From d0d1eb0b878ddc1bb46914acf18676a5a98b732b Mon Sep 17 00:00:00 2001
From: DavidStein7 <david.steinhaeuser@study.hs-duesseldorf.de>
Date: Mon, 28 Mar 2022 11:21:33 +0200
Subject: [PATCH 1/4] Erster Commit

---
 first_commits/HalloDavidStein7.txt | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 first_commits/HalloDavidStein7.txt

diff --git a/first_commits/HalloDavidStein7.txt b/first_commits/HalloDavidStein7.txt
new file mode 100644
index 0000000..a92550c
--- /dev/null
+++ b/first_commits/HalloDavidStein7.txt
@@ -0,0 +1 @@
+Hallo Welt
\ No newline at end of file

From a0a09b1cff29d5cfec35d38dcd72bc5df7f50d59 Mon Sep 17 00:00:00 2001
From: DavidStein7 <david.steinhaeuser@study.hs-duesseldorf.de>
Date: Mon, 28 Mar 2022 15:22:14 +0200
Subject: [PATCH 2/4] First App

---
 src/app/App.py | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 src/app/App.py

diff --git a/src/app/App.py b/src/app/App.py
new file mode 100644
index 0000000..f6e4c2c
--- /dev/null
+++ b/src/app/App.py
@@ -0,0 +1,37 @@
+import streamlit as st
+
+st.title('Vorhersage der deutschen Co2 Emissionen')
+
+"Autor: David Steinhäuser (https://github.com/DavidStein7)"
+
+from scipy.stats import linregress
+st.subheader("Vorhersage")
+years = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]
+emissions_per_year = [10.3, 10.0, 10.1, 10.2, 9.7, 9.7, 9.7, 9.5, 9.1, 8.5, 7.7]
+
+regression_result = linregress(years, emissions_per_year)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+desired_year = st.number_input('Jahr', value=2022)
+
+prediction = scipy_model(desired_year)
+prediction_rounded = round(prediction, 2)
+
+"Die Vorhersage der Emissionen für das ausgewählte Jahr ist:"
+
+st.write(prediction_rounded)
+
+"Tonnen pro Kopf pro Jahr"
+
+st.subheader("Über das Modell und die Daten")
+
+"Das Modell ist ein lineares Regressionsmodell auf Grundlage von Daen von 2010 bis 2020."
+"Es steht ein Datenpunkt pro Jahr zur Verfügung"
+
+"Die Daten stammen aus den folgenden Quellen:"
+
+"- Global Carbon Project. (2021). Supplemental data of Global Carbon Project 2021 (1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/gcp-2021"
+"- Andrew, Robbie M., & Peters, Glen P. (2021). The Global Carbon Project's fossil CO2 emissions dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5569235"
\ No newline at end of file

From 36f5f47cd98b4ebd918d00fae8f7d8609ceb9762 Mon Sep 17 00:00:00 2001
From: DavidStein7 <david.steinhaeuser@study.hs-duesseldorf.de>
Date: Fri, 29 Apr 2022 11:33:53 +0200
Subject: [PATCH 3/4] First App

---
 .idea/biz_analytics_a_starter.iml |  20 +-
 environment.yml                   |   3 +
 notebooks/data_overview.ipynb     | 335 ++++++++++++++++++++++++++++++
 src/App2.py                       |   1 +
 src/app/App.py                    |   3 +-
 src/app/OwnApp1.py                |  48 +++++
 src/app/OwnApp2.py                |  53 +++++
 src/hello_world.py                |  95 +++++++++
 8 files changed, 547 insertions(+), 11 deletions(-)
 create mode 100644 notebooks/data_overview.ipynb
 create mode 100644 src/App2.py
 create mode 100644 src/app/OwnApp1.py
 create mode 100644 src/app/OwnApp2.py
 create mode 100644 src/hello_world.py

diff --git a/.idea/biz_analytics_a_starter.iml b/.idea/biz_analytics_a_starter.iml
index 99d1c63..c748ac2 100644
--- a/.idea/biz_analytics_a_starter.iml
+++ b/.idea/biz_analytics_a_starter.iml
@@ -7,14 +7,14 @@
     <orderEntry type="jdk" jdkName="Python 3.8 (biz_analytics_a)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
-    <component name="PackageRequirementsSettings">
-      <option name="requirementsPath" value="$MODULE_DIR$/environment.yml" />
-    </component>
-    <component name="PyDocumentationSettings">
-      <option name="format" value="GOOGLE" />
-      <option name="myDocStringFormat" value="Google" />
-    </component>
-    <component name="TestRunnerService">
-      <option name="PROJECT_TEST_RUNNER" value="py.test" />
-    </component>
+  <component name="PackageRequirementsSettings">
+    <option name="requirementsPath" value="$MODULE_DIR$/environment.yml" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="GOOGLE" />
+    <option name="myDocStringFormat" value="Google" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="py.test" />
+  </component>
 </module>
\ No newline at end of file
diff --git a/environment.yml b/environment.yml
index fe172a4..240ae6d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -20,3 +20,6 @@ dependencies:
   - yellowbrick=1.3
   - pip:
     - streamlit==1.7.0
+
+streamlit~=1.7.0
+scipy~=1.5.0
\ No newline at end of file
diff --git a/notebooks/data_overview.ipynb b/notebooks/data_overview.ipynb
new file mode 100644
index 0000000..dc7ad9c
--- /dev/null
+++ b/notebooks/data_overview.ipynb
@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "data = pd.read_stata('../data/external/ZA6647_v1-0-0.dta')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "               studyno                  doi             version  \\\n0  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n1  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n2  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n3  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n4  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n\n           edition                                    survey  caseid   uniqid  \\\n0  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       1  1000001   \n1  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       2  1000002   \n2  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       3  1000003   \n3  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       4  1000004   \n4  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       5  1000005   \n\n   tnscntry       country isocntry  ...  w82  w84       w89  w90       w95  \\\n0  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.377414  0.0  0.399552   \n1  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.398224  0.0  0.421584   \n2  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.203295  0.0  0.215220   \n3  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.313204  0.0  0.331576   \n4  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.384786  0.0  0.407357   \n\n   w96       w97  w83  w98          wex  \n0  0.0  0.419225  0.0  0.0  6446.127930  \n1  0.0  0.442341  0.0  0.0  6801.566895  \n2  0.0  0.225817  0.0  0.0  3472.228516  \n3  0.0  0.347902  0.0  0.0  5349.446777  \n4  0.0  0.427415  0.0  0.0  6572.049805  \n\n[5 rows x 165 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>studyno</th>\n      <th>doi</th>\n      <th>version</th>\n      <th>edition</th>\n      <th>survey</th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>1</td>\n      <td>1000001</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.377414</td>\n      <td>0.0</td>\n      <td>0.399552</td>\n      <td>0.0</td>\n      <td>0.419225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6446.127930</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>2</td>\n      <td>1000002</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.398224</td>\n      <td>0.0</td>\n      <td>0.421584</td>\n      <td>0.0</td>\n      <td>0.442341</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6801.566895</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>3</td>\n      <td>1000003</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.203295</td>\n      <td>0.0</td>\n      <td>0.215220</td>\n      <td>0.0</td>\n      <td>0.225817</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3472.228516</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>4</td>\n      <td>1000004</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.313204</td>\n      <td>0.0</td>\n      <td>0.331576</td>\n      <td>0.0</td>\n      <td>0.347902</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5349.446777</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>5</td>\n      <td>1000005</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.384786</td>\n      <td>0.0</td>\n      <td>0.407357</td>\n      <td>0.0</td>\n      <td>0.427415</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6572.049805</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 165 columns</p>\n</div>"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n                9,\n            ...\n            26591, 26592, 26593, 26594, 26595, 26596, 26597, 26598, 26599,\n            26600],\n           dtype='int64', length=26601)"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Index auslesen\n",
+    "data.index"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Index(['studyno', 'doi', 'version', 'edition', 'survey', 'caseid', 'uniqid',\n       'tnscntry', 'country', 'isocntry',\n       ...\n       'w82', 'w84', 'w89', 'w90', 'w95', 'w96', 'w97', 'w83', 'w98', 'wex'],\n      dtype='object', length=165)"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Spalten auslesen\n",
+    "data.columns"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['studyno',\n 'doi',\n 'version',\n 'edition',\n 'survey',\n 'caseid',\n 'uniqid',\n 'tnscntry',\n 'country',\n 'isocntry',\n 'mode',\n 'q1_1',\n 'q1_2',\n 'q1_3',\n 'q1_4',\n 'q1_5',\n 'q1_6',\n 'q1_7',\n 'q2_1',\n 'q2_2',\n 'q2_3',\n 'q2_4',\n 'q2_5',\n 'q2_6',\n 'q2_7',\n 'q2_8',\n 'q3',\n 'q4',\n 'q5',\n 'q6',\n 'q7',\n 'q8',\n 'd1',\n 'd1r1',\n 'd1r2',\n 'd2',\n 'd3a_1',\n 'd3a_2',\n 'd3a_3',\n 'd3a_4',\n 'd3a_5',\n 'd3a_6',\n 'd3a_7',\n 'd3a_8',\n 'd3a_9',\n 'd3a_10',\n 'd3a_11',\n 'd3a_12',\n 'd3a_13',\n 'd3a_14',\n 'd3a_15',\n 'd3a_16',\n 'd3a_17',\n 'd3a_18',\n 'd3a_19',\n 'd3a_20',\n 'd3a_21',\n 'd3a_22',\n 'd3a_23',\n 'd3a_24',\n 'd3a_25',\n 'd3a_26',\n 'd3a_27',\n 'd3a_28',\n 'd3a_98',\n 'd3a_99',\n 'd3b',\n 'd4',\n 'd4r1',\n 'd4r2',\n 'd5',\n 'd5r',\n 'd13',\n 'd18',\n 'd20',\n 'd18_d20',\n 'd22',\n 'd22r',\n 'd12be',\n 'd12be_r',\n 'd12at',\n 'd12at_r',\n 'd12bg',\n 'd12cy',\n 'd12cz',\n 'd12dk',\n 'd12ee',\n 'd12de',\n 'd12gr',\n 'd12gr_r',\n 'd12es',\n 'd12es_r',\n 'd12fi',\n 'd12fr',\n 'd12fr_r',\n 'd12gb',\n 'd12hu',\n 'd12ie',\n 'd12it',\n 'd12it_r',\n 'd12lt',\n 'd12lu',\n 'd12lv',\n 'd12nl',\n 'd12nl_r',\n 'd12pl',\n 'd12pl_r',\n 'd12pt',\n 'd12ro',\n 'd12se',\n 'd12si',\n 'd12sk',\n 'd12mt',\n 'd12hr',\n 'eu6',\n 'eu9',\n 'eu10',\n 'eu12_2',\n 'eu_nms3',\n 'eu15',\n 'eu_nms10',\n 'eu25',\n 'eu_nms12',\n 'eu27',\n 'eu_nms13',\n 'eu28',\n 'euroz13',\n 'euroz15',\n 'euronz15',\n 'euroz16',\n 'euronz16',\n 'euroz17',\n 'euronz17',\n 'euroz18',\n 'euronz18',\n 'euronzms',\n 'euronznm',\n 'euroz19',\n 'euronz19',\n 'w1',\n 'w5',\n 'w6',\n 'w7',\n 'w9',\n 'w10',\n 'w11',\n 'w13',\n 'w14',\n 'w24',\n 'w22',\n 'w94',\n 'w23',\n 'w29',\n 'w30',\n 'w81',\n 'w82',\n 'w84',\n 'w89',\n 'w90',\n 'w95',\n 'w96',\n 'w97',\n 'w83',\n 'w98',\n 'wex']"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Spalten auslesen bessere Lesbarkeit\n",
+    "list(data.columns)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'BE - Belgium'"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bestimmten Wert auslesen\n",
+    "data.loc[2, 'country']"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'BE - Belgium'"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bestimmten Wert mit numerischer Notation auslesen\n",
+    "data.iloc[2,8]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "studyno    category\ndoi          object\nversion      object\nedition    category\nsurvey     category\n             ...   \nw96         float64\nw97         float64\nw83         float64\nw98         float64\nwex         float64\nLength: 165, dtype: object"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Datentypen der Spalten auslesen\n",
+    "data.dtypes"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                   studyno                  doi             version  \\\n25173  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n18174  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n1849   GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n15540  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n24356  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n\n               edition                                    survey  caseid  \\\n25173  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   25174   \n18174  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   18175   \n1849   ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)    1850   \n15540  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   15541   \n24356  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   24357   \n\n         uniqid            tnscntry                 country isocntry  ...  \\\n25173  42025174           SLOVENIJA           SI - Slovenia       SI  ...   \n18174  35018175        MAGYARORSZAG            HU - Hungary       HU  ...   \n1849    2001850             DANMARK            DK - Denmark       DK  ...   \n15540  32015541              KYPROS  CY - Cyprus (Republic)       CY  ...   \n24356  41024357  SLOVENSKA REPUBLIC           SK - Slovakia       SK  ...   \n\n            w82       w84       w89       w90       w95       w96       w97  \\\n25173  0.000000  0.000000  0.257511  0.000000  0.272616  0.000000  0.286039   \n18174  1.638508  1.440078  0.000000  1.607205  0.000000  1.475640  0.000000   \n1849   0.219173  0.000000  0.000000  0.214986  0.000000  0.197387  0.000000   \n15540  0.000000  0.000000  0.067801  0.000000  0.071778  0.000000  0.075313   \n24356  0.000000  0.000000  0.161409  0.000000  0.170877  0.000000  0.179291   \n\n            w83       w98           wex  \n25173  0.000000  0.000000   4398.215820  \n18174  1.237390  1.354887  21380.603516  \n1849   0.165518  0.181235   2859.951904  \n15540  0.000000  0.000000   1158.028076  \n24356  0.000000  0.000000   2756.826660  \n\n[5 rows x 165 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>studyno</th>\n      <th>doi</th>\n      <th>version</th>\n      <th>edition</th>\n      <th>survey</th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>25173</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>25174</td>\n      <td>42025174</td>\n      <td>SLOVENIJA</td>\n      <td>SI - Slovenia</td>\n      <td>SI</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.257511</td>\n      <td>0.000000</td>\n      <td>0.272616</td>\n      <td>0.000000</td>\n      <td>0.286039</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4398.215820</td>\n    </tr>\n    <tr>\n      <th>18174</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>18175</td>\n      <td>35018175</td>\n      <td>MAGYARORSZAG</td>\n      <td>HU - Hungary</td>\n      <td>HU</td>\n      <td>...</td>\n      <td>1.638508</td>\n      <td>1.440078</td>\n      <td>0.000000</td>\n      <td>1.607205</td>\n      <td>0.000000</td>\n      <td>1.475640</td>\n      <td>0.000000</td>\n      <td>1.237390</td>\n      <td>1.354887</td>\n      <td>21380.603516</td>\n    </tr>\n    <tr>\n      <th>1849</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>1850</td>\n      <td>2001850</td>\n      <td>DANMARK</td>\n      <td>DK - Denmark</td>\n      <td>DK</td>\n      <td>...</td>\n      <td>0.219173</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.214986</td>\n      <td>0.000000</td>\n      <td>0.197387</td>\n      <td>0.000000</td>\n      <td>0.165518</td>\n      <td>0.181235</td>\n      <td>2859.951904</td>\n    </tr>\n    <tr>\n      <th>15540</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>15541</td>\n      <td>32015541</td>\n      <td>KYPROS</td>\n      <td>CY - Cyprus (Republic)</td>\n      <td>CY</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.067801</td>\n      <td>0.000000</td>\n      <td>0.071778</td>\n      <td>0.000000</td>\n      <td>0.075313</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1158.028076</td>\n    </tr>\n    <tr>\n      <th>24356</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>24357</td>\n      <td>41024357</td>\n      <td>SLOVENSKA REPUBLIC</td>\n      <td>SK - Slovakia</td>\n      <td>SK</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.161409</td>\n      <td>0.000000</td>\n      <td>0.170877</td>\n      <td>0.000000</td>\n      <td>0.179291</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2756.826660</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 165 columns</p>\n</div>"
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 5 Zeilen zufällig aus dem DataFrame auswählen\n",
+    "data.sample(5)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid  tnscntry              country isocntry  \\\n7609     7610   9007610    ITALIA           IT - Italy       IT   \n4644     4645   6004645     SUOMI         FI - Finland       FI   \n20058   20059  37020059   LIETUVA       LT - Lithuania       LT   \n13868   13869  15013869        UK  GB - United Kingdom       GB   \n11049   11050  13011050  PORTUGAL        PT - Portugal       PT   \n\n                       mode           q1_1                q1_2  \\\n7609      Mobile phone line  Not mentioned       Not mentioned   \n4644      Mobile phone line  Not mentioned  Food manufacturers   \n20058  Fixed telephone line        Farmers       Not mentioned   \n13868  Fixed telephone line        Farmers  Food manufacturers   \n11049  Fixed telephone line        Farmers  Food manufacturers   \n\n                      q1_3                                      q1_4  ...  \\\n7609   Shops and retailers  no  Hospitality and food service sectors  ...   \n4644   Shops and retailers  no  Hospitality and food service sectors  ...   \n20058        Not mentioned                             Not mentioned  ...   \n13868  Shops and retailers  no  Hospitality and food service sectors  ...   \n11049  Shops and retailers  no  Hospitality and food service sectors  ...   \n\n            w82  w84       w89       w90       w95       w96       w97  \\\n7609   0.000000  0.0  1.939347  0.000000  2.053107  0.000000  2.154197   \n4644   0.000000  0.0  0.200278  0.000000  0.212026  0.000000  0.222466   \n20058  0.557824  0.0  0.000000  0.547167  0.000000  0.502376  0.473388   \n13868  7.124311  0.0  0.000000  6.988205  0.000000  6.416151  0.000000   \n11049  0.000000  0.0  0.340113  0.000000  0.360064  0.000000  0.377793   \n\n            w83       w98           wex  \n7609   0.000000  0.000000  33123.546875  \n4644   0.000000  0.000000   3420.700439  \n20058  0.000000  0.000000   7278.943848  \n13868  5.380231  5.891114  92963.890625  \n11049  0.000000  0.000000   5809.047363  \n\n[5 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7609</th>\n      <td>7610</td>\n      <td>9007610</td>\n      <td>ITALIA</td>\n      <td>IT - Italy</td>\n      <td>IT</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.939347</td>\n      <td>0.000000</td>\n      <td>2.053107</td>\n      <td>0.000000</td>\n      <td>2.154197</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>33123.546875</td>\n    </tr>\n    <tr>\n      <th>4644</th>\n      <td>4645</td>\n      <td>6004645</td>\n      <td>SUOMI</td>\n      <td>FI - Finland</td>\n      <td>FI</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.200278</td>\n      <td>0.000000</td>\n      <td>0.212026</td>\n      <td>0.000000</td>\n      <td>0.222466</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3420.700439</td>\n    </tr>\n    <tr>\n      <th>20058</th>\n      <td>20059</td>\n      <td>37020059</td>\n      <td>LIETUVA</td>\n      <td>LT - Lithuania</td>\n      <td>LT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.557824</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.547167</td>\n      <td>0.000000</td>\n      <td>0.502376</td>\n      <td>0.473388</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7278.943848</td>\n    </tr>\n    <tr>\n      <th>13868</th>\n      <td>13869</td>\n      <td>15013869</td>\n      <td>UK</td>\n      <td>GB - United Kingdom</td>\n      <td>GB</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>7.124311</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>6.988205</td>\n      <td>0.000000</td>\n      <td>6.416151</td>\n      <td>0.000000</td>\n      <td>5.380231</td>\n      <td>5.891114</td>\n      <td>92963.890625</td>\n    </tr>\n    <tr>\n      <th>11049</th>\n      <td>11050</td>\n      <td>13011050</td>\n      <td>PORTUGAL</td>\n      <td>PT - Portugal</td>\n      <td>PT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.340113</td>\n      <td>0.000000</td>\n      <td>0.360064</td>\n      <td>0.000000</td>\n      <td>0.377793</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5809.047363</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Unerwünschte Spalten definieren und löschen\n",
+    "cols_to_delete = ['studyno', 'doi', 'version', 'edition', 'survey']\n",
+    "data = data.drop(columns=cols_to_delete)\n",
+    "data.sample(5)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid    tnscntry              country isocntry  \\\n8221     8222  10008222  LUXEMBOURG      LU - Luxembourg       LU   \n6698     6699   8006699     IRELAND         IE - Ireland       IE   \n25495   25496  42025496   SLOVENIJA        SI - Slovenia       SI   \n11143   11144  13011144    PORTUGAL        PT - Portugal       PT   \n14413   14414  15014414          UK  GB - United Kingdom       GB   \n\n                       mode           q1_1                q1_2  \\\n8221   Fixed telephone line  Not mentioned       Not mentioned   \n6698   Fixed telephone line        Farmers  Food manufacturers   \n25495  Fixed telephone line  Not mentioned       Not mentioned   \n11143  Fixed telephone line        Farmers  Food manufacturers   \n14413  Fixed telephone line        Farmers  Food manufacturers   \n\n                      q1_3                                      q1_4  ...  \\\n8221   Shops and retailers  no  Hospitality and food service sectors  ...   \n6698   Shops and retailers  no  Hospitality and food service sectors  ...   \n25495  Shops and retailers  no  Hospitality and food service sectors  ...   \n11143  Shops and retailers  no  Hospitality and food service sectors  ...   \n14413  Shops and retailers  no  Hospitality and food service sectors  ...   \n\n            w82  w84       w89       w90       w95      w96       w97  \\\n8221   0.000000  0.0  0.028708  0.000000  0.030392  0.00000  0.031888   \n6698   0.000000  0.0  0.111559  0.000000  0.118103  0.00000  0.123918   \n25495  0.000000  0.0  0.033513  0.000000  0.035478  0.00000  0.037225   \n11143  0.000000  0.0  0.407588  0.000000  0.431497  0.00000  0.452743   \n14413  8.376597  0.0  0.000000  8.216568  0.000000  7.54396  0.000000   \n\n            w83       w98            wex  \n8221   0.000000  0.000000     490.321014  \n6698   0.000000  0.000000    1905.406128  \n25495  0.000000  0.000000     572.386169  \n11143  0.000000  0.000000    6961.501953  \n14413  6.325949  6.926633  109304.757812  \n\n[5 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8221</th>\n      <td>8222</td>\n      <td>10008222</td>\n      <td>LUXEMBOURG</td>\n      <td>LU - Luxembourg</td>\n      <td>LU</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.028708</td>\n      <td>0.000000</td>\n      <td>0.030392</td>\n      <td>0.00000</td>\n      <td>0.031888</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>490.321014</td>\n    </tr>\n    <tr>\n      <th>6698</th>\n      <td>6699</td>\n      <td>8006699</td>\n      <td>IRELAND</td>\n      <td>IE - Ireland</td>\n      <td>IE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.111559</td>\n      <td>0.000000</td>\n      <td>0.118103</td>\n      <td>0.00000</td>\n      <td>0.123918</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1905.406128</td>\n    </tr>\n    <tr>\n      <th>25495</th>\n      <td>25496</td>\n      <td>42025496</td>\n      <td>SLOVENIJA</td>\n      <td>SI - Slovenia</td>\n      <td>SI</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033513</td>\n      <td>0.000000</td>\n      <td>0.035478</td>\n      <td>0.00000</td>\n      <td>0.037225</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>572.386169</td>\n    </tr>\n    <tr>\n      <th>11143</th>\n      <td>11144</td>\n      <td>13011144</td>\n      <td>PORTUGAL</td>\n      <td>PT - Portugal</td>\n      <td>PT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.407588</td>\n      <td>0.000000</td>\n      <td>0.431497</td>\n      <td>0.00000</td>\n      <td>0.452743</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6961.501953</td>\n    </tr>\n    <tr>\n      <th>14413</th>\n      <td>14414</td>\n      <td>15014414</td>\n      <td>UK</td>\n      <td>GB - United Kingdom</td>\n      <td>GB</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>8.376597</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>8.216568</td>\n      <td>0.000000</td>\n      <td>7.54396</td>\n      <td>0.000000</td>\n      <td>6.325949</td>\n      <td>6.926633</td>\n      <td>109304.757812</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_rename = {\n",
+    "    'd1': 'age',\n",
+    "    'd2': 'gender',\n",
+    "}\n",
+    "data = data.rename(columns=cols_to_rename)\n",
+    "data.sample(5)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid      tnscntry               country isocntry  \\\n8951     8952  11008952     NEDERLAND  NL - The Netherlands       NL   \n17880   17881  34017881         EESTI          EE - Estonia       EE   \n5910     5911   7005911        FRANCE           FR - France       FR   \n15030   15031  31015031     BALGARIJA         BG - Bulgaria       BG   \n12622   12623   3012623   DEUTSCHLAND          DE - Germany       DE   \n3986     3987   5003987        ESPANA             ES -Spain       ES   \n9921     9922  12009922    ÖSTERREICH          AT - Austria       AT   \n467       468   1000468      BELGIQUE          BE - Belgium       BE   \n2577     2578   4002578        ELLADA           GR - Greece       GR   \n18088   18089  35018089  MAGYARORSZAG          HU - Hungary       HU   \n\n                       mode           q1_1                q1_2  \\\n8951   Fixed telephone line  Not mentioned  Food manufacturers   \n17880  Fixed telephone line        Farmers  Food manufacturers   \n5910   Fixed telephone line        Farmers  Food manufacturers   \n15030  Fixed telephone line  Not mentioned       Not mentioned   \n12622  Fixed telephone line  Not mentioned  Food manufacturers   \n3986   Fixed telephone line  Not mentioned  Food manufacturers   \n9921   Fixed telephone line  Not mentioned       Not mentioned   \n467       Mobile phone line  Not mentioned  Food manufacturers   \n2577   Fixed telephone line  Not mentioned       Not mentioned   \n18088     Mobile phone line  Not mentioned       Not mentioned   \n\n                      q1_3                                      q1_4  ...  \\\n8951         Not mentioned                             Not mentioned  ...   \n17880  Shops and retailers  no  Hospitality and food service sectors  ...   \n5910   Shops and retailers  no  Hospitality and food service sectors  ...   \n15030  Shops and retailers  no  Hospitality and food service sectors  ...   \n12622  Shops and retailers  no  Hospitality and food service sectors  ...   \n3986         Not mentioned  no  Hospitality and food service sectors  ...   \n9921   Shops and retailers  no  Hospitality and food service sectors  ...   \n467    Shops and retailers  no  Hospitality and food service sectors  ...   \n2577         Not mentioned                             Not mentioned  ...   \n18088        Not mentioned                             Not mentioned  ...   \n\n            w82       w84       w89       w90       w95       w96       w97  \\\n8951   0.000000  0.000000  2.382976  0.000000  2.522759  0.000000  2.646974   \n17880  0.029498  0.000000  0.022536  0.000000  0.023858  0.000000  0.025033   \n5910   0.000000  0.000000  5.377200  0.000000  5.692622  0.000000  5.972913   \n15030  0.299301  0.263054  0.000000  0.293583  0.000000  0.269550  0.000000   \n12622  0.000000  0.000000  2.660488  0.000000  2.816550  0.000000  2.955230   \n3986   0.000000  0.000000  2.369598  0.000000  2.508597  0.000000  2.632114   \n9921   0.000000  0.000000  0.353662  0.000000  0.374407  0.000000  0.392842   \n467    0.000000  0.000000  0.843141  0.000000  0.892599  0.000000  0.936549   \n2577   0.000000  0.000000  0.570128  0.000000  0.603572  0.000000  0.633290   \n18088  0.276740  0.243226  0.000000  0.271453  0.000000  0.249232  0.000000   \n\n            w83       w98           wex  \n8951   0.000000  0.000000  40700.621094  \n17880  0.000000  0.000000    384.912231  \n5910   0.000000  0.000000  91841.210938  \n15030  0.226030  0.247493   3905.526611  \n12622  0.000000  0.000000  45440.453125  \n3986   0.000000  0.000000  40472.132812  \n9921   0.000000  0.000000   6040.449219  \n467    0.000000  0.000000  14400.638672  \n2577   0.000000  0.000000   9737.649414  \n18088  0.208992  0.228837   3611.134521  \n\n[10 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8951</th>\n      <td>8952</td>\n      <td>11008952</td>\n      <td>NEDERLAND</td>\n      <td>NL - The Netherlands</td>\n      <td>NL</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.382976</td>\n      <td>0.000000</td>\n      <td>2.522759</td>\n      <td>0.000000</td>\n      <td>2.646974</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40700.621094</td>\n    </tr>\n    <tr>\n      <th>17880</th>\n      <td>17881</td>\n      <td>34017881</td>\n      <td>EESTI</td>\n      <td>EE - Estonia</td>\n      <td>EE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.029498</td>\n      <td>0.000000</td>\n      <td>0.022536</td>\n      <td>0.000000</td>\n      <td>0.023858</td>\n      <td>0.000000</td>\n      <td>0.025033</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>384.912231</td>\n    </tr>\n    <tr>\n      <th>5910</th>\n      <td>5911</td>\n      <td>7005911</td>\n      <td>FRANCE</td>\n      <td>FR - France</td>\n      <td>FR</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5.377200</td>\n      <td>0.000000</td>\n      <td>5.692622</td>\n      <td>0.000000</td>\n      <td>5.972913</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>91841.210938</td>\n    </tr>\n    <tr>\n      <th>15030</th>\n      <td>15031</td>\n      <td>31015031</td>\n      <td>BALGARIJA</td>\n      <td>BG - Bulgaria</td>\n      <td>BG</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.299301</td>\n      <td>0.263054</td>\n      <td>0.000000</td>\n      <td>0.293583</td>\n      <td>0.000000</td>\n      <td>0.269550</td>\n      <td>0.000000</td>\n      <td>0.226030</td>\n      <td>0.247493</td>\n      <td>3905.526611</td>\n    </tr>\n    <tr>\n      <th>12622</th>\n      <td>12623</td>\n      <td>3012623</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.660488</td>\n      <td>0.000000</td>\n      <td>2.816550</td>\n      <td>0.000000</td>\n      <td>2.955230</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45440.453125</td>\n    </tr>\n    <tr>\n      <th>3986</th>\n      <td>3987</td>\n      <td>5003987</td>\n      <td>ESPANA</td>\n      <td>ES -Spain</td>\n      <td>ES</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.369598</td>\n      <td>0.000000</td>\n      <td>2.508597</td>\n      <td>0.000000</td>\n      <td>2.632114</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40472.132812</td>\n    </tr>\n    <tr>\n      <th>9921</th>\n      <td>9922</td>\n      <td>12009922</td>\n      <td>ÖSTERREICH</td>\n      <td>AT - Austria</td>\n      <td>AT</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.353662</td>\n      <td>0.000000</td>\n      <td>0.374407</td>\n      <td>0.000000</td>\n      <td>0.392842</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6040.449219</td>\n    </tr>\n    <tr>\n      <th>467</th>\n      <td>468</td>\n      <td>1000468</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.843141</td>\n      <td>0.000000</td>\n      <td>0.892599</td>\n      <td>0.000000</td>\n      <td>0.936549</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14400.638672</td>\n    </tr>\n    <tr>\n      <th>2577</th>\n      <td>2578</td>\n      <td>4002578</td>\n      <td>ELLADA</td>\n      <td>GR - Greece</td>\n      <td>GR</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.570128</td>\n      <td>0.000000</td>\n      <td>0.603572</td>\n      <td>0.000000</td>\n      <td>0.633290</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>9737.649414</td>\n    </tr>\n    <tr>\n      <th>18088</th>\n      <td>18089</td>\n      <td>35018089</td>\n      <td>MAGYARORSZAG</td>\n      <td>HU - Hungary</td>\n      <td>HU</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.276740</td>\n      <td>0.243226</td>\n      <td>0.000000</td>\n      <td>0.271453</td>\n      <td>0.000000</td>\n      <td>0.249232</td>\n      <td>0.000000</td>\n      <td>0.208992</td>\n      <td>0.228837</td>\n      <td>3611.134521</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.sample(10)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/src/App2.py b/src/App2.py
new file mode 100644
index 0000000..e975c40
--- /dev/null
+++ b/src/App2.py
@@ -0,0 +1 @@
+import streamlit as st
\ No newline at end of file
diff --git a/src/app/App.py b/src/app/App.py
index f6e4c2c..e519ee3 100644
--- a/src/app/App.py
+++ b/src/app/App.py
@@ -34,4 +34,5 @@ def scipy_model(desired_year):
 "Die Daten stammen aus den folgenden Quellen:"
 
 "- Global Carbon Project. (2021). Supplemental data of Global Carbon Project 2021 (1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/gcp-2021"
-"- Andrew, Robbie M., & Peters, Glen P. (2021). The Global Carbon Project's fossil CO2 emissions dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5569235"
\ No newline at end of file
+"- Andrew, Robbie M., & Peters, Glen P. (2021). The Global Carbon Project's fossil CO2 emissions dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5569235"
+#%%
diff --git a/src/app/OwnApp1.py b/src/app/OwnApp1.py
new file mode 100644
index 0000000..d0837c2
--- /dev/null
+++ b/src/app/OwnApp1.py
@@ -0,0 +1,48 @@
+import pandas as pd
+import streamlit as st
+
+st.title('Vorhersage des Anteils der erneuerbaren Energien am gesamten Energieverbrauch in Deutschland')
+
+"Autor: David Steinhäuser (https://github.com/DavidStein7)"
+
+from scipy.stats import linregress
+
+st.subheader("Vorhersage")
+years = [2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]
+r_energy = [10.72, 11.61, 12.54, 13.64, 13.63, 14.02, 14.55, 14.24, 15.22, 16.12, 17.17]
+
+
+regression_result = linregress(years, r_energy)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+desired_year = st.number_input('Jahr', value = 2022)
+
+prediction = scipy_model(desired_year)
+prediction_rounded = round(prediction, 2)
+
+"Die Vorhersage des Anteils der erneuerbaren Energien für das ausgewählte Jahr ist"
+
+st.write(prediction_rounded,"%")
+
+"des gesamten Energieverbrauchs."
+
+st.subheader("Genutzte Datenpunkte:")
+chart_data=pd.DataFrame(r_energy,years)
+st.bar_chart(chart_data)
+
+clicked = st.button("Drück mich! :)",help="Balloons")
+if clicked:
+    st.balloons()
+
+st.subheader("Über das Modell und Daten")
+
+"Das Modell ist ein lineares Regressionsmodell auf Grundlage von Daten von 2009 bis 2019."
+"Es steht ein Datenpunkt pro Jahr zur Verfügung"
+
+"Die Daten stammen aus den folgenden Quellen:"
+"- IEA. (2021). World Energy Balances."
+"- UN Statistics Division. (2021). Energy Balances."
+"- https://unstats.un.org/sdgs/dataportal/countryprofiles/DEU#goal-7"
\ No newline at end of file
diff --git a/src/app/OwnApp2.py b/src/app/OwnApp2.py
new file mode 100644
index 0000000..738f95d
--- /dev/null
+++ b/src/app/OwnApp2.py
@@ -0,0 +1,53 @@
+import pandas as pd
+import streamlit as st
+
+st.title('Number of Nuclear Warheads - USA vs Russia')
+
+"Autor: David Steinhäuser (https://github.com/DavidStein7)"
+
+from scipy.stats import linregress
+
+st.subheader("Vorhersage 1")
+years = [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]
+warheadsR = [12188, 11152, 10114, 9076, 8038, 7000, 6643, 6286, 5929, 5527, 5215, 4858, 4750, 4650, 4600, 4500, 4490, 4300, 4350, 4330, 4310, 4495, 4477]
+warheadsU = [10577, 10526, 10457, 10027, 8570, 8360, 7853, 5709, 5273, 5113, 5066, 4897, 4881, 4804, 4717, 4571, 4018, 3822, 3785, 3805, 3750, 3708, 3708]
+
+regression_result = linregress(years, warheadsR)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+desired_year = st.number_input('Jahr', value = 2022)
+
+prediction = scipy_model(desired_year)
+prediction_rounded = round(prediction, 2)
+
+"Die Vorhersage"
+
+st.write(prediction_rounded)
+
+"Lineare Vorhersage"
+slope = (warheadsR[22]-warheadsR[0])/(years[22]-years[0])
+intercept = warheadsR[0]-(slope*years[0])
+
+def model(desired_year):
+    model_result2 = slope * desired_year + intercept
+    return model_result2
+
+st.write(model(desired_year))
+
+"Daten"
+
+st.write(warheadsR[22])
+
+st.success("nice!")
+
+st.subheader("Data points used:")
+chart_data=pd.DataFrame(warheadsR,years)
+st.bar_chart(chart_data)
+
+clicked = st.button("Click me",help="Balloons")
+if clicked:
+    st.balloons()
+
diff --git a/src/hello_world.py b/src/hello_world.py
new file mode 100644
index 0000000..9d4c75b
--- /dev/null
+++ b/src/hello_world.py
@@ -0,0 +1,95 @@
+print(5+5)
+#%%
+print(5*2)
+#%%
+print(5*3)
+#%%
+multiplier = 2
+
+result = multiplier * -3
+print(result)
+#%%
+recent_years = [2020, 2021, 2022]
+
+print(recent_years[1])
+#%%
+for year in recent_years:
+    print(year)
+#%%
+
+#%%
+
+#%%
+for year in recent_years:
+    print(year)
+#%%
+for number in range (10):
+    print(number)
+#%%
+years = [2020, 2021]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/years[1]-years[0])
+#%%
+years = [2020, 2021]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/years[1]-years[0]
+
+#%%
+years = [2010, 2020]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/(years[1]-years[0])
+print(slope)
+#%%
+years = [2010, 2020]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/(years[1]-years[0])
+intercept = emissions_per_year[0]-(slope*years[0])
+print(intercept)
+#%%
+years = [2010, 2020]
+emissions_per_year = [10.3 , 7.7]
+
+slope = (emissions_per_year[1]-emissions_per_year[0])/(years[1]-years[0])
+intercept = emissions_per_year[0]-(slope*years[0])
+
+def model(desired_year):
+    model_result = slope * desired_year + intercept
+    return model_result
+
+emissions_in_2022 = model(2022)
+print(emissions_in_2022)
+#%%
+# Modell mit SciPy
+years = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]
+emissions_per_year = [10.3, 10.0, 10.1, 10.2, 9.7, 9.7, 9.7, 9.5, 9.1, 8.5, 7.7]
+from scipy.stats import linregress
+regression_result = linregress(years, emissions_per_year)
+scipy_slope = regression_result.slope
+scipy_intercept = regression_result.intercept
+def scipy_model(desired_year):
+    model_result = scipy_slope * desired_year + scipy_intercept
+    return model_result
+emissions_in_2022_scipy = scipy_model(2022)
+print(emissions_in_2022_scipy)
+#%%
+model_results = []
+scipy_model_results = []
+
+for year in years:
+    model_result = model(year)
+    scipy_model_result = scipy_model(year)
+
+    model_results.append(model_result)
+    scipy_model_results.append(scipy_model_result)
+
+print(emissions_per_year)
+print(model_results)
+print(scipy_model_results)
+
+#%%
+
+#%%

From 0916ee61e4e6971c98a1224a991216abb4d8d167 Mon Sep 17 00:00:00 2001
From: DavidStein7 <david.steinhaeuser@study.hs-duesseldorf.de>
Date: Tue, 17 May 2022 23:27:37 +0200
Subject: [PATCH 4/4] Update Notebooks

---
 "notebooks/Ergebnisse_f\303\274r_Piotr.ipynb" |  266 +++
 notebooks/data_clustering.ipynb               | 1719 +++++++++++++++++
 notebooks/data_modeling_beans.ipynb           | 1610 +++++++++++++++
 notebooks/data_overview.ipynb                 |  940 ++++++++-
 notebooks/data_penguin_clustering.ipynb       | 1538 +++++++++++++++
 notebooks/spaghetti_classification.ipynb      |  634 ++++++
 6 files changed, 6636 insertions(+), 71 deletions(-)
 create mode 100644 "notebooks/Ergebnisse_f\303\274r_Piotr.ipynb"
 create mode 100644 notebooks/data_clustering.ipynb
 create mode 100644 notebooks/data_modeling_beans.ipynb
 create mode 100644 notebooks/data_penguin_clustering.ipynb
 create mode 100644 notebooks/spaghetti_classification.ipynb

diff --git "a/notebooks/Ergebnisse_f\303\274r_Piotr.ipynb" "b/notebooks/Ergebnisse_f\303\274r_Piotr.ipynb"
new file mode 100644
index 0000000..b06e225
--- /dev/null
+++ "b/notebooks/Ergebnisse_f\303\274r_Piotr.ipynb"
@@ -0,0 +1,266 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Daten der Befragung auslesen\n",
+    "dataset = pd.read_stata('../data/external/ZA6647_v1-0-0.dta')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Unerwünschte Spalten definieren und löschen\n",
+    "cols_to_delete = ['studyno', 'doi', 'version', 'edition', 'survey']\n",
+    "dataset = dataset.drop(columns=cols_to_delete)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Spalten für Alter und Geschlecht umbenennen\n",
+    "cols_to_rename = {\n",
+    "    'd1': 'age',\n",
+    "    'd2': 'gender',\n",
+    "}\n",
+    "dataset = dataset.rename(columns=cols_to_rename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "country                                             FR - France  BE - Belgium  \\\nq8                                                                              \nUse it if the product looks all right and the p...          637           531   \nYou  throw it away                                          266           368   \nYou never look at  dates (DO NOT READ OUT)                    2             1   \nIt depends on the type of food (DO NOT READ OUT)             93            81   \nDK/NA                                                        10            20   \n\ncountry                                             NL - The Netherlands  \\\nq8                                                                         \nUse it if the product looks all right and the p...                   598   \nYou  throw it away                                                   284   \nYou never look at  dates (DO NOT READ OUT)                             4   \nIt depends on the type of food (DO NOT READ OUT)                      93   \nDK/NA                                                                 23   \n\ncountry                                             DE - Germany  IT - Italy  \\\nq8                                                                             \nUse it if the product looks all right and the p...           764         564   \nYou  throw it away                                           147         313   \nYou never look at  dates (DO NOT READ OUT)                     2           1   \nIt depends on the type of food (DO NOT READ OUT)              52         104   \nDK/NA                                                         35          21   \n\ncountry                                             LU - Luxembourg  \\\nq8                                                                    \nUse it if the product looks all right and the p...              317   \nYou  throw it away                                              130   \nYou never look at  dates (DO NOT READ OUT)                        2   \nIt depends on the type of food (DO NOT READ OUT)                 50   \nDK/NA                                                             3   \n\ncountry                                             DK - Denmark  \\\nq8                                                                 \nUse it if the product looks all right and the p...           563   \nYou  throw it away                                           296   \nYou never look at  dates (DO NOT READ OUT)                     2   \nIt depends on the type of food (DO NOT READ OUT)             119   \nDK/NA                                                         27   \n\ncountry                                             IE - Ireland  \\\nq8                                                                 \nUse it if the product looks all right and the p...           474   \nYou  throw it away                                           439   \nYou never look at  dates (DO NOT READ OUT)                     0   \nIt depends on the type of food (DO NOT READ OUT)              76   \nDK/NA                                                         11   \n\ncountry                                             GB - United Kingdom  \\\nq8                                                                        \nUse it if the product looks all right and the p...                  566   \nYou  throw it away                                                  329   \nYou never look at  dates (DO NOT READ OUT)                            0   \nIt depends on the type of food (DO NOT READ OUT)                     86   \nDK/NA                                                                21   \n\ncountry                                             GR - Greece  ...  \\\nq8                                                               ...   \nUse it if the product looks all right and the p...          316  ...   \nYou  throw it away                                          609  ...   \nYou never look at  dates (DO NOT READ OUT)                    1  ...   \nIt depends on the type of food (DO NOT READ OUT)             64  ...   \nDK/NA                                                        10  ...   \n\ncountry                                             HU - Hungary  LV - Latvia  \\\nq8                                                                              \nUse it if the product looks all right and the p...           564          608   \nYou  throw it away                                           319          268   \nYou never look at  dates (DO NOT READ OUT)                     9            3   \nIt depends on the type of food (DO NOT READ OUT)              80           84   \nDK/NA                                                         31           40   \n\ncountry                                             LT - Lithuania  \\\nq8                                                                   \nUse it if the product looks all right and the p...             487   \nYou  throw it away                                             371   \nYou never look at  dates (DO NOT READ OUT)                       6   \nIt depends on the type of food (DO NOT READ OUT)                69   \nDK/NA                                                           67   \n\ncountry                                             MT - Malta  PL - Poland  \\\nq8                                                                            \nUse it if the product looks all right and the p...         169          523   \nYou  throw it away                                         266          368   \nYou never look at  dates (DO NOT READ OUT)                   2            0   \nIt depends on the type of food (DO NOT READ OUT)            66           60   \nDK/NA                                                       12           49   \n\ncountry                                             SK - Slovakia  \\\nq8                                                                  \nUse it if the product looks all right and the p...            493   \nYou  throw it away                                            416   \nYou never look at  dates (DO NOT READ OUT)                      2   \nIt depends on the type of food (DO NOT READ OUT)               79   \nDK/NA                                                          27   \n\ncountry                                             SI - Slovenia  \\\nq8                                                                  \nUse it if the product looks all right and the p...            699   \nYou  throw it away                                            193   \nYou never look at  dates (DO NOT READ OUT)                      4   \nIt depends on the type of food (DO NOT READ OUT)               89   \nDK/NA                                                          20   \n\ncountry                                             BG - Bulgaria  \\\nq8                                                                  \nUse it if the product looks all right and the p...            361   \nYou  throw it away                                            559   \nYou never look at  dates (DO NOT READ OUT)                      2   \nIt depends on the type of food (DO NOT READ OUT)               60   \nDK/NA                                                          19   \n\ncountry                                             RO - Romania  HR - Croatia  \nq8                                                                              \nUse it if the product looks all right and the p...           256           553  \nYou  throw it away                                           654           333  \nYou never look at  dates (DO NOT READ OUT)                     1             6  \nIt depends on the type of food (DO NOT READ OUT)              57            89  \nDK/NA                                                         48            19  \n\n[5 rows x 28 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>country</th>\n      <th>FR - France</th>\n      <th>BE - Belgium</th>\n      <th>NL - The Netherlands</th>\n      <th>DE - Germany</th>\n      <th>IT - Italy</th>\n      <th>LU - Luxembourg</th>\n      <th>DK - Denmark</th>\n      <th>IE - Ireland</th>\n      <th>GB - United Kingdom</th>\n      <th>GR - Greece</th>\n      <th>...</th>\n      <th>HU - Hungary</th>\n      <th>LV - Latvia</th>\n      <th>LT - Lithuania</th>\n      <th>MT - Malta</th>\n      <th>PL - Poland</th>\n      <th>SK - Slovakia</th>\n      <th>SI - Slovenia</th>\n      <th>BG - Bulgaria</th>\n      <th>RO - Romania</th>\n      <th>HR - Croatia</th>\n    </tr>\n    <tr>\n      <th>q8</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Use it if the product looks all right and the packaging is not damaged</th>\n      <td>637</td>\n      <td>531</td>\n      <td>598</td>\n      <td>764</td>\n      <td>564</td>\n      <td>317</td>\n      <td>563</td>\n      <td>474</td>\n      <td>566</td>\n      <td>316</td>\n      <td>...</td>\n      <td>564</td>\n      <td>608</td>\n      <td>487</td>\n      <td>169</td>\n      <td>523</td>\n      <td>493</td>\n      <td>699</td>\n      <td>361</td>\n      <td>256</td>\n      <td>553</td>\n    </tr>\n    <tr>\n      <th>You  throw it away</th>\n      <td>266</td>\n      <td>368</td>\n      <td>284</td>\n      <td>147</td>\n      <td>313</td>\n      <td>130</td>\n      <td>296</td>\n      <td>439</td>\n      <td>329</td>\n      <td>609</td>\n      <td>...</td>\n      <td>319</td>\n      <td>268</td>\n      <td>371</td>\n      <td>266</td>\n      <td>368</td>\n      <td>416</td>\n      <td>193</td>\n      <td>559</td>\n      <td>654</td>\n      <td>333</td>\n    </tr>\n    <tr>\n      <th>You never look at  dates (DO NOT READ OUT)</th>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>9</td>\n      <td>3</td>\n      <td>6</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>It depends on the type of food (DO NOT READ OUT)</th>\n      <td>93</td>\n      <td>81</td>\n      <td>93</td>\n      <td>52</td>\n      <td>104</td>\n      <td>50</td>\n      <td>119</td>\n      <td>76</td>\n      <td>86</td>\n      <td>64</td>\n      <td>...</td>\n      <td>80</td>\n      <td>84</td>\n      <td>69</td>\n      <td>66</td>\n      <td>60</td>\n      <td>79</td>\n      <td>89</td>\n      <td>60</td>\n      <td>57</td>\n      <td>89</td>\n    </tr>\n    <tr>\n      <th>DK/NA</th>\n      <td>10</td>\n      <td>20</td>\n      <td>23</td>\n      <td>35</td>\n      <td>21</td>\n      <td>3</td>\n      <td>27</td>\n      <td>11</td>\n      <td>21</td>\n      <td>10</td>\n      <td>...</td>\n      <td>31</td>\n      <td>40</td>\n      <td>67</td>\n      <td>12</td>\n      <td>49</td>\n      <td>27</td>\n      <td>20</td>\n      <td>19</td>\n      <td>48</td>\n      <td>19</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 28 columns</p>\n</div>"
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Übersicht über Antworten zu Frage 8 nach Land\n",
+    "pd.crosstab(dataset['q8'], dataset['country'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Anzahl der Antworten \"Wegwerfen\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "RO - Romania              654\nGR - Greece               609\nBG - Bulgaria             559\nIE - Ireland              439\nPT - Portugal             419\nSK - Slovakia             416\nLT - Lithuania            371\nBE - Belgium              368\nPL - Poland               368\nHR - Croatia              333\nGB - United Kingdom       329\nHU - Hungary              319\nES -Spain                 316\nIT - Italy                313\nCY - Cyprus (Republic)    300\nDK - Denmark              296\nNL - The Netherlands      284\nCZ - Czech Republic       283\nLV - Latvia               268\nFR - France               266\nMT - Malta                266\nFI - Finland              257\nSI - Slovenia             193\nEE - Estonia              192\nSE - Sweden               167\nDE - Germany              147\nLU - Luxembourg           130\nAT - Austria              116\nName: country, dtype: int64"
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "throw_per_country= dataset[dataset['q8']=='You  throw it away']['country'].value_counts()\n",
+    "throw_per_country"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Anzahl der Befragten je Land:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "SK - Slovakia             1017\nRO - Romania              1016\nFR - France               1008\nDK - Denmark              1007\nES -Spain                 1007\nSI - Slovenia             1005\nLV - Latvia               1003\nIT - Italy                1003\nAT - Austria              1003\nHU - Hungary              1003\nNL - The Netherlands      1002\nGB - United Kingdom       1002\nPT - Portugal             1002\nBE - Belgium              1001\nBG - Bulgaria             1001\nFI - Finland              1001\nDE - Germany              1000\nIE - Ireland              1000\nHR - Croatia              1000\nGR - Greece               1000\nCZ - Czech Republic       1000\nEE - Estonia              1000\nLT - Lithuania            1000\nPL - Poland               1000\nSE - Sweden               1000\nMT - Malta                 515\nCY - Cyprus (Republic)     503\nLU - Luxembourg            502\nName: country, dtype: int64"
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "participants_per_country = dataset['country'].value_counts()\n",
+    "participants_per_country"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prozentanteil der \"Wegwerfer\" an der Gesamtheit der Befragten:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "AT - Austria              11.565304\nDE - Germany              14.700000\nSE - Sweden               16.700000\nEE - Estonia              19.200000\nSI - Slovenia             19.203980\nFI - Finland              25.674326\nLU - Luxembourg           25.896414\nFR - France               26.388889\nLV - Latvia               26.719840\nCZ - Czech Republic       28.300000\nNL - The Netherlands      28.343313\nDK - Denmark              29.394240\nIT - Italy                31.206381\nES -Spain                 31.380338\nHU - Hungary              31.804586\nGB - United Kingdom       32.834331\nHR - Croatia              33.300000\nBE - Belgium              36.763237\nPL - Poland               36.800000\nLT - Lithuania            37.100000\nSK - Slovakia             40.904621\nPT - Portugal             41.816367\nIE - Ireland              43.900000\nMT - Malta                51.650485\nBG - Bulgaria             55.844156\nCY - Cyprus (Republic)    59.642147\nGR - Greece               60.900000\nRO - Romania              64.370079\nName: country, dtype: float64"
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percentage_throw_per_country = throw_per_country / participants_per_country*100\n",
+    "percentage_throw_per_country\n",
+    "#Prozente aufsteigend sortieren\n",
+    "sorted_percentage_throw_per_country = percentage_throw_per_country.sort_values(ascending=True)\n",
+    "sorted_percentage_throw_per_country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x864 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "throwplot = sorted_percentage_throw_per_country.plot.barh(title='Prozent des Wegwurfs von Lebensmitteln nach empfohlener Vebrauchsdauer pro Land', figsize=(8,12),width=0.75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Ergebnis:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "In Rumänien, Griechenland und Zypern werden Lebensmittel nach der empfohlenen Verbrauchsdauer am ehesten weggeworfen, wohingegen sie in Schweden, Deutschland und Österreich am wenigsten weggeworfen werden."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Download des Graphs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig = throwplot.get_figure()\n",
+    "fig.savefig('throwplot.png', dpi=300, facecolor='white', bbox_inches = 'tight')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/notebooks/data_clustering.ipynb b/notebooks/data_clustering.ipynb
new file mode 100644
index 0000000..8e8271a
--- /dev/null
+++ b/notebooks/data_clustering.ipynb
@@ -0,0 +1,1719 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data=pd.read_parquet('../data/interim/data_numeric.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n14710            3.0   39          16.0             2.0   43.0000   25.0000   \n23335            4.0   61          22.0             2.0   46.0000   25.0000   \n2210             4.0   53          22.0             3.0   39.0000   22.0000   \n25173            4.0   53          18.0             1.0   46.0000   15.0000   \n10242            3.0   64          45.0             2.0   47.3333   13.3333   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n14710                      0.0                   0.0         1.0   \n23335                      0.0                   0.0         2.0   \n2210                       1.0                   0.0         3.0   \n25173                      1.0                   0.0         1.0   \n10242                      NaN                   NaN         0.0   \n\n       population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n14710                 2.0  0.2000                             1              1  \n23335                 2.0  0.5000                             0              1  \n2210                  1.0  1.0000                             0              0  \n25173                 1.0  0.2850                             0              1  \n10242                 0.0  0.1139                             0              0  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14710</th>\n      <td>3.0</td>\n      <td>39</td>\n      <td>16.0</td>\n      <td>2.0</td>\n      <td>43.0000</td>\n      <td>25.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.2000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>23335</th>\n      <td>4.0</td>\n      <td>61</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>46.0000</td>\n      <td>25.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.5000</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2210</th>\n      <td>4.0</td>\n      <td>53</td>\n      <td>22.0</td>\n      <td>3.0</td>\n      <td>39.0000</td>\n      <td>22.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25173</th>\n      <td>4.0</td>\n      <td>53</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>46.0000</td>\n      <td>15.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.2850</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10242</th>\n      <td>3.0</td>\n      <td>64</td>\n      <td>45.0</td>\n      <td>2.0</td>\n      <td>47.3333</td>\n      <td>13.3333</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.sample(5)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0            2.0   64          13.0             2.0   50.8333       4.0   \n1            4.0   87          15.0             1.0   50.8333       4.0   \n2            4.0   73          30.0             1.0   50.8333       4.0   \n3            4.0   55          14.0             2.0   50.8333       4.0   \n4            4.0   74          24.0             1.0   50.8333       4.0   \n\n   best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                      2.0                   1.0         0.0   \n1                      1.0                   0.0         0.0   \n2                      1.0                   1.0         0.0   \n3                      NaN                   NaN         0.0   \n4                      0.0                   0.0         0.0   \n\n   population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n0                 0.0  0.1139                             0              1  \n1                 1.0  0.1139                             0              1  \n2                 1.0  0.1139                             0              0  \n3                 2.0  0.0000                             1              1  \n4                 1.0  0.1139                             1              1  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>64</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>55</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Daten ersetzen Beispiel!\n",
+    "\n",
+    "data.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "   look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0            2.0  164          13.0             2.0   50.8333       4.0   \n1            4.0   87          15.0             1.0   50.8333       4.0   \n2            4.0   73          30.0             1.0   50.8333       4.0   \n3            4.0   55          14.0             2.0   50.8333       4.0   \n4            4.0   74          24.0             1.0   50.8333       4.0   \n\n   best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                      2.0                   1.0         0.0   \n1                      1.0                   0.0         0.0   \n2                      1.0                   1.0         0.0   \n3                      NaN                   NaN         0.0   \n4                      0.0                   0.0         0.0   \n\n   population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n0                 0.0  0.1139                             0              1  \n1                 1.0  0.1139                             0              1  \n2                 1.0  0.1139                             0              0  \n3                 2.0  0.0000                             1              1  \n4                 1.0  0.1139                             1              1  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>164</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>55</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_copy = data.copy(deep=True)\n",
+    "data_copy['age'] = data_copy['age'].replace({64:164})\n",
+    "data_copy.head()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates    age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0              False  False         False           False     False     False   \n1              False  False         False           False     False     False   \n2              False  False         False           False     False     False   \n3              False  False         False           False     False     False   \n4              False  False         False           False     False     False   \n...              ...    ...           ...             ...       ...       ...   \n26596          False  False         False           False     False     False   \n26597          False  False         False           False     False     False   \n26598          False  False         False           False     False     False   \n26599          False  False         False           False     False     False   \n26600          False  False         False           False     False     False   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                        False                 False       False   \n1                        False                 False       False   \n2                        False                 False       False   \n3                         True                  True       False   \n4                        False                 False       False   \n...                        ...                   ...         ...   \n26596                    False                 False       False   \n26597                    False                 False       False   \n26598                    False                  True       False   \n26599                    False                 False       False   \n26600                    False                 False       False   \n\n       population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n0                   False   False                         False          False  \n1                   False   False                         False          False  \n2                   False   False                         False          False  \n3                   False   False                         False          False  \n4                   False   False                         False          False  \n...                   ...     ...                           ...            ...  \n26596               False   False                         False          False  \n26597               False   False                         False          False  \n26598               False   False                         False          False  \n26599               False   False                         False          False  \n26600               False   False                         False          False  \n\n[26601 rows x 13 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>26598</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>26601 rows × 13 columns</p>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "look_at_dates                    193\nage                                0\nage_stop_edu                    1920\nhousehold_size                   159\ncntrylat                           0\ncntrylon                           0\nbest_before_meaning_map         1122\nvalidity_meaning_map            1126\nwork_scale                        85\npopulation_density               199\nsalary                           111\nno_date_spaghetti_throw_away       0\ngender_Female                      0\ndtype: int64"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "60    837\n70    724\n50    700\n65    680\n55    586\n     ... \n94     11\n95      5\n98      2\n97      2\n96      1\nName: age, Length: 84, dtype: int64"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['age'].value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.8509454531784519"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data.dropna())/len(data)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "data = data.dropna()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0        False\n1        False\n2        False\n4        False\n5        False\n         ...  \n26594    False\n26596    False\n26597    False\n26599    False\n26600    False\nLength: 22636, dtype: bool"
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.duplicated()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n963              4.0   62          20.0             2.0   50.8333    4.0000   \n1744             4.0   86          23.0             1.0   56.0000   10.0000   \n1997             4.0   66          22.0             2.0   56.0000   10.0000   \n2789             4.0   41          18.0             3.0   39.0000   22.0000   \n2802             4.0   40          18.0             2.0   39.0000   22.0000   \n2911             4.0   50          22.0             5.0   39.0000   22.0000   \n3668             4.0   49          25.0             2.0   40.0000   -4.0000   \n5703             4.0   68          17.0             2.0   46.0000    2.0000   \n6405             4.0   56          22.0             1.0   53.0000   -8.0000   \n10266            4.0   62          19.0             2.0   47.3333   13.3333   \n11639            3.0   74          15.0             2.0   62.0000   15.0000   \n11803            4.0   78          40.0             1.0   62.0000   15.0000   \n11911            4.0   82          14.0             2.0   62.0000   15.0000   \n13239            4.0   70          16.0             1.0   51.0000    9.0000   \n13799            4.0   46          21.0             2.0   54.0000   -2.0000   \n14013            4.0   78          15.0             1.0   54.0000   -2.0000   \n14724            4.0   60          23.0             2.0   43.0000   25.0000   \n15060            4.0   70          25.0             3.0   43.0000   25.0000   \n17912            4.0   47          25.0             3.0   59.0000   26.0000   \n18304            4.0   70          18.0             1.0   47.0000   20.0000   \n18400            4.0   68          18.0             1.0   47.0000   20.0000   \n18589            4.0   65          23.0             2.0   47.0000   20.0000   \n18792            4.0   67          18.0             2.0   47.0000   20.0000   \n18876            4.0   67          14.0             2.0   47.0000   20.0000   \n19997            1.0   74          19.0             1.0   57.0000   25.0000   \n22264            3.0   59          24.0             2.0   52.0000   20.0000   \n22310            4.0   65          21.0             1.0   52.0000   20.0000   \n22916            4.0   19          19.0             1.0   46.0000   25.0000   \n23810            4.0   76          19.0             1.0   48.6667   19.5000   \n24203            4.0   43          18.0             4.0   48.6667   19.5000   \n24669            4.0   62          19.0             2.0   46.0000   15.0000   \n25211            4.0   75          20.0             2.0   46.0000   15.0000   \n25900            4.0   60          18.0             2.0   45.1667   15.5000   \n25982            4.0   60          18.0             2.0   45.1667   15.5000   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n963                        1.0                   1.0         0.0   \n1744                       1.0                   1.0         0.0   \n1997                       2.0                   2.0         0.0   \n2789                       1.0                   0.0         0.0   \n2802                       2.0                   2.0         0.0   \n2911                       0.0                   0.0         2.0   \n3668                       1.0                   0.0         3.0   \n5703                       2.0                   2.0         0.0   \n6405                       1.0                   0.0         2.0   \n10266                      2.0                   2.0         0.0   \n11639                      1.0                   1.0         0.0   \n11803                      2.0                   1.0         0.0   \n11911                      1.0                   1.0         0.0   \n13239                      1.0                   1.0         0.0   \n13799                      1.0                   0.0         2.0   \n14013                      2.0                   2.0         0.0   \n14724                      1.0                   1.0         0.0   \n15060                      0.0                   0.0         0.0   \n17912                      1.0                   0.0         2.0   \n18304                      1.0                   1.0         0.0   \n18400                      0.0                   0.0         0.0   \n18589                      0.0                   0.0         0.0   \n18792                      0.0                   0.0         0.0   \n18876                      1.0                   0.0         0.0   \n19997                      2.0                   0.0         0.0   \n22264                      0.0                   0.0         2.0   \n22310                      2.0                   0.0         0.0   \n22916                      0.0                   0.0         0.0   \n23810                      0.0                   0.0         0.0   \n24203                      0.0                   0.0         2.0   \n24669                      2.0                   2.0         0.0   \n25211                      1.0                   2.0         0.0   \n25900                      2.0                   2.0         0.0   \n25982                      0.0                   0.0         0.0   \n\n       population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n963                   0.0  0.1139                             0              1  \n1744                  2.0  0.1139                             1              1  \n1997                  1.0  0.1139                             0              1  \n2789                  2.0  0.0000                             1              1  \n2802                  2.0  0.0000                             1              1  \n2911                  1.0  0.3200                             0              1  \n3668                  2.0  1.0000                             0              0  \n5703                  0.0  0.1139                             0              1  \n6405                  2.0  0.5000                             0              1  \n10266                 0.0  0.1139                             0              1  \n11639                 1.0  0.1139                             0              1  \n11803                 2.0  0.1139                             0              1  \n11911                 1.0  0.1139                             0              1  \n13239                 1.0  0.1139                             0              0  \n13799                 2.0  0.5000                             0              1  \n14013                 1.0  0.1139                             1              1  \n14724                 2.0  0.1139                             0              1  \n15060                 2.0  0.1139                             0              1  \n17912                 0.0  0.5800                             0              1  \n18304                 2.0  0.1139                             0              1  \n18400                 2.0  0.1139                             0              1  \n18589                 2.0  0.1139                             1              1  \n18792                 1.0  0.1139                             1              1  \n18876                 1.0  0.1139                             0              1  \n19997                 1.0  0.1139                             0              1  \n22264                 1.0  0.3200                             0              1  \n22310                 1.0  0.1139                             0              1  \n22916                 1.0  0.0200                             1              1  \n23810                 1.0  0.1139                             0              1  \n24203                 0.0  0.3645                             0              1  \n24669                 1.0  0.1139                             0              1  \n25211                 2.0  0.1139                             0              1  \n25900                 1.0  0.1139                             0              1  \n25982                 0.0  0.0000                             1              1  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>963</th>\n      <td>4.0</td>\n      <td>62</td>\n      <td>20.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1744</th>\n      <td>4.0</td>\n      <td>86</td>\n      <td>23.0</td>\n      <td>1.0</td>\n      <td>56.0000</td>\n      <td>10.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1997</th>\n      <td>4.0</td>\n      <td>66</td>\n      <td>22.0</td>\n      <td>2.0</td>\n      <td>56.0000</td>\n      <td>10.0000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2789</th>\n      <td>4.0</td>\n      <td>41</td>\n      <td>18.0</td>\n      <td>3.0</td>\n      <td>39.0000</td>\n      <td>22.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2802</th>\n      <td>4.0</td>\n      <td>40</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>39.0000</td>\n      <td>22.0000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2911</th>\n      <td>4.0</td>\n      <td>50</td>\n      <td>22.0</td>\n      <td>5.0</td>\n      <td>39.0000</td>\n      <td>22.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3668</th>\n      <td>4.0</td>\n      <td>49</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>40.0000</td>\n      <td>-4.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5703</th>\n      <td>4.0</td>\n      <td>68</td>\n      <td>17.0</td>\n      <td>2.0</td>\n      <td>46.0000</td>\n      <td>2.0000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6405</th>\n      <td>4.0</td>\n      <td>56</td>\n      <td>22.0</td>\n      <td>1.0</td>\n      <td>53.0000</td>\n      <td>-8.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.5000</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10266</th>\n      <td>4.0</td>\n      <td>62</td>\n      <td>19.0</td>\n      <td>2.0</td>\n      <td>47.3333</td>\n      <td>13.3333</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11639</th>\n      <td>3.0</td>\n      <td>74</td>\n      <td>15.0</td>\n      <td>2.0</td>\n      <td>62.0000</td>\n      <td>15.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11803</th>\n      <td>4.0</td>\n      <td>78</td>\n      <td>40.0</td>\n      <td>1.0</td>\n      <td>62.0000</td>\n      <td>15.0000</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>11911</th>\n      <td>4.0</td>\n      <td>82</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>62.0000</td>\n      <td>15.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13239</th>\n      <td>4.0</td>\n      <td>70</td>\n      <td>16.0</td>\n      <td>1.0</td>\n      <td>51.0000</td>\n      <td>9.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13799</th>\n      <td>4.0</td>\n      <td>46</td>\n      <td>21.0</td>\n      <td>2.0</td>\n      <td>54.0000</td>\n      <td>-2.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.5000</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14013</th>\n      <td>4.0</td>\n      <td>78</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>54.0000</td>\n      <td>-2.0000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14724</th>\n      <td>4.0</td>\n      <td>60</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>43.0000</td>\n      <td>25.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15060</th>\n      <td>4.0</td>\n      <td>70</td>\n      <td>25.0</td>\n      <td>3.0</td>\n      <td>43.0000</td>\n      <td>25.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>17912</th>\n      <td>4.0</td>\n      <td>47</td>\n      <td>25.0</td>\n      <td>3.0</td>\n      <td>59.0000</td>\n      <td>26.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.5800</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18304</th>\n      <td>4.0</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>47.0000</td>\n      <td>20.0000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18400</th>\n      <td>4.0</td>\n      <td>68</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>47.0000</td>\n      <td>20.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18589</th>\n      <td>4.0</td>\n      <td>65</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>47.0000</td>\n      <td>20.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18792</th>\n      <td>4.0</td>\n      <td>67</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>47.0000</td>\n      <td>20.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18876</th>\n      <td>4.0</td>\n      <td>67</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>47.0000</td>\n      <td>20.0000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19997</th>\n      <td>1.0</td>\n      <td>74</td>\n      <td>19.0</td>\n      <td>1.0</td>\n      <td>57.0000</td>\n      <td>25.0000</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>22264</th>\n      <td>3.0</td>\n      <td>59</td>\n      <td>24.0</td>\n      <td>2.0</td>\n      <td>52.0000</td>\n      <td>20.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>22310</th>\n      <td>4.0</td>\n      <td>65</td>\n      <td>21.0</td>\n      <td>1.0</td>\n      <td>52.0000</td>\n      <td>20.0000</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>22916</th>\n      <td>4.0</td>\n      <td>19</td>\n      <td>19.0</td>\n      <td>1.0</td>\n      <td>46.0000</td>\n      <td>25.0000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0200</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>23810</th>\n      <td>4.0</td>\n      <td>76</td>\n      <td>19.0</td>\n      <td>1.0</td>\n      <td>48.6667</td>\n      <td>19.5000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>24203</th>\n      <td>4.0</td>\n      <td>43</td>\n      <td>18.0</td>\n      <td>4.0</td>\n      <td>48.6667</td>\n      <td>19.5000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.3645</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>24669</th>\n      <td>4.0</td>\n      <td>62</td>\n      <td>19.0</td>\n      <td>2.0</td>\n      <td>46.0000</td>\n      <td>15.0000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>25211</th>\n      <td>4.0</td>\n      <td>75</td>\n      <td>20.0</td>\n      <td>2.0</td>\n      <td>46.0000</td>\n      <td>15.0000</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>25900</th>\n      <td>4.0</td>\n      <td>60</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5000</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>25982</th>\n      <td>4.0</td>\n      <td>60</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.duplicated()==True]"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zeilen vor Duplikatentfernung 22636\n",
+      "Zeilen nach Duplikatentfernung 22602\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Zeilen vor Duplikatentfernung', len(data))\n",
+    "\n",
+    "data = data[data.duplicated()==False]\n",
+    "\n",
+    "print('Zeilen nach Duplikatentfernung', len(data))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [],
+   "source": [
+    "labels = data['no_date_spaghetti_throw_away']\n",
+    "data = data.drop(columns='no_date_spaghetti_throw_away')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         look_at_dates       age  age_stop_edu  \\\nlook_at_dates                 1.000000 -0.018086      0.048131   \nage                          -0.018086  1.000000     -0.067765   \nage_stop_edu                  0.048131 -0.067765      1.000000   \nhousehold_size               -0.007688 -0.246758     -0.033383   \ncntrylat                     -0.046038  0.116217      0.154812   \ncntrylon                     -0.023512 -0.024962      0.095928   \nbest_before_meaning_map      -0.026345  0.065229      0.013119   \nvalidity_meaning_map         -0.059237  0.106370     -0.015260   \nwork_scale                    0.002063 -0.505467      0.155339   \npopulation_density            0.058242 -0.013458      0.116166   \nsalary                        0.001092 -0.338823      0.182410   \ngender_Female                 0.120618  0.074436     -0.047995   \n\n                         household_size  cntrylat  cntrylon  \\\nlook_at_dates                 -0.007688 -0.046038 -0.023512   \nage                           -0.246758  0.116217 -0.024962   \nage_stop_edu                  -0.033383  0.154812  0.095928   \nhousehold_size                 1.000000 -0.166927  0.052551   \ncntrylat                      -0.166927  1.000000  0.156853   \ncntrylon                       0.052551  0.156853  1.000000   \nbest_before_meaning_map       -0.059370  0.128519 -0.127889   \nvalidity_meaning_map          -0.044758  0.101711 -0.085519   \nwork_scale                     0.149625 -0.017027 -0.011246   \npopulation_density            -0.082184 -0.010266  0.114569   \nsalary                         0.077844  0.038361  0.009702   \ngender_Female                 -0.022459 -0.038730  0.011561   \n\n                         best_before_meaning_map  validity_meaning_map  \\\nlook_at_dates                          -0.026345             -0.059237   \nage                                     0.065229              0.106370   \nage_stop_edu                            0.013119             -0.015260   \nhousehold_size                         -0.059370             -0.044758   \ncntrylat                                0.128519              0.101711   \ncntrylon                               -0.127889             -0.085519   \nbest_before_meaning_map                 1.000000              0.378513   \nvalidity_meaning_map                    0.378513              1.000000   \nwork_scale                             -0.032185             -0.064258   \npopulation_density                     -0.044443             -0.056563   \nsalary                                 -0.016188             -0.052016   \ngender_Female                           0.000627              0.011457   \n\n                         work_scale  population_density    salary  \\\nlook_at_dates              0.002063            0.058242  0.001092   \nage                       -0.505467           -0.013458 -0.338823   \nage_stop_edu               0.155339            0.116166  0.182410   \nhousehold_size             0.149625           -0.082184  0.077844   \ncntrylat                  -0.017027           -0.010266  0.038361   \ncntrylon                  -0.011246            0.114569  0.009702   \nbest_before_meaning_map   -0.032185           -0.044443 -0.016188   \nvalidity_meaning_map      -0.064258           -0.056563 -0.052016   \nwork_scale                 1.000000            0.034063  0.835259   \npopulation_density         0.034063            1.000000  0.063467   \nsalary                     0.835259            0.063467  1.000000   \ngender_Female             -0.126720            0.002421 -0.161365   \n\n                         gender_Female  \nlook_at_dates                 0.120618  \nage                           0.074436  \nage_stop_edu                 -0.047995  \nhousehold_size               -0.022459  \ncntrylat                     -0.038730  \ncntrylon                      0.011561  \nbest_before_meaning_map       0.000627  \nvalidity_meaning_map          0.011457  \nwork_scale                   -0.126720  \npopulation_density            0.002421  \nsalary                       -0.161365  \ngender_Female                 1.000000  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>look_at_dates</th>\n      <td>1.000000</td>\n      <td>-0.018086</td>\n      <td>0.048131</td>\n      <td>-0.007688</td>\n      <td>-0.046038</td>\n      <td>-0.023512</td>\n      <td>-0.026345</td>\n      <td>-0.059237</td>\n      <td>0.002063</td>\n      <td>0.058242</td>\n      <td>0.001092</td>\n      <td>0.120618</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>-0.018086</td>\n      <td>1.000000</td>\n      <td>-0.067765</td>\n      <td>-0.246758</td>\n      <td>0.116217</td>\n      <td>-0.024962</td>\n      <td>0.065229</td>\n      <td>0.106370</td>\n      <td>-0.505467</td>\n      <td>-0.013458</td>\n      <td>-0.338823</td>\n      <td>0.074436</td>\n    </tr>\n    <tr>\n      <th>age_stop_edu</th>\n      <td>0.048131</td>\n      <td>-0.067765</td>\n      <td>1.000000</td>\n      <td>-0.033383</td>\n      <td>0.154812</td>\n      <td>0.095928</td>\n      <td>0.013119</td>\n      <td>-0.015260</td>\n      <td>0.155339</td>\n      <td>0.116166</td>\n      <td>0.182410</td>\n      <td>-0.047995</td>\n    </tr>\n    <tr>\n      <th>household_size</th>\n      <td>-0.007688</td>\n      <td>-0.246758</td>\n      <td>-0.033383</td>\n      <td>1.000000</td>\n      <td>-0.166927</td>\n      <td>0.052551</td>\n      <td>-0.059370</td>\n      <td>-0.044758</td>\n      <td>0.149625</td>\n      <td>-0.082184</td>\n      <td>0.077844</td>\n      <td>-0.022459</td>\n    </tr>\n    <tr>\n      <th>cntrylat</th>\n      <td>-0.046038</td>\n      <td>0.116217</td>\n      <td>0.154812</td>\n      <td>-0.166927</td>\n      <td>1.000000</td>\n      <td>0.156853</td>\n      <td>0.128519</td>\n      <td>0.101711</td>\n      <td>-0.017027</td>\n      <td>-0.010266</td>\n      <td>0.038361</td>\n      <td>-0.038730</td>\n    </tr>\n    <tr>\n      <th>cntrylon</th>\n      <td>-0.023512</td>\n      <td>-0.024962</td>\n      <td>0.095928</td>\n      <td>0.052551</td>\n      <td>0.156853</td>\n      <td>1.000000</td>\n      <td>-0.127889</td>\n      <td>-0.085519</td>\n      <td>-0.011246</td>\n      <td>0.114569</td>\n      <td>0.009702</td>\n      <td>0.011561</td>\n    </tr>\n    <tr>\n      <th>best_before_meaning_map</th>\n      <td>-0.026345</td>\n      <td>0.065229</td>\n      <td>0.013119</td>\n      <td>-0.059370</td>\n      <td>0.128519</td>\n      <td>-0.127889</td>\n      <td>1.000000</td>\n      <td>0.378513</td>\n      <td>-0.032185</td>\n      <td>-0.044443</td>\n      <td>-0.016188</td>\n      <td>0.000627</td>\n    </tr>\n    <tr>\n      <th>validity_meaning_map</th>\n      <td>-0.059237</td>\n      <td>0.106370</td>\n      <td>-0.015260</td>\n      <td>-0.044758</td>\n      <td>0.101711</td>\n      <td>-0.085519</td>\n      <td>0.378513</td>\n      <td>1.000000</td>\n      <td>-0.064258</td>\n      <td>-0.056563</td>\n      <td>-0.052016</td>\n      <td>0.011457</td>\n    </tr>\n    <tr>\n      <th>work_scale</th>\n      <td>0.002063</td>\n      <td>-0.505467</td>\n      <td>0.155339</td>\n      <td>0.149625</td>\n      <td>-0.017027</td>\n      <td>-0.011246</td>\n      <td>-0.032185</td>\n      <td>-0.064258</td>\n      <td>1.000000</td>\n      <td>0.034063</td>\n      <td>0.835259</td>\n      <td>-0.126720</td>\n    </tr>\n    <tr>\n      <th>population_density</th>\n      <td>0.058242</td>\n      <td>-0.013458</td>\n      <td>0.116166</td>\n      <td>-0.082184</td>\n      <td>-0.010266</td>\n      <td>0.114569</td>\n      <td>-0.044443</td>\n      <td>-0.056563</td>\n      <td>0.034063</td>\n      <td>1.000000</td>\n      <td>0.063467</td>\n      <td>0.002421</td>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <td>0.001092</td>\n      <td>-0.338823</td>\n      <td>0.182410</td>\n      <td>0.077844</td>\n      <td>0.038361</td>\n      <td>0.009702</td>\n      <td>-0.016188</td>\n      <td>-0.052016</td>\n      <td>0.835259</td>\n      <td>0.063467</td>\n      <td>1.000000</td>\n      <td>-0.161365</td>\n    </tr>\n    <tr>\n      <th>gender_Female</th>\n      <td>0.120618</td>\n      <td>0.074436</td>\n      <td>-0.047995</td>\n      <td>-0.022459</td>\n      <td>-0.038730</td>\n      <td>0.011561</td>\n      <td>0.000627</td>\n      <td>0.011457</td>\n      <td>-0.126720</td>\n      <td>0.002421</td>\n      <td>-0.161365</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr = data.corr()\n",
+    "corr"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                         look_at_dates   age  age_stop_edu  household_size  \\\nlook_at_dates                     1.00 -0.02          0.05           -0.01   \nage                              -0.02  1.00         -0.07           -0.25   \nage_stop_edu                      0.05 -0.07          1.00           -0.03   \nhousehold_size                   -0.01 -0.25         -0.03            1.00   \ncntrylat                         -0.05  0.12          0.15           -0.17   \ncntrylon                         -0.02 -0.02          0.10            0.05   \nbest_before_meaning_map          -0.03  0.07          0.01           -0.06   \nvalidity_meaning_map             -0.06  0.11         -0.02           -0.04   \nwork_scale                        0.00 -0.51          0.16            0.15   \npopulation_density                0.06 -0.01          0.12           -0.08   \nsalary                            0.00 -0.34          0.18            0.08   \ngender_Female                     0.12  0.07         -0.05           -0.02   \n\n                         cntrylat  cntrylon  best_before_meaning_map  \\\nlook_at_dates               -0.05     -0.02                    -0.03   \nage                          0.12     -0.02                     0.07   \nage_stop_edu                 0.15      0.10                     0.01   \nhousehold_size              -0.17      0.05                    -0.06   \ncntrylat                     1.00      0.16                     0.13   \ncntrylon                     0.16      1.00                    -0.13   \nbest_before_meaning_map      0.13     -0.13                     1.00   \nvalidity_meaning_map         0.10     -0.09                     0.38   \nwork_scale                  -0.02     -0.01                    -0.03   \npopulation_density          -0.01      0.11                    -0.04   \nsalary                       0.04      0.01                    -0.02   \ngender_Female               -0.04      0.01                     0.00   \n\n                         validity_meaning_map  work_scale  population_density  \\\nlook_at_dates                           -0.06        0.00                0.06   \nage                                      0.11       -0.51               -0.01   \nage_stop_edu                            -0.02        0.16                0.12   \nhousehold_size                          -0.04        0.15               -0.08   \ncntrylat                                 0.10       -0.02               -0.01   \ncntrylon                                -0.09       -0.01                0.11   \nbest_before_meaning_map                  0.38       -0.03               -0.04   \nvalidity_meaning_map                     1.00       -0.06               -0.06   \nwork_scale                              -0.06        1.00                0.03   \npopulation_density                      -0.06        0.03                1.00   \nsalary                                  -0.05        0.84                0.06   \ngender_Female                            0.01       -0.13                0.00   \n\n                         salary  gender_Female  \nlook_at_dates              0.00           0.12  \nage                       -0.34           0.07  \nage_stop_edu               0.18          -0.05  \nhousehold_size             0.08          -0.02  \ncntrylat                   0.04          -0.04  \ncntrylon                   0.01           0.01  \nbest_before_meaning_map   -0.02           0.00  \nvalidity_meaning_map      -0.05           0.01  \nwork_scale                 0.84          -0.13  \npopulation_density         0.06           0.00  \nsalary                     1.00          -0.16  \ngender_Female             -0.16           1.00  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>look_at_dates</th>\n      <td>1.00</td>\n      <td>-0.02</td>\n      <td>0.05</td>\n      <td>-0.01</td>\n      <td>-0.05</td>\n      <td>-0.02</td>\n      <td>-0.03</td>\n      <td>-0.06</td>\n      <td>0.00</td>\n      <td>0.06</td>\n      <td>0.00</td>\n      <td>0.12</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>-0.02</td>\n      <td>1.00</td>\n      <td>-0.07</td>\n      <td>-0.25</td>\n      <td>0.12</td>\n      <td>-0.02</td>\n      <td>0.07</td>\n      <td>0.11</td>\n      <td>-0.51</td>\n      <td>-0.01</td>\n      <td>-0.34</td>\n      <td>0.07</td>\n    </tr>\n    <tr>\n      <th>age_stop_edu</th>\n      <td>0.05</td>\n      <td>-0.07</td>\n      <td>1.00</td>\n      <td>-0.03</td>\n      <td>0.15</td>\n      <td>0.10</td>\n      <td>0.01</td>\n      <td>-0.02</td>\n      <td>0.16</td>\n      <td>0.12</td>\n      <td>0.18</td>\n      <td>-0.05</td>\n    </tr>\n    <tr>\n      <th>household_size</th>\n      <td>-0.01</td>\n      <td>-0.25</td>\n      <td>-0.03</td>\n      <td>1.00</td>\n      <td>-0.17</td>\n      <td>0.05</td>\n      <td>-0.06</td>\n      <td>-0.04</td>\n      <td>0.15</td>\n      <td>-0.08</td>\n      <td>0.08</td>\n      <td>-0.02</td>\n    </tr>\n    <tr>\n      <th>cntrylat</th>\n      <td>-0.05</td>\n      <td>0.12</td>\n      <td>0.15</td>\n      <td>-0.17</td>\n      <td>1.00</td>\n      <td>0.16</td>\n      <td>0.13</td>\n      <td>0.10</td>\n      <td>-0.02</td>\n      <td>-0.01</td>\n      <td>0.04</td>\n      <td>-0.04</td>\n    </tr>\n    <tr>\n      <th>cntrylon</th>\n      <td>-0.02</td>\n      <td>-0.02</td>\n      <td>0.10</td>\n      <td>0.05</td>\n      <td>0.16</td>\n      <td>1.00</td>\n      <td>-0.13</td>\n      <td>-0.09</td>\n      <td>-0.01</td>\n      <td>0.11</td>\n      <td>0.01</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>best_before_meaning_map</th>\n      <td>-0.03</td>\n      <td>0.07</td>\n      <td>0.01</td>\n      <td>-0.06</td>\n      <td>0.13</td>\n      <td>-0.13</td>\n      <td>1.00</td>\n      <td>0.38</td>\n      <td>-0.03</td>\n      <td>-0.04</td>\n      <td>-0.02</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>validity_meaning_map</th>\n      <td>-0.06</td>\n      <td>0.11</td>\n      <td>-0.02</td>\n      <td>-0.04</td>\n      <td>0.10</td>\n      <td>-0.09</td>\n      <td>0.38</td>\n      <td>1.00</td>\n      <td>-0.06</td>\n      <td>-0.06</td>\n      <td>-0.05</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>work_scale</th>\n      <td>0.00</td>\n      <td>-0.51</td>\n      <td>0.16</td>\n      <td>0.15</td>\n      <td>-0.02</td>\n      <td>-0.01</td>\n      <td>-0.03</td>\n      <td>-0.06</td>\n      <td>1.00</td>\n      <td>0.03</td>\n      <td>0.84</td>\n      <td>-0.13</td>\n    </tr>\n    <tr>\n      <th>population_density</th>\n      <td>0.06</td>\n      <td>-0.01</td>\n      <td>0.12</td>\n      <td>-0.08</td>\n      <td>-0.01</td>\n      <td>0.11</td>\n      <td>-0.04</td>\n      <td>-0.06</td>\n      <td>0.03</td>\n      <td>1.00</td>\n      <td>0.06</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <td>0.00</td>\n      <td>-0.34</td>\n      <td>0.18</td>\n      <td>0.08</td>\n      <td>0.04</td>\n      <td>0.01</td>\n      <td>-0.02</td>\n      <td>-0.05</td>\n      <td>0.84</td>\n      <td>0.06</td>\n      <td>1.00</td>\n      <td>-0.16</td>\n    </tr>\n    <tr>\n      <th>gender_Female</th>\n      <td>0.12</td>\n      <td>0.07</td>\n      <td>-0.05</td>\n      <td>-0.02</td>\n      <td>-0.04</td>\n      <td>0.01</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>-0.13</td>\n      <td>0.00</td>\n      <td>-0.16</td>\n      <td>1.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr.round(2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 2 Axes>",
+      "image/png": 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1GmHYpmd54Fe2VyEtau4AnAV8w/ZqwETg+wPI2A84zvYoYDTwpKavXj0KmEIuQwPMCdxue3VSeZov5fYTgbPyfc8Bjq9yr9OBg2yv28B7m0Gv8ou2v5evbUxa1D1xAL2DIOhhZh45ouGj1+g9jQeXx22Pz6/vApYjVbW+IbedCWw0gIzbgG9J+gZp38ab1K5eDfAO8Oeyey6dX68LnJtfn01FZWxJ81bodnYLek2HktP9HOBY23cNoHfl2PcqaJ9++qBnDguCoE36uR5bBI9Mz9tlr6cA89Xp+151aVJ2fwBsnyvp78DWwFWSvkiN6tWZyTk6qHTPWv8nlZsWVaWtJtX0sv23im5jgCdzXsvSPWrpXSn/vQ2ck159NSpoB0HQNWLGVp9XgJckbZjP9wBKM6QnmFZdesfSAEnLAo/ZPh64HFiN2tWr63EraU8HJPffzeUXbb8MvCJpg7I+NamhV/n1T5ESkR5U1tyK3kEQ9AAxYxve7AmcJGkO4DFg79z+c+APkvYAymc+OwO7S5oM/Af4oe0XJZWqV48AJpOCPf5Z574HAadJOgz4b9l9y9k793mDtA+kHjPoVXH9q8BiwB35F/ly299rQe8gCHqAovaxDUU0zQsWBMVQhCvy9X9MLEIV5lhmxULkDMkkyAsVkwR5jinFJAtWQUEGT74+uRA5i808tRA5r800RyFy5i0wCfLc887XtlW66LZ7Gv473XHdj/SUFYwZWxAEwTBkRA+G8TdKGLY+Q9IngCMrmh+3vX039AmCYGjSz67IMGx9hu2rGHi9LQiCYU4vBoU0Shi2IAiCYUgYtiBoAr/zVtsyXl/sgwVoAi++9vbAnRqgqIAPgB3Pv2HgTg1wwl6fLkTOrDMV8xiYqZhYDZaYr5iK1UUx69SCsuC/W0xQTDAwYdiCIAiGIX28xBaGLQiCYDgy08j+zWcehi0IgmAY0sdLbJFSKwiCIOgvwrD1CZKWlvS5FsZtIunPA/QZJWmr1rULgmCoMVIjGj56jd7TOKjF0kBVwyapXZfzKCAMWxD0EUUmQZb0SUkPS3pU0uFVru+WCxxPkHSrpNXLrj0haWIuZnxnEe8t1tiGOJI+D3yNVKJmAqm0zaukYqHvB75u+yLgCOBDuW7amcBLpBI1swFzSnoKuMj2ZVnuOcAFWVbpXmsDvwRmB94kJVl+nJQwefZcSeBnti8Y3HcdBMFgU1RUpKSRwK9I1UGeJNVvvNz2A2XdHgc2tv2SpC1JJa4+WnZ9U9vPF6NRzNiGNJJWAb4NfCxX2D44X1qUVHj0UySDBnA4cJPtUbaPzW3rAnva/hjwW3KFgFykdD3giopbPgRsZPsjwPeAn9p+J7++IMsOoxYEfUCBM7a1gUdtP5afF+cD25V3sH2r7Zfy6e3ABwp/Q2XEjG1o8zHSLOt5gFz+BuBS21OBByQtUmf8NbZfzGNvkPSrXFvtM8DFtt+t+KWdFzhT0vKkGeLMxb+lIAiGAiNHFDavWRz4d9n5k0w/G6tkH+CvZecmlcYycHIuWtwWMWMb2tSqkv12RZ9avF5xfjapIOnewOkzdudHwPW2VwW2oawy+ICKSvtKulPSnaefdVajw4Ig6BJSM8e0v+987Fsuqor4qiVxJG1KMmzfKGte3/YawJbA/pI2ave9xYxtaHMdcImkY22/IGmBOn0nAXMPIO8M4A7gP7bvr3J9XuCp/HqvZmTnb1mnALz6/HNR5C8IhjgjmtjIVv73XYUngSXKzj8APF3ZSdJqpCWRLW2/UCb76fzzOUmXkFybNzasXBVixjaEycbnJ8ANku4FflGn+wTgXUn3Sjq0hrxngQepPlsDOAr4maRbgPK0BNcDK+eopZ2bfR9BEPQ144DlJS0jaRZgF+Dy8g6SlgT+COxh+x9l7XNKmrv0Gvg4cF+7CsWMbYhj+0xSlGOt63Pln5OBzSoun1F+ImkOYHngvLLxY4Gx+fVtwAplQ76b218E1mrtHQRBMBSZqaA1trxWfwCpXNZI4DTb90vaL18/iRSAtiDw67yu/67t0cAiJK8UJHt0ru0r29UpDNswQdLmwGnAL2y/0m19giDoLkWWrbF9BRVR1tmglV5/EfhilXGPAatXtrdLGLZhgu1rgSW7rUcQBMFgE4YtCIJgGNJM8EivEYYtCIJgGDKijwuyhWELimdE+3WeFm54B119XixogXzynIsXIgeKq3x94BmXFiLn4s9/ohA5I2cr6D+NWQqR8tzrxVRPX2CuOQqRM2XkrIXIKYqYsQVBEAR9RZHBI0ONMGxBEATDkJFh2IIgCIJ+ImZsQRAEQV/Rz2tskVKrxxnMytlBEPQvRRYaHWqEYet9lmbwKmcHQdCnjByhho9eIx58Q5QOV85egJRua1ngDWBf2xMkjSFlK1k2//yl7eMH950HQdAJenEm1igxYxuCdKFy9g+Ae2yvBnwLKC+othLwCVIpie9LiuKjQdAHjJAaPnqNMGxDkxkqZ+f2S21Ptf0AKSt2LaarnA18MFfO3pVcObui/wakIqTY/huwYDaCAH+x/XbW5bkB7hsEQdB1wrANTTpdObteBdzye06hhvt6ugraZ9asshMEwRBhxAg1fPQascY2NOl05ewbSYbvR5I2AZ63/WozPvjpKmi/+EJU0A6CIc6Iut+Ne5swbEOQXKSvVDl7CnBPne7vVc4mGbCXqsh7VtKDwKU1ZIwBTpc0gRQ8smfr2gdB0Av0YrRjo4RhG6J0uHL2i8B2Ve4xpuJ81YbfQBAEQZeINbY+J1fOfgg4ISpnB0FQYsSIEQ0fvUbvaRw0he1rbS9p+5fd1iUIgqFDkeH+kj4p6WFJj0o6vMp1STo+X58gaY1Gx7b03ooQEgRBEPQWUuNHfTkaCfwK2BJYGdhV0soV3bYkLYcsD+wL/KaJsU0Ta2xBEATDkJHFuRjXBh61/RiApPNJa/YPlPXZDjjLtoHbJc0naVFSSsCBxjZNGLZgSDJpSjERW/POUUxV5xFvVm4NbJ1ZZyrmz66oytc7nHVVIXIO+MT6hcj5+AqzFyJn4VmK+R2SpxQiZ2oBleWLpMCgyMWBf5edPwl8tIE+izc4tmnCsAVBEAxDRrjx7aaS9iW5EEuckveuQv0EDwzQp5GxTROGLQiCYDjiqY13LUvAUIUngSXKzj8APN1gn1kaGNs0ETwSBEEQtMM4YHlJy0iaBdgFuLyiz+XA53N05DrAK7afaXBs08SMLQiCYBjiKcWsHdp+V9IBwFXASOC0nD1pv3z9JFJFka2AR0nZjfauN7ZdnQY0bJKWBv7cTtaJnH/wHdu31ulzRr7PRU3IPZr0YV1h+7BW9RsqSBoNfN72Qd3WJQiCPmdqMYYNwPYVVJTDygat9NrA/o2ObZdOzdg2AV4Dahq2FvkfYGHbbw/Yk1RRukrJliGD7TuBO7utRxAE/Y+nNL7G1ms0usY2k6Qz847xiyTNIWlNSTdIukvSVXlPApIOkvRA7nt+nvHtBxwqabykDevcZ3NJN0n6h6RPZXkjJR0taVyW+T+5/XJgTuDvknaWtJSk63Kf6yQtmfudIekXkq4HjpS0nKQrs943SVqpljJ57G8kXS/pMUkbSzpN0oN5hlnq93FJt0m6W9KFkubK7d/Let8n6RTldPmSxko6UtId+b1umNs3kfTn/HpMvtfYfO+Dyu73XUkPSbpG0nmSvlbnPYyVdKykG7Pea0n6o6RHJP24rN+l+TO5P0dAldpfk3RMfm/XSVq4zv9fEARB12nUsK1ICu9cDXiVNKU8AdjR9prAacBPct/DgY/kvvvZfgI4CTg2V3m+qc59lgY2BrYGTpI0G7APaaFxLWAt4EuSlrG9LfBmlnkBcCJpA+BqwDnA8WVyVwA2t/1VUmTPgVnvrwG/HuC9z08q/Hko8CfgWGAV4MOSRklaCPhOlr8Gacb1lTz2RNtrZTfu7KTK1yVmsr02cAjw/Rr3nqF6dXZX7gB8BPgMMHoA/SG5gTci/T9cRvr/WxXYS9KCuc8X8mcyGjiorH1O4O783m6oo2sQBD2EPbXho9do1BX5b9u35Ne/B75FejBekychI4Fn8vUJwDmSLqV2mZRa/MHpU3xE0mOkB/vHgdUk7Zj7zEtKy/J4xdh1SQ96SIU1jyq7dqHtKXkmtR5woabliZl1AJ3+ZNuSJgLP2p4IIOl+kiH+ACkVzC1Z5izAbXnsppK+DswBLADcTzKOAH/MP+/Kcqrxl+xmfVtSqXr1BsBltt/MevypxthySlFGE4H7czQS+TNeAniBZMy2z/2WIH3GLwBTgQty++/L9A6CoJdpYh9br9GoYav8BCaRHpDrVum7NbARsC3wXUmrNKFP5X1KG/gOtN1seoRyWaW0ESOAl22PakJOaf1uKtNXk55K+vymANfY3rV8UJ5t/hoYbfvfksYA5WkwSrJqVqWmevXqVvIF1H0PObhnc2Bd229IGluhazlV/xpUtoHzl784hr33jJJuQTCk6cGZWKM06opcUlLJiO0K3A4sXGrLLrJVJI0AlrB9PfB1YD5gLhqr8gywk6QRkpYDlgUeJoWBflnSzPleK0ias8rYW0l7ICBVg765soPtV4HHJe2UZUnS6g3oVY/bgfUlfTDLnEPSCkwzDM/nmeKOtQQ0yc3ANpJmy3K3LkDmvMBL2aitBKxTdm0E03T/HFU+V0gbOG2Ptj06jFoQDH08ZUrDR6/R6IztQWBPSScDj5DW164Cjpc0b5bzS+AfwO9zm0jrai9nd9lFkrYjzb5qrbM9TFrHWYS0PveWpN+SXHV35+CL/wKfrjL2IOA0SYflPnvXuMduwG8kfQeYGTgfuLfBz2EGbP9X0l7AeZJKbs3v2P6HpFNJ7r8nSBsR28b2OKXAmXuBf5LW9Nqts3YlsJ9SBe2HSca6xOvAKpLuyvfZuc17BUEwFOhjV6Tcx2+uX5E0l+3XlCpj3wjsa/vuQbrXa6Vq3Y3y6osvtP1LVVQS5DlmnbkQOUUmQX52cjFyFnxnUiFyhl4S5CUG7tQIBbnaNPMshcgpMgnyvHPP3fYfyIv/fKzhv9MFllq2uJTJHSAyj/QmpyjVLJoNOHOwjFoQBP1LL7oYG6Xjhk3St4GdKpovtP2Tav07wVDUqR62P1fZJulXQOVX5uNsn97mvZqarQVBEHSbjhu2bCyGlMEYijo1i+2q6WqCIAiqUmBKraFGuCKDIAiGIb248bpRwrAFhePJ7UdHvD21mIpK88xSjByPLK7C00wFPU9GzlZMdfCigj5OvOqWgTs1wObLfGbgTg2Qdh+1j6e8VYicqbNX26XURfp4jS3qsQVBEAR9RczYgiAIhiH9vNUrDFsQBMFwJAxbEARB0E/EPrYgCIKgv+jjqMieCB6R9Fr+uZiki2r0GZtrlSHpCknz5eN/O6lrO0gqusJ4EARBVWw3fLSDpAVyUeRH8s/5q/RZQqmg84O52PHBZdfGSHpKqVD1eElbDXTPnjBsJWw/bXvALPm2t7L9Mqm6QM8YNtvrdVuHIAiGCZ7a+NEehwPX2V4euC6fV/Iu8FXbHyJVF9k/pw0sUSpUPcr2FQPdsCuGTdKR5TOpbJG/L+k6SXdLmpgrAVSOW1rSffn17JLOlzRB0gWkCtWlfk/kytZHAMtlK3+0pLPL5Uo6R9K2NXTcS9Klkv4k6XFJB0j6iqR7JN0uaYHcbzlJV0q6S9JNuewLkraR9Pfc/1pJi5S919PyDPMxSQeV3bM0M90kX79I0kNZT+VrW+W2myUdL+nPdT7nMZLOlHR1/kw+I+mo/PleqWmlgL4naZyk+ySdUnavsZJ+KenWfG3tAf9zgyDoCTxlasNHm2wHnJlfn0mV6iy2nynlvLU9iVRRZvFWb9itGdv5TF/+5LPA6cD2ttcANgWOKT1ga/Bl4A3bq5HSYa1Zpc/hwP9lK38Y8FtyORul0jrrAfWs/6qkGmRr53u8YfsjpArZn899TiGV4lkT+BqpuCikumXr5P7nk+rTlVgJ+ESW+/2SgangI8AhpOrcy5Jqvs0GnAxsaXsDYOE6updYjlSzbTtSBezrbX8YeJNptdxOtL2W7VVJXxA+VTZ+zjyT/F/gtAbuFwRBUM4itp+BZMCA99XrLGlp0vPv72XNB+RJzGnVXJmVdMWw2b4HeF9eM1sdeAl4Bvhprgl2LclaL1JHzEakBzW2JwATGrjvDcAHJb2PVDD1Ytvv1hlyve1Jtv9LqkX2p9w+EVhaqdDnesCFksaTjM6iuc8HgKskTQQOA8orif/F9tu2nweeq/E+77D9pFPem/GkmnQrAY/Zfjz3OW+g9wz81fbkrPNIUu21995Dfr1pnl1OBD5Woet5ALZvBOaRNF+1m0jaV9Kdku484+yzG1ArCIJu4qlTGj7K/77zsW+5rOyVuq/KMYPnrR75mXoxcEguDA3wG9IX9FEkO3HMQHK6GRV5Eaky8/tJM5rdSDOQNW1PlvQE06pQ16KVVc2z8712Ab4wQN+3y15PLTufSvrsRgAv2x5VZewJwC9sXy5pE2BMDblTqP7/UK1PKzWR3gawPVXSZE9bCZ4KzJRngb8GRtv+t6QxTP+5V37GVT9z26eQZq+88ux/+neDTBD0C02E+5f/fde4vnmta5KelbSo7WckLUr6Ml+t38wko3aO7T+WyX62rM+pQM3llxLdDB45n2RcdiQZuXmB57JR2xRYaoDxN5IMFJJWBVar0mcSMHdF2xkkFx+2729Rd/L4V4HHJe2U9VCegUJ6P0/l13u2c58yHgKWzVN1KKaadcmIPZ+/LVUG5+wMIGkD4BXb7VbrDoJgCGBPbfhok8uZ9gzcE7isskNedvod8KDtX1RcW7TsdHvgvoFu2DXDlo3K3MBT2e96DjBa0p0kg/XQACJ+A8yVXZdfB+6oco8XgFvylPjo3PYsaWGyrTplZewG7CPpXuB+0loWpBnahZJuAp4v4ka23yStdV0p6WbgWZKLtB2ZLwOnklyTlwLjKrq8pLQN4SRgn3buFQTBsOQIYAtJjwBb5PPS9q1SjMP6wB7AxzRjWH8p4G0CKf7i0IFuqH7OF1YNSXOQHuJr9OLsQ9Jctl/L33B+BTxi+9hButdY4Gu272xmXBGuyBcKyu6/8ByzFCLHk98pRA7A8+0XPwBgAb89cKcGuPafhXzvKiy7/6X7Da3s/kVRZHb/+eeZu5Vliel4+ta/Nfx3uth6H2v7fp1kaP3PDzKSNifNBE/oRaOW+VIOVLmf5O48ubvqBEHQi/jddxs+eo1hlVLL9rXAkuVtkj4BHFnR9XHb23dMsSbIs7PpZmiS9gYOruh6S7tVtW1v0s74IAiCbjCsDFs1bF8FXNVtPdrB9ukUt2YYBMEwICpoB0EzzNH+WsKUS88qQBH490afLkROkSwxX1FrLcWsH358hdkH7tQARVW+/vRJfxy4UwNcePAehcg59OzLC5Fz3KfWKUQOAPNUBnu3QB/HV4RhC4IgGIZE2ZogCIKgvwhXZBAEQdBXhCsyCIIg6Cf6eQ9zGLYgCIJhSD+vsQ2rDdq9iqbVl+vEvd6reRcEQR/TuUKjHScM2xBH0shu6xAEQdBLhGEbRCR9vVQhW9Kxkv6WX28m6feSds3JPe+TdGTZuNck/VDS34F1y9pnz5Wvv1TjfnNK+ouke7PMUmb+tXIV7Hsl3SFp7jwzu0mpYvndktarIm+kUuXxcbnI3/8U/BEFQdAt7MaPHiPW2AaXG4GvAscDo4FZc82hDYBHSKm81iQVWr1a0qdtXwrMCdxn+3sAKd8xc5FK/Zxlu9bu5U8CT9veOo+bV9IswAXAzrbHSZqHVD37OWAL229JWp5UUHR0hbx9SKVq1pI0K6lSwtVlhU6DIOhRejEHZKPEjG1wuQtYU9LcpIKft5GMx4bAy8BY2//NVbzPIVUFh1RY9OIKWZcBp9cxapCqFmwu6UhJG+ZEzysCz9geB6mGXL7fzMCpuWr2hcDKVeR9HPh8Trr8d2BBYPlqN56ugvbpkd0rCILuETO2QaSsEvjewK1AqZ7QcsC/SLO1arxluzJk6RZgS0nnukacru1/SFoT2Ar4maSrSTXWqvU/lFTPbXXSF5y3qvQRcGDOp1mX6SpoT5rUe76LIBhm9HOuyJixDT43Al/LP28C9gPGA7cDG0taKAeI7ArcUEfO94AXgF/X6iBpMeAN278Hfg6sQSrTs5iktXKfuSXNRCp584zTb/ceQLUglauAL2f3KZJWkFRcUakgCLpHH6+xhWEbfG4CFgVuy9W73wJuylXDvwlcD9wL3G17hpLpFRwCzCbpqBrXPwzckV2H3wZ+bPsdYGfghFzl+xpgNpKB3FPS7cAKwOtV5P0WeAC4O28BOJmY5QdBX+ApUxo+eo14SA0ytq8jrWeVzlcoe30ucG6VMXNVnC9ddrp3nXtVLcGT19cqU4s/AqxWdv7N3PcJYNX8eirwrXwEQdBP9LErMgxbEATBMKRTKbUkLUCKzF4aeAL4rO2XqvR7AphECp571/boZsaXE67IHkTSgpLGVzkW7LZuQRD0CFOmNH60x+HAdbaXB67L57XY1PaoklFrYTwQM7aexPYLwKhu6xEEQe/SwSTI2wGb5NdnAmOBbwzm+JixBUEQDEc6lytykRwsR/75vloakRJV3CVp3xbGv0fM2ILCeePtyW3LeP+2uxWgCTz3+tuFyFmQdwqRUyRFvbeFZ1EhcqRividfePAehcjZ6bizC5Fzzv6fK0TOTLPNPHCnIUo2NOXG5pS8d7V0/Vrg/VWGfruJ26xv+2lJ7wOukfSQ7Rtb0TcMWxAEwTDE7zb+BbQ8AUON65vXuibpWUmL2n5G0qKkdH7VZDydfz4n6RJgbdL+34bGlxOuyCAIgmGI7YaPNrkc2DO/3pOUHnA6cgL3uUuvSen87mt0fCVh2IIgCILB5AhgC0mPAFvkcyQtJumK3GcR4OacROIO4C+2r6w3vh7higyCIBiOTO3MBu0cxb1ZlfanSXltsf0YKW9tw+PrETO2AWikonTu87my89GSjh9EncZI+lrBMq+QNF8+/rdI2UEQDD08dUrDR68Rhq0YlgbeM2y277R9UPfUaR7bW9l+GZgPCMMWBP1OJEEeuuTZ0kOSzsxVni+SNEeuUn1PrlB9Wi6UiaQncr2yO/Lxwdx+hqQdy+S+VuNe1apOHwFsmLN/HCppE0l/zmMWkHRp1u12Savl9jFZr7GSHlOutF3nfX5b0sM5rHbFsvbllKpq35V1W6ns/RyvVDn7sdJ7k7SopBuzrvdJ2rDsc1kov5fl8vWjJZ0tabuy+50jadum/6OCIBhSeKobPnqNnjdsmRVJ+ypWA14FvgKcQaoa/WHSWuKXy/q/antt4ETgl03cp1R1eg1SxvySu/FwUsb+UbaPrRjzA+CerNu3gPJCoSsBnyCFtX6/VB6mklxjbRfgI8BngLXKLp9Cqpm2Jqk8TnlZm0VJ1bo/xbQF188BV9keRfJpj6+43eHA/+X3chgpw//eWY95gfWAKwiCoLfp3AbtjtMvhu3ftm/Jr39PWmh83PY/ctuZTKtODXBe2c91m7hPI1WnK9kAOBvA9t+ABbOBgBT587bt50lGc5EaMjYELrH9hu1XSeGvSJqLZGguzKVqTiYZsxKX2p5q+4Ey2eOAvSWNAT5se1I95W3fAHwwb5rcFbg4V+CeDpVV0P79WWfWExkEwRCgn9fY+iUqstm5squ8fpds6CUJmKXKuEaqTldSLa1D6Z7lqSOmUP//o9p7HAG8nGdf1SiXLwDbN0raCNgaOFvS0bbPqjp6GmcDu5FmjV+oqlzZBs5nnn+x93wXQTDc6MG1s0bplxnbkpJKM69dgWuBpUvrZ6QK0eXVqXcu+3lbfv0EsGZ+vR1lNdTKqFV1ehIwdw3dbiQZBSRtAjyfZ13NcCOwvaTZ8ybGbQCynMcl7ZTlS1LVkNkSkpYCnrN9KvA7UpXtcqq9lzNIRU6xfX+TugdBEHSUfpmxPUiqBn0yqYDmwcDtJBfdTCT320ll/WeV9HeSYd81t50KXCbpDlJphGoVpX8NXJwNyfVlfSYA7+bNhWcA95SNGQOcLmkC8AbTdtA3jO27JV1AWg/7J6kqd4ndgN9I+g7JGJ9Pqshdi02AwyRNBl4DPl9xrxck3ZK3OPzV9mG2n5X0IHBps7oHQTA0aSalVq+hDpYuGBQkLQ382faqDfZ/Ahid17WCBpA0BzARWMP2KwP1L8IVOdfMxTgThmIS5JFz1prcN8dQS4LsKcUEGbwzy+yFyBlqSZDnKjAJ8rxzz932f9r9v/xew3+nqxzyw2J+STpEv7gig0FC0ubAQ8AJjRi1IAh6hKlu/Ogxet4VafsJoKHZWu6/9KAp0yZKFbCvq3Jps5xWpuPYvhZYshv3DoIgaIWeN2z9RFTGDoKgY/Tg/rRGCcMWBEEwDOnF/WmNEoYtKJw5Zm1/kfzlv19fgCYwadlRhciZda45C5EDMOvUYtbhF5hrjkLkyMU84DylkW2dA3Po2ZcXIqeooI/dfnVuIXIu2mXjQuQAsNKH2xbhKf1r2CJ4JAiCIOgrYsYWBEEwDHGH6rF1gzBsQRAEw5E+Dh4JV+QworI0TxAEw5g+3scWhi2oSU5HFgRBH2JPbfhoh1yT8hpJj+Sf81fps2KuAVk6XpV0SL42RtJTZde2GuieYdh6HElzSvqLpHtz4dCdJX1P0rh8fkquVlA5rmqfXPj0p5JuAL4t6fFSnThJ8+SCpMXlBgqCoCt0sGzN4cB1tpcnJaA4fAZd7IdzDchRpGT0bwCXlHU5tnTd9oD1IMOw9T6fBJ62vXrOl3klcKLttfL57KRCo5XU6zOf7Y1t/wAYSypxA6lszcW2+zd7ahAMF+zGj/bYjlQTk/zz0wP034xU7Pifrd4wDFvvMxHYXNKRkjbM+Rw3lfT3XBD1Y8AqVcbV63NB2ev3Kmjnn6cX/xaCIOg4nVtjW8T2MwD55/sG6L8L04pBlzhA0gRJp1VzZVYShq3HyVXC1yQZuJ9J+h6pvM6Otj9MKsczW/kYSbMN0Oe9kj25MvnSkjYGRtq+r5oe5RW0zzg9bF8Q9BPlf9/52Lfi+rV5WaPy2K7J+8wCbAtcWNb8G2A5UrrBZ4BjBpITwQE9jqTFgBdt/17Sa8Be+dLzkuYCdgQuqhg2WwN9yjmL9A3qR7U6lFfQfmXSpN4LowqCYUYza2flf981rm9e65qkZyUtavsZSYsCz9W51ZbA3bafLZP93mtJpwJ/HkjfMGy9z4eBoyVNBSYDXyb5sCeSqoKPqxxg++X8C1KzTwXnAD9mRvdAEAQ9SgdzRV5OKrB8RP55WZ2+u1LxnCkZxXy6PVDVa1ROGLYex/ZVwFUVzXcC36nSd6+y19+p0WeTKrfZALjI9sttqBoEwVCic5lHjgD+IGkf4F/ATvCet+m3trfK53MAWwD/UzH+KEmjAJO+iFden4EwbEFdJJ1Acg8MuHckCIKgklyOa7Mq7U9T9lyx/QawYJV+ezR7zzBsQV1sH9htHYIgKJ4oWxMEQRD0F+3vTxuyhGELgiAYhrgHc0A2Shi2IAiC4Ui4IoOgcZ595fWBOw3AHKutX4AmsExB2b9GzDqyEDkAfrcYnaaMnLUQOVNHFPPeps5eTJXx4z61TiFyZpqtmJSmRVW+3vH8GwqRAzB2TAEVtPu4bE0YtiAIguFIrLEFQRAE/UQ/V9COXJFBEARBXxEztiAIgmGIp7zbbRUGjZixDSKSzpC0YwFyKivIHlGEfjXutZekEwdLfhAEQ4TOla3pODFjG0JImsl2ra9Rx9r+eUcVCoKgb+nnzCMxY8tI+q6khyRdI+k8SV+TtJykKyXdJekmSSvlvmdIOl7SrZIeK83KlDhR0gOS/kJZQT1Ja0q6Icu6KpdvQNJYST+VdANwcBP6jpR0tKRxuQDf/+T2TfJ9/iDpH5KOkLSbpDskTZS0XO63TS40ek+upbRIlXssLOnifI9xkoqJwQ+CIBhEYsYGSBoN7AB8hPSZ3A3cRao/tJ/tRyR9lFSc82N52KKkrPcrkcoyXEQqqbAiqZTMIsADwGmSZgZOALaz/V9JOwM/Ab6QZc1ne6DNModK2j2//gawFPCK7bUkzQrcIunqfH114EPAi8BjpAzaa0s6GDgQOAS4GVjHtiV9Efg68NWKex5HmineLGlJUhWBDw2gZxAEvUDsY+t7NgAus/0mgKQ/kYpxrgdcKKnUr3xH7KVOOxwfKJvtbAScZ3sK8LSkv+X2FYFVgWuyrJGkSrAlLmhAx+lckZIuAlYrW8ObF1geeAcYV6pfJOn/gJLBmwhsml9/ALggzxxnAR6vcs/NgZXL3v88kua2PamyY66ouy/AD484ip13azohdxAEHcSxj63vUZW2EcDLtkfVGPN2jfHVflsE3G973RqyWknVIeDAXI9tWqO0SYVuU8vOpzLt//wE4Be2L89jxlS5xwhg3ZLBr0d5hd1/PPmf/v2LCYJ+oQeDQhol1tgSNwPbSJpN0lzA1sAbwOOSSkXxJGn1AeTcCOyS178WZdrs6GFgYUnrZlkzS1qlTZ2vAr6c3ZxIWkFSMzmN5gWeyq/3rNHnauCA0kku9hcEQR/gqVMaPnqNMGyA7XGkdbJ7gT+SKlC/AuwG7CPpXuB+YLsBRF0CPEJy+f0GuCHLfwfYETgyyxpPcnO2w29Ja3h3S7oPOJnmZuBjSG7Wm4Dna/Q5CBidg1MeAPZrQ98gCIYSntr40WOon/2szSBpLtuv5fLkNwL72r6723r1IkW4IueYpZgEtnMXlQR5ttkKkQPgd4vZGDtl5mKSII+o5ohvgaI8W1Of/08hcmZa+P2FyJn81BOFyCk2CfIBbf+v3br7xxr+H1vv938r6LekM8Qa2zROkbQyKWjkzDBqQRD0M73oYmyUcEVmbH/O9ijbK9n+WTd0kPTtsuwipePb3dAlCIKgCCTtJOl+SVPz1qpa/T4p6WFJj0o6vKx9gby/+JH8c/6B7hmGbQhh+yfZuJYfP+m2XkEQ9B8dDB65D/gMaYmnKpJGAr8CtgRWBnbNHjSAw4HrbC8PXJfP6xKGLQiCYDjSoVyRth+0/fAA3dYGHrX9WA62O59pwXrbAWfm12cCn27kpnHE0fGDFJwTckJO12X1q5wiD1LyhTvLjqZ1BMYCo2tc25GUIal0vgdwYn79ckXflwa6V8zYgm6xb8gJOUNEVr/KKQzbp9geXXacUn4955u9r8ox0Bap90RUu22r+kZUZBAEQdAWtjdvU8STwBJl5x8Ans6vn5W0qO1ncuKL5wYSFjO2IAiCoNuMA5aXtIykWYBdSEkzyD9L2ZH2BC4bSFgYtqBbnDJwl5ATcjoiq1/lDAkkbS/pSWBd4C+Srsrti0m6AsCpDuUBpFSBDwJ/sH1/FnEEsIWkR4At8nn9e+bFuCAIgiDoC2LGFgRBEPQVYdiCIAiCviIMWxAEQdBXhGELepIma8/1BKXafwO19SqS5pE0d7f1AJD0KUltPf9yGqhC6cff624Qhi3oGJIOzg83SfqdpLslfbxJGevl2nAP5vPVJf16UBRuXCdJ2l3S9/L5kpLWbkHUNxtsa0SnkTnqbMnS0aKcBSWdkP+v7pJ0nKQFm5QxWtJEYAJwn6R7Ja3Zoj5LSdo8v569DUO5C/CIpKMkfahFGY9KOrosp2HLDMXf614moiKDjiHpXturS/oEsD/wXeB022s0IePvpPQ7l9v+SG67z/aqLegziWnZDWYBZgZetz1Pk3J+A0wFPmb7Qzn7+NW212pw/JbAVsBngQvKLs0DrGy7KSMp6UDg+8CzWS8A216tGTlZ1jWk5LW/z027AZs0syFX0gRgf9s35fMNgF83q4+kL5GycixgezlJywMn2d6sGTll8uYBdgX2Jv0enA6cZ3tSg+PnJhnIvUmThNOA822/2oIuhf1eBzFjCzpLKW3OViSDdm9ZW8PY/ndFU0vpx23PbXuefMwG7ACc2IKoj9reH3gry32JZCgb5WlS/r23gLvKjsuBT7Sgz8HAirZXsf3hfDRt1DIL2P6R7cfz8WNgviZlTCoZNQDbNwMNGY8K9gfWB17Nch4B3teCnJIerwIXkxLuLgpsT6pIf2CD4yfZPtX2esDXSV8mnpF0pqQPtqBPIb/XQaTUCjrLXZKuBpYBvpm/8TZbd/7fktYDnDMUHER237SL7UvL60A1weS83mIASQvTxPvKBv5eSefahZT8/jfwSgFyAK6XtAvwh3y+I/CXJmXcIelk4DzSZ7QzMFbSGgBuvKjv27bfkdJ3IUkz0WI+QUnbkmZaywFnA2vbfk7SHKTfpxMakDES2DrLWRo4BjgH2BC4AlihCZUG7fd6OBKuyKBj5MX6UcBjtl/OazWL257QhIyFgOOAzUmzvauBg22/0II+nyk7HQGMBja2vW6TcnYjPazXIJXV2BH4ju0Lm5SzPPAzUj2q2UrttpdtUs7vgBVJBujtMjm/aEZOljUJmJNphnoE8Po0kQO7bSVdX+eybX+sQV2OAl4GPg8cCPwv8IDtpovxSjqLlE1+hhphkjazfV0DMh4Drgd+Z/vWimvH2z6oCX0K+70OwrAFHUTpq/ZuwLK2f5gDGt5v+44u6XN62em7wBPAqbYHTLJaRdZKwGakh9J1tpv+ti3pZpI761hgG9JMQLa/36Scqv1t/6BZnYYS+YvRPsDHSZ/zVSTj1PRDTNKRtr8xUFud8SOBb9v+YbP3DgafMGxBx2g3yCLLOL5K8yvAnbYHTI46GOQZ0gm2x5e1jbE9pkk5d9leU9JE2x/ObTfZ3rBFveYmzYhea2V8mZz5geWZfhZZsxpyDRlbA6tUyOiaUZB0d2XQkqQJzaxFSrre9qZt6nECddypzcz6gmnEGlvQST5qew1J90AKssjrCc0wG7ASUHLz7QDcD+wjaVPbhwwkYBAeJp8A1pT0C9tn5bZtgTFNynkrz0oekXQA8BQtBEdIWpW0brRAPn8e+HxZUtlmZH2RFIzyAWA8sA5wG9CQ+zDLOAmYA9gU+C3JVdvwLD1vFaj3/9WMMfoyyYW5XI7WLDE3cEujcjK3SjqRFMlacs82s2YIKWgoKJgwbEEnaSvIIvNB0ozv3SzjN6T1iC2AiQ3KKD1M1ietZ5VC7HciRSM2y3PAJsA5kj5KMgRNR3sCh5AMwEHAj0jGY896A2pwCvAV29cDSNoEOBVYrwVZBwNrAbfb3jS7XJt1aa5ne7U8I/qBpGOAPzYx/lNN3q8e5wJ/Ja1llgcKTbL9YpOySp9n+czTNGH0bZ/Z5D2DBgjDFnSS44FLgPdJ+gnpm/t3m5SxOCmYoRT1NyewmO0pkt6uPWwapYeJpL2ATUuRiHlmcXWT+kBy6b8KbCNpDHADMG+zQmyPyy9fI62vtcqcJaOW5Y5V6xkt3rL9liQkzWr7IUkrNinjzfzzDUmLAS+QImMbwvY/m7zfAOL8hKT9Ky9IWqAZ49auG7Li3gsD32DGwKGGjWQwjTBsQcewfY6ku5gWZPHpFoIsjgLGSxqbZWwE/DQ/uK9tUtZiJBdU6WE2V25rllJBRGyPkXQn8JVGB0v6E/Vdbds2qc9jkr5LckcC7A483qSMEk9Kmg+4FLhG0ktMq2zcKH/OMo4G7ia91982q4ikdUhh+B8i7RMcSfMb6s8lzQDvynqUz6wNNBuBWtTa4Tkkz8HWwH6kmfp/W5ATEMEjQQeRdLbtPQZqa0DOYsAewEOkGduTzQYzZDl7k9bBSrObjYExnXYPSdq43nXbNzQpb36Su3AD0oP7RtL7eqllJXlPz3mBK22/06KMWYHZbDe9zy5/YdiFtL46mhT2/8FWwv2LoNbaoe19WpBVChx6L4BF0g226/5uBNUJwxZ0jMpItLzeNtF2w7n2agUztOqykfR+4KP59O+2/9PE2Jttb6DpU3NBMiYN7fGqkPcp4Arbza47DirZUC5BmYenmQCJso3MS1fIaGpfnaQ7bY+uePjfmjN/NIWk9YHxtl+XtDtpD+Ivbf+rCRkTytYOV5M0F/BH203lP82ybre9jlJ16eNJs+KLbC/XrKwgXJFBB5D0TeBbwOySXmWa++cdUqBDMxQRzFDSS6QNse/tq5O0dqP76mxvkH8WlbF+F+A4SReTUo415aYdBJcmkn4E7AU8RlneSZoIkAD+REoXNpHmg4XKeSNH0Y7Pm7WfIc3YW+E3wOqSVielw/odyXXbzAyprbXDCn4saV7gqyR36zzAoS3KGvbEjC3oGJJ+ZrulbPVlMsbZXkvSeNL2gbcljbc9qgVZbe+ry3KWI7lD384RiKsBZ9l+uQWdWk7MW7RLM8t8GPhwq67HLKOp/WF15CxFSuw8C+mhPy8pmfKjLci6O289+R7wlO3fVdvbNoCM75KM0GbAr8hrh7abDYgKCiYMW9BR2t3sK+kS0kP/ENKs4SVgZttbtaBL6eF2j6dlVL/X9upNyhlPWvNZmpQN43JSEuKmdcryFiIFfBxCyhf4QeB42wPmL8zjC3Np5tnjl91CNpYyGUeSsrG0EnFaLmdO4M3S+8ouzlltv9GCrBuAK0m/SxuRAjXGlzbGtyCv5bXDPH4ZUpqwpZneXdv0LDsIV2TQQYrY7Gt7+/xyjFIOwnlJD6hWKGJfHcBU2+9K2p60TnOC8ib0ZlABiXkzbbk0K/gZcI+k+5g+72QzD9zbgUuUNp9PpsU1SOA6kuu4lElldtL2jFb25+0MfA7Yx/Z/lNK7Hd3IQE2fY7TyGrab2aNX4lKSO/RPtOeuDQjDFnSWwtbHoDXXWgXV9tV9pwU5kyXtSgrR3ia3zdyCnB2AYytnsLbfkPSFRoXY3r3MpXm6pKZrjZVxJnAk7a2PHQOsSwoUasdFNJvL0oPZfi0b/abJQUK/KDv/F3BW7RHTsU2da6a5zecl3rJdLV1c0AJh2IJOUsRm38IYaF+dpPkbDJHfm7T36Ce2H89upd8PMGY68sxx8VpuWTeQbb6i/6t5xjY7yaW5PXCYUtb5Rmd+AM8X8MB9BLivTaMG8LqkNUoRmUpVuN8cYExV8qzrSFLKMtHELNJ2O5vna3GcUvLqq5l+ZtxMeq4gE2tsQccocn2sEzQbTFBHzsW2d2ig3+XAHq2u05TJqXRpnlnu0rS9VBOyfkF60F5Oiw9cSWeQNj7/lTbK6Ehai1QUtLRBfFFgZ9tNp0GT9CiwTZtu2sI2aEv6GWlv5v8xfdXzyDzSAjFjCzpGwetjnaCVfI/VaDSbxVvAREnXMH1S3WaTMhfi0sx8JP9cp1wUzYX7P56PWWiusvh02B6X3dcrkv5vHnLrhVmfLcCotZXcuYLtSdtOWo4+DaYRM7Zg0JG0QL3rbj75bEcocMbWkBxJ1RIe29MqBjRyr5HAVbY3b0bHVpG0p9vM1CLpBNsHNtBvJ1LWk0mSvkPaVP3jVtx1ko4D3k8K2iifRTa8PlbwBu0LgAPbiT4NphEztqATlOflW5LkghQwH/AvWt/U2m/MZ/u48gZJBzcjwCkZ9BuS5m3XpdkgB5MCTNph/Qb7fdf2hZI2IJUK+jlpo/VH6w+ryjzAG6SipSWaDfx4K/8sbdB+kdZ/lxcBHpI0jtajT4NMGLZg0LG9DLznurnc9hX5fEtS+PZQpShXZKNy9gSOq2jbq0rbQBTl0myEoj6jRpiSf24N/Mb2ZUrVFJqmoACQP2nG5M6ntiirqSrpQX3CsAWdZC3b+5VObP81p2zqGpLWICULNnBLhVtrs4Ju840BdNiVtKdqmRxAUmJuUpqmZvlLPsoZrDWHTq5lPCXpZNKXoSPzpugRrQiStAJptreI7VUlrQZsa/vHTYh5CJhi+2JJK5Nco5e2oo/tG3JmleVtX5sDfUa2IisIwxZ0lufz2sjvSQ/E3WntwV0IOZ3STkxzP50u6cLSw22gtT81WNm5gYwbt5LyHi5E2vNVYhIwoeqI+rTt0myCImZsjcr4LPBJ4Oe2X5a0KHDYe0Ia354BaWZ1GHAygO0Jks4FmjFs5a7RLUj/dy25RiV9CdiXVPV8OVLdwZMo7svVsCKCR4KOkYNIvk9KYWRSOZUfdit4RNKDwEdsv5XPZwfutv2hBseXwuZLRStL9c92A95oJey7CKoFq5SnDSv4XifaPqBNGXvZPqMAXRoO9tG0nKPvfS5qMudoaWwO1Z9o+9xWP2eltGxrkypMlPSZ6BZTfA13YsYWdIxswGrOHBqNjiuQJ0j7j0pBALOS9hE1hHNlZ0nr2y4PgDhc0i1AU4atnU3DeXzRLk0kVSuY+gpwl+3xjRg1Va868ApwJ3ByEUatdKsm+j6vlLy6lE5tR9KsuRkKc40Cb9t+R0pvQdJMdNbN21eEYQuGEo1GxxXF28D9OcjCJHfSzZKOh6aCLeaUtIHtmwEkrUdr5VSOor1Nw0W7NCEldx5NymEIKXBjHLBfdtse1YCMx4CFgfPy+c6kLP0rkFyCTRWarUMzhmB/UsmklSQ9Rdpnt3uT96vrGm2SGySVSjttAfwv0z7zoEnCFRkMGYraN9bE/artG3uPRvdn5dROp5E2nAO8DHyh2f1Vkm6pmPl1HaXClzs452jMe7UuIm0ovssNFImVdKPtjaq1Sbrf9ioF6dr0749SxYARbj6HZqEoJYjeh7T9QKQqEb8tIA3ZsCRmbMGwxfaZSoUrV8hND7eSySKndFpdKfGw2tg/dmfeqHspLW4ahvZdmhUsSSoIW2IysJTtNyW9XWNMJQtLWtK5OrVSJv2F8rUiM20M6Iqs4Vql5AJsNs1Xu0j6qe1v2Z4q6QnbO3Xy/v1KGLZgKNHJPVEoFQU9k7TWJmCJnEmjofpwg/CQLGLTMLTv0iznXOB2SZfl822A8/JM54EGZXyV5OL9P9LnvAzwv1lGw5u7Je1j+3cVbUfYPjyfNhJBWKp2viKp0kRpLXIbUjBTp/kkqbo8pC8j13RBh74jDFvQMSTtZPvCOm3NbkRul2OAj9t+OOuyAmkdaM0Gx889cJemGAEc7Fx5W6ko6zF1R1Sn7TyIJWz/SNJfSeufAvazfWe+vFuDMq6QtDywEtNyPJYCdn7ZhDo7SnrL9jkAkn5NCvgp3WfA6FrbP8hjrwbWKLkg80bvC+sMDXqIWGMLOkaNMPSOrqtV3HtCaa9ZvbYO6jNDqHgr4eMqIA9ihbyRpJRP5ZWd/9WkjPWYsTp0wzkws4zZSTOs04AtgRdtH9KMjDJZDwGr2347n88K3Gt7pVbktYqkJ0l14QQcSlmNOOi8a7RfiBlbMOjk1FlbAYuXIg4z8wDvdkcrIK1p/Y7p95+1UgLlA6Tq1uuTXIc3k2ZeTzYpakT5JuO876+Vv9GiXJpIOpC09/BZUkorZVkNG39JZ5M2HY9nWlos02BhT02fRPuLJIN9C/BDSQu0uA/ybOAOpVJKJgXDtJvzshVOZdrMv/x10AYxYwsGHUmrA6NI+7q+V3ZpEnB9E9kiitZrVlLY9wakB/aNwK/cZOmQvF3gXKYZyN2B3Wxv0aSczwPfJEUdmhRO/hPbZ9cdOKOcM6ni0rTdbMmaUt2yj9puOUNM3gi/cqsRfpIeZ/pQ/vK1WNtutCxQpdw1gA3z6Y227ym71kwWk0FH0jdt/6zbevQKYdiCjiFp5laiDgcLSQe7SuqpyrYG5MyQsaLZLBZl41Ym1ToTcJ3tRgM0ymUU4tLM464HtrDd8sxa0oXAQbab3QBdLmMEsK7tW1qV0eT9uuYir8ZQ02eoE67IoJMsndMPrcz0FYdb+sZdAEVl039e0u5M24C8Ky1m+siGrGljVkFRLk1Im6vHSvoLrVe/Xgh4QNIdtFiSJYfD/xxYt4n7tkNHI3QbYKjpM6QJwxZ0ktNJ6zXHkqoO700X/mDrpJ6ah9YM0heAE0nvy6QMIE27/QrkGOBWSdO5NFuU9a98tFP9ekyL4yq5WtIOpGKeg+1qGmqurKGmz5AmXJFBx5B0l+01VZbcVdJNtjccaGzBeixF2kv1M+DwskuTgAntuN2GCkW4NIcakiaRUpVNAd6kvY3nA91rSLn+WnUlD1dixhZ0krfyWskjkg4AniJlx+goTsmL/ylpc+DN7OZagbTPamKz8iQtDHyJGcPZuzZra9elKemXtg9R9QTGDbkRJd1se4NskCqDP5o2SLY7GTHYMU9C3k5xkO1j63SLPXZNEDO2oGNIWgt4EJgP+BHJ9Xe07du7pM9dpKi4+YHbSdnm37Dd0MbjMjm3AjeRtgqUwtmxfXFx2nYWSWvavkvSxtWu276h0zoBSNqWVPYIYKztP7chq+b+vDa2EbSqy1jbm3Tqfv1OGLZgyKAOl60puZvyXq3ZbR/V4oboliIghxMFbfI+gpQG65zctCspEfPhtUfVlFW+P2/qNJW6tjn/J6Qk2hcAr5fa3WQi7SARhi0YMnR6XUPSPaTyIMcC+9i+Xy0Ud5T0Y+BW21cMhp7dRNL6pOCPpUhGqeRGbDiStSgjImkCMMr21Hw+ErinFWNUxP68IsnbKiqx7Y91XJk+INbYguHMwaQN0Zdko7YsUO0BU5WytSMB35L0Dilb/aAFNXSB35FSPU3nZm2Sg4EVCzIi8wElF+G8dfoNxL9JxU6HBLY37bYO/UQYtmDY4pTF/8ay88eA94qLDuQa7XAwQ7d4xfZf25RRlBH5KXC3pLGkLw8bkb6YtEIR+/MKQ9IipPe3mO0tc1Truq6oZhA0Rhi2YCgx1DahNlT0U5JIeSaXccqGvwSwqO07BlW7znC9pKNJeSbLDUAzaz9FGZGtSQmQXyLtrfuG7f80KaNEEfvziuQM0j7Pb+fzf5DW28KwtUAYtqBjSFra9hMVbWvZHpdPO122pih+TVo7+hgp2vM14FekQIde56P55+iyNpPea6MUZUROJ+X13BZYFhivVIm76d8bTytfM3c6TRXCu8hCtv8g6Zskhd6V1Krrd9gThi3oJH+UtI3tpwByKPmJwIcBbJ/RRd3a4aM5uvIeANsvKVXm7nmKWPspGZEC5PxN0g2kLwybAvsBq9DCFyJJq5KSVi+Qz58HPm/7/iJ0bYHXJS1I3u8naR2G0BpgrxGGLegk/wNcKmkbYA3SmsJW3VWpLo26RifnCL3SQ2lhpkX/9TyStiYZkPL8nj9sYvzCwNeryGgq4k/SdaTMI7eR9g2uZfu5ZmSUcQrwFdvXZ9mbkMrGrNeivHb5CqnW3HKSbgEWBnbski49Txi2oGPYHifpIOBq4C1S1vj/dlktJM1p+/UqlxqdCRwPXAK8L+9H2hH4TlH6dRNJJwFzkGZIvyW9t2bXDs8hrRd9ijTL2hNo5f99Aqm6+aqk2czLkm6z/WYLsuYsGTUA22MlzdmCnEKwfXf2YKxI+kL1sIdQJYxeI/axBYNOlbRMKwPPkIIAmsryXrBe65Ee1nPZXlKpbtz/2P7fFmStBGzGtNyMDxarbXdQrihe9nMuUhLijw84eJqMUo7Q96qTS7rBdtWsJg3Im4uUQPtrwPttz9qCjEuAu5m+ht5o259uRadWkfSZetfdYtXz4U7M2IJO8PNuK1CDY4FPkFxA2L5X0kb1h8yIpOWAx23/Kru0tpD0jHOhzx6nNBt6Q9JipOoHyzQpozTzeCa7NZ8GPtCsIjm/6IakWds/SRGSNzUrJ/MF4AekaM9Skdm9W5TVDtvkn+8juUH/ls83BcbSQtXzIAxb0AHK8wrm/TqlaME72lgjKQTb/07R+u/RSiTaxcBoSR8kzQD/RKqoPZTXDxvlz5LmA44mzXBMeo/N8GNJ8wJfBU4g5Qg9tAVdZgd+QUqj1VYFBqdadQcN2HGQsb03gKQ/k6qMP5PPFyVF1gYtEK7IoGNI+izpATmW9C15Q+Aw2xd1SZ+LSA/KE4F1SA+60bZ3aVJOKefk10nVAk7oxzIjkmYFZrPds9F6RVQtGAwk3Wd71bLzEaQSSqvWGRbUIGZsQSf5NmWRbDla7lqgK4aNFMhwHLA48CQpqGX/FuRMVipe+nmmuZZmLkTDLiNpDtJMa0nbX5K0pKQNm8mqn0sC/QZYxPaqklYDtrX940FSux6lNbWh5h4fK+kqUhV2A7vQRHq3YHpixhZ0jMoEw/lb6b3NJh0eauT0R/sBt9k+T9IywM62j+iyam0j6QJSnsjPZ6M0O+l9jmpCxg3AYcDJpVls5Qyl00g6uHJjd7W2Duv0GZIXA+BG25d0S5deJwxb0DFyaqbVSN9KAXYmuVu+0SV9jq/S/Apwp+3LOq3PUETSnbZHl7tWJd1re/UmZIyzvVaFjK6W+qlWSaIf3cfDlXBFBh3D9mH5W+kGpDW2U7r8rXQ2UtXsUnXiHYD7gX0kbWr7kEaESHqc6us1DZd2GcK8k2dppc3ny1GW77FBns/jSjJ2JG336DjZZfw5YBlJl5ddmpsU8dkV8t/FkaToSNFfFSI6Thi2oNPcQgr/Ns1v9C2aDwIfK0XYSfoNaZ1tC2BiE3LK8yjOBuxETtXUB3wfuBJYQtI5pMTQezUpY39Spo+VJD0FPE7aN9YNbiUZ1YWAY8raJ5E2gHeLo4Bt+mX/Y7cJV2TQMYZgVOTDwNqlKL8ckv532yu165aSdLPtDYrStZvkHIbrkP7Pbrf9fIty5gRG2J5UpH79gKRbbDdUTSIYmJixBZ1kqEVFHkXKED+WafW9fpofwNc2KkRS+VrNCNIMrp9qtS0OjCQ9LzaS1FRGjLwP7vPA0sBMpX2Dtru2jywnGT4B+BCp4sBI4PUuuv7uzIE6lzJ9aZ/YoN0CYdiCTjKiYkP2CyRD0BVs/07SX4E9gIdIbsgnc97Iw5oQVe7Sehd4AvhsUXp2E0mnkQJ+7mdaYmfTXEaMK4DbSe7doZIc+kRSSP2FpC8inye5prvFPMAbQHmqsmY/5yAThi3oJFeW7dWBFBV5RbeUkfRF4GBSeqfxJHfbbTRXa6yQ0i5DmHVsr9ymjNlsf6UQbQrE9qOSRtqeApwu6dYu6tKNdF59S9e+LQfDD9uHkYIIVgNWJ0VFdiXUP3MwKb3XP7Nx+ggtZJ2XNK+kX0i6Mx/H5PW6fuC2vE+vHc6W9CVJi0paoHQUol3rvKFUM2+8pKMkHUoqidMVJK0g6TpJ9+Xz1ST1RYWIbhDBI8GwpWx/1XhSsdC3W9lfJeli4D7gzNy0B7C67bqZ23uBnBT6T8B/SGs/pTD01ZqQsT/wE+Blpm2LcDe3Q0haCniOlCHmUGBe4Ne2H+2SPkNuE3svE67IYNCRNIkq+7zo/l6dJ3Ngw6XANZJeImWeb5blbO9Qdv6DbCz7gdNIhrqd9bGvAB9sNZpyMLD9z/zyTVKW/24zh+07KhJyt5XoeTgThi0YdGwPyQhB29vnl2MkXU/61n5lC6LelLSB7ZsBJK3PtHIvvc6/bF8+cLe63E8KjOg6kiZS/UsWAM3MRAtmyGxi7wfCFRkEbSJpFMkNWVpXewnY03Y3N/wWgqRfA/OR3JEthaErFfVchZTUt1xGx8P9swuyJmUzuY4iaVnS+vN6pN+fx4HduqVPrxOGLQjaJJdz2RFYjmQEXiG5WH/YTb2KQNLpVZpt+wtNyNizWrvtM6u1D0cklaJGZycF9b1O+j26y/b4bunVq4RhC4I2kXQlKTDibsoKldo+ptaYfkHSN23/rE0ZF1esUQ46Feu+s5CCSLq2QVvSuaT9dJeT1p63BsaRc5naPqobevUqYdiCoE2Gc/RatSz5LcjoelZ9SZ8mpVf7VpfufxWwg+3X8vlcpIw825Nmbe1uuRhWxD62IGifWyX1dE25NtDAXQak69+ubV9KkxvzC2ZJ4J2y88nAUrbfpPlqCsOeiIoMghYpi7CbCdhb0mO0uNerh+m6UWqFXCamRCm/Zzffy7nA7ZJKdQC3Ac7LeUsf6J5avUm4IoOgRYZqhF0nKcKN2A1XZEVQTCm/56kVuUw7iqQ1mVar8Gbbd3ZLl14nDFsQBC0j6Vu2fzpAn08BV9iuusFb0sdtXz0oCgbDklhjC4KgJgPlMBzIqGV2AR7JORk/VHmxG0ZN0rKS/iTpv5Kek3RZ3ksW9AFh2IIgqMepwDdJwQzkTee7NCPA9u6kBNP/R8qif5ukfSV1MyPNucAfgEWBxUjla86rOyLoGcKwBUFQjzls31HR1nQOQ9uvAhcD55OMyfbA3ZIObF/FlpDts22/m4/f06OBMMGMhGELgqAebecwlLRtTqv1N9JG6LVtb0kqXfS1gvVtlOslHS5paUlLSfo68JchUlInaJMIHgmCoCZF5DCUdBbwW9s3Vrm2me3ritK3CZ0er3O5qyV1gvaJfWxBEFRF0kjgy7Y3z/upRtie1IKoZyqNmqQjbX+jG0YNwPYy3bhv0BnCFRkEQVVsTwHWzK9fb9GoAWxRpW3LlhUrAEkzSzpI0kX5OEDSzN3UKSiOcEUGQVATSccAy5OiBl8vtTdStkbSl4H/JVU9KK9MPTdwS46W7AqSfkta7yuvej7F9he7pVNQHGHYgiCoSTtlayTNC8wP/Aw4vOzSJNsvFqRiS0i61/bqA7UFvUmssQVBUBPbe7c33E9I2r/ygqQFumzcpkhazvb/ZX2WpazkUNDbhGELgqAmkj4AnACsTwr5vxk42PaTDQw/F/gUcFceW14JwEA3Iw8PI4X8P5bPlwbaMeLBECJckUEQ1ETSNSQDdXZu2p0U7l8tIKRnkDQb8FVgs9x0DXCs7be6p1VQFGHYgiCoiaTxtkcN1FZjbN0CpLbvbk+71pH0B+BV4JzctCswv+2duqVTUBzhigyCoB7PS9qdaXkUdwVeaHDsMfnnbKR6Z/eS3JGrAX8nlWjpFitWBIpcL+nermkTFErsYwuCoB5fAD4L/IeUSmvH3DYgtje1vSnwT2AN26Ntr0lKiPxo/dGDzj2S1imdSPoocEsX9QkKJFyRQRDMQCkziKSdbF/YpqyW3ZmDhaQHgRWBf+WmJYEHgakMn+rnfUsYtiAIZkDSRGAN4O+2666VNSDrPNLm7lIG/d2BuWzv2raires07Kuf9zNh2IIgmAFJRwP7AnMCb5DWxkoh+7Y9TxOyZgO+DGyUm24EfhMRiMFgEYYtCIKaSLrM9nbd1iMImiEMWxAEdcluu+VtXytpdmCmRhIiS/qD7c9mt+YMD5pYxwoGizBsQRDURNKXSC7JBWwvJ2l54CTbmw0wFEmL2n6m1npWrGMFg0UYtiAIaiJpPLA2KYjkI7ltou0Pd1WxIKhDbNAOgqAeb9t+R0ppHiXNRBW3YjUkTarRt+kAlCBohjBsQRDU4wZJ3wJml7QFqb7anxoZaHvuQdUsCGoQrsggCGoiaQSwD/Bx0kzrKuC3buHBIel9pPRaANj+V53uQdAyYdiCIKiLpFmAlUhuxYdtv9Pk+G1JeSMXA54DlgIetL1K0boGAUSuyCAI6iBpa+D/gOOBE4FHJW3ZpJgfAesA/7C9DKlUTORlDAaNWGMLgqAexwCb2n4UQNJywF+AvzYhY7LtFySNkDTC9vWSjhwMZYMAwrAFQVCf50pGLfMYyZ3YDC9Lmgu4CThH0nPAu0UpGASVhGELgmAGJH0mv7xf0hXAH0hrbDsB45oUdyMwH3AwKQHyvMAPi9E0CGYkDFsQBNXYpuz1s8DG+fV/gfmblFWKpnwROB+4wHajxUqDoGkiKjIIgpaR9E3bP2uw72rAzsAOwJO2Nx9U5YJhS0RFBkHQDjs10fc5UiXuF4D3DY46QRCGLQiC9tCAHaQvSxoLXAcsBHwpMvsHg0mssQVB0A6NrGUsBRxie/wg6xIEQKyxBUHQBpLuKWX9D4KhQrgigyCoiaT1B2i7sIPqBEFDxIwtCIKaSLrb9hoDtQXBUCLW2IIgmAFJ6wLrAQtL+krZpXmAkd3RKggaIwxbEATVmAWYi/SMKK+r9iqwY1c0CoIGCVdkEAQ1kbSU7X/m1yOAuWy/2mW1gqAuETwSBEE9fiZpHklzAg8AD0s6rNtKBUE9wrAFQVCPlfMM7dPAFcCSwB5d1SgIBiAMWxAE9ZhZ0swkw3aZ7ck0tik7CLpGGLYgCOpxMvAEMCdwo6SlSAEkQTBkieCRIAiaQtJMtqNQaDBkiRlbEAQ1kbSIpN9J+ms+XxnYs8tqBUFdwrAFQVCPM0hFQhfL5/8ADumWMkHQCGHYgiCox0K2/wBMBcguyCndVSkI6hOGLQiCerwuaUFyJKSkdYBXuqtSENQnUmoFQVCPrwCXA8tKugVYmEipFQxxwrAFQVCPB4BLgDeAScClpHW2IBiyRLh/EAQ1kfQH0r61c3LTrsD8tnfqnlZBUJ8wbEEQ1ETSvbZXH6gtCIYSETwSBEE97skBIwBI+ihwSxf1CYIBiRlbEAQzIGkiKRJyZmBF4F/5fCngAdurdlG9IKhLGLYgCGYg54SsSalGWxAMRcKwBUEQBH1FrLEFQRAEfUUYtiAIgqCvCMMWBEEQ9BVh2IIgCIK+IgxbEARB0Ff8P/3Vor03vxDDAAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "ax = sns.heatmap(\n",
+    "    corr,\n",
+    "    vmin=-1, vmax=1, center=0,\n",
+    "    cmap=sns.diverging_palette(20,220, n=200),\n",
+    "    square=True\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='age', ylabel='work_scale'>"
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(data=data, x='age',y='work_scale')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='age', ylabel='work_scale_cat'>"
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_tmp = data.copy()\n",
+    "df_tmp['work_scale_cat'] = df_tmp['work_scale'].astype('category')\n",
+    "sns.swarmplot(\n",
+    "    data=df_tmp.sample(500),\n",
+    "    x='age',\n",
+    "    y='work_scale_cat',\n",
+    "    size=3\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0                2.0   64          13.0             2.0   50.8333       4.0   \n1                4.0   87          15.0             1.0   50.8333       4.0   \n2                4.0   73          30.0             1.0   50.8333       4.0   \n4                4.0   74          24.0             1.0   50.8333       4.0   \n5                4.0   21          18.0             2.0   50.8333       4.0   \n...              ...  ...           ...             ...       ...       ...   \n26594            4.0   28          18.0             1.0   45.1667      15.5   \n26596            1.0   42          18.0             1.0   45.1667      15.5   \n26597            4.0   37          19.0             4.0   45.1667      15.5   \n26599            4.0   57          23.0             2.0   45.1667      15.5   \n26600            1.0   60          25.0             2.0   45.1667      15.5   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                          2.0                   1.0         0.0   \n1                          1.0                   0.0         0.0   \n2                          1.0                   1.0         0.0   \n4                          0.0                   0.0         0.0   \n5                          0.0                   0.0         1.0   \n...                        ...                   ...         ...   \n26594                      0.0                   0.0         2.0   \n26596                      1.0                   1.0         0.0   \n26597                      0.0                   0.0         2.0   \n26599                      0.0                   2.0         0.0   \n26600                      0.0                   0.0         2.0   \n\n       population_density  salary  gender_Female  \n0                     0.0  0.1139              1  \n1                     1.0  0.1139              1  \n2                     1.0  0.1139              0  \n4                     1.0  0.1139              1  \n5                     1.0  0.2850              1  \n...                   ...     ...            ...  \n26594                 1.0  0.3645              1  \n26596                 2.0  0.0000              1  \n26597                 1.0  0.3200              0  \n26599                 1.0  0.1139              1  \n26600                 2.0  0.3645              0  \n\n[22602 rows x 12 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>64</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4.0</td>\n      <td>21</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.2850</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26594</th>\n      <td>4.0</td>\n      <td>28</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3645</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>1.0</td>\n      <td>42</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>4.0</td>\n      <td>37</td>\n      <td>19.0</td>\n      <td>4.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>4.0</td>\n      <td>57</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>1.0</td>\n      <td>60</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.3645</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>22602 rows × 12 columns</p>\n</div>"
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "look_at_dates               3.328511\nage                        54.438855\nage_stop_edu               20.679586\nhousehold_size              2.290240\ncntrylat                   49.334213\ncntrylon                   13.431187\nbest_before_meaning_map     0.998717\nvalidity_meaning_map        0.863198\nwork_scale                  1.048934\npopulation_density          0.988320\nsalary                      0.274835\ngender_Female               0.624900\ndtype: float64"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.mean()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "look_at_dates               1.027471\nage                        16.151461\nage_stop_edu                5.779848\nhousehold_size              1.165178\ncntrylat                    6.996555\ncntrylon                   10.624097\nbest_before_meaning_map     0.746153\nvalidity_meaning_map        0.831097\nwork_scale                  1.099168\npopulation_density          0.791668\nsalary                      0.245872\ngender_Female               0.484159\ndtype: float64"
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.std()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-1.2930194 ,  0.5919809 , -1.32871239, ..., -1.24842899,\n        -0.65456171,  0.77476118],\n       [ 0.65355032,  2.01603218, -0.98267488, ...,  0.01475447,\n        -0.65456171,  0.77476118],\n       [ 0.65355032,  1.14921836,  1.61260645, ...,  0.01475447,\n        -0.65456171, -1.29072032],\n       ...,\n       [ 0.65355032, -1.07973146, -0.29059986, ...,  0.01475447,\n         0.18369862, -1.29072032],\n       [ 0.65355032,  0.15857399,  0.40147516, ...,  0.01475447,\n        -0.65456171,  0.77476118],\n       [-2.26630425,  0.34431981,  0.74751267, ...,  1.27793792,\n         0.36469127, -1.29072032]])"
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "scaler = preprocessing.StandardScaler()\n",
+    "X = scaler.fit_transform(data)\n",
+    "X\n",
+    "#Fit = Model an Daten anpassen"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "numpy.ndarray"
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([-1.20718702e-16, -1.15688756e-16, -5.40719187e-17, -1.40838486e-16,\n        4.20000484e-16,  2.01197837e-17,  4.02395674e-17, -2.01197837e-17,\n        4.02395674e-17, -5.28144322e-17, -1.23233675e-16, -1.25748648e-16])"
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.mean(axis=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.std(axis=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates       age  age_stop_edu  household_size  cntrylat  \\\n0          -1.293019  0.591981     -1.328712       -0.249100  0.214266   \n1           0.653550  2.016032     -0.982675       -1.107357  0.214266   \n2           0.653550  1.149218      1.612606       -1.107357  0.214266   \n4           0.653550  1.211134      0.574494       -1.107357  0.214266   \n5           0.653550 -2.070376     -0.463619       -0.249100  0.214266   \n...              ...       ...           ...             ...       ...   \n26594       0.653550 -1.636969     -0.463619       -1.107357 -0.595665   \n26596      -2.266304 -0.770155     -0.463619       -1.107357 -0.595665   \n26597       0.653550 -1.079731     -0.290600        1.467413 -0.595665   \n26599       0.653550  0.158574      0.401475       -0.249100 -0.595665   \n26600      -2.266304  0.344320      0.747513       -0.249100 -0.595665   \n\n       cntrylon  best_before_meaning_map  validity_meaning_map  work_scale  \\\n0     -0.887736                 1.341958              0.164608   -0.954319   \n1     -0.887736                 0.001720             -1.038648   -0.954319   \n2     -0.887736                 0.001720              0.164608   -0.954319   \n4     -0.887736                -1.338519             -1.038648   -0.954319   \n5     -0.887736                -1.338519             -1.038648   -0.044520   \n...         ...                      ...                   ...         ...   \n26594  0.194733                -1.338519             -1.038648    0.865279   \n26596  0.194733                 0.001720              0.164608   -0.954319   \n26597  0.194733                -1.338519             -1.038648    0.865279   \n26599  0.194733                -1.338519              1.367864   -0.954319   \n26600  0.194733                -1.338519             -1.038648    0.865279   \n\n       population_density    salary  gender_Female  \n0               -1.248429 -0.654562       0.774761  \n1                0.014754 -0.654562       0.774761  \n2                0.014754 -0.654562      -1.290720  \n4                0.014754 -0.654562       0.774761  \n5                0.014754  0.041345       0.774761  \n...                   ...       ...            ...  \n26594            0.014754  0.364691       0.774761  \n26596            1.277938 -1.117822       0.774761  \n26597            0.014754  0.183699      -1.290720  \n26599            0.014754 -0.654562       0.774761  \n26600            1.277938  0.364691      -1.290720  \n\n[22602 rows x 12 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1.293019</td>\n      <td>0.591981</td>\n      <td>-1.328712</td>\n      <td>-0.249100</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>1.341958</td>\n      <td>0.164608</td>\n      <td>-0.954319</td>\n      <td>-1.248429</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.653550</td>\n      <td>2.016032</td>\n      <td>-0.982675</td>\n      <td>-1.107357</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>0.001720</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.653550</td>\n      <td>1.149218</td>\n      <td>1.612606</td>\n      <td>-1.107357</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>0.001720</td>\n      <td>0.164608</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>-1.290720</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.653550</td>\n      <td>1.211134</td>\n      <td>0.574494</td>\n      <td>-1.107357</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.653550</td>\n      <td>-2.070376</td>\n      <td>-0.463619</td>\n      <td>-0.249100</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.044520</td>\n      <td>0.014754</td>\n      <td>0.041345</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26594</th>\n      <td>0.653550</td>\n      <td>-1.636969</td>\n      <td>-0.463619</td>\n      <td>-1.107357</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>0.865279</td>\n      <td>0.014754</td>\n      <td>0.364691</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>-2.266304</td>\n      <td>-0.770155</td>\n      <td>-0.463619</td>\n      <td>-1.107357</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>0.001720</td>\n      <td>0.164608</td>\n      <td>-0.954319</td>\n      <td>1.277938</td>\n      <td>-1.117822</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>0.653550</td>\n      <td>-1.079731</td>\n      <td>-0.290600</td>\n      <td>1.467413</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>0.865279</td>\n      <td>0.014754</td>\n      <td>0.183699</td>\n      <td>-1.290720</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>0.653550</td>\n      <td>0.158574</td>\n      <td>0.401475</td>\n      <td>-0.249100</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>1.367864</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>-2.266304</td>\n      <td>0.344320</td>\n      <td>0.747513</td>\n      <td>-0.249100</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>0.865279</td>\n      <td>1.277938</td>\n      <td>0.364691</td>\n      <td>-1.290720</td>\n    </tr>\n  </tbody>\n</table>\n<p>22602 rows × 12 columns</p>\n</div>"
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scaled = pd.DataFrame(X, index=data.index, columns=data.columns)\n",
+    "data_scaled"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "pandas.core.frame.DataFrame"
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-1.2930194 ,  0.5919809 , -1.32871239, ..., -1.24842899,\n        -0.65456171,  0.77476118],\n       [ 0.65355032,  2.01603218, -0.98267488, ...,  0.01475447,\n        -0.65456171,  0.77476118],\n       [ 0.65355032,  1.14921836,  1.61260645, ...,  0.01475447,\n        -0.65456171, -1.29072032],\n       ...,\n       [ 0.65355032, -1.07973146, -0.29059986, ...,  0.01475447,\n         0.18369862, -1.29072032],\n       [ 0.65355032,  0.15857399,  0.40147516, ...,  0.01475447,\n        -0.65456171,  0.77476118],\n       [-2.26630425,  0.34431981,  0.74751267, ...,  1.27793792,\n         0.36469127, -1.29072032]])"
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scaled.values"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "numpy.ndarray"
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data_scaled.values)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KMeans(n_clusters=2)"
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "model = KMeans(n_clusters=2)\n",
+    "model.fit(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 0, 0, ..., 1, 0, 1])"
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions = model.labels_\n",
+    "predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0                2.0   64          13.0             2.0   50.8333       4.0   \n1                4.0   87          15.0             1.0   50.8333       4.0   \n2                4.0   73          30.0             1.0   50.8333       4.0   \n4                4.0   74          24.0             1.0   50.8333       4.0   \n5                4.0   21          18.0             2.0   50.8333       4.0   \n...              ...  ...           ...             ...       ...       ...   \n26594            4.0   28          18.0             1.0   45.1667      15.5   \n26596            1.0   42          18.0             1.0   45.1667      15.5   \n26597            4.0   37          19.0             4.0   45.1667      15.5   \n26599            4.0   57          23.0             2.0   45.1667      15.5   \n26600            1.0   60          25.0             2.0   45.1667      15.5   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                          2.0                   1.0         0.0   \n1                          1.0                   0.0         0.0   \n2                          1.0                   1.0         0.0   \n4                          0.0                   0.0         0.0   \n5                          0.0                   0.0         1.0   \n...                        ...                   ...         ...   \n26594                      0.0                   0.0         2.0   \n26596                      1.0                   1.0         0.0   \n26597                      0.0                   0.0         2.0   \n26599                      0.0                   2.0         0.0   \n26600                      0.0                   0.0         2.0   \n\n       population_density  salary  gender_Female  cluster  \n0                     0.0  0.1139              1        0  \n1                     1.0  0.1139              1        0  \n2                     1.0  0.1139              0        0  \n4                     1.0  0.1139              1        0  \n5                     1.0  0.2850              1        1  \n...                   ...     ...            ...      ...  \n26594                 1.0  0.3645              1        1  \n26596                 2.0  0.0000              1        0  \n26597                 1.0  0.3200              0        1  \n26599                 1.0  0.1139              1        0  \n26600                 2.0  0.3645              0        1  \n\n[22602 rows x 13 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n      <th>cluster</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>64</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4.0</td>\n      <td>21</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.2850</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26594</th>\n      <td>4.0</td>\n      <td>28</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3645</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>1.0</td>\n      <td>42</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>4.0</td>\n      <td>37</td>\n      <td>19.0</td>\n      <td>4.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>4.0</td>\n      <td>57</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>1.0</td>\n      <td>60</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.3645</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>22602 rows × 13 columns</p>\n</div>"
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_predictions = data.copy()\n",
+    "data_with_predictions['cluster'] = predictions\n",
+    "data_with_predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "sns.set()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n",
+     "is_executing": true
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='age', ylabel='salary'>"
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(\n",
+    "    data=data_with_predictions,\n",
+    "    x='age',\n",
+    "    y='salary',\n",
+    "    alpha=0.04,\n",
+    "    hue='cluster')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "         look_at_dates        age  age_stop_edu  household_size   cntrylat  \\\ncluster                                                                      \n0             3.317369  63.583181     19.755998        2.081043  49.386939   \n1             3.339977  45.028548     21.630039        2.505521  49.279953   \n\n          cntrylon  best_before_meaning_map  validity_meaning_map  work_scale  \\\ncluster                                                                         \n0        13.425039                 1.028963              0.931781    0.045363   \n1        13.437514                 0.967591              0.792621    2.081695   \n\n         population_density    salary  gender_Female  \ncluster                                               \n0                  0.956120  0.092940       0.690395  \n1                  1.021456  0.462021       0.557501  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n    <tr>\n      <th>cluster</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.317369</td>\n      <td>63.583181</td>\n      <td>19.755998</td>\n      <td>2.081043</td>\n      <td>49.386939</td>\n      <td>13.425039</td>\n      <td>1.028963</td>\n      <td>0.931781</td>\n      <td>0.045363</td>\n      <td>0.956120</td>\n      <td>0.092940</td>\n      <td>0.690395</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.339977</td>\n      <td>45.028548</td>\n      <td>21.630039</td>\n      <td>2.505521</td>\n      <td>49.279953</td>\n      <td>13.437514</td>\n      <td>0.967591</td>\n      <td>0.792621</td>\n      <td>2.081695</td>\n      <td>1.021456</td>\n      <td>0.462021</td>\n      <td>0.557501</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_predictions.groupby('cluster').mean()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_with_predictions.groupby('cluster').mean().T.plot.bar()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "cluster                          0          1\nlook_at_dates             3.317369   3.339977\nage                      63.583181  45.028548\nage_stop_edu             19.755998  21.630039\nhousehold_size            2.081043   2.505521\ncntrylat                 49.386939  49.279953\ncntrylon                 13.425039  13.437514\nbest_before_meaning_map   1.028963   0.967591\nvalidity_meaning_map      0.931781   0.792621\nwork_scale                0.045363   2.081695\npopulation_density        0.956120   1.021456\nsalary                    0.092940   0.462021\ngender_Female             0.690395   0.557501",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>cluster</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>look_at_dates</th>\n      <td>3.317369</td>\n      <td>3.339977</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>63.583181</td>\n      <td>45.028548</td>\n    </tr>\n    <tr>\n      <th>age_stop_edu</th>\n      <td>19.755998</td>\n      <td>21.630039</td>\n    </tr>\n    <tr>\n      <th>household_size</th>\n      <td>2.081043</td>\n      <td>2.505521</td>\n    </tr>\n    <tr>\n      <th>cntrylat</th>\n      <td>49.386939</td>\n      <td>49.279953</td>\n    </tr>\n    <tr>\n      <th>cntrylon</th>\n      <td>13.425039</td>\n      <td>13.437514</td>\n    </tr>\n    <tr>\n      <th>best_before_meaning_map</th>\n      <td>1.028963</td>\n      <td>0.967591</td>\n    </tr>\n    <tr>\n      <th>validity_meaning_map</th>\n      <td>0.931781</td>\n      <td>0.792621</td>\n    </tr>\n    <tr>\n      <th>work_scale</th>\n      <td>0.045363</td>\n      <td>2.081695</td>\n    </tr>\n    <tr>\n      <th>population_density</th>\n      <td>0.956120</td>\n      <td>1.021456</td>\n    </tr>\n    <tr>\n      <th>salary</th>\n      <td>0.092940</td>\n      <td>0.462021</td>\n    </tr>\n    <tr>\n      <th>gender_Female</th>\n      <td>0.690395</td>\n      <td>0.557501</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_predictions.groupby('cluster').mean().T"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "outputs": [],
+   "source": [
+    "data_scaled_with_predictions = data_scaled.copy()\n",
+    "data_scaled_with_predictions['cluster'] = predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates       age  age_stop_edu  household_size  cntrylat  \\\n0          -1.293019  0.591981     -1.328712       -0.249100  0.214266   \n1           0.653550  2.016032     -0.982675       -1.107357  0.214266   \n2           0.653550  1.149218      1.612606       -1.107357  0.214266   \n4           0.653550  1.211134      0.574494       -1.107357  0.214266   \n5           0.653550 -2.070376     -0.463619       -0.249100  0.214266   \n...              ...       ...           ...             ...       ...   \n26594       0.653550 -1.636969     -0.463619       -1.107357 -0.595665   \n26596      -2.266304 -0.770155     -0.463619       -1.107357 -0.595665   \n26597       0.653550 -1.079731     -0.290600        1.467413 -0.595665   \n26599       0.653550  0.158574      0.401475       -0.249100 -0.595665   \n26600      -2.266304  0.344320      0.747513       -0.249100 -0.595665   \n\n       cntrylon  best_before_meaning_map  validity_meaning_map  work_scale  \\\n0     -0.887736                 1.341958              0.164608   -0.954319   \n1     -0.887736                 0.001720             -1.038648   -0.954319   \n2     -0.887736                 0.001720              0.164608   -0.954319   \n4     -0.887736                -1.338519             -1.038648   -0.954319   \n5     -0.887736                -1.338519             -1.038648   -0.044520   \n...         ...                      ...                   ...         ...   \n26594  0.194733                -1.338519             -1.038648    0.865279   \n26596  0.194733                 0.001720              0.164608   -0.954319   \n26597  0.194733                -1.338519             -1.038648    0.865279   \n26599  0.194733                -1.338519              1.367864   -0.954319   \n26600  0.194733                -1.338519             -1.038648    0.865279   \n\n       population_density    salary  gender_Female  cluster  \n0               -1.248429 -0.654562       0.774761        0  \n1                0.014754 -0.654562       0.774761        0  \n2                0.014754 -0.654562      -1.290720        0  \n4                0.014754 -0.654562       0.774761        0  \n5                0.014754  0.041345       0.774761        1  \n...                   ...       ...            ...      ...  \n26594            0.014754  0.364691       0.774761        1  \n26596            1.277938 -1.117822       0.774761        0  \n26597            0.014754  0.183699      -1.290720        1  \n26599            0.014754 -0.654562       0.774761        0  \n26600            1.277938  0.364691      -1.290720        1  \n\n[22602 rows x 13 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n      <th>cluster</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1.293019</td>\n      <td>0.591981</td>\n      <td>-1.328712</td>\n      <td>-0.249100</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>1.341958</td>\n      <td>0.164608</td>\n      <td>-0.954319</td>\n      <td>-1.248429</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.653550</td>\n      <td>2.016032</td>\n      <td>-0.982675</td>\n      <td>-1.107357</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>0.001720</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.653550</td>\n      <td>1.149218</td>\n      <td>1.612606</td>\n      <td>-1.107357</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>0.001720</td>\n      <td>0.164608</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>-1.290720</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.653550</td>\n      <td>1.211134</td>\n      <td>0.574494</td>\n      <td>-1.107357</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.653550</td>\n      <td>-2.070376</td>\n      <td>-0.463619</td>\n      <td>-0.249100</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.044520</td>\n      <td>0.014754</td>\n      <td>0.041345</td>\n      <td>0.774761</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26594</th>\n      <td>0.653550</td>\n      <td>-1.636969</td>\n      <td>-0.463619</td>\n      <td>-1.107357</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>0.865279</td>\n      <td>0.014754</td>\n      <td>0.364691</td>\n      <td>0.774761</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>-2.266304</td>\n      <td>-0.770155</td>\n      <td>-0.463619</td>\n      <td>-1.107357</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>0.001720</td>\n      <td>0.164608</td>\n      <td>-0.954319</td>\n      <td>1.277938</td>\n      <td>-1.117822</td>\n      <td>0.774761</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>0.653550</td>\n      <td>-1.079731</td>\n      <td>-0.290600</td>\n      <td>1.467413</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>0.865279</td>\n      <td>0.014754</td>\n      <td>0.183699</td>\n      <td>-1.290720</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>0.653550</td>\n      <td>0.158574</td>\n      <td>0.401475</td>\n      <td>-0.249100</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>1.367864</td>\n      <td>-0.954319</td>\n      <td>0.014754</td>\n      <td>-0.654562</td>\n      <td>0.774761</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>-2.266304</td>\n      <td>0.344320</td>\n      <td>0.747513</td>\n      <td>-0.249100</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>0.865279</td>\n      <td>1.277938</td>\n      <td>0.364691</td>\n      <td>-1.290720</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>22602 rows × 13 columns</p>\n</div>"
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scaled_with_predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_scaled_with_predictions.groupby('cluster').mean().T.plot.bar()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from yellowbrick.cluster.silhouette import SilhouetteVisualizer\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "for k in [2,3,4]:\n",
+    "    sil = SilhouetteVisualizer(\n",
+    "        KMeans(n_clusters=k, random_state=42))\n",
+    "    sil.fit(X)\n",
+    "    sil.finalize()\n",
+    "    plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "model = KMeans(n_clusters=4, random_state=42)\n",
+    "model.fit(X)\n",
+    "predictions = model.labels_\n",
+    "\n",
+    "data_scaled_with_predictions = data_scaled.copy()\n",
+    "data_scaled_with_predictions['cluster'] = predictions\n",
+    "\n",
+    "data_scaled_with_predictions.groupby('cluster').mean().T.plot.bar()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "         look_at_dates   age  age_stop_edu  household_size  cntrylat  \\\ncluster                                                                \n0                 6639  6639          6639            6639      6639   \n1                 5177  5177          5177            5177      5177   \n2                 5514  5514          5514            5514      5514   \n3                 5272  5272          5272            5272      5272   \n\n         cntrylon  best_before_meaning_map  validity_meaning_map  work_scale  \\\ncluster                                                                        \n0            6639                     6639                  6639        6639   \n1            5177                     5177                  5177        5177   \n2            5514                     5514                  5514        5514   \n3            5272                     5272                  5272        5272   \n\n         population_density  salary  gender_Female  \ncluster                                             \n0                      6639    6639           6639  \n1                      5177    5177           5177  \n2                      5514    5514           5514  \n3                      5272    5272           5272  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n    <tr>\n      <th>cluster</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n      <td>6639</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n      <td>5177</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n      <td>5514</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n      <td>5272</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows_per_cluster_per_column = data_scaled_with_predictions.groupby('cluster').count()\n",
+    "rows_per_cluster_per_column"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='cluster', ylabel='Anzahl der Befragten'>"
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rows_per_cluster = rows_per_cluster_per_column.iloc[:,0]\n",
+    "#Dopplepunkt heißt alles , 0 heißt erste Spalte hier\n",
+    "rows_per_cluster.plot.bar(ylabel='Anzahl der Befragten')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1000x500 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:title={'center':'Principal Component Plot'}, xlabel='$PC_1$', ylabel='$PC_2$'>"
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from yellowbrick.features import PCA\n",
+    "\n",
+    "classes = ['0', '1', '2', '3']\n",
+    "\n",
+    "visualizer = PCA(classes=classes,\n",
+    "                 proj_features=True,\n",
+    "                 alpha=0.2,\n",
+    "                 colormap='sns_muted',\n",
+    "                 size=(1000,500)\n",
+    "                 )\n",
+    "\n",
+    "visualizer.fit_transform(data_scaled, predictions)\n",
+    "visualizer.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "outputs": [],
+   "source": [
+    "# PCA mit Scikit-learn durchführen\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=12)\n",
+    "pca_fit = pca.fit(data_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:label='7810a667-e65f-46a5-afa9-c7530a1501bf'>"
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scree-Plot mit Pareto-Plot ausgeben\n",
+    "pd.Series(pca.explained_variance_ratio_).plot.bar(ylabel='Explained Variance')\n",
+    "pd.Series(pca.explained_variance_ratio_).cumsum().plot.line(\n",
+    "               secondary_y=True,\n",
+    "               color='orange')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    16702\n1     5900\nName: no_date_spaghetti_throw_away, dtype: int64"
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KNeighborsClassifier()"
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "model = KNeighborsClassifier()\n",
+    "model.fit(data_scaled, labels)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates       age  age_stop_edu  household_size  cntrylat  \\\n26033       0.653550 -0.460579      0.228456        1.467413 -0.595665   \n2279        0.653550  0.468150      0.055438        1.467413 -1.477076   \n17839      -1.293019  0.096659     -0.290600       -0.249100  1.381537   \n2332       -2.266304  0.096659     -0.463619        0.609157 -1.477076   \n16060       0.653550 -0.584409     -0.290600       -0.249100  0.059429   \n\n       cntrylon  best_before_meaning_map  validity_meaning_map  work_scale  \\\n26033  0.194733                 1.341958              1.367864    1.775078   \n2279   0.806563                -1.338519             -1.038648   -0.954319   \n17839  1.183074                 0.001720             -1.038648   -0.954319   \n2332   0.806563                -1.338519              1.367864   -0.954319   \n16060  0.194733                 1.341958              0.164608   -0.044520   \n\n       population_density    salary  gender_Female  \n26033            0.014754  2.257996       0.774761  \n2279            -1.248429 -0.654562      -1.290720  \n17839            1.277938 -1.036477      -1.290720  \n2332             1.277938 -1.117822       0.774761  \n16060           -1.248429  0.041345      -1.290720  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>26033</th>\n      <td>0.653550</td>\n      <td>-0.460579</td>\n      <td>0.228456</td>\n      <td>1.467413</td>\n      <td>-0.595665</td>\n      <td>0.194733</td>\n      <td>1.341958</td>\n      <td>1.367864</td>\n      <td>1.775078</td>\n      <td>0.014754</td>\n      <td>2.257996</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>2279</th>\n      <td>0.653550</td>\n      <td>0.468150</td>\n      <td>0.055438</td>\n      <td>1.467413</td>\n      <td>-1.477076</td>\n      <td>0.806563</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>-1.248429</td>\n      <td>-0.654562</td>\n      <td>-1.290720</td>\n    </tr>\n    <tr>\n      <th>17839</th>\n      <td>-1.293019</td>\n      <td>0.096659</td>\n      <td>-0.290600</td>\n      <td>-0.249100</td>\n      <td>1.381537</td>\n      <td>1.183074</td>\n      <td>0.001720</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>1.277938</td>\n      <td>-1.036477</td>\n      <td>-1.290720</td>\n    </tr>\n    <tr>\n      <th>2332</th>\n      <td>-2.266304</td>\n      <td>0.096659</td>\n      <td>-0.463619</td>\n      <td>0.609157</td>\n      <td>-1.477076</td>\n      <td>0.806563</td>\n      <td>-1.338519</td>\n      <td>1.367864</td>\n      <td>-0.954319</td>\n      <td>1.277938</td>\n      <td>-1.117822</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>16060</th>\n      <td>0.653550</td>\n      <td>-0.584409</td>\n      <td>-0.290600</td>\n      <td>-0.249100</td>\n      <td>0.059429</td>\n      <td>0.194733</td>\n      <td>1.341958</td>\n      <td>0.164608</td>\n      <td>-0.044520</td>\n      <td>-1.248429</td>\n      <td>0.041345</td>\n      <td>-1.290720</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_to_predict = data_scaled.sample(5)\n",
+    "data_to_predict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "26033    0\n2279     1\n17839    0\n2332     1\n16060    1\nName: no_date_spaghetti_throw_away, dtype: uint8"
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels_to_predict = labels[data_to_predict.index]\n",
+    "labels_to_predict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 1, 0, 1, 0], dtype=uint8)"
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(data_to_predict)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "outputs": [],
+   "source": [
+    "all_predictions = model.predict(data_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0         True\n1         True\n2         True\n4         True\n5        False\n         ...  \n26594    False\n26596     True\n26597     True\n26599    False\n26600    False\nName: no_date_spaghetti_throw_away, Length: 22602, dtype: bool"
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correct_predictions = all_predictions==labels\n",
+    "correct_predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "True     18077\nFalse     4525\nName: no_date_spaghetti_throw_away, dtype: int64"
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correct_predictions.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "look_at_dates               2.000\nage                        30.000\nage_stop_edu               22.000\nhousehold_size              2.000\ncntrylat                   51.230\ncntrylon                    6.783\nbest_before_meaning_map     1.000\nvalidity_meaning_map        1.000\nwork_scale                  2.000\npopulation_density          3.000\nsalary                      0.250\ngender_Female               0.000\ndtype: float64"
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Daten für Fantasieperson definieren\n",
+    "\n",
+    "new_data_to_predict = pd.Series({\n",
+    "    'look_at_dates': 2, # manchmal\n",
+    "    'age': 30,\n",
+    "    'age_stop_edu': 22,\n",
+    "    'household_size': 2,\n",
+    "    'cntrylat': 51.23, # Düsseldorf\n",
+    "    'cntrylon': 6.783,\n",
+    "    'best_before_meaning_map': 1, # Kommt aufs Lebensmittel an\n",
+    "    'validity_meaning_map': 1,\n",
+    "    'work_scale': 2, # Angestellt\n",
+    "    'population_density': 3, # Große Stadt\n",
+    "    'salary': 0.25, # Ca. 2.5k Euro\n",
+    "    'gender_Female': 0\n",
+    "})\n",
+    "new_data_to_predict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-1.29301940e+00, -1.51313838e+00,  2.28456408e-01,\n        -2.49100286e-01,  2.70966106e-01, -6.25778754e-01,\n         1.71962245e-03,  1.64607863e-01,  8.65278943e-01,\n         2.54112137e+00, -1.01008912e-01, -1.29072032e+00]])"
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data_to_predict_for_scaler = new_data_to_predict.values.reshape(1,-1)\n",
+    "new_data_to_predict_scaled = scaler.transform(new_data_to_predict_for_scaler)\n",
+    "new_data_to_predict_scaled"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0], dtype=uint8)"
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(new_data_to_predict_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Aufgabe"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    16702\n1     5900\nName: no_date_spaghetti_throw_away, dtype: int64"
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KNeighborsClassifier(n_neighbors=6)"
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = KNeighborsClassifier(n_neighbors=6)\n",
+    "model.fit(data_scaled, labels)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates       age  age_stop_edu  household_size  cntrylat  \\\n170        -0.319735 -0.212918     -0.290600       -0.249100  0.214266   \n6411       -1.293019  1.211134     -1.155694       -1.107357  0.523953   \n23527       0.653550 -1.636969      0.228456        0.609157 -0.476561   \n17724       0.653550 -0.893986      0.401475        0.609157  1.381537   \n10573       0.653550  0.158574     -1.501731       -0.249100 -1.405610   \n\n       cntrylon  best_before_meaning_map  validity_meaning_map  work_scale  \\\n170   -0.887736                 0.001720              1.367864    0.865279   \n6411  -2.017269                -1.338519             -1.038648   -0.954319   \n23527  1.088946                -1.338519             -1.038648   -0.044520   \n17724  1.183074                -1.338519              0.164608    0.865279   \n10573 -2.017269                 1.341958              1.367864   -0.954319   \n\n       population_density    salary  gender_Female  \n170             -1.248429  0.364691       0.774761  \n6411             1.277938 -0.654562      -1.290720  \n23527            1.277938 -0.304371       0.774761  \n17724            1.277938  0.915804       0.774761  \n10573           -1.248429 -1.117822       0.774761  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>170</th>\n      <td>-0.319735</td>\n      <td>-0.212918</td>\n      <td>-0.290600</td>\n      <td>-0.249100</td>\n      <td>0.214266</td>\n      <td>-0.887736</td>\n      <td>0.001720</td>\n      <td>1.367864</td>\n      <td>0.865279</td>\n      <td>-1.248429</td>\n      <td>0.364691</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>6411</th>\n      <td>-1.293019</td>\n      <td>1.211134</td>\n      <td>-1.155694</td>\n      <td>-1.107357</td>\n      <td>0.523953</td>\n      <td>-2.017269</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.954319</td>\n      <td>1.277938</td>\n      <td>-0.654562</td>\n      <td>-1.290720</td>\n    </tr>\n    <tr>\n      <th>23527</th>\n      <td>0.653550</td>\n      <td>-1.636969</td>\n      <td>0.228456</td>\n      <td>0.609157</td>\n      <td>-0.476561</td>\n      <td>1.088946</td>\n      <td>-1.338519</td>\n      <td>-1.038648</td>\n      <td>-0.044520</td>\n      <td>1.277938</td>\n      <td>-0.304371</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>17724</th>\n      <td>0.653550</td>\n      <td>-0.893986</td>\n      <td>0.401475</td>\n      <td>0.609157</td>\n      <td>1.381537</td>\n      <td>1.183074</td>\n      <td>-1.338519</td>\n      <td>0.164608</td>\n      <td>0.865279</td>\n      <td>1.277938</td>\n      <td>0.915804</td>\n      <td>0.774761</td>\n    </tr>\n    <tr>\n      <th>10573</th>\n      <td>0.653550</td>\n      <td>0.158574</td>\n      <td>-1.501731</td>\n      <td>-0.249100</td>\n      <td>-1.405610</td>\n      <td>-2.017269</td>\n      <td>1.341958</td>\n      <td>1.367864</td>\n      <td>-0.954319</td>\n      <td>-1.248429</td>\n      <td>-1.117822</td>\n      <td>0.774761</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_to_predict2 = data_scaled.sample(5)\n",
+    "data_to_predict2"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "170      0\n6411     0\n23527    0\n17724    0\n10573    0\nName: no_date_spaghetti_throw_away, dtype: uint8"
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels_to_predict2 = labels[data_to_predict2.index]\n",
+    "labels_to_predict2"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 1, 1, 0, 0], dtype=uint8)"
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(data_to_predict2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "outputs": [],
+   "source": [
+    "all_predictions2 = model.predict(data_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "True     0.78409\nFalse    0.21591\nName: no_date_spaghetti_throw_away, dtype: float64"
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correct_predictions2 = all_predictions2==labels\n",
+    "correct_predictions2.value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/notebooks/data_modeling_beans.ipynb b/notebooks/data_modeling_beans.ipynb
new file mode 100644
index 0000000..3035b77
--- /dev/null
+++ b/notebooks/data_modeling_beans.ipynb
@@ -0,0 +1,1610 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: openpyxl in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (3.0.9)\n",
+      "Requirement already satisfied: et-xmlfile in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from openpyxl) (1.1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install openpyxl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.read_excel(r'C:\\Users\\daves\\AppData\\Roaming\\JetBrains\\DataSpell2021.3\\projects\\biz_analytics_a_starter\\data\\external\\Dry_Bean_Dataset.xlsx', engine='openpyxl')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "        Area  Perimeter  MajorAxisLength  MinorAxisLength  AspectRation  \\\n0      28395    610.291       208.178117       173.888747      1.197191   \n1      28734    638.018       200.524796       182.734419      1.097356   \n2      29380    624.110       212.826130       175.931143      1.209713   \n3      30008    645.884       210.557999       182.516516      1.153638   \n4      30140    620.134       201.847882       190.279279      1.060798   \n...      ...        ...              ...              ...           ...   \n13606  42097    759.696       288.721612       185.944705      1.552728   \n13607  42101    757.499       281.576392       190.713136      1.476439   \n13608  42139    759.321       281.539928       191.187979      1.472582   \n13609  42147    763.779       283.382636       190.275731      1.489326   \n13610  42159    772.237       295.142741       182.204716      1.619841   \n\n       Eccentricity  ConvexArea  EquivDiameter    Extent  Solidity  roundness  \\\n0          0.549812       28715     190.141097  0.763923  0.988856   0.958027   \n1          0.411785       29172     191.272750  0.783968  0.984986   0.887034   \n2          0.562727       29690     193.410904  0.778113  0.989559   0.947849   \n3          0.498616       30724     195.467062  0.782681  0.976696   0.903936   \n4          0.333680       30417     195.896503  0.773098  0.990893   0.984877   \n...             ...         ...            ...       ...       ...        ...   \n13606      0.765002       42508     231.515799  0.714574  0.990331   0.916603   \n13607      0.735702       42494     231.526798  0.799943  0.990752   0.922015   \n13608      0.734065       42569     231.631261  0.729932  0.989899   0.918424   \n13609      0.741055       42667     231.653248  0.705389  0.987813   0.907906   \n13610      0.786693       42600     231.686223  0.788962  0.989648   0.888380   \n\n       Compactness  ShapeFactor1  ShapeFactor2  ShapeFactor3  ShapeFactor4  \\\n0         0.913358      0.007332      0.003147      0.834222      0.998724   \n1         0.953861      0.006979      0.003564      0.909851      0.998430   \n2         0.908774      0.007244      0.003048      0.825871      0.999066   \n3         0.928329      0.007017      0.003215      0.861794      0.994199   \n4         0.970516      0.006697      0.003665      0.941900      0.999166   \n...            ...           ...           ...           ...           ...   \n13606     0.801865      0.006858      0.001749      0.642988      0.998385   \n13607     0.822252      0.006688      0.001886      0.676099      0.998219   \n13608     0.822730      0.006681      0.001888      0.676884      0.996767   \n13609     0.817457      0.006724      0.001852      0.668237      0.995222   \n13610     0.784997      0.007001      0.001640      0.616221      0.998180   \n\n          Class  \n0         SEKER  \n1         SEKER  \n2         SEKER  \n3         SEKER  \n4         SEKER  \n...         ...  \n13606  DERMASON  \n13607  DERMASON  \n13608  DERMASON  \n13609  DERMASON  \n13610  DERMASON  \n\n[13611 rows x 17 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Area</th>\n      <th>Perimeter</th>\n      <th>MajorAxisLength</th>\n      <th>MinorAxisLength</th>\n      <th>AspectRation</th>\n      <th>Eccentricity</th>\n      <th>ConvexArea</th>\n      <th>EquivDiameter</th>\n      <th>Extent</th>\n      <th>Solidity</th>\n      <th>roundness</th>\n      <th>Compactness</th>\n      <th>ShapeFactor1</th>\n      <th>ShapeFactor2</th>\n      <th>ShapeFactor3</th>\n      <th>ShapeFactor4</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>28395</td>\n      <td>610.291</td>\n      <td>208.178117</td>\n      <td>173.888747</td>\n      <td>1.197191</td>\n      <td>0.549812</td>\n      <td>28715</td>\n      <td>190.141097</td>\n      <td>0.763923</td>\n      <td>0.988856</td>\n      <td>0.958027</td>\n      <td>0.913358</td>\n      <td>0.007332</td>\n      <td>0.003147</td>\n      <td>0.834222</td>\n      <td>0.998724</td>\n      <td>SEKER</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>28734</td>\n      <td>638.018</td>\n      <td>200.524796</td>\n      <td>182.734419</td>\n      <td>1.097356</td>\n      <td>0.411785</td>\n      <td>29172</td>\n      <td>191.272750</td>\n      <td>0.783968</td>\n      <td>0.984986</td>\n      <td>0.887034</td>\n      <td>0.953861</td>\n      <td>0.006979</td>\n      <td>0.003564</td>\n      <td>0.909851</td>\n      <td>0.998430</td>\n      <td>SEKER</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>29380</td>\n      <td>624.110</td>\n      <td>212.826130</td>\n      <td>175.931143</td>\n      <td>1.209713</td>\n      <td>0.562727</td>\n      <td>29690</td>\n      <td>193.410904</td>\n      <td>0.778113</td>\n      <td>0.989559</td>\n      <td>0.947849</td>\n      <td>0.908774</td>\n      <td>0.007244</td>\n      <td>0.003048</td>\n      <td>0.825871</td>\n      <td>0.999066</td>\n      <td>SEKER</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>30008</td>\n      <td>645.884</td>\n      <td>210.557999</td>\n      <td>182.516516</td>\n      <td>1.153638</td>\n      <td>0.498616</td>\n      <td>30724</td>\n      <td>195.467062</td>\n      <td>0.782681</td>\n      <td>0.976696</td>\n      <td>0.903936</td>\n      <td>0.928329</td>\n      <td>0.007017</td>\n      <td>0.003215</td>\n      <td>0.861794</td>\n      <td>0.994199</td>\n      <td>SEKER</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>30140</td>\n      <td>620.134</td>\n      <td>201.847882</td>\n      <td>190.279279</td>\n      <td>1.060798</td>\n      <td>0.333680</td>\n      <td>30417</td>\n      <td>195.896503</td>\n      <td>0.773098</td>\n      <td>0.990893</td>\n      <td>0.984877</td>\n      <td>0.970516</td>\n      <td>0.006697</td>\n      <td>0.003665</td>\n      <td>0.941900</td>\n      <td>0.999166</td>\n      <td>SEKER</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13606</th>\n      <td>42097</td>\n      <td>759.696</td>\n      <td>288.721612</td>\n      <td>185.944705</td>\n      <td>1.552728</td>\n      <td>0.765002</td>\n      <td>42508</td>\n      <td>231.515799</td>\n      <td>0.714574</td>\n      <td>0.990331</td>\n      <td>0.916603</td>\n      <td>0.801865</td>\n      <td>0.006858</td>\n      <td>0.001749</td>\n      <td>0.642988</td>\n      <td>0.998385</td>\n      <td>DERMASON</td>\n    </tr>\n    <tr>\n      <th>13607</th>\n      <td>42101</td>\n      <td>757.499</td>\n      <td>281.576392</td>\n      <td>190.713136</td>\n      <td>1.476439</td>\n      <td>0.735702</td>\n      <td>42494</td>\n      <td>231.526798</td>\n      <td>0.799943</td>\n      <td>0.990752</td>\n      <td>0.922015</td>\n      <td>0.822252</td>\n      <td>0.006688</td>\n      <td>0.001886</td>\n      <td>0.676099</td>\n      <td>0.998219</td>\n      <td>DERMASON</td>\n    </tr>\n    <tr>\n      <th>13608</th>\n      <td>42139</td>\n      <td>759.321</td>\n      <td>281.539928</td>\n      <td>191.187979</td>\n      <td>1.472582</td>\n      <td>0.734065</td>\n      <td>42569</td>\n      <td>231.631261</td>\n      <td>0.729932</td>\n      <td>0.989899</td>\n      <td>0.918424</td>\n      <td>0.822730</td>\n      <td>0.006681</td>\n      <td>0.001888</td>\n      <td>0.676884</td>\n      <td>0.996767</td>\n      <td>DERMASON</td>\n    </tr>\n    <tr>\n      <th>13609</th>\n      <td>42147</td>\n      <td>763.779</td>\n      <td>283.382636</td>\n      <td>190.275731</td>\n      <td>1.489326</td>\n      <td>0.741055</td>\n      <td>42667</td>\n      <td>231.653248</td>\n      <td>0.705389</td>\n      <td>0.987813</td>\n      <td>0.907906</td>\n      <td>0.817457</td>\n      <td>0.006724</td>\n      <td>0.001852</td>\n      <td>0.668237</td>\n      <td>0.995222</td>\n      <td>DERMASON</td>\n    </tr>\n    <tr>\n      <th>13610</th>\n      <td>42159</td>\n      <td>772.237</td>\n      <td>295.142741</td>\n      <td>182.204716</td>\n      <td>1.619841</td>\n      <td>0.786693</td>\n      <td>42600</td>\n      <td>231.686223</td>\n      <td>0.788962</td>\n      <td>0.989648</td>\n      <td>0.888380</td>\n      <td>0.784997</td>\n      <td>0.007001</td>\n      <td>0.001640</td>\n      <td>0.616221</td>\n      <td>0.998180</td>\n      <td>DERMASON</td>\n    </tr>\n  </tbody>\n</table>\n<p>13611 rows × 17 columns</p>\n</div>"
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DERMASON    3546\nSIRA        2636\nSEKER       2027\nHOROZ       1928\nCALI        1630\nBARBUNYA    1322\nBOMBAY       522\nName: Class, dtype: int64"
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Class'].value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "outputs": [],
+   "source": [
+    "#Pandas immer Zeilen,Spalten\n",
+    "data['Class'] = data['Class'] == 'BARBUNYA'"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "False    12289\nTrue      1322\nName: Class, dtype: int64"
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Class'].value_counts()\n",
+    "# Klassen haben unterschiedliche Größen - imbalanced Problem bspw. bei Betrug vorhersagen"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       EquivDiameter  ShapeFactor1  ShapeFactor2  ShapeFactor3  Class\n0         190.141097      0.007332      0.003147      0.834222  False\n1         191.272750      0.006979      0.003564      0.909851  False\n2         193.410904      0.007244      0.003048      0.825871  False\n3         195.467062      0.007017      0.003215      0.861794  False\n4         195.896503      0.006697      0.003665      0.941900  False\n...              ...           ...           ...           ...    ...\n13606     231.515799      0.006858      0.001749      0.642988  False\n13607     231.526798      0.006688      0.001886      0.676099  False\n13608     231.631261      0.006681      0.001888      0.676884  False\n13609     231.653248      0.006724      0.001852      0.668237  False\n13610     231.686223      0.007001      0.001640      0.616221  False\n\n[13611 rows x 5 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EquivDiameter</th>\n      <th>ShapeFactor1</th>\n      <th>ShapeFactor2</th>\n      <th>ShapeFactor3</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>190.141097</td>\n      <td>0.007332</td>\n      <td>0.003147</td>\n      <td>0.834222</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>191.272750</td>\n      <td>0.006979</td>\n      <td>0.003564</td>\n      <td>0.909851</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>193.410904</td>\n      <td>0.007244</td>\n      <td>0.003048</td>\n      <td>0.825871</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>195.467062</td>\n      <td>0.007017</td>\n      <td>0.003215</td>\n      <td>0.861794</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>195.896503</td>\n      <td>0.006697</td>\n      <td>0.003665</td>\n      <td>0.941900</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13606</th>\n      <td>231.515799</td>\n      <td>0.006858</td>\n      <td>0.001749</td>\n      <td>0.642988</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>13607</th>\n      <td>231.526798</td>\n      <td>0.006688</td>\n      <td>0.001886</td>\n      <td>0.676099</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>13608</th>\n      <td>231.631261</td>\n      <td>0.006681</td>\n      <td>0.001888</td>\n      <td>0.676884</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>13609</th>\n      <td>231.653248</td>\n      <td>0.006724</td>\n      <td>0.001852</td>\n      <td>0.668237</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>13610</th>\n      <td>231.686223</td>\n      <td>0.007001</td>\n      <td>0.001640</td>\n      <td>0.616221</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>13611 rows × 5 columns</p>\n</div>"
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_keep = [7,12,13,14,16]\n",
+    "#Nehme nur von data alle zeilen und diese Spalten\n",
+    "data = data.iloc[:,cols_to_keep]\n",
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "outputs": [],
+   "source": [
+    "labels = data['Class']\n",
+    "\n",
+    "data = data.drop(columns=['Class'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0        False\n1        False\n2        False\n3        False\n4        False\n         ...  \n13606    False\n13607    False\n13608    False\n13609    False\n13610    False\nName: Class, Length: 13611, dtype: bool"
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-1.0633406 ,  0.68078638,  2.40217287,  1.92572347],\n       [-1.04421674,  0.3679669 ,  3.10089314,  2.68970162],\n       [-1.00808399,  0.60312889,  2.23509147,  1.84135576],\n       ...,\n       [-0.3621965 ,  0.10426946,  0.28920441,  0.33632829],\n       [-0.36182496,  0.14190638,  0.22837538,  0.2489734 ],\n       [-0.3612677 ,  0.38751213, -0.12777587, -0.2764814 ]])"
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "scaler = preprocessing.StandardScaler()\n",
+    "X = scaler.fit_transform(data)\n",
+    "X"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([False, False, False, ..., False, False, False])"
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = labels.values\n",
+    "y"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KNeighborsClassifier()"
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "model = KNeighborsClassifier()\n",
+    "model.fit(X,y)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "outputs": [],
+   "source": [
+    "all_predictions = model.predict(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "True     0.962824\nFalse    0.037176\nName: Class, dtype: float64"
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction_comparison = (all_predictions==labels)\n",
+    "prediction_comparison.value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Train-Test-Splitting"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.7999412240099919"
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# y barbuny oder nicht x daten\n",
+    "#x test vorhersage y test war die vorersage korrekt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=6)\n",
+    "\n",
+    "len(X_train)/len(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "False    0.902186\nTrue     0.097814\ndtype: float64"
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# mach aus y_Train( numpy array ) in eine Panda series damit ich die tollen statistischen Funktionen von Pandas nutzen kann\n",
+    "pd.Series(y_train).value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Erklärung pd.Series(y_train) numpy array zu pd series"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "numpy.ndarray"
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(y_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "pandas.core.series.Series"
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(pd.Series(y_test))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "False    0.905619\nTrue     0.094381\ndtype: float64"
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(y_test).value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Stratifikation"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "False    0.902829\nTrue     0.097171\ndtype: float64"
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(y_train).value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "False    0.903048\nTrue     0.096952\ndtype: float64"
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(y_test).value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Übung"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 1)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "outputs": [],
+   "source": [
+    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+    "\n",
+    "#len(X_test)/len(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 2)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "outputs": [],
+   "source": [
+    "#pd.Series(y_test).value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 3)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "outputs": [],
+   "source": [
+    " #keine_barbunya_bohne = y_test==False"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "outputs": [],
+   "source": [
+    "#X_test[keine_barbunya_bohne,:]\n",
+    "#Eckige Klammern könnt ihr Sachen rausfiltern\n",
+    "# Doppelpunkt wir nehmen alle Spalten"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "outputs": [],
+   "source": [
+    "#keine_barbunya_bohne"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Modellauswertung\n",
+    " Confusion Matrix\n",
+    "|true negative | false positive |\n",
+    "|false negative| true positive  |"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KNeighborsClassifier()"
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = KNeighborsClassifier()\n",
+    "model.fit(X_train, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "outputs": [],
+   "source": [
+    "y_predicted_test = model.predict(X_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([False, False, False, ..., False, False, False])"
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted_test"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "outputs": [],
+   "source": [
+    "y_prediction_correct = y_predicted_test==y_test"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "True     2590\nFalse     133\ndtype: int64"
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(y_prediction_correct).value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      prediction_correct  label\n0                  False   True\n1                   True  False\n2                   True  False\n3                   True  False\n4                   True  False\n...                  ...    ...\n2718                True  False\n2719                True  False\n2720                True  False\n2721                True  False\n2722                True  False\n\n[2723 rows x 2 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>prediction_correct</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2718</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2719</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2720</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2721</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2722</th>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>2723 rows × 2 columns</p>\n</div>"
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions_and_labels = pd.DataFrame({\n",
+    "    'prediction_correct': y_prediction_correct,\n",
+    "    'label': y_test})\n",
+    "predictions_and_labels"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "prediction_correct  False  True \nlabel                           \nFalse                  49   2410\nTrue                   84    180",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>prediction_correct</th>\n      <th>False</th>\n      <th>True</th>\n    </tr>\n    <tr>\n      <th>label</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>False</th>\n      <td>49</td>\n      <td>2410</td>\n    </tr>\n    <tr>\n      <th>True</th>\n      <td>84</td>\n      <td>180</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(predictions_and_labels['label'],\n",
+    "            predictions_and_labels['prediction_correct'],\n",
+    "            predictions_and_labels['prediction_correct'],\n",
+    "            aggfunc='count')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Korrektur Confusion Matrix"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      prediction_correct  label\n0                  False   True\n1                  False  False\n2                  False  False\n3                  False  False\n4                  False  False\n...                  ...    ...\n2718               False  False\n2719               False  False\n2720               False  False\n2721               False  False\n2722               False  False\n\n[2723 rows x 2 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>prediction_correct</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2718</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2719</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2720</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2721</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2722</th>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>2723 rows × 2 columns</p>\n</div>"
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted_test = model.predict(X_test)\n",
+    "\n",
+    "predictions_and_labels = pd.DataFrame({\n",
+    "    'prediction_correct': y_predicted_test,\n",
+    "    'label': y_test})\n",
+    "predictions_and_labels"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "prediction_correct  False  True \nlabel                           \nFalse                2410     49\nTrue                   84    180",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>prediction_correct</th>\n      <th>False</th>\n      <th>True</th>\n    </tr>\n    <tr>\n      <th>label</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>False</th>\n      <td>2410</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>True</th>\n      <td>84</td>\n      <td>180</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(predictions_and_labels['label'],\n",
+    "            predictions_and_labels['prediction_correct'],\n",
+    "            predictions_and_labels['prediction_correct'],\n",
+    "            aggfunc='count')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "True = Babunya False = Nicht Babunya"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages\\yellowbrick\\classifier\\base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:title={'center':'KNeighborsClassifier Confusion Matrix'}, xlabel='Predicted Class', ylabel='True Class'>"
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Erste klasse immer True und zweite Klasse False\n",
+    "\n",
+    "from yellowbrick.classifier import ConfusionMatrix\n",
+    "\n",
+    "confusion_matrix = ConfusionMatrix(\n",
+    "    model,\n",
+    "    classes=['Not BARBUNYA','Is BARBUNYA']\n",
+    ")\n",
+    "confusion_matrix.score(X_test, y_test)\n",
+    "confusion_matrix.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Accuracy"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "outputs": [],
+   "source": [
+    "tp = 180\n",
+    "tn = 2410\n",
+    "fp = 49\n",
+    "fn = 84"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.9511568123393316"
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy = (tp+tn)/(tp+tn+fp+fn)\n",
+    "accuracy"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.9511568123393316"
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "accuracy_score(y_test, y_predicted_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Recall"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.6818181818181818"
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import recall_score\n",
+    "\n",
+    "recall_score(y_test, y_predicted_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.6818181818181818"
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall = tp/(tp+fn)\n",
+    "recall"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Precision"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.7860262008733624"
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "\n",
+    "precision_score(y_test, y_predicted_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7860262008733624\n"
+     ]
+    }
+   ],
+   "source": [
+    "precision = tp/(tp+fp)\n",
+    "print(precision)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.97      0.98      0.97      2459\n",
+      "        True       0.79      0.68      0.73       264\n",
+      "\n",
+      "    accuracy                           0.95      2723\n",
+      "   macro avg       0.88      0.83      0.85      2723\n",
+      "weighted avg       0.95      0.95      0.95      2723\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "print(classification_report(y_test, y_predicted_test))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.7302231237322515"
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1 = 2*precision*recall/(precision+recall)\n",
+    "f1"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.7302231237322515"
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "\n",
+    "f1_score(y_test,y_predicted_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Entscheidungsbaummodelle"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Gini impurity bestraft falsch klassifizierte Zeilen stärker als accuracy\n",
+    "Kriterium finden, dass die Daten amgenauesten splittet in rechts rot links grün oder rockts falsch links wahr. Dafür ist die Gini impurity"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DecisionTreeClassifier(random_state=42)"
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "model_dt = DecisionTreeClassifier(random_state=42)\n",
+    "model_dt.fit(X_train, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1200x825 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotte die ersten Ebenen des Entscheidungsbaummmodells\n",
+    "from sklearn.tree import plot_tree\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "plt.figure(dpi=150)\n",
+    "plot_tree(model_dt,\n",
+    "          max_depth=2,\n",
+    "          feature_names=data.columns,\n",
+    "          class_names=['Not BARBUNYA','BARBUNYA'],\n",
+    "          rounded=True,\n",
+    "          filled=True,\n",
+    "          fontsize=8);"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Index(['EquivDiameter', 'ShapeFactor1', 'ShapeFactor2', 'ShapeFactor3'], dtype='object')"
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.96      0.96      0.96      2459\n",
+      "        True       0.65      0.66      0.66       264\n",
+      "\n",
+      "    accuracy                           0.93      2723\n",
+      "   macro avg       0.81      0.81      0.81      2723\n",
+      "weighted avg       0.93      0.93      0.93      2723\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_predicted_test_dt = model_dt.predict(X_test)\n",
+    "print(classification_report(y_test,y_predicted_test_dt))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Random Forest"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "model_rf = RandomForestClassifier(random_state=20)\n",
+    "model_rf.fit(X_train, y_train)\n",
+    "y_predicted_test_rf = model_rf.predict(X_test)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.97      0.98      0.97      2459\n",
+      "        True       0.78      0.70      0.74       264\n",
+      "\n",
+      "    accuracy                           0.95      2723\n",
+      "   macro avg       0.87      0.84      0.86      2723\n",
+      "weighted avg       0.95      0.95      0.95      2723\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_predicted_test_rf))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0.23895268, 0.2495993 , 0.30430666, 0.20714136])"
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_rf.feature_importances_"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "EquivDiameter    0.238953\nShapeFactor1     0.249599\nShapeFactor2     0.304307\nShapeFactor3     0.207141\ndtype: float64"
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(model_rf.feature_importances_,index=data.columns)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "ShapeFactor2     0.304307\nShapeFactor1     0.249599\nEquivDiameter    0.238953\nShapeFactor3     0.207141\ndtype: float64"
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(model_rf.feature_importances_,index=data.columns).sort_values(ascending=False)\n",
+    "# Wie oft habe ich nach einem feature gesplittet"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Modelle optimieren"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zeilen in Training und Testing:  10888 2723\n",
+      "Zeilen in Training und Testing:  10889 2722\n",
+      "Zeilen in Training und Testing:  10889 2722\n",
+      "Zeilen in Training und Testing:  10889 2722\n",
+      "Zeilen in Training und Testing:  10889 2722\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "\n",
+    "cv_splitter = StratifiedKFold(shuffle=True, random_state=42)\n",
+    "splits = cv_splitter.split(X, y)\n",
+    "for train_index, test_index in splits:\n",
+    "    print('Zeilen in Training und Testing: ', len(train_index), len(test_index))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([    0,     1,     3, ..., 13608, 13609, 13610])"
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_index\n",
+    "# Einträge in X die in dem Trainingset drin sind\n",
+    "# In dem letzten Fold waren Zeilen 0, 1, 3, 4, 5, 6, 8..."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_validate\n",
+    "\n",
+    "model = RandomForestClassifier(max_depth=5, min_samples_split=100,n_estimators=90, min_samples_leaf= 1,random_state=42)\n",
+    "scores = cross_validate(model, X, y,\n",
+    "                        cv=cv_splitter,\n",
+    "                        scoring=['f1', 'precision', 'recall'],\n",
+    "                        return_train_score=True)\n",
+    "scores['test_f1'].mean().round(4)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:title={'center':'Validation Curve for RandomForestClassifier'}, xlabel='max_depth', ylabel='score'>"
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from yellowbrick.model_selection import ValidationCurve\n",
+    "\n",
+    "viz = ValidationCurve(\n",
+    "    model,\n",
+    "    param_name=\"max_depth\",\n",
+    "    param_range=range(1,10),\n",
+    "# range(1,10) heißt 1-9 x)\n",
+    "    cv=cv_splitter,\n",
+    "    scoring=\"f1\"\n",
+    ")\n",
+    "viz.fit(X, y)\n",
+    "viz.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/notebooks/data_overview.ipynb b/notebooks/data_overview.ipynb
index dc7ad9c..6761897 100644
--- a/notebooks/data_overview.ipynb
+++ b/notebooks/data_overview.ipynb
@@ -11,36 +11,596 @@
     "import pandas as pd"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_stata('../data/external/ZA6647_v1-0-0.dta')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "               studyno                  doi             version  \\\n0  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n1  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n2  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n3  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n4  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n\n           edition                                    survey  caseid   uniqid  \\\n0  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       1  1000001   \n1  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       2  1000002   \n2  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       3  1000003   \n3  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       4  1000004   \n4  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       5  1000005   \n\n   tnscntry       country isocntry  ...  w82  w84       w89  w90       w95  \\\n0  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.377414  0.0  0.399552   \n1  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.398224  0.0  0.421584   \n2  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.203295  0.0  0.215220   \n3  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.313204  0.0  0.331576   \n4  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.384786  0.0  0.407357   \n\n   w96       w97  w83  w98          wex  \n0  0.0  0.419225  0.0  0.0  6446.127930  \n1  0.0  0.442341  0.0  0.0  6801.566895  \n2  0.0  0.225817  0.0  0.0  3472.228516  \n3  0.0  0.347902  0.0  0.0  5349.446777  \n4  0.0  0.427415  0.0  0.0  6572.049805  \n\n[5 rows x 165 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>studyno</th>\n      <th>doi</th>\n      <th>version</th>\n      <th>edition</th>\n      <th>survey</th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>1</td>\n      <td>1000001</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.377414</td>\n      <td>0.0</td>\n      <td>0.399552</td>\n      <td>0.0</td>\n      <td>0.419225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6446.127930</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>2</td>\n      <td>1000002</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.398224</td>\n      <td>0.0</td>\n      <td>0.421584</td>\n      <td>0.0</td>\n      <td>0.442341</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6801.566895</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>3</td>\n      <td>1000003</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.203295</td>\n      <td>0.0</td>\n      <td>0.215220</td>\n      <td>0.0</td>\n      <td>0.225817</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3472.228516</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>4</td>\n      <td>1000004</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.313204</td>\n      <td>0.0</td>\n      <td>0.331576</td>\n      <td>0.0</td>\n      <td>0.347902</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5349.446777</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>5</td>\n      <td>1000005</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.384786</td>\n      <td>0.0</td>\n      <td>0.407357</td>\n      <td>0.0</td>\n      <td>0.427415</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6572.049805</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 165 columns</p>\n</div>"
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n                9,\n            ...\n            26591, 26592, 26593, 26594, 26595, 26596, 26597, 26598, 26599,\n            26600],\n           dtype='int64', length=26601)"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Index auslesen\n",
+    "data.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Index(['studyno', 'doi', 'version', 'edition', 'survey', 'caseid', 'uniqid',\n       'tnscntry', 'country', 'isocntry',\n       ...\n       'w82', 'w84', 'w89', 'w90', 'w95', 'w96', 'w97', 'w83', 'w98', 'wex'],\n      dtype='object', length=165)"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Spalten auslesen\n",
+    "data.columns"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['studyno',\n 'doi',\n 'version',\n 'edition',\n 'survey',\n 'caseid',\n 'uniqid',\n 'tnscntry',\n 'country',\n 'isocntry',\n 'mode',\n 'q1_1',\n 'q1_2',\n 'q1_3',\n 'q1_4',\n 'q1_5',\n 'q1_6',\n 'q1_7',\n 'q2_1',\n 'q2_2',\n 'q2_3',\n 'q2_4',\n 'q2_5',\n 'q2_6',\n 'q2_7',\n 'q2_8',\n 'q3',\n 'q4',\n 'q5',\n 'q6',\n 'q7',\n 'q8',\n 'd1',\n 'd1r1',\n 'd1r2',\n 'd2',\n 'd3a_1',\n 'd3a_2',\n 'd3a_3',\n 'd3a_4',\n 'd3a_5',\n 'd3a_6',\n 'd3a_7',\n 'd3a_8',\n 'd3a_9',\n 'd3a_10',\n 'd3a_11',\n 'd3a_12',\n 'd3a_13',\n 'd3a_14',\n 'd3a_15',\n 'd3a_16',\n 'd3a_17',\n 'd3a_18',\n 'd3a_19',\n 'd3a_20',\n 'd3a_21',\n 'd3a_22',\n 'd3a_23',\n 'd3a_24',\n 'd3a_25',\n 'd3a_26',\n 'd3a_27',\n 'd3a_28',\n 'd3a_98',\n 'd3a_99',\n 'd3b',\n 'd4',\n 'd4r1',\n 'd4r2',\n 'd5',\n 'd5r',\n 'd13',\n 'd18',\n 'd20',\n 'd18_d20',\n 'd22',\n 'd22r',\n 'd12be',\n 'd12be_r',\n 'd12at',\n 'd12at_r',\n 'd12bg',\n 'd12cy',\n 'd12cz',\n 'd12dk',\n 'd12ee',\n 'd12de',\n 'd12gr',\n 'd12gr_r',\n 'd12es',\n 'd12es_r',\n 'd12fi',\n 'd12fr',\n 'd12fr_r',\n 'd12gb',\n 'd12hu',\n 'd12ie',\n 'd12it',\n 'd12it_r',\n 'd12lt',\n 'd12lu',\n 'd12lv',\n 'd12nl',\n 'd12nl_r',\n 'd12pl',\n 'd12pl_r',\n 'd12pt',\n 'd12ro',\n 'd12se',\n 'd12si',\n 'd12sk',\n 'd12mt',\n 'd12hr',\n 'eu6',\n 'eu9',\n 'eu10',\n 'eu12_2',\n 'eu_nms3',\n 'eu15',\n 'eu_nms10',\n 'eu25',\n 'eu_nms12',\n 'eu27',\n 'eu_nms13',\n 'eu28',\n 'euroz13',\n 'euroz15',\n 'euronz15',\n 'euroz16',\n 'euronz16',\n 'euroz17',\n 'euronz17',\n 'euroz18',\n 'euronz18',\n 'euronzms',\n 'euronznm',\n 'euroz19',\n 'euronz19',\n 'w1',\n 'w5',\n 'w6',\n 'w7',\n 'w9',\n 'w10',\n 'w11',\n 'w13',\n 'w14',\n 'w24',\n 'w22',\n 'w94',\n 'w23',\n 'w29',\n 'w30',\n 'w81',\n 'w82',\n 'w84',\n 'w89',\n 'w90',\n 'w95',\n 'w96',\n 'w97',\n 'w83',\n 'w98',\n 'wex']"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Spalten auslesen bessere Lesbarkeit\n",
+    "list(data.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'BE - Belgium'"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bestimmten Wert auslesen\n",
+    "data.loc[2, 'country']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "'BE - Belgium'"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Bestimmten Wert mit numerischer Notation auslesen\n",
+    "data.iloc[2,8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "studyno    category\ndoi          object\nversion      object\nedition    category\nsurvey     category\n             ...   \nw96         float64\nw97         float64\nw83         float64\nw98         float64\nwex         float64\nLength: 165, dtype: object"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Datentypen der Spalten auslesen\n",
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                   studyno                  doi             version  \\\n21975  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n20535  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n7158   GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n5714   GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n20010  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n\n               edition                                    survey  caseid  \\\n21975  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   21976   \n20535  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   20536   \n7158   ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)    7159   \n5714   ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)    5715   \n20010  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   20011   \n\n         uniqid tnscntry         country isocntry  ...       w82       w84  \\\n21975  39021976   POLSKA     PL - Poland       PL  ...  5.590352  4.913337   \n20535  37020536  LIETUVA  LT - Lithuania       LT  ...  0.148483  0.000000   \n7158    9007159   ITALIA      IT - Italy       IT  ...  0.000000  0.000000   \n5714    7005715   FRANCE     FR - France       FR  ...  0.000000  0.000000   \n20010  36020011   LATVIA     LV - Latvia       LV  ...  0.044858  0.000000   \n\n            w89       w90       w95       w96       w97       w83       w98  \\\n21975  0.000000  5.483552  0.000000  5.034668  0.000000  4.221796  4.622679   \n20535  0.000000  0.145646  0.000000  0.133723  0.126007  0.000000  0.000000   \n7158   2.038948  0.000000  2.158551  0.000000  2.264832  0.000000  0.000000   \n5714   2.119258  0.000000  2.243572  0.000000  2.354039  0.000000  0.000000   \n20010  0.000000  0.044001  0.036282  0.000000  0.038068  0.000000  0.000000   \n\n                wex  \n21975  72947.523438  \n20535   1937.523926  \n7158   34824.710938  \n5714   36196.382812  \n20010    585.348450  \n\n[5 rows x 165 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>studyno</th>\n      <th>doi</th>\n      <th>version</th>\n      <th>edition</th>\n      <th>survey</th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>21975</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>21976</td>\n      <td>39021976</td>\n      <td>POLSKA</td>\n      <td>PL - Poland</td>\n      <td>PL</td>\n      <td>...</td>\n      <td>5.590352</td>\n      <td>4.913337</td>\n      <td>0.000000</td>\n      <td>5.483552</td>\n      <td>0.000000</td>\n      <td>5.034668</td>\n      <td>0.000000</td>\n      <td>4.221796</td>\n      <td>4.622679</td>\n      <td>72947.523438</td>\n    </tr>\n    <tr>\n      <th>20535</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>20536</td>\n      <td>37020536</td>\n      <td>LIETUVA</td>\n      <td>LT - Lithuania</td>\n      <td>LT</td>\n      <td>...</td>\n      <td>0.148483</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.145646</td>\n      <td>0.000000</td>\n      <td>0.133723</td>\n      <td>0.126007</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1937.523926</td>\n    </tr>\n    <tr>\n      <th>7158</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>7159</td>\n      <td>9007159</td>\n      <td>ITALIA</td>\n      <td>IT - Italy</td>\n      <td>IT</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.038948</td>\n      <td>0.000000</td>\n      <td>2.158551</td>\n      <td>0.000000</td>\n      <td>2.264832</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>34824.710938</td>\n    </tr>\n    <tr>\n      <th>5714</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>5715</td>\n      <td>7005715</td>\n      <td>FRANCE</td>\n      <td>FR - France</td>\n      <td>FR</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.119258</td>\n      <td>0.000000</td>\n      <td>2.243572</td>\n      <td>0.000000</td>\n      <td>2.354039</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>36196.382812</td>\n    </tr>\n    <tr>\n      <th>20010</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>20011</td>\n      <td>36020011</td>\n      <td>LATVIA</td>\n      <td>LV - Latvia</td>\n      <td>LV</td>\n      <td>...</td>\n      <td>0.044858</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.044001</td>\n      <td>0.036282</td>\n      <td>0.000000</td>\n      <td>0.038068</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>585.348450</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 165 columns</p>\n</div>"
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 5 Zeilen zufällig aus dem DataFrame auswählen\n",
+    "data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid     tnscntry       country isocntry  \\\n9849     9850  12009850   ÖSTERREICH  AT - Austria       AT   \n1014     1015   2001015      DANMARK  DK - Denmark       DK   \n12584   12585   3012585  DEUTSCHLAND  DE - Germany       DE   \n13264   13265   3013265  DEUTSCHLAND  DE - Germany       DE   \n50         51   1000051     BELGIQUE  BE - Belgium       BE   \n\n                       mode           q1_1                q1_2  \\\n9849   Fixed telephone line        Farmers  Food manufacturers   \n1014      Mobile phone line  Not mentioned       Not mentioned   \n12584  Fixed telephone line  Not mentioned       Not mentioned   \n13264  Fixed telephone line  Not mentioned       Not mentioned   \n50     Fixed telephone line  Not mentioned       Not mentioned   \n\n                      q1_3                                      q1_4  ...  \\\n9849   Shops and retailers  no  Hospitality and food service sectors  ...   \n1014         Not mentioned                             Not mentioned  ...   \n12584        Not mentioned                             Not mentioned  ...   \n13264  Shops and retailers                             Not mentioned  ...   \n50           Not mentioned                             Not mentioned  ...   \n\n            w82  w84       w89       w90       w95       w96       w97  \\\n9849   0.000000  0.0  0.163553  0.000000  0.173146  0.000000  0.181672   \n1014   0.180955  0.0  0.000000  0.177498  0.000000  0.162968  0.000000   \n12584  0.000000  0.0  3.592318  0.000000  3.803041  0.000000  3.990293   \n13264  0.000000  0.0  6.924810  0.000000  7.331014  0.000000  7.691975   \n50     0.000000  0.0  0.384577  0.000000  0.407136  0.000000  0.427182   \n\n            w83       w98            wex  \n9849   0.000000  0.000000    2793.437012  \n1014   0.136656  0.149632    2361.249512  \n12584  0.000000  0.000000   61355.882812  \n13264  0.000000  0.000000  118273.992188  \n50     0.000000  0.000000    6568.477539  \n\n[5 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9849</th>\n      <td>9850</td>\n      <td>12009850</td>\n      <td>ÖSTERREICH</td>\n      <td>AT - Austria</td>\n      <td>AT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.163553</td>\n      <td>0.000000</td>\n      <td>0.173146</td>\n      <td>0.000000</td>\n      <td>0.181672</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2793.437012</td>\n    </tr>\n    <tr>\n      <th>1014</th>\n      <td>1015</td>\n      <td>2001015</td>\n      <td>DANMARK</td>\n      <td>DK - Denmark</td>\n      <td>DK</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.180955</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.177498</td>\n      <td>0.000000</td>\n      <td>0.162968</td>\n      <td>0.000000</td>\n      <td>0.136656</td>\n      <td>0.149632</td>\n      <td>2361.249512</td>\n    </tr>\n    <tr>\n      <th>12584</th>\n      <td>12585</td>\n      <td>3012585</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>3.592318</td>\n      <td>0.000000</td>\n      <td>3.803041</td>\n      <td>0.000000</td>\n      <td>3.990293</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>61355.882812</td>\n    </tr>\n    <tr>\n      <th>13264</th>\n      <td>13265</td>\n      <td>3013265</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>6.924810</td>\n      <td>0.000000</td>\n      <td>7.331014</td>\n      <td>0.000000</td>\n      <td>7.691975</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>118273.992188</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>51</td>\n      <td>1000051</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.384577</td>\n      <td>0.000000</td>\n      <td>0.407136</td>\n      <td>0.000000</td>\n      <td>0.427182</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6568.477539</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Unerwünschte Spalten definieren und löschen\n",
+    "cols_to_delete = ['studyno', 'doi', 'version', 'edition', 'survey']\n",
+    "data = data.drop(columns=cols_to_delete)\n",
+    "data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid     tnscntry         country isocntry  \\\n19586   19587  36019587       LATVIA     LV - Latvia       LV   \n6389     6390   8006390      IRELAND    IE - Ireland       IE   \n20202   20203  37020203      LIETUVA  LT - Lithuania       LT   \n12579   12580   3012580  DEUTSCHLAND    DE - Germany       DE   \n2983     2984   4002984       ELLADA     GR - Greece       GR   \n\n                       mode           q1_1                q1_2  \\\n19586  Fixed telephone line  Not mentioned       Not mentioned   \n6389   Fixed telephone line        Farmers  Food manufacturers   \n20202     Mobile phone line  Not mentioned       Not mentioned   \n12579  Fixed telephone line  Not mentioned  Food manufacturers   \n2983      Mobile phone line  Not mentioned       Not mentioned   \n\n                      q1_3                                      q1_4  ...  \\\n19586  Shops and retailers  no  Hospitality and food service sectors  ...   \n6389   Shops and retailers  no  Hospitality and food service sectors  ...   \n20202  Shops and retailers  no  Hospitality and food service sectors  ...   \n12579  Shops and retailers  no  Hospitality and food service sectors  ...   \n2983   Shops and retailers                             Not mentioned  ...   \n\n            w82  w84       w89       w90       w95       w96       w97  w83  \\\n19586  0.105470  0.0  0.000000  0.103455  0.085305  0.000000  0.089505  0.0   \n6389   0.000000  0.0  0.063409  0.000000  0.067129  0.000000  0.070434  0.0   \n20202  0.357035  0.0  0.000000  0.350214  0.000000  0.321546  0.302992  0.0   \n12579  0.000000  0.0  7.513096  0.000000  7.953808  0.000000  8.345435  0.0   \n2983   0.000000  0.0  0.610034  0.000000  0.645818  0.000000  0.677617  0.0   \n\n       w98            wex  \n19586  0.0    1376.254517  \n6389   0.0    1083.017334  \n20202  0.0    4658.886230  \n12579  0.0  128321.781250  \n2983   0.0   10419.224609  \n\n[5 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19586</th>\n      <td>19587</td>\n      <td>36019587</td>\n      <td>LATVIA</td>\n      <td>LV - Latvia</td>\n      <td>LV</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.105470</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.103455</td>\n      <td>0.085305</td>\n      <td>0.000000</td>\n      <td>0.089505</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1376.254517</td>\n    </tr>\n    <tr>\n      <th>6389</th>\n      <td>6390</td>\n      <td>8006390</td>\n      <td>IRELAND</td>\n      <td>IE - Ireland</td>\n      <td>IE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.063409</td>\n      <td>0.000000</td>\n      <td>0.067129</td>\n      <td>0.000000</td>\n      <td>0.070434</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1083.017334</td>\n    </tr>\n    <tr>\n      <th>20202</th>\n      <td>20203</td>\n      <td>37020203</td>\n      <td>LIETUVA</td>\n      <td>LT - Lithuania</td>\n      <td>LT</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.357035</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.350214</td>\n      <td>0.000000</td>\n      <td>0.321546</td>\n      <td>0.302992</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4658.886230</td>\n    </tr>\n    <tr>\n      <th>12579</th>\n      <td>12580</td>\n      <td>3012580</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>7.513096</td>\n      <td>0.000000</td>\n      <td>7.953808</td>\n      <td>0.000000</td>\n      <td>8.345435</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>128321.781250</td>\n    </tr>\n    <tr>\n      <th>2983</th>\n      <td>2984</td>\n      <td>4002984</td>\n      <td>ELLADA</td>\n      <td>GR - Greece</td>\n      <td>GR</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.610034</td>\n      <td>0.000000</td>\n      <td>0.645818</td>\n      <td>0.000000</td>\n      <td>0.677617</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10419.224609</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols_to_rename = {\n",
+    "    'd1': 'age',\n",
+    "    'd2': 'gender',\n",
+    "}\n",
+    "data = data.rename(columns=cols_to_rename)\n",
+    "data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid         tnscntry              country isocntry  \\\n14280   14281  15014281               UK  GB - United Kingdom       GB   \n14693   14694  31014694        BALGARIJA        BG - Bulgaria       BG   \n14901   14902  31014902        BALGARIJA        BG - Bulgaria       BG   \n6513     6514   8006514          IRELAND         IE - Ireland       IE   \n16846   16847  33016847  CESKA REPUBLIKA  CZ - Czech Republic       CZ   \n4535     4536   6004536            SUOMI         FI - Finland       FI   \n12861   12862   3012862      DEUTSCHLAND         DE - Germany       DE   \n7956     7957   9007957           ITALIA           IT - Italy       IT   \n7281     7282   9007282           ITALIA           IT - Italy       IT   \n2634     2635   4002635           ELLADA          GR - Greece       GR   \n\n                       mode           q1_1                q1_2  \\\n14280  Fixed telephone line  Not mentioned  Food manufacturers   \n14693     Mobile phone line  Not mentioned       Not mentioned   \n14901     Mobile phone line  Not mentioned       Not mentioned   \n6513   Fixed telephone line        Farmers  Food manufacturers   \n16846     Mobile phone line        Farmers       Not mentioned   \n4535      Mobile phone line  Not mentioned  Food manufacturers   \n12861  Fixed telephone line        Farmers  Food manufacturers   \n7956   Fixed telephone line  Not mentioned       Not mentioned   \n7281      Mobile phone line  Not mentioned       Not mentioned   \n2634   Fixed telephone line  Not mentioned       Not mentioned   \n\n                      q1_3                                      q1_4  ...  \\\n14280  Shops and retailers  no  Hospitality and food service sectors  ...   \n14693        Not mentioned  no  Hospitality and food service sectors  ...   \n14901  Shops and retailers                             Not mentioned  ...   \n6513   Shops and retailers  no  Hospitality and food service sectors  ...   \n16846  Shops and retailers  no  Hospitality and food service sectors  ...   \n4535   Shops and retailers  no  Hospitality and food service sectors  ...   \n12861  Shops and retailers                             Not mentioned  ...   \n7956         Not mentioned                             Not mentioned  ...   \n7281         Not mentioned  no  Hospitality and food service sectors  ...   \n2634         Not mentioned                             Not mentioned  ...   \n\n            w82       w84       w89       w90       w95       w96       w97  \\\n14280  2.699276  0.000000  0.000000  2.647708  0.000000  2.430966  0.000000   \n14693  0.656060  0.576608  0.000000  0.643526  0.000000  0.590847  0.000000   \n14901  0.949355  0.834384  0.000000  0.931218  0.000000  0.854988  0.000000   \n6513   0.000000  0.000000  0.390396  0.000000  0.413296  0.000000  0.433646   \n16846  0.366747  0.322332  0.000000  0.359740  0.000000  0.330292  0.000000   \n4535   0.000000  0.000000  0.392350  0.000000  0.415365  0.000000  0.435816   \n12861  0.000000  0.000000  3.556493  0.000000  3.765114  0.000000  3.950499   \n7956   0.000000  0.000000  2.808208  0.000000  2.972935  0.000000  3.119315   \n7281   0.000000  0.000000  1.471312  0.000000  1.557618  0.000000  1.634311   \n2634   0.000000  0.000000  0.152699  0.000000  0.161657  0.000000  0.169616   \n\n            w83       w98           wex  \n14280  2.038475  2.232039  35222.375000  \n14693  0.495452  0.542498   8560.809570  \n14901  0.716946  0.785024  12387.964844  \n6513   0.000000  0.000000   6667.864746  \n16846  0.276965  0.303264   4785.617188  \n4535   0.000000  0.000000   6701.237305  \n12861  0.000000  0.000000  60744.000000  \n7956   0.000000  0.000000  47963.476562  \n7281   0.000000  0.000000  25129.634766  \n2634   0.000000  0.000000   2608.066162  \n\n[10 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14280</th>\n      <td>14281</td>\n      <td>15014281</td>\n      <td>UK</td>\n      <td>GB - United Kingdom</td>\n      <td>GB</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>2.699276</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.647708</td>\n      <td>0.000000</td>\n      <td>2.430966</td>\n      <td>0.000000</td>\n      <td>2.038475</td>\n      <td>2.232039</td>\n      <td>35222.375000</td>\n    </tr>\n    <tr>\n      <th>14693</th>\n      <td>14694</td>\n      <td>31014694</td>\n      <td>BALGARIJA</td>\n      <td>BG - Bulgaria</td>\n      <td>BG</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.656060</td>\n      <td>0.576608</td>\n      <td>0.000000</td>\n      <td>0.643526</td>\n      <td>0.000000</td>\n      <td>0.590847</td>\n      <td>0.000000</td>\n      <td>0.495452</td>\n      <td>0.542498</td>\n      <td>8560.809570</td>\n    </tr>\n    <tr>\n      <th>14901</th>\n      <td>14902</td>\n      <td>31014902</td>\n      <td>BALGARIJA</td>\n      <td>BG - Bulgaria</td>\n      <td>BG</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.949355</td>\n      <td>0.834384</td>\n      <td>0.000000</td>\n      <td>0.931218</td>\n      <td>0.000000</td>\n      <td>0.854988</td>\n      <td>0.000000</td>\n      <td>0.716946</td>\n      <td>0.785024</td>\n      <td>12387.964844</td>\n    </tr>\n    <tr>\n      <th>6513</th>\n      <td>6514</td>\n      <td>8006514</td>\n      <td>IRELAND</td>\n      <td>IE - Ireland</td>\n      <td>IE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.390396</td>\n      <td>0.000000</td>\n      <td>0.413296</td>\n      <td>0.000000</td>\n      <td>0.433646</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6667.864746</td>\n    </tr>\n    <tr>\n      <th>16846</th>\n      <td>16847</td>\n      <td>33016847</td>\n      <td>CESKA REPUBLIKA</td>\n      <td>CZ - Czech Republic</td>\n      <td>CZ</td>\n      <td>Mobile phone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.366747</td>\n      <td>0.322332</td>\n      <td>0.000000</td>\n      <td>0.359740</td>\n      <td>0.000000</td>\n      <td>0.330292</td>\n      <td>0.000000</td>\n      <td>0.276965</td>\n      <td>0.303264</td>\n      <td>4785.617188</td>\n    </tr>\n    <tr>\n      <th>4535</th>\n      <td>4536</td>\n      <td>6004536</td>\n      <td>SUOMI</td>\n      <td>FI - Finland</td>\n      <td>FI</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.392350</td>\n      <td>0.000000</td>\n      <td>0.415365</td>\n      <td>0.000000</td>\n      <td>0.435816</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6701.237305</td>\n    </tr>\n    <tr>\n      <th>12861</th>\n      <td>12862</td>\n      <td>3012862</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3.556493</td>\n      <td>0.000000</td>\n      <td>3.765114</td>\n      <td>0.000000</td>\n      <td>3.950499</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>60744.000000</td>\n    </tr>\n    <tr>\n      <th>7956</th>\n      <td>7957</td>\n      <td>9007957</td>\n      <td>ITALIA</td>\n      <td>IT - Italy</td>\n      <td>IT</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.808208</td>\n      <td>0.000000</td>\n      <td>2.972935</td>\n      <td>0.000000</td>\n      <td>3.119315</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>47963.476562</td>\n    </tr>\n    <tr>\n      <th>7281</th>\n      <td>7282</td>\n      <td>9007282</td>\n      <td>ITALIA</td>\n      <td>IT - Italy</td>\n      <td>IT</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.471312</td>\n      <td>0.000000</td>\n      <td>1.557618</td>\n      <td>0.000000</td>\n      <td>1.634311</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>25129.634766</td>\n    </tr>\n    <tr>\n      <th>2634</th>\n      <td>2635</td>\n      <td>4002635</td>\n      <td>ELLADA</td>\n      <td>GR - Greece</td>\n      <td>GR</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.152699</td>\n      <td>0.000000</td>\n      <td>0.161657</td>\n      <td>0.000000</td>\n      <td>0.169616</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2608.066162</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['Sometimes', 'Always', 'Rarely', 'Often', 'Never', 'DK/NA']\nCategories (6, object): ['Always' < 'Often' < 'Sometimes' < 'Rarely' < 'Never' < 'DK/NA']"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['q3'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid    uniqid  tnscntry       country isocntry  \\\n48         49   1000049  BELGIQUE  BE - Belgium       BE   \n114       115   1000115  BELGIQUE  BE - Belgium       BE   \n135       136   1000136  BELGIQUE  BE - Belgium       BE   \n249       250   1000250  BELGIQUE  BE - Belgium       BE   \n258       259   1000259  BELGIQUE  BE - Belgium       BE   \n...       ...       ...       ...           ...      ...   \n26502   26503  46026503  HRVATSKA  HR - Croatia       HR   \n26525   26526  46026526  HRVATSKA  HR - Croatia       HR   \n26528   26529  46026529  HRVATSKA  HR - Croatia       HR   \n26540   26541  46026541  HRVATSKA  HR - Croatia       HR   \n26548   26549  46026549  HRVATSKA  HR - Croatia       HR   \n\n                       mode           q1_1                q1_2  \\\n48     Fixed telephone line        Farmers  Food manufacturers   \n114    Fixed telephone line        Farmers       Not mentioned   \n135    Fixed telephone line  Not mentioned  Food manufacturers   \n249    Fixed telephone line  Not mentioned  Food manufacturers   \n258    Fixed telephone line        Farmers  Food manufacturers   \n...                     ...            ...                 ...   \n26502  Fixed telephone line  Not mentioned       Not mentioned   \n26525     Mobile phone line  Not mentioned  Food manufacturers   \n26528  Fixed telephone line        Farmers  Food manufacturers   \n26540     Mobile phone line        Farmers  Food manufacturers   \n26548  Fixed telephone line  Not mentioned       Not mentioned   \n\n                      q1_3                                      q1_4  ...  \\\n48     Shops and retailers  no  Hospitality and food service sectors  ...   \n114    Shops and retailers  no  Hospitality and food service sectors  ...   \n135    Shops and retailers  no  Hospitality and food service sectors  ...   \n249    Shops and retailers                             Not mentioned  ...   \n258    Shops and retailers  no  Hospitality and food service sectors  ...   \n...                    ...                                       ...  ...   \n26502  Shops and retailers                             Not mentioned  ...   \n26525  Shops and retailers  no  Hospitality and food service sectors  ...   \n26528  Shops and retailers  no  Hospitality and food service sectors  ...   \n26540  Shops and retailers  no  Hospitality and food service sectors  ...   \n26548        Not mentioned                             Not mentioned  ...   \n\n       w82  w84       w89       w90       w95       w96       w97  w83  \\\n48     0.0  0.0  0.518591  0.000000  0.549011  0.000000  0.576043  0.0   \n114    0.0  0.0  0.365440  0.000000  0.386876  0.000000  0.405925  0.0   \n135    0.0  0.0  0.712317  0.000000  0.754101  0.000000  0.791231  0.0   \n249    0.0  0.0  0.465675  0.000000  0.492992  0.000000  0.517265  0.0   \n258    0.0  0.0  0.365440  0.000000  0.386876  0.000000  0.405925  0.0   \n...    ...  ...       ...       ...       ...       ...       ...  ...   \n26502  0.0  0.0  0.000000  0.217276  0.000000  0.199490  0.000000  0.0   \n26525  0.0  0.0  0.000000  0.851884  0.000000  0.782149  0.000000  0.0   \n26528  0.0  0.0  0.000000  0.100986  0.000000  0.092719  0.000000  0.0   \n26540  0.0  0.0  0.000000  0.353944  0.000000  0.324970  0.000000  0.0   \n26548  0.0  0.0  0.000000  0.851856  0.000000  0.782123  0.000000  0.0   \n\n            w98           wex  \n48     0.000000   8857.397461  \n114    0.000000   6241.616211  \n135    0.000000  12166.195312  \n249    0.000000   7953.618164  \n258    0.000000   6241.616211  \n...         ...           ...  \n26502  0.183166   2890.419189  \n26525  0.718145  11332.587891  \n26528  0.085132   1343.413086  \n26540  0.298378   4708.507324  \n26548  0.718121  11332.212891  \n\n[894 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>48</th>\n      <td>49</td>\n      <td>1000049</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.518591</td>\n      <td>0.000000</td>\n      <td>0.549011</td>\n      <td>0.000000</td>\n      <td>0.576043</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>8857.397461</td>\n    </tr>\n    <tr>\n      <th>114</th>\n      <td>115</td>\n      <td>1000115</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365440</td>\n      <td>0.000000</td>\n      <td>0.386876</td>\n      <td>0.000000</td>\n      <td>0.405925</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>6241.616211</td>\n    </tr>\n    <tr>\n      <th>135</th>\n      <td>136</td>\n      <td>1000136</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.712317</td>\n      <td>0.000000</td>\n      <td>0.754101</td>\n      <td>0.000000</td>\n      <td>0.791231</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>12166.195312</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>250</td>\n      <td>1000250</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.465675</td>\n      <td>0.000000</td>\n      <td>0.492992</td>\n      <td>0.000000</td>\n      <td>0.517265</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>7953.618164</td>\n    </tr>\n    <tr>\n      <th>258</th>\n      <td>259</td>\n      <td>1000259</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.365440</td>\n      <td>0.000000</td>\n      <td>0.386876</td>\n      <td>0.000000</td>\n      <td>0.405925</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>6241.616211</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26502</th>\n      <td>26503</td>\n      <td>46026503</td>\n      <td>HRVATSKA</td>\n      <td>HR - Croatia</td>\n      <td>HR</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.217276</td>\n      <td>0.000000</td>\n      <td>0.199490</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.183166</td>\n      <td>2890.419189</td>\n    </tr>\n    <tr>\n      <th>26525</th>\n      <td>26526</td>\n      <td>46026526</td>\n      <td>HRVATSKA</td>\n      <td>HR - Croatia</td>\n      <td>HR</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.851884</td>\n      <td>0.000000</td>\n      <td>0.782149</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.718145</td>\n      <td>11332.587891</td>\n    </tr>\n    <tr>\n      <th>26528</th>\n      <td>26529</td>\n      <td>46026529</td>\n      <td>HRVATSKA</td>\n      <td>HR - Croatia</td>\n      <td>HR</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.100986</td>\n      <td>0.000000</td>\n      <td>0.092719</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.085132</td>\n      <td>1343.413086</td>\n    </tr>\n    <tr>\n      <th>26540</th>\n      <td>26541</td>\n      <td>46026541</td>\n      <td>HRVATSKA</td>\n      <td>HR - Croatia</td>\n      <td>HR</td>\n      <td>Mobile phone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.353944</td>\n      <td>0.000000</td>\n      <td>0.324970</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.298378</td>\n      <td>4708.507324</td>\n    </tr>\n    <tr>\n      <th>26548</th>\n      <td>26549</td>\n      <td>46026549</td>\n      <td>HRVATSKA</td>\n      <td>HR - Croatia</td>\n      <td>HR</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.851856</td>\n      <td>0.000000</td>\n      <td>0.782123</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.718121</td>\n      <td>11332.212891</td>\n    </tr>\n  </tbody>\n</table>\n<p>894 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data['q3']=='Never']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid   uniqid     tnscntry       country isocntry  \\\n12558   12559  3012559  DEUTSCHLAND  DE - Germany       DE   \n12562   12563  3012563  DEUTSCHLAND  DE - Germany       DE   \n12566   12567  3012567  DEUTSCHLAND  DE - Germany       DE   \n12567   12568  3012568  DEUTSCHLAND  DE - Germany       DE   \n12642   12643  3012643  DEUTSCHLAND  DE - Germany       DE   \n12673   12674  3012674  DEUTSCHLAND  DE - Germany       DE   \n12682   12683  3012683  DEUTSCHLAND  DE - Germany       DE   \n12696   12697  3012697  DEUTSCHLAND  DE - Germany       DE   \n12794   12795  3012795  DEUTSCHLAND  DE - Germany       DE   \n12805   12806  3012806  DEUTSCHLAND  DE - Germany       DE   \n12817   12818  3012818  DEUTSCHLAND  DE - Germany       DE   \n12885   12886  3012886  DEUTSCHLAND  DE - Germany       DE   \n12898   12899  3012899  DEUTSCHLAND  DE - Germany       DE   \n12899   12900  3012900  DEUTSCHLAND  DE - Germany       DE   \n12985   12986  3012986  DEUTSCHLAND  DE - Germany       DE   \n13025   13026  3013026  DEUTSCHLAND  DE - Germany       DE   \n13056   13057  3013057  DEUTSCHLAND  DE - Germany       DE   \n13061   13062  3013062  DEUTSCHLAND  DE - Germany       DE   \n13065   13066  3013066  DEUTSCHLAND  DE - Germany       DE   \n13096   13097  3013097  DEUTSCHLAND  DE - Germany       DE   \n13108   13109  3013109  DEUTSCHLAND  DE - Germany       DE   \n13113   13114  3013114  DEUTSCHLAND  DE - Germany       DE   \n13118   13119  3013119  DEUTSCHLAND  DE - Germany       DE   \n13148   13149  3013149  DEUTSCHLAND  DE - Germany       DE   \n13156   13157  3013157  DEUTSCHLAND  DE - Germany       DE   \n13193   13194  3013194  DEUTSCHLAND  DE - Germany       DE   \n13197   13198  3013198  DEUTSCHLAND  DE - Germany       DE   \n13206   13207  3013207  DEUTSCHLAND  DE - Germany       DE   \n13208   13209  3013209  DEUTSCHLAND  DE - Germany       DE   \n13217   13218  3013218  DEUTSCHLAND  DE - Germany       DE   \n13220   13221  3013221  DEUTSCHLAND  DE - Germany       DE   \n13233   13234  3013234  DEUTSCHLAND  DE - Germany       DE   \n13249   13250  3013250  DEUTSCHLAND  DE - Germany       DE   \n13255   13256  3013256  DEUTSCHLAND  DE - Germany       DE   \n13293   13294  3013294  DEUTSCHLAND  DE - Germany       DE   \n13296   13297  3013297  DEUTSCHLAND  DE - Germany       DE   \n13334   13335  3013335  DEUTSCHLAND  DE - Germany       DE   \n13338   13339  3013339  DEUTSCHLAND  DE - Germany       DE   \n13350   13351  3013351  DEUTSCHLAND  DE - Germany       DE   \n13389   13390  3013390  DEUTSCHLAND  DE - Germany       DE   \n13426   13427  3013427  DEUTSCHLAND  DE - Germany       DE   \n13440   13441  3013441  DEUTSCHLAND  DE - Germany       DE   \n13465   13466  3013466  DEUTSCHLAND  DE - Germany       DE   \n13524   13525  3013525  DEUTSCHLAND  DE - Germany       DE   \n13529   13530  3013530  DEUTSCHLAND  DE - Germany       DE   \n\n                       mode           q1_1                q1_2  \\\n12558  Fixed telephone line  Not mentioned       Not mentioned   \n12562  Fixed telephone line  Not mentioned       Not mentioned   \n12566  Fixed telephone line  Not mentioned       Not mentioned   \n12567  Fixed telephone line  Not mentioned  Food manufacturers   \n12642  Fixed telephone line  Not mentioned       Not mentioned   \n12673  Fixed telephone line  Not mentioned       Not mentioned   \n12682  Fixed telephone line        Farmers       Not mentioned   \n12696  Fixed telephone line        Farmers  Food manufacturers   \n12794  Fixed telephone line        Farmers  Food manufacturers   \n12805  Fixed telephone line  Not mentioned       Not mentioned   \n12817     Mobile phone line  Not mentioned       Not mentioned   \n12885  Fixed telephone line        Farmers  Food manufacturers   \n12898  Fixed telephone line  Not mentioned       Not mentioned   \n12899  Fixed telephone line  Not mentioned       Not mentioned   \n12985  Fixed telephone line  Not mentioned       Not mentioned   \n13025  Fixed telephone line  Not mentioned       Not mentioned   \n13056  Fixed telephone line  Not mentioned  Food manufacturers   \n13061  Fixed telephone line        Farmers  Food manufacturers   \n13065  Fixed telephone line  Not mentioned       Not mentioned   \n13096  Fixed telephone line        Farmers  Food manufacturers   \n13108  Fixed telephone line  Not mentioned       Not mentioned   \n13113  Fixed telephone line  Not mentioned       Not mentioned   \n13118  Fixed telephone line  Not mentioned  Food manufacturers   \n13148  Fixed telephone line        Farmers  Food manufacturers   \n13156  Fixed telephone line        Farmers  Food manufacturers   \n13193  Fixed telephone line        Farmers       Not mentioned   \n13197  Fixed telephone line        Farmers  Food manufacturers   \n13206  Fixed telephone line  Not mentioned       Not mentioned   \n13208  Fixed telephone line        Farmers  Food manufacturers   \n13217  Fixed telephone line        Farmers       Not mentioned   \n13220  Fixed telephone line        Farmers       Not mentioned   \n13233  Fixed telephone line  Not mentioned  Food manufacturers   \n13249  Fixed telephone line  Not mentioned       Not mentioned   \n13255  Fixed telephone line  Not mentioned       Not mentioned   \n13293  Fixed telephone line  Not mentioned       Not mentioned   \n13296  Fixed telephone line  Not mentioned       Not mentioned   \n13334  Fixed telephone line        Farmers       Not mentioned   \n13338     Mobile phone line  Not mentioned       Not mentioned   \n13350  Fixed telephone line  Not mentioned  Food manufacturers   \n13389     Mobile phone line  Not mentioned  Food manufacturers   \n13426  Fixed telephone line  Not mentioned  Food manufacturers   \n13440  Fixed telephone line  Not mentioned       Not mentioned   \n13465  Fixed telephone line        Farmers  Food manufacturers   \n13524  Fixed telephone line        Farmers  Food manufacturers   \n13529     Mobile phone line  Not mentioned  Food manufacturers   \n\n                      q1_3                                      q1_4  ...  \\\n12558  Shops and retailers  no  Hospitality and food service sectors  ...   \n12562        Not mentioned                             Not mentioned  ...   \n12566        Not mentioned  no  Hospitality and food service sectors  ...   \n12567  Shops and retailers                             Not mentioned  ...   \n12642        Not mentioned                             Not mentioned  ...   \n12673        Not mentioned                             Not mentioned  ...   \n12682        Not mentioned                             Not mentioned  ...   \n12696  Shops and retailers  no  Hospitality and food service sectors  ...   \n12794  Shops and retailers  no  Hospitality and food service sectors  ...   \n12805        Not mentioned                             Not mentioned  ...   \n12817        Not mentioned  no  Hospitality and food service sectors  ...   \n12885  Shops and retailers  no  Hospitality and food service sectors  ...   \n12898  Shops and retailers                             Not mentioned  ...   \n12899        Not mentioned                             Not mentioned  ...   \n12985        Not mentioned                             Not mentioned  ...   \n13025        Not mentioned                             Not mentioned  ...   \n13056        Not mentioned                             Not mentioned  ...   \n13061  Shops and retailers  no  Hospitality and food service sectors  ...   \n13065  Shops and retailers                             Not mentioned  ...   \n13096  Shops and retailers  no  Hospitality and food service sectors  ...   \n13108  Shops and retailers  no  Hospitality and food service sectors  ...   \n13113        Not mentioned                             Not mentioned  ...   \n13118  Shops and retailers  no  Hospitality and food service sectors  ...   \n13148  Shops and retailers  no  Hospitality and food service sectors  ...   \n13156  Shops and retailers  no  Hospitality and food service sectors  ...   \n13193        Not mentioned                             Not mentioned  ...   \n13197  Shops and retailers  no  Hospitality and food service sectors  ...   \n13206        Not mentioned                             Not mentioned  ...   \n13208  Shops and retailers                             Not mentioned  ...   \n13217  Shops and retailers  no  Hospitality and food service sectors  ...   \n13220        Not mentioned                             Not mentioned  ...   \n13233  Shops and retailers                             Not mentioned  ...   \n13249  Shops and retailers                             Not mentioned  ...   \n13255  Shops and retailers                             Not mentioned  ...   \n13293        Not mentioned                             Not mentioned  ...   \n13296        Not mentioned                             Not mentioned  ...   \n13334  Shops and retailers  no  Hospitality and food service sectors  ...   \n13338        Not mentioned                             Not mentioned  ...   \n13350  Shops and retailers  no  Hospitality and food service sectors  ...   \n13389  Shops and retailers  no  Hospitality and food service sectors  ...   \n13426  Shops and retailers                             Not mentioned  ...   \n13440        Not mentioned                             Not mentioned  ...   \n13465  Shops and retailers  no  Hospitality and food service sectors  ...   \n13524  Shops and retailers                             Not mentioned  ...   \n13529        Not mentioned                             Not mentioned  ...   \n\n       w82  w84       w89  w90       w95  w96       w97  w83  w98  \\\n12558  0.0  0.0  5.121486  0.0  5.421908  0.0  5.688869  0.0  0.0   \n12562  0.0  0.0  6.637455  0.0  7.026802  0.0  7.372785  0.0  0.0   \n12566  0.0  0.0  2.342210  0.0  2.479602  0.0  2.601691  0.0  0.0   \n12567  0.0  0.0  2.741565  0.0  2.902384  0.0  3.045290  0.0  0.0   \n12642  0.0  0.0  5.121486  0.0  5.421908  0.0  5.688869  0.0  0.0   \n12673  0.0  0.0  2.226438  0.0  2.357039  0.0  2.473094  0.0  0.0   \n12682  0.0  0.0  1.544629  0.0  1.635236  0.0  1.715751  0.0  0.0   \n12696  0.0  0.0  1.970382  0.0  2.085963  0.0  2.188671  0.0  0.0   \n12794  0.0  0.0  1.970382  0.0  2.085963  0.0  2.188671  0.0  0.0   \n12805  0.0  0.0  2.216633  0.0  2.346659  0.0  2.462203  0.0  0.0   \n12817  0.0  0.0  5.081512  0.0  5.379590  0.0  5.644468  0.0  0.0   \n12885  0.0  0.0  2.241145  0.0  2.372609  0.0  2.489430  0.0  0.0   \n12898  0.0  0.0  2.411974  0.0  2.553459  0.0  2.679185  0.0  0.0   \n12899  0.0  0.0  2.197778  0.0  2.326698  0.0  2.441258  0.0  0.0   \n12985  0.0  0.0  2.905607  0.0  3.076047  0.0  3.227504  0.0  0.0   \n13025  0.0  0.0  7.513096  0.0  7.953808  0.0  8.345435  0.0  0.0   \n13056  0.0  0.0  6.637455  0.0  7.026802  0.0  7.372785  0.0  0.0   \n13061  0.0  0.0  2.173643  0.0  2.301147  0.0  2.414450  0.0  0.0   \n13065  0.0  0.0  2.533403  0.0  2.682010  0.0  2.814066  0.0  0.0   \n13096  0.0  0.0  5.594000  0.0  5.922140  0.0  6.213732  0.0  0.0   \n13108  0.0  0.0  1.603835  0.0  1.697914  0.0  1.781515  0.0  0.0   \n13113  0.0  0.0  1.516346  0.0  1.605294  0.0  1.684334  0.0  0.0   \n13118  0.0  0.0  3.592318  0.0  3.803041  0.0  3.990293  0.0  0.0   \n13148  0.0  0.0  2.372001  0.0  2.511141  0.0  2.634783  0.0  0.0   \n13156  0.0  0.0  2.923331  0.0  3.094811  0.0  3.247192  0.0  0.0   \n13193  0.0  0.0  2.745714  0.0  2.906775  0.0  3.049898  0.0  0.0   \n13197  0.0  0.0  2.741565  0.0  2.902384  0.0  3.045290  0.0  0.0   \n13206  0.0  0.0  2.296957  0.0  2.431694  0.0  2.551425  0.0  0.0   \n13208  0.0  0.0  2.741565  0.0  2.902384  0.0  3.045290  0.0  0.0   \n13217  0.0  0.0  2.197024  0.0  2.325899  0.0  2.440421  0.0  0.0   \n13220  0.0  0.0  3.107736  0.0  3.290033  0.0  3.452027  0.0  0.0   \n13233  0.0  0.0  1.603835  0.0  1.697914  0.0  1.781515  0.0  0.0   \n13249  0.0  0.0  3.344182  0.0  3.540349  0.0  3.714667  0.0  0.0   \n13255  0.0  0.0  6.072549  0.0  6.428760  0.0  6.745296  0.0  0.0   \n13293  0.0  0.0  3.034577  0.0  3.212583  0.0  3.370763  0.0  0.0   \n13296  0.0  0.0  3.112638  0.0  3.295223  0.0  3.457472  0.0  0.0   \n13334  0.0  0.0  7.669219  0.0  8.119088  0.0  8.518853  0.0  0.0   \n13338  0.0  0.0  6.637077  0.0  7.026403  0.0  7.372366  0.0  0.0   \n13350  0.0  0.0  2.905607  0.0  3.076047  0.0  3.227504  0.0  0.0   \n13389  0.0  0.0  3.112638  0.0  3.295223  0.0  3.457472  0.0  0.0   \n13426  0.0  0.0  2.677835  0.0  2.834914  0.0  2.974499  0.0  0.0   \n13440  0.0  0.0  2.921068  0.0  3.092416  0.0  3.244679  0.0  0.0   \n13465  0.0  0.0  3.107736  0.0  3.290033  0.0  3.452027  0.0  0.0   \n13524  0.0  0.0  1.919473  0.0  2.032068  0.0  2.132122  0.0  0.0   \n13529  0.0  0.0  6.491514  0.0  6.872301  0.0  7.210677  0.0  0.0   \n\n                 wex  \n12558   87473.679688  \n12562  113366.039062  \n12566   40004.343750  \n12567   46825.242188  \n12642   87473.679688  \n12673   38026.992188  \n12682   26381.871094  \n12696   33653.632812  \n12794   33653.632812  \n12805   37859.531250  \n12817   86790.945312  \n12885   38278.187500  \n12898   41195.910156  \n12899   37537.484375  \n12985   49627.027344  \n13025  128321.781250  \n13056  113366.039062  \n13061   37125.269531  \n13065   43269.875000  \n13096   95544.109375  \n13108   27393.089844  \n13113   25898.804688  \n13118   61355.882812  \n13148   40513.175781  \n13156   49929.750000  \n13193   46896.093750  \n13197   46825.242188  \n13206   39231.437500  \n13208   46825.242188  \n13217   37524.605469  \n13220   53079.343750  \n13233   27393.089844  \n13249   57117.781250  \n13255  103717.593750  \n13293   51829.812500  \n13296   53163.074219  \n13334  130988.304688  \n13338  113359.601562  \n13350   49627.027344  \n13389   53163.074219  \n13426   45736.734375  \n13440   49891.105469  \n13465   53079.343750  \n13524   32784.109375  \n13529  110873.414062  \n\n[45 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12558</th>\n      <td>12559</td>\n      <td>3012559</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.121486</td>\n      <td>0.0</td>\n      <td>5.421908</td>\n      <td>0.0</td>\n      <td>5.688869</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>87473.679688</td>\n    </tr>\n    <tr>\n      <th>12562</th>\n      <td>12563</td>\n      <td>3012563</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.637455</td>\n      <td>0.0</td>\n      <td>7.026802</td>\n      <td>0.0</td>\n      <td>7.372785</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>113366.039062</td>\n    </tr>\n    <tr>\n      <th>12566</th>\n      <td>12567</td>\n      <td>3012567</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.342210</td>\n      <td>0.0</td>\n      <td>2.479602</td>\n      <td>0.0</td>\n      <td>2.601691</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40004.343750</td>\n    </tr>\n    <tr>\n      <th>12567</th>\n      <td>12568</td>\n      <td>3012568</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.741565</td>\n      <td>0.0</td>\n      <td>2.902384</td>\n      <td>0.0</td>\n      <td>3.045290</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>46825.242188</td>\n    </tr>\n    <tr>\n      <th>12642</th>\n      <td>12643</td>\n      <td>3012643</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.121486</td>\n      <td>0.0</td>\n      <td>5.421908</td>\n      <td>0.0</td>\n      <td>5.688869</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>87473.679688</td>\n    </tr>\n    <tr>\n      <th>12673</th>\n      <td>12674</td>\n      <td>3012674</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.226438</td>\n      <td>0.0</td>\n      <td>2.357039</td>\n      <td>0.0</td>\n      <td>2.473094</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>38026.992188</td>\n    </tr>\n    <tr>\n      <th>12682</th>\n      <td>12683</td>\n      <td>3012683</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.544629</td>\n      <td>0.0</td>\n      <td>1.635236</td>\n      <td>0.0</td>\n      <td>1.715751</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>26381.871094</td>\n    </tr>\n    <tr>\n      <th>12696</th>\n      <td>12697</td>\n      <td>3012697</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.970382</td>\n      <td>0.0</td>\n      <td>2.085963</td>\n      <td>0.0</td>\n      <td>2.188671</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>33653.632812</td>\n    </tr>\n    <tr>\n      <th>12794</th>\n      <td>12795</td>\n      <td>3012795</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.970382</td>\n      <td>0.0</td>\n      <td>2.085963</td>\n      <td>0.0</td>\n      <td>2.188671</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>33653.632812</td>\n    </tr>\n    <tr>\n      <th>12805</th>\n      <td>12806</td>\n      <td>3012806</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.216633</td>\n      <td>0.0</td>\n      <td>2.346659</td>\n      <td>0.0</td>\n      <td>2.462203</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>37859.531250</td>\n    </tr>\n    <tr>\n      <th>12817</th>\n      <td>12818</td>\n      <td>3012818</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.081512</td>\n      <td>0.0</td>\n      <td>5.379590</td>\n      <td>0.0</td>\n      <td>5.644468</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>86790.945312</td>\n    </tr>\n    <tr>\n      <th>12885</th>\n      <td>12886</td>\n      <td>3012886</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.241145</td>\n      <td>0.0</td>\n      <td>2.372609</td>\n      <td>0.0</td>\n      <td>2.489430</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>38278.187500</td>\n    </tr>\n    <tr>\n      <th>12898</th>\n      <td>12899</td>\n      <td>3012899</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.411974</td>\n      <td>0.0</td>\n      <td>2.553459</td>\n      <td>0.0</td>\n      <td>2.679185</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>41195.910156</td>\n    </tr>\n    <tr>\n      <th>12899</th>\n      <td>12900</td>\n      <td>3012900</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.197778</td>\n      <td>0.0</td>\n      <td>2.326698</td>\n      <td>0.0</td>\n      <td>2.441258</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>37537.484375</td>\n    </tr>\n    <tr>\n      <th>12985</th>\n      <td>12986</td>\n      <td>3012986</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.905607</td>\n      <td>0.0</td>\n      <td>3.076047</td>\n      <td>0.0</td>\n      <td>3.227504</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49627.027344</td>\n    </tr>\n    <tr>\n      <th>13025</th>\n      <td>13026</td>\n      <td>3013026</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.513096</td>\n      <td>0.0</td>\n      <td>7.953808</td>\n      <td>0.0</td>\n      <td>8.345435</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>128321.781250</td>\n    </tr>\n    <tr>\n      <th>13056</th>\n      <td>13057</td>\n      <td>3013057</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.637455</td>\n      <td>0.0</td>\n      <td>7.026802</td>\n      <td>0.0</td>\n      <td>7.372785</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>113366.039062</td>\n    </tr>\n    <tr>\n      <th>13061</th>\n      <td>13062</td>\n      <td>3013062</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.173643</td>\n      <td>0.0</td>\n      <td>2.301147</td>\n      <td>0.0</td>\n      <td>2.414450</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>37125.269531</td>\n    </tr>\n    <tr>\n      <th>13065</th>\n      <td>13066</td>\n      <td>3013066</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.533403</td>\n      <td>0.0</td>\n      <td>2.682010</td>\n      <td>0.0</td>\n      <td>2.814066</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>43269.875000</td>\n    </tr>\n    <tr>\n      <th>13096</th>\n      <td>13097</td>\n      <td>3013097</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.594000</td>\n      <td>0.0</td>\n      <td>5.922140</td>\n      <td>0.0</td>\n      <td>6.213732</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>95544.109375</td>\n    </tr>\n    <tr>\n      <th>13108</th>\n      <td>13109</td>\n      <td>3013109</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.603835</td>\n      <td>0.0</td>\n      <td>1.697914</td>\n      <td>0.0</td>\n      <td>1.781515</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>27393.089844</td>\n    </tr>\n    <tr>\n      <th>13113</th>\n      <td>13114</td>\n      <td>3013114</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.516346</td>\n      <td>0.0</td>\n      <td>1.605294</td>\n      <td>0.0</td>\n      <td>1.684334</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25898.804688</td>\n    </tr>\n    <tr>\n      <th>13118</th>\n      <td>13119</td>\n      <td>3013119</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.592318</td>\n      <td>0.0</td>\n      <td>3.803041</td>\n      <td>0.0</td>\n      <td>3.990293</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>61355.882812</td>\n    </tr>\n    <tr>\n      <th>13148</th>\n      <td>13149</td>\n      <td>3013149</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.372001</td>\n      <td>0.0</td>\n      <td>2.511141</td>\n      <td>0.0</td>\n      <td>2.634783</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40513.175781</td>\n    </tr>\n    <tr>\n      <th>13156</th>\n      <td>13157</td>\n      <td>3013157</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.923331</td>\n      <td>0.0</td>\n      <td>3.094811</td>\n      <td>0.0</td>\n      <td>3.247192</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49929.750000</td>\n    </tr>\n    <tr>\n      <th>13193</th>\n      <td>13194</td>\n      <td>3013194</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.745714</td>\n      <td>0.0</td>\n      <td>2.906775</td>\n      <td>0.0</td>\n      <td>3.049898</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>46896.093750</td>\n    </tr>\n    <tr>\n      <th>13197</th>\n      <td>13198</td>\n      <td>3013198</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.741565</td>\n      <td>0.0</td>\n      <td>2.902384</td>\n      <td>0.0</td>\n      <td>3.045290</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>46825.242188</td>\n    </tr>\n    <tr>\n      <th>13206</th>\n      <td>13207</td>\n      <td>3013207</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.296957</td>\n      <td>0.0</td>\n      <td>2.431694</td>\n      <td>0.0</td>\n      <td>2.551425</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>39231.437500</td>\n    </tr>\n    <tr>\n      <th>13208</th>\n      <td>13209</td>\n      <td>3013209</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.741565</td>\n      <td>0.0</td>\n      <td>2.902384</td>\n      <td>0.0</td>\n      <td>3.045290</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>46825.242188</td>\n    </tr>\n    <tr>\n      <th>13217</th>\n      <td>13218</td>\n      <td>3013218</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.197024</td>\n      <td>0.0</td>\n      <td>2.325899</td>\n      <td>0.0</td>\n      <td>2.440421</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>37524.605469</td>\n    </tr>\n    <tr>\n      <th>13220</th>\n      <td>13221</td>\n      <td>3013221</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.107736</td>\n      <td>0.0</td>\n      <td>3.290033</td>\n      <td>0.0</td>\n      <td>3.452027</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>53079.343750</td>\n    </tr>\n    <tr>\n      <th>13233</th>\n      <td>13234</td>\n      <td>3013234</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.603835</td>\n      <td>0.0</td>\n      <td>1.697914</td>\n      <td>0.0</td>\n      <td>1.781515</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>27393.089844</td>\n    </tr>\n    <tr>\n      <th>13249</th>\n      <td>13250</td>\n      <td>3013250</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.344182</td>\n      <td>0.0</td>\n      <td>3.540349</td>\n      <td>0.0</td>\n      <td>3.714667</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>57117.781250</td>\n    </tr>\n    <tr>\n      <th>13255</th>\n      <td>13256</td>\n      <td>3013256</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.072549</td>\n      <td>0.0</td>\n      <td>6.428760</td>\n      <td>0.0</td>\n      <td>6.745296</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>103717.593750</td>\n    </tr>\n    <tr>\n      <th>13293</th>\n      <td>13294</td>\n      <td>3013294</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.034577</td>\n      <td>0.0</td>\n      <td>3.212583</td>\n      <td>0.0</td>\n      <td>3.370763</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>51829.812500</td>\n    </tr>\n    <tr>\n      <th>13296</th>\n      <td>13297</td>\n      <td>3013297</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.112638</td>\n      <td>0.0</td>\n      <td>3.295223</td>\n      <td>0.0</td>\n      <td>3.457472</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>53163.074219</td>\n    </tr>\n    <tr>\n      <th>13334</th>\n      <td>13335</td>\n      <td>3013335</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.669219</td>\n      <td>0.0</td>\n      <td>8.119088</td>\n      <td>0.0</td>\n      <td>8.518853</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>130988.304688</td>\n    </tr>\n    <tr>\n      <th>13338</th>\n      <td>13339</td>\n      <td>3013339</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.637077</td>\n      <td>0.0</td>\n      <td>7.026403</td>\n      <td>0.0</td>\n      <td>7.372366</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>113359.601562</td>\n    </tr>\n    <tr>\n      <th>13350</th>\n      <td>13351</td>\n      <td>3013351</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.905607</td>\n      <td>0.0</td>\n      <td>3.076047</td>\n      <td>0.0</td>\n      <td>3.227504</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49627.027344</td>\n    </tr>\n    <tr>\n      <th>13389</th>\n      <td>13390</td>\n      <td>3013390</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.112638</td>\n      <td>0.0</td>\n      <td>3.295223</td>\n      <td>0.0</td>\n      <td>3.457472</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>53163.074219</td>\n    </tr>\n    <tr>\n      <th>13426</th>\n      <td>13427</td>\n      <td>3013427</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.677835</td>\n      <td>0.0</td>\n      <td>2.834914</td>\n      <td>0.0</td>\n      <td>2.974499</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>45736.734375</td>\n    </tr>\n    <tr>\n      <th>13440</th>\n      <td>13441</td>\n      <td>3013441</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.921068</td>\n      <td>0.0</td>\n      <td>3.092416</td>\n      <td>0.0</td>\n      <td>3.244679</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49891.105469</td>\n    </tr>\n    <tr>\n      <th>13465</th>\n      <td>13466</td>\n      <td>3013466</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.107736</td>\n      <td>0.0</td>\n      <td>3.290033</td>\n      <td>0.0</td>\n      <td>3.452027</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>53079.343750</td>\n    </tr>\n    <tr>\n      <th>13524</th>\n      <td>13525</td>\n      <td>3013525</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.919473</td>\n      <td>0.0</td>\n      <td>2.032068</td>\n      <td>0.0</td>\n      <td>2.132122</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>32784.109375</td>\n    </tr>\n    <tr>\n      <th>13529</th>\n      <td>13530</td>\n      <td>3013530</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.491514</td>\n      <td>0.0</td>\n      <td>6.872301</td>\n      <td>0.0</td>\n      <td>7.210677</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>110873.414062</td>\n    </tr>\n  </tbody>\n</table>\n<p>45 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[(data['q3']=='Never') & (data['isocntry']=='DE')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       caseid   uniqid     tnscntry       country isocntry  \\\n12538   12539  3012539  DEUTSCHLAND  DE - Germany       DE   \n12558   12559  3012559  DEUTSCHLAND  DE - Germany       DE   \n12562   12563  3012563  DEUTSCHLAND  DE - Germany       DE   \n12565   12566  3012566  DEUTSCHLAND  DE - Germany       DE   \n12566   12567  3012567  DEUTSCHLAND  DE - Germany       DE   \n...       ...      ...          ...           ...      ...   \n13497   13498  3013498  DEUTSCHLAND  DE - Germany       DE   \n13500   13501  3013501  DEUTSCHLAND  DE - Germany       DE   \n13504   13505  3013505  DEUTSCHLAND  DE - Germany       DE   \n13524   13525  3013525  DEUTSCHLAND  DE - Germany       DE   \n13529   13530  3013530  DEUTSCHLAND  DE - Germany       DE   \n\n                       mode           q1_1                q1_2  \\\n12538  Fixed telephone line  Not mentioned       Not mentioned   \n12558  Fixed telephone line  Not mentioned       Not mentioned   \n12562  Fixed telephone line  Not mentioned       Not mentioned   \n12565  Fixed telephone line        Farmers  Food manufacturers   \n12566  Fixed telephone line  Not mentioned       Not mentioned   \n...                     ...            ...                 ...   \n13497  Fixed telephone line        Farmers  Food manufacturers   \n13500  Fixed telephone line  Not mentioned       Not mentioned   \n13504  Fixed telephone line        Farmers  Food manufacturers   \n13524  Fixed telephone line        Farmers  Food manufacturers   \n13529     Mobile phone line  Not mentioned  Food manufacturers   \n\n                      q1_3                                      q1_4  ...  \\\n12538  Shops and retailers                             Not mentioned  ...   \n12558  Shops and retailers  no  Hospitality and food service sectors  ...   \n12562        Not mentioned                             Not mentioned  ...   \n12565  Shops and retailers  no  Hospitality and food service sectors  ...   \n12566        Not mentioned  no  Hospitality and food service sectors  ...   \n...                    ...                                       ...  ...   \n13497  Shops and retailers  no  Hospitality and food service sectors  ...   \n13500        Not mentioned                             Not mentioned  ...   \n13504  Shops and retailers  no  Hospitality and food service sectors  ...   \n13524  Shops and retailers                             Not mentioned  ...   \n13529        Not mentioned                             Not mentioned  ...   \n\n       w82  w84       w89  w90       w95  w96       w97  w83  w98  \\\n12538  0.0  0.0  3.592318  0.0  3.803041  0.0  3.990293  0.0  0.0   \n12558  0.0  0.0  5.121486  0.0  5.421908  0.0  5.688869  0.0  0.0   \n12562  0.0  0.0  6.637455  0.0  7.026802  0.0  7.372785  0.0  0.0   \n12565  0.0  0.0  2.917674  0.0  3.088823  0.0  3.240909  0.0  0.0   \n12566  0.0  0.0  2.342210  0.0  2.479602  0.0  2.601691  0.0  0.0   \n...    ...  ...       ...  ...       ...  ...       ...  ...  ...   \n13497  0.0  0.0  1.611377  0.0  1.705899  0.0  1.789893  0.0  0.0   \n13500  0.0  0.0  2.364459  0.0  2.503156  0.0  2.626405  0.0  0.0   \n13504  0.0  0.0  7.056420  0.0  7.470344  0.0  7.838166  0.0  0.0   \n13524  0.0  0.0  1.919473  0.0  2.032068  0.0  2.132122  0.0  0.0   \n13529  0.0  0.0  6.491514  0.0  6.872301  0.0  7.210677  0.0  0.0   \n\n                 wex  \n12538   61355.882812  \n12558   87473.679688  \n12562  113366.039062  \n12565   49833.136719  \n12566   40004.343750  \n...              ...  \n13497   27521.906250  \n13500   40384.355469  \n13504  120521.867188  \n13524   32784.109375  \n13529  110873.414062  \n\n[131 rows x 160 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12538</th>\n      <td>12539</td>\n      <td>3012539</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.592318</td>\n      <td>0.0</td>\n      <td>3.803041</td>\n      <td>0.0</td>\n      <td>3.990293</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>61355.882812</td>\n    </tr>\n    <tr>\n      <th>12558</th>\n      <td>12559</td>\n      <td>3012559</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.121486</td>\n      <td>0.0</td>\n      <td>5.421908</td>\n      <td>0.0</td>\n      <td>5.688869</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>87473.679688</td>\n    </tr>\n    <tr>\n      <th>12562</th>\n      <td>12563</td>\n      <td>3012563</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.637455</td>\n      <td>0.0</td>\n      <td>7.026802</td>\n      <td>0.0</td>\n      <td>7.372785</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>113366.039062</td>\n    </tr>\n    <tr>\n      <th>12565</th>\n      <td>12566</td>\n      <td>3012566</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.917674</td>\n      <td>0.0</td>\n      <td>3.088823</td>\n      <td>0.0</td>\n      <td>3.240909</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>49833.136719</td>\n    </tr>\n    <tr>\n      <th>12566</th>\n      <td>12567</td>\n      <td>3012567</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.342210</td>\n      <td>0.0</td>\n      <td>2.479602</td>\n      <td>0.0</td>\n      <td>2.601691</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40004.343750</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>13497</th>\n      <td>13498</td>\n      <td>3013498</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.611377</td>\n      <td>0.0</td>\n      <td>1.705899</td>\n      <td>0.0</td>\n      <td>1.789893</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>27521.906250</td>\n    </tr>\n    <tr>\n      <th>13500</th>\n      <td>13501</td>\n      <td>3013501</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.364459</td>\n      <td>0.0</td>\n      <td>2.503156</td>\n      <td>0.0</td>\n      <td>2.626405</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>40384.355469</td>\n    </tr>\n    <tr>\n      <th>13504</th>\n      <td>13505</td>\n      <td>3013505</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.056420</td>\n      <td>0.0</td>\n      <td>7.470344</td>\n      <td>0.0</td>\n      <td>7.838166</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>120521.867188</td>\n    </tr>\n    <tr>\n      <th>13524</th>\n      <td>13525</td>\n      <td>3013525</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.919473</td>\n      <td>0.0</td>\n      <td>2.032068</td>\n      <td>0.0</td>\n      <td>2.132122</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>32784.109375</td>\n    </tr>\n    <tr>\n      <th>13529</th>\n      <td>13530</td>\n      <td>3013530</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.491514</td>\n      <td>0.0</td>\n      <td>6.872301</td>\n      <td>0.0</td>\n      <td>7.210677</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>110873.414062</td>\n    </tr>\n  </tbody>\n</table>\n<p>131 rows × 160 columns</p>\n</div>"
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[((data['q3']=='Never') | (data['q3']=='Rarely')) & (data['isocntry']=='DE')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "data = pd.read_stata('../data/external/ZA6647_v1-0-0.dta')"
+    "group1 = data[((data['q3']=='Never') | (data['q3']=='Rarely')) & (data['isocntry']=='DE')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "caseid      131\nuniqid      131\ntnscntry    131\ncountry     131\nisocntry    131\n           ... \nw96         131\nw97         131\nw83         131\nw98         131\nwex         131\nLength: 160, dtype: int64"
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "group1.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Female    68\nMale      63\nName: gender, dtype: int64"
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "group1['gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "gender                  Male  Female\nd12de                               \nSchleswig-Holstein       2.0     6.0\nHamburg                  3.0     4.0\nNiedersachsen            5.0     4.0\nBremen                   NaN     1.0\nNordrhein-Westfalen      6.0     9.0\nHessen                   3.0     3.0\nRheinland-Pfalz          3.0     3.0\nBaden-Wurttemberg       11.0    11.0\nBayern                  12.0    10.0\nSaarland                 2.0     1.0\nBerlin                   3.0     1.0\nBrandenburg              NaN     4.0\nMecklenburg-Vorpommern   1.0     2.0\nSachsen                  8.0     3.0\nSachsen-Anhalt           1.0     4.0\nThuringen                3.0     2.0",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>gender</th>\n      <th>Male</th>\n      <th>Female</th>\n    </tr>\n    <tr>\n      <th>d12de</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Schleswig-Holstein</th>\n      <td>2.0</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>Hamburg</th>\n      <td>3.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>Niedersachsen</th>\n      <td>5.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>Bremen</th>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Nordrhein-Westfalen</th>\n      <td>6.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>Hessen</th>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>Rheinland-Pfalz</th>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>Baden-Wurttemberg</th>\n      <td>11.0</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>Bayern</th>\n      <td>12.0</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>Saarland</th>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Berlin</th>\n      <td>3.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Brandenburg</th>\n      <td>NaN</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>Mecklenburg-Vorpommern</th>\n      <td>1.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>Sachsen</th>\n      <td>8.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>Sachsen-Anhalt</th>\n      <td>1.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>Thuringen</th>\n      <td>3.0</td>\n      <td>2.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(group1['d12de'], group1['gender'], group1['gender'], aggfunc='count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "['Sometimes', 'Always', 'Rarely', 'Often', 'Never', 'DK/NA']\nCategories (6, object): ['Always' < 'Often' < 'Sometimes' < 'Rarely' < 'Never' < 'DK/NA']"
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['q3'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "gender     Male  Female\nq3                     \nAlways     5367   10461\nOften      2413    3344\nSometimes   993    1330\nRarely      794     812\nNever       508     386\nDK/NA        94      99",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>gender</th>\n      <th>Male</th>\n      <th>Female</th>\n    </tr>\n    <tr>\n      <th>q3</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Always</th>\n      <td>5367</td>\n      <td>10461</td>\n    </tr>\n    <tr>\n      <th>Often</th>\n      <td>2413</td>\n      <td>3344</td>\n    </tr>\n    <tr>\n      <th>Sometimes</th>\n      <td>993</td>\n      <td>1330</td>\n    </tr>\n    <tr>\n      <th>Rarely</th>\n      <td>794</td>\n      <td>812</td>\n    </tr>\n    <tr>\n      <th>Never</th>\n      <td>508</td>\n      <td>386</td>\n    </tr>\n    <tr>\n      <th>DK/NA</th>\n      <td>94</td>\n      <td>99</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
    ],
+   "source": [
+    "pd.crosstab(data['q3'], data['gender'], data['gender'], aggfunc='count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Female    16432\nMale      10169\nName: gender, dtype: int64"
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {
-    "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
     }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage of females:\n",
+      "63.662366114897765\n",
+      "Percentage of males:\n",
+      "52.77805093912873\n"
+     ]
+    }
+   ],
+   "source": [
+    "females = 10461/16432*100\n",
+    "males = 5367/10169*100\n",
+    "\n",
+    "print('Percentage of females:')\n",
+    "print(females)\n",
+    "print('Percentage of males:')\n",
+    "print(males)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Das ist eine große Überschrift\n",
+    "\n",
+    "Hier erkläre ich etwas. Und dieser Text ist **fett**.\n",
+    "\n",
+    "Liste mit Früchten:\n",
+    "- Apfel\n",
+    "- Birne\n",
+    "- Traube"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Wichtige Punkte zur Analyse für Piotr\n",
+    "\n",
+    "Nach kurzem Einblick sind folgende Dinge aufgefallen.\n",
+    "\n",
+    "Themen:\n",
+    "- Hoher Frauenanteil in der Umfrage\n",
+    "- Mehr Frauen (66%) achten immer auf das Verfallsdatum (Männer 52%)"
+   ],
+   "metadata": {
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "outputs": [
     {
      "data": {
-      "text/plain": "               studyno                  doi             version  \\\n0  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n1  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n2  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n3  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n4  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n\n           edition                                    survey  caseid   uniqid  \\\n0  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       1  1000001   \n1  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       2  1000002   \n2  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       3  1000003   \n3  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       4  1000004   \n4  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)       5  1000005   \n\n   tnscntry       country isocntry  ...  w82  w84       w89  w90       w95  \\\n0  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.377414  0.0  0.399552   \n1  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.398224  0.0  0.421584   \n2  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.203295  0.0  0.215220   \n3  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.313204  0.0  0.331576   \n4  BELGIQUE  BE - Belgium       BE  ...  0.0  0.0  0.384786  0.0  0.407357   \n\n   w96       w97  w83  w98          wex  \n0  0.0  0.419225  0.0  0.0  6446.127930  \n1  0.0  0.442341  0.0  0.0  6801.566895  \n2  0.0  0.225817  0.0  0.0  3472.228516  \n3  0.0  0.347902  0.0  0.0  5349.446777  \n4  0.0  0.427415  0.0  0.0  6572.049805  \n\n[5 rows x 165 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>studyno</th>\n      <th>doi</th>\n      <th>version</th>\n      <th>edition</th>\n      <th>survey</th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>1</td>\n      <td>1000001</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.377414</td>\n      <td>0.0</td>\n      <td>0.399552</td>\n      <td>0.0</td>\n      <td>0.419225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6446.127930</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>2</td>\n      <td>1000002</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.398224</td>\n      <td>0.0</td>\n      <td>0.421584</td>\n      <td>0.0</td>\n      <td>0.442341</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6801.566895</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>3</td>\n      <td>1000003</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.203295</td>\n      <td>0.0</td>\n      <td>0.215220</td>\n      <td>0.0</td>\n      <td>0.225817</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3472.228516</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>4</td>\n      <td>1000004</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.313204</td>\n      <td>0.0</td>\n      <td>0.331576</td>\n      <td>0.0</td>\n      <td>0.347902</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5349.446777</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>5</td>\n      <td>1000005</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.384786</td>\n      <td>0.0</td>\n      <td>0.407357</td>\n      <td>0.0</td>\n      <td>0.427415</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6572.049805</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 165 columns</p>\n</div>"
+      "text/plain": "Nordrhein-Westfalen       185\nBayern                    169\nBaden-Wurttemberg         126\nNiedersachsen              83\nHessen                     65\nSachsen                    61\nRheinland-Pfalz            42\nThuringen                  41\nBrandenburg                40\nSchleswig-Holstein         40\nSachsen-Anhalt             39\nBerlin                     39\nHamburg                    26\nMecklenburg-Vorpommern     20\nSaarland                   17\nBremen                      7\nName: d12de, dtype: int64"
      },
-     "execution_count": 7,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.head()"
+    "answers_per_state = data[data['isocntry']=='DE']['d12de'].value_counts()\n",
+    "answers_per_state"
    ],
    "metadata": {
     "collapsed": false,
@@ -51,20 +611,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "outputs": [
     {
      "data": {
-      "text/plain": "Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n                9,\n            ...\n            26591, 26592, 26593, 26594, 26595, 26596, 26597, 26598, 26599,\n            26600],\n           dtype='int64', length=26601)"
+      "text/plain": "<AxesSubplot:>"
      },
-     "execution_count": 8,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Index auslesen\n",
-    "data.index"
+    "answers_per_state.plot.bar()"
    ],
    "metadata": {
     "collapsed": false,
@@ -75,20 +644,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 28,
    "outputs": [
     {
      "data": {
-      "text/plain": "Index(['studyno', 'doi', 'version', 'edition', 'survey', 'caseid', 'uniqid',\n       'tnscntry', 'country', 'isocntry',\n       ...\n       'w82', 'w84', 'w89', 'w90', 'w95', 'w96', 'w97', 'w83', 'w98', 'wex'],\n      dtype='object', length=165)"
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
      },
-     "execution_count": 12,
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = answers_per_state.plot.barh(title='Antworten in Deutschland pro Bundesland')\n",
+    "ax.invert_yaxis()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [],
+   "source": [
+    "fig =ax.get_figure()\n",
+    "fig.savefig('plot.png', dpi=150, facecolor='white', bbox_inches =\"tight\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Always       527\n",
+      "Often        234\n",
+      "Sometimes     91\n",
+      "Rarely        86\n",
+      "Never         45\n",
+      "DK/NA         17\n",
+      "Name: q3, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "Answers_q3 = data[data['isocntry']=='DE']['q3'].value_counts()\n",
+    "print(Answers_q3)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:ylabel='q3'>"
+     },
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Spalten auslesen\n",
-    "data.columns"
+    "Answers_q3.plot.pie()"
    ],
    "metadata": {
     "collapsed": false,
@@ -99,20 +745,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "outputs": [
     {
      "data": {
-      "text/plain": "['studyno',\n 'doi',\n 'version',\n 'edition',\n 'survey',\n 'caseid',\n 'uniqid',\n 'tnscntry',\n 'country',\n 'isocntry',\n 'mode',\n 'q1_1',\n 'q1_2',\n 'q1_3',\n 'q1_4',\n 'q1_5',\n 'q1_6',\n 'q1_7',\n 'q2_1',\n 'q2_2',\n 'q2_3',\n 'q2_4',\n 'q2_5',\n 'q2_6',\n 'q2_7',\n 'q2_8',\n 'q3',\n 'q4',\n 'q5',\n 'q6',\n 'q7',\n 'q8',\n 'd1',\n 'd1r1',\n 'd1r2',\n 'd2',\n 'd3a_1',\n 'd3a_2',\n 'd3a_3',\n 'd3a_4',\n 'd3a_5',\n 'd3a_6',\n 'd3a_7',\n 'd3a_8',\n 'd3a_9',\n 'd3a_10',\n 'd3a_11',\n 'd3a_12',\n 'd3a_13',\n 'd3a_14',\n 'd3a_15',\n 'd3a_16',\n 'd3a_17',\n 'd3a_18',\n 'd3a_19',\n 'd3a_20',\n 'd3a_21',\n 'd3a_22',\n 'd3a_23',\n 'd3a_24',\n 'd3a_25',\n 'd3a_26',\n 'd3a_27',\n 'd3a_28',\n 'd3a_98',\n 'd3a_99',\n 'd3b',\n 'd4',\n 'd4r1',\n 'd4r2',\n 'd5',\n 'd5r',\n 'd13',\n 'd18',\n 'd20',\n 'd18_d20',\n 'd22',\n 'd22r',\n 'd12be',\n 'd12be_r',\n 'd12at',\n 'd12at_r',\n 'd12bg',\n 'd12cy',\n 'd12cz',\n 'd12dk',\n 'd12ee',\n 'd12de',\n 'd12gr',\n 'd12gr_r',\n 'd12es',\n 'd12es_r',\n 'd12fi',\n 'd12fr',\n 'd12fr_r',\n 'd12gb',\n 'd12hu',\n 'd12ie',\n 'd12it',\n 'd12it_r',\n 'd12lt',\n 'd12lu',\n 'd12lv',\n 'd12nl',\n 'd12nl_r',\n 'd12pl',\n 'd12pl_r',\n 'd12pt',\n 'd12ro',\n 'd12se',\n 'd12si',\n 'd12sk',\n 'd12mt',\n 'd12hr',\n 'eu6',\n 'eu9',\n 'eu10',\n 'eu12_2',\n 'eu_nms3',\n 'eu15',\n 'eu_nms10',\n 'eu25',\n 'eu_nms12',\n 'eu27',\n 'eu_nms13',\n 'eu28',\n 'euroz13',\n 'euroz15',\n 'euronz15',\n 'euroz16',\n 'euronz16',\n 'euroz17',\n 'euronz17',\n 'euroz18',\n 'euronz18',\n 'euronzms',\n 'euronznm',\n 'euroz19',\n 'euronz19',\n 'w1',\n 'w5',\n 'w6',\n 'w7',\n 'w9',\n 'w10',\n 'w11',\n 'w13',\n 'w14',\n 'w24',\n 'w22',\n 'w94',\n 'w23',\n 'w29',\n 'w30',\n 'w81',\n 'w82',\n 'w84',\n 'w89',\n 'w90',\n 'w95',\n 'w96',\n 'w97',\n 'w83',\n 'w98',\n 'wex']"
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
      },
-     "execution_count": 13,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Spalten auslesen bessere Lesbarkeit\n",
-    "list(data.columns)"
+    "pl2 = Answers_q3.plot.pie(title='Beachten des Verfallsdatums beim Kauf',ylabel='')"
    ],
    "metadata": {
     "collapsed": false,
@@ -123,20 +768,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
+   "outputs": [],
+   "source": [
+    "fig2 = pl2.get_figure()\n",
+    "fig2.savefig('verfallsdatum_beachten_deutschland.pdf', dpi=150, facecolor='white', bbox_inches = \"tight\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Unable to parse string \"15 years\" at position 89",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
+      "\u001B[1;32mpandas\\_libs\\lib.pyx\u001B[0m in \u001B[0;36mpandas._libs.lib.maybe_convert_numeric\u001B[1;34m()\u001B[0m\n",
+      "\u001B[1;31mValueError\u001B[0m: Unable to parse string \"15 years\"",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
+      "\u001B[1;32m<ipython-input-34-303cf6c89804>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mpd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mto_numeric\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'age'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
+      "\u001B[1;32m~\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages\\pandas\\core\\tools\\numeric.py\u001B[0m in \u001B[0;36mto_numeric\u001B[1;34m(arg, errors, downcast)\u001B[0m\n\u001B[0;32m    150\u001B[0m         \u001B[0mcoerce_numeric\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0merrors\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32min\u001B[0m \u001B[1;33m(\u001B[0m\u001B[1;34m\"ignore\"\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m\"raise\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    151\u001B[0m         \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 152\u001B[1;33m             values = lib.maybe_convert_numeric(\n\u001B[0m\u001B[0;32m    153\u001B[0m                 \u001B[0mvalues\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mset\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcoerce_numeric\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mcoerce_numeric\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    154\u001B[0m             )\n",
+      "\u001B[1;32mpandas\\_libs\\lib.pyx\u001B[0m in \u001B[0;36mpandas._libs.lib.maybe_convert_numeric\u001B[1;34m()\u001B[0m\n",
+      "\u001B[1;31mValueError\u001B[0m: Unable to parse string \"15 years\" at position 89"
+     ]
+    }
+   ],
+   "source": [
+    "pd.to_numeric(data['age'])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
    "outputs": [
     {
      "data": {
-      "text/plain": "'BE - Belgium'"
+      "text/plain": "[64,\n 87,\n 73,\n 55,\n 74,\n 21,\n 76,\n 79,\n 81,\n 59,\n 52,\n 66,\n 51,\n 63,\n 72,\n 27,\n 75,\n 68,\n 26,\n 36,\n 82,\n 61,\n 57,\n 39,\n 20,\n 60,\n 70,\n 47,\n 77,\n 40,\n 62,\n 17,\n 78,\n 19,\n 50,\n 24,\n 58,\n 41,\n 84,\n 23,\n 65,\n 80,\n 48,\n 18,\n 33,\n 54,\n 38,\n 46,\n 34,\n 43,\n '15 years',\n 69,\n 45,\n 49,\n 32,\n 30,\n 93,\n 31,\n 35,\n 67,\n 28,\n 25,\n 53,\n 86,\n 22,\n 37,\n 44,\n 56,\n 42,\n 83,\n 71,\n 16,\n 29,\n 94,\n 85,\n 88,\n 90,\n 91,\n 92,\n 89,\n 97,\n 95,\n '98 years',\n 96]"
      },
-     "execution_count": 14,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Bestimmten Wert auslesen\n",
-    "data.loc[2, 'country']"
+    "list(data['age'].unique())"
    ],
    "metadata": {
     "collapsed": false,
@@ -147,20 +838,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 38,
    "outputs": [
     {
      "data": {
-      "text/plain": "'BE - Belgium'"
+      "text/plain": "0        64.0\n1        87.0\n2        73.0\n3        55.0\n4        74.0\n         ... \n26596    42.0\n26597    37.0\n26598    61.0\n26599    57.0\n26600    60.0\nName: age, Length: 26601, dtype: float64"
      },
-     "execution_count": 15,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Bestimmten Wert mit numerischer Notation auslesen\n",
-    "data.iloc[2,8]"
+    "pd.to_numeric(data['age'], errors='coerce')"
    ],
    "metadata": {
     "collapsed": false,
@@ -171,20 +861,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 39,
+   "outputs": [],
+   "source": [
+    "data['age_number'] = pd.to_numeric(data['age'], errors='coerce')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "outputs": [
     {
      "data": {
-      "text/plain": "studyno    category\ndoi          object\nversion      object\nedition    category\nsurvey     category\n             ...   \nw96         float64\nw97         float64\nw83         float64\nw98         float64\nwex         float64\nLength: 165, dtype: object"
+      "text/plain": "0        64.0\n1        87.0\n2        73.0\n3        55.0\n4        74.0\n         ... \n26596    42.0\n26597    37.0\n26598    61.0\n26599    57.0\n26600    60.0\nName: age_number, Length: 26601, dtype: float64"
      },
-     "execution_count": 16,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Datentypen der Spalten auslesen\n",
-    "data.dtypes"
+    "data['age_number']"
    ],
    "metadata": {
     "collapsed": false,
@@ -195,21 +898,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 43,
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'seaborn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
+      "\u001B[1;32m<ipython-input-43-d1038722f2c5>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[1;32mimport\u001B[0m \u001B[0mseaborn\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0msns\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      2\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[0msns\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdisplot\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'age_number'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'seaborn'"
+     ]
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.displot(data, x='age_number')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting seaborn\n",
+      "  Downloading seaborn-0.11.2-py3-none-any.whl (292 kB)\n",
+      "Requirement already satisfied: numpy>=1.15 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from seaborn) (1.19.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from seaborn) (1.1.3)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from seaborn) (3.3.1)\n",
+      "Requirement already satisfied: scipy>=1.0 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from seaborn) (1.5.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from pandas>=0.23->seaborn) (2020.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from pandas>=0.23->seaborn) (2.8.1)\n",
+      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2020.6.20)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.2.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from matplotlib>=2.2->seaborn) (8.0.0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\daves\\anaconda3\\envs\\biz_analytics_a\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.15.0)\n",
+      "Installing collected packages: seaborn\n",
+      "Successfully installed seaborn-0.11.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
    "outputs": [
     {
      "data": {
-      "text/plain": "                   studyno                  doi             version  \\\n25173  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n18174  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n1849   GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n15540  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n24356  GESIS STUDY ID 6647  doi:10.4232/1.12515  1.0.0 (2016-04-21)   \n\n               edition                                    survey  caseid  \\\n25173  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   25174   \n18174  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   18175   \n1849   ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)    1850   \n15540  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   15541   \n24356  ARCHIVE RELEASE  Flash Eurobarometer 425 (September 2015)   24357   \n\n         uniqid            tnscntry                 country isocntry  ...  \\\n25173  42025174           SLOVENIJA           SI - Slovenia       SI  ...   \n18174  35018175        MAGYARORSZAG            HU - Hungary       HU  ...   \n1849    2001850             DANMARK            DK - Denmark       DK  ...   \n15540  32015541              KYPROS  CY - Cyprus (Republic)       CY  ...   \n24356  41024357  SLOVENSKA REPUBLIC           SK - Slovakia       SK  ...   \n\n            w82       w84       w89       w90       w95       w96       w97  \\\n25173  0.000000  0.000000  0.257511  0.000000  0.272616  0.000000  0.286039   \n18174  1.638508  1.440078  0.000000  1.607205  0.000000  1.475640  0.000000   \n1849   0.219173  0.000000  0.000000  0.214986  0.000000  0.197387  0.000000   \n15540  0.000000  0.000000  0.067801  0.000000  0.071778  0.000000  0.075313   \n24356  0.000000  0.000000  0.161409  0.000000  0.170877  0.000000  0.179291   \n\n            w83       w98           wex  \n25173  0.000000  0.000000   4398.215820  \n18174  1.237390  1.354887  21380.603516  \n1849   0.165518  0.181235   2859.951904  \n15540  0.000000  0.000000   1158.028076  \n24356  0.000000  0.000000   2756.826660  \n\n[5 rows x 165 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>studyno</th>\n      <th>doi</th>\n      <th>version</th>\n      <th>edition</th>\n      <th>survey</th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>25173</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>25174</td>\n      <td>42025174</td>\n      <td>SLOVENIJA</td>\n      <td>SI - Slovenia</td>\n      <td>SI</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.257511</td>\n      <td>0.000000</td>\n      <td>0.272616</td>\n      <td>0.000000</td>\n      <td>0.286039</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4398.215820</td>\n    </tr>\n    <tr>\n      <th>18174</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>18175</td>\n      <td>35018175</td>\n      <td>MAGYARORSZAG</td>\n      <td>HU - Hungary</td>\n      <td>HU</td>\n      <td>...</td>\n      <td>1.638508</td>\n      <td>1.440078</td>\n      <td>0.000000</td>\n      <td>1.607205</td>\n      <td>0.000000</td>\n      <td>1.475640</td>\n      <td>0.000000</td>\n      <td>1.237390</td>\n      <td>1.354887</td>\n      <td>21380.603516</td>\n    </tr>\n    <tr>\n      <th>1849</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>1850</td>\n      <td>2001850</td>\n      <td>DANMARK</td>\n      <td>DK - Denmark</td>\n      <td>DK</td>\n      <td>...</td>\n      <td>0.219173</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.214986</td>\n      <td>0.000000</td>\n      <td>0.197387</td>\n      <td>0.000000</td>\n      <td>0.165518</td>\n      <td>0.181235</td>\n      <td>2859.951904</td>\n    </tr>\n    <tr>\n      <th>15540</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>15541</td>\n      <td>32015541</td>\n      <td>KYPROS</td>\n      <td>CY - Cyprus (Republic)</td>\n      <td>CY</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.067801</td>\n      <td>0.000000</td>\n      <td>0.071778</td>\n      <td>0.000000</td>\n      <td>0.075313</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1158.028076</td>\n    </tr>\n    <tr>\n      <th>24356</th>\n      <td>GESIS STUDY ID 6647</td>\n      <td>doi:10.4232/1.12515</td>\n      <td>1.0.0 (2016-04-21)</td>\n      <td>ARCHIVE RELEASE</td>\n      <td>Flash Eurobarometer 425 (September 2015)</td>\n      <td>24357</td>\n      <td>41024357</td>\n      <td>SLOVENSKA REPUBLIC</td>\n      <td>SK - Slovakia</td>\n      <td>SK</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.161409</td>\n      <td>0.000000</td>\n      <td>0.170877</td>\n      <td>0.000000</td>\n      <td>0.179291</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2756.826660</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 165 columns</p>\n</div>"
+      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x2353a3be280>"
      },
-     "execution_count": 17,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 360x360 with 1 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# 5 Zeilen zufällig aus dem DataFrame auswählen\n",
-    "data.sample(5)"
+    "import seaborn as sns\n",
+    "\n",
+    "sns.displot(data, x='age_number')"
    ],
    "metadata": {
     "collapsed": false,
@@ -220,23 +998,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 47,
    "outputs": [
     {
      "data": {
-      "text/plain": "       caseid    uniqid  tnscntry              country isocntry  \\\n7609     7610   9007610    ITALIA           IT - Italy       IT   \n4644     4645   6004645     SUOMI         FI - Finland       FI   \n20058   20059  37020059   LIETUVA       LT - Lithuania       LT   \n13868   13869  15013869        UK  GB - United Kingdom       GB   \n11049   11050  13011050  PORTUGAL        PT - Portugal       PT   \n\n                       mode           q1_1                q1_2  \\\n7609      Mobile phone line  Not mentioned       Not mentioned   \n4644      Mobile phone line  Not mentioned  Food manufacturers   \n20058  Fixed telephone line        Farmers       Not mentioned   \n13868  Fixed telephone line        Farmers  Food manufacturers   \n11049  Fixed telephone line        Farmers  Food manufacturers   \n\n                      q1_3                                      q1_4  ...  \\\n7609   Shops and retailers  no  Hospitality and food service sectors  ...   \n4644   Shops and retailers  no  Hospitality and food service sectors  ...   \n20058        Not mentioned                             Not mentioned  ...   \n13868  Shops and retailers  no  Hospitality and food service sectors  ...   \n11049  Shops and retailers  no  Hospitality and food service sectors  ...   \n\n            w82  w84       w89       w90       w95       w96       w97  \\\n7609   0.000000  0.0  1.939347  0.000000  2.053107  0.000000  2.154197   \n4644   0.000000  0.0  0.200278  0.000000  0.212026  0.000000  0.222466   \n20058  0.557824  0.0  0.000000  0.547167  0.000000  0.502376  0.473388   \n13868  7.124311  0.0  0.000000  6.988205  0.000000  6.416151  0.000000   \n11049  0.000000  0.0  0.340113  0.000000  0.360064  0.000000  0.377793   \n\n            w83       w98           wex  \n7609   0.000000  0.000000  33123.546875  \n4644   0.000000  0.000000   3420.700439  \n20058  0.000000  0.000000   7278.943848  \n13868  5.380231  5.891114  92963.890625  \n11049  0.000000  0.000000   5809.047363  \n\n[5 rows x 160 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7609</th>\n      <td>7610</td>\n      <td>9007610</td>\n      <td>ITALIA</td>\n      <td>IT - Italy</td>\n      <td>IT</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.939347</td>\n      <td>0.000000</td>\n      <td>2.053107</td>\n      <td>0.000000</td>\n      <td>2.154197</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>33123.546875</td>\n    </tr>\n    <tr>\n      <th>4644</th>\n      <td>4645</td>\n      <td>6004645</td>\n      <td>SUOMI</td>\n      <td>FI - Finland</td>\n      <td>FI</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.200278</td>\n      <td>0.000000</td>\n      <td>0.212026</td>\n      <td>0.000000</td>\n      <td>0.222466</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>3420.700439</td>\n    </tr>\n    <tr>\n      <th>20058</th>\n      <td>20059</td>\n      <td>37020059</td>\n      <td>LIETUVA</td>\n      <td>LT - Lithuania</td>\n      <td>LT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.557824</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.547167</td>\n      <td>0.000000</td>\n      <td>0.502376</td>\n      <td>0.473388</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>7278.943848</td>\n    </tr>\n    <tr>\n      <th>13868</th>\n      <td>13869</td>\n      <td>15013869</td>\n      <td>UK</td>\n      <td>GB - United Kingdom</td>\n      <td>GB</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>7.124311</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>6.988205</td>\n      <td>0.000000</td>\n      <td>6.416151</td>\n      <td>0.000000</td>\n      <td>5.380231</td>\n      <td>5.891114</td>\n      <td>92963.890625</td>\n    </tr>\n    <tr>\n      <th>11049</th>\n      <td>11050</td>\n      <td>13011050</td>\n      <td>PORTUGAL</td>\n      <td>PT - Portugal</td>\n      <td>PT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.340113</td>\n      <td>0.000000</td>\n      <td>0.360064</td>\n      <td>0.000000</td>\n      <td>0.377793</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5809.047363</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 160 columns</p>\n</div>"
+      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x2353da0fb80>"
      },
-     "execution_count": 18,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 360x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Unerwünschte Spalten definieren und löschen\n",
-    "cols_to_delete = ['studyno', 'doi', 'version', 'edition', 'survey']\n",
-    "data = data.drop(columns=cols_to_delete)\n",
-    "data.sample(5)"
+    "sns.displot(data, x='age_number', binwidth = 5)"
    ],
    "metadata": {
     "collapsed": false,
@@ -247,25 +1031,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 48,
    "outputs": [
     {
      "data": {
-      "text/plain": "       caseid    uniqid    tnscntry              country isocntry  \\\n8221     8222  10008222  LUXEMBOURG      LU - Luxembourg       LU   \n6698     6699   8006699     IRELAND         IE - Ireland       IE   \n25495   25496  42025496   SLOVENIJA        SI - Slovenia       SI   \n11143   11144  13011144    PORTUGAL        PT - Portugal       PT   \n14413   14414  15014414          UK  GB - United Kingdom       GB   \n\n                       mode           q1_1                q1_2  \\\n8221   Fixed telephone line  Not mentioned       Not mentioned   \n6698   Fixed telephone line        Farmers  Food manufacturers   \n25495  Fixed telephone line  Not mentioned       Not mentioned   \n11143  Fixed telephone line        Farmers  Food manufacturers   \n14413  Fixed telephone line        Farmers  Food manufacturers   \n\n                      q1_3                                      q1_4  ...  \\\n8221   Shops and retailers  no  Hospitality and food service sectors  ...   \n6698   Shops and retailers  no  Hospitality and food service sectors  ...   \n25495  Shops and retailers  no  Hospitality and food service sectors  ...   \n11143  Shops and retailers  no  Hospitality and food service sectors  ...   \n14413  Shops and retailers  no  Hospitality and food service sectors  ...   \n\n            w82  w84       w89       w90       w95      w96       w97  \\\n8221   0.000000  0.0  0.028708  0.000000  0.030392  0.00000  0.031888   \n6698   0.000000  0.0  0.111559  0.000000  0.118103  0.00000  0.123918   \n25495  0.000000  0.0  0.033513  0.000000  0.035478  0.00000  0.037225   \n11143  0.000000  0.0  0.407588  0.000000  0.431497  0.00000  0.452743   \n14413  8.376597  0.0  0.000000  8.216568  0.000000  7.54396  0.000000   \n\n            w83       w98            wex  \n8221   0.000000  0.000000     490.321014  \n6698   0.000000  0.000000    1905.406128  \n25495  0.000000  0.000000     572.386169  \n11143  0.000000  0.000000    6961.501953  \n14413  6.325949  6.926633  109304.757812  \n\n[5 rows x 160 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8221</th>\n      <td>8222</td>\n      <td>10008222</td>\n      <td>LUXEMBOURG</td>\n      <td>LU - Luxembourg</td>\n      <td>LU</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.028708</td>\n      <td>0.000000</td>\n      <td>0.030392</td>\n      <td>0.00000</td>\n      <td>0.031888</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>490.321014</td>\n    </tr>\n    <tr>\n      <th>6698</th>\n      <td>6699</td>\n      <td>8006699</td>\n      <td>IRELAND</td>\n      <td>IE - Ireland</td>\n      <td>IE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.111559</td>\n      <td>0.000000</td>\n      <td>0.118103</td>\n      <td>0.00000</td>\n      <td>0.123918</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1905.406128</td>\n    </tr>\n    <tr>\n      <th>25495</th>\n      <td>25496</td>\n      <td>42025496</td>\n      <td>SLOVENIJA</td>\n      <td>SI - Slovenia</td>\n      <td>SI</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.033513</td>\n      <td>0.000000</td>\n      <td>0.035478</td>\n      <td>0.00000</td>\n      <td>0.037225</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>572.386169</td>\n    </tr>\n    <tr>\n      <th>11143</th>\n      <td>11144</td>\n      <td>13011144</td>\n      <td>PORTUGAL</td>\n      <td>PT - Portugal</td>\n      <td>PT</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.407588</td>\n      <td>0.000000</td>\n      <td>0.431497</td>\n      <td>0.00000</td>\n      <td>0.452743</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6961.501953</td>\n    </tr>\n    <tr>\n      <th>14413</th>\n      <td>14414</td>\n      <td>15014414</td>\n      <td>UK</td>\n      <td>GB - United Kingdom</td>\n      <td>GB</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>8.376597</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>8.216568</td>\n      <td>0.000000</td>\n      <td>7.54396</td>\n      <td>0.000000</td>\n      <td>6.325949</td>\n      <td>6.926633</td>\n      <td>109304.757812</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 160 columns</p>\n</div>"
+      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x2353a12a460>"
      },
-     "execution_count": 19,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 561.5x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "cols_to_rename = {\n",
-    "    'd1': 'age',\n",
-    "    'd2': 'gender',\n",
-    "}\n",
-    "data = data.rename(columns=cols_to_rename)\n",
-    "data.sample(5)"
+    "sns.displot(data, x='age_number', binwidth = 5, hue='q6')"
    ],
    "metadata": {
     "collapsed": false,
@@ -276,20 +1064,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 51,
    "outputs": [
     {
      "data": {
-      "text/plain": "       caseid    uniqid      tnscntry               country isocntry  \\\n8951     8952  11008952     NEDERLAND  NL - The Netherlands       NL   \n17880   17881  34017881         EESTI          EE - Estonia       EE   \n5910     5911   7005911        FRANCE           FR - France       FR   \n15030   15031  31015031     BALGARIJA         BG - Bulgaria       BG   \n12622   12623   3012623   DEUTSCHLAND          DE - Germany       DE   \n3986     3987   5003987        ESPANA             ES -Spain       ES   \n9921     9922  12009922    ÖSTERREICH          AT - Austria       AT   \n467       468   1000468      BELGIQUE          BE - Belgium       BE   \n2577     2578   4002578        ELLADA           GR - Greece       GR   \n18088   18089  35018089  MAGYARORSZAG          HU - Hungary       HU   \n\n                       mode           q1_1                q1_2  \\\n8951   Fixed telephone line  Not mentioned  Food manufacturers   \n17880  Fixed telephone line        Farmers  Food manufacturers   \n5910   Fixed telephone line        Farmers  Food manufacturers   \n15030  Fixed telephone line  Not mentioned       Not mentioned   \n12622  Fixed telephone line  Not mentioned  Food manufacturers   \n3986   Fixed telephone line  Not mentioned  Food manufacturers   \n9921   Fixed telephone line  Not mentioned       Not mentioned   \n467       Mobile phone line  Not mentioned  Food manufacturers   \n2577   Fixed telephone line  Not mentioned       Not mentioned   \n18088     Mobile phone line  Not mentioned       Not mentioned   \n\n                      q1_3                                      q1_4  ...  \\\n8951         Not mentioned                             Not mentioned  ...   \n17880  Shops and retailers  no  Hospitality and food service sectors  ...   \n5910   Shops and retailers  no  Hospitality and food service sectors  ...   \n15030  Shops and retailers  no  Hospitality and food service sectors  ...   \n12622  Shops and retailers  no  Hospitality and food service sectors  ...   \n3986         Not mentioned  no  Hospitality and food service sectors  ...   \n9921   Shops and retailers  no  Hospitality and food service sectors  ...   \n467    Shops and retailers  no  Hospitality and food service sectors  ...   \n2577         Not mentioned                             Not mentioned  ...   \n18088        Not mentioned                             Not mentioned  ...   \n\n            w82       w84       w89       w90       w95       w96       w97  \\\n8951   0.000000  0.000000  2.382976  0.000000  2.522759  0.000000  2.646974   \n17880  0.029498  0.000000  0.022536  0.000000  0.023858  0.000000  0.025033   \n5910   0.000000  0.000000  5.377200  0.000000  5.692622  0.000000  5.972913   \n15030  0.299301  0.263054  0.000000  0.293583  0.000000  0.269550  0.000000   \n12622  0.000000  0.000000  2.660488  0.000000  2.816550  0.000000  2.955230   \n3986   0.000000  0.000000  2.369598  0.000000  2.508597  0.000000  2.632114   \n9921   0.000000  0.000000  0.353662  0.000000  0.374407  0.000000  0.392842   \n467    0.000000  0.000000  0.843141  0.000000  0.892599  0.000000  0.936549   \n2577   0.000000  0.000000  0.570128  0.000000  0.603572  0.000000  0.633290   \n18088  0.276740  0.243226  0.000000  0.271453  0.000000  0.249232  0.000000   \n\n            w83       w98           wex  \n8951   0.000000  0.000000  40700.621094  \n17880  0.000000  0.000000    384.912231  \n5910   0.000000  0.000000  91841.210938  \n15030  0.226030  0.247493   3905.526611  \n12622  0.000000  0.000000  45440.453125  \n3986   0.000000  0.000000  40472.132812  \n9921   0.000000  0.000000   6040.449219  \n467    0.000000  0.000000  14400.638672  \n2577   0.000000  0.000000   9737.649414  \n18088  0.208992  0.228837   3611.134521  \n\n[10 rows x 160 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>caseid</th>\n      <th>uniqid</th>\n      <th>tnscntry</th>\n      <th>country</th>\n      <th>isocntry</th>\n      <th>mode</th>\n      <th>q1_1</th>\n      <th>q1_2</th>\n      <th>q1_3</th>\n      <th>q1_4</th>\n      <th>...</th>\n      <th>w82</th>\n      <th>w84</th>\n      <th>w89</th>\n      <th>w90</th>\n      <th>w95</th>\n      <th>w96</th>\n      <th>w97</th>\n      <th>w83</th>\n      <th>w98</th>\n      <th>wex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8951</th>\n      <td>8952</td>\n      <td>11008952</td>\n      <td>NEDERLAND</td>\n      <td>NL - The Netherlands</td>\n      <td>NL</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.382976</td>\n      <td>0.000000</td>\n      <td>2.522759</td>\n      <td>0.000000</td>\n      <td>2.646974</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40700.621094</td>\n    </tr>\n    <tr>\n      <th>17880</th>\n      <td>17881</td>\n      <td>34017881</td>\n      <td>EESTI</td>\n      <td>EE - Estonia</td>\n      <td>EE</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.029498</td>\n      <td>0.000000</td>\n      <td>0.022536</td>\n      <td>0.000000</td>\n      <td>0.023858</td>\n      <td>0.000000</td>\n      <td>0.025033</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>384.912231</td>\n    </tr>\n    <tr>\n      <th>5910</th>\n      <td>5911</td>\n      <td>7005911</td>\n      <td>FRANCE</td>\n      <td>FR - France</td>\n      <td>FR</td>\n      <td>Fixed telephone line</td>\n      <td>Farmers</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5.377200</td>\n      <td>0.000000</td>\n      <td>5.692622</td>\n      <td>0.000000</td>\n      <td>5.972913</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>91841.210938</td>\n    </tr>\n    <tr>\n      <th>15030</th>\n      <td>15031</td>\n      <td>31015031</td>\n      <td>BALGARIJA</td>\n      <td>BG - Bulgaria</td>\n      <td>BG</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.299301</td>\n      <td>0.263054</td>\n      <td>0.000000</td>\n      <td>0.293583</td>\n      <td>0.000000</td>\n      <td>0.269550</td>\n      <td>0.000000</td>\n      <td>0.226030</td>\n      <td>0.247493</td>\n      <td>3905.526611</td>\n    </tr>\n    <tr>\n      <th>12622</th>\n      <td>12623</td>\n      <td>3012623</td>\n      <td>DEUTSCHLAND</td>\n      <td>DE - Germany</td>\n      <td>DE</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.660488</td>\n      <td>0.000000</td>\n      <td>2.816550</td>\n      <td>0.000000</td>\n      <td>2.955230</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45440.453125</td>\n    </tr>\n    <tr>\n      <th>3986</th>\n      <td>3987</td>\n      <td>5003987</td>\n      <td>ESPANA</td>\n      <td>ES -Spain</td>\n      <td>ES</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Not mentioned</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.369598</td>\n      <td>0.000000</td>\n      <td>2.508597</td>\n      <td>0.000000</td>\n      <td>2.632114</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40472.132812</td>\n    </tr>\n    <tr>\n      <th>9921</th>\n      <td>9922</td>\n      <td>12009922</td>\n      <td>ÖSTERREICH</td>\n      <td>AT - Austria</td>\n      <td>AT</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.353662</td>\n      <td>0.000000</td>\n      <td>0.374407</td>\n      <td>0.000000</td>\n      <td>0.392842</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>6040.449219</td>\n    </tr>\n    <tr>\n      <th>467</th>\n      <td>468</td>\n      <td>1000468</td>\n      <td>BELGIQUE</td>\n      <td>BE - Belgium</td>\n      <td>BE</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Food manufacturers</td>\n      <td>Shops and retailers</td>\n      <td>no  Hospitality and food service sectors</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.843141</td>\n      <td>0.000000</td>\n      <td>0.892599</td>\n      <td>0.000000</td>\n      <td>0.936549</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>14400.638672</td>\n    </tr>\n    <tr>\n      <th>2577</th>\n      <td>2578</td>\n      <td>4002578</td>\n      <td>ELLADA</td>\n      <td>GR - Greece</td>\n      <td>GR</td>\n      <td>Fixed telephone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.570128</td>\n      <td>0.000000</td>\n      <td>0.603572</td>\n      <td>0.000000</td>\n      <td>0.633290</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>9737.649414</td>\n    </tr>\n    <tr>\n      <th>18088</th>\n      <td>18089</td>\n      <td>35018089</td>\n      <td>MAGYARORSZAG</td>\n      <td>HU - Hungary</td>\n      <td>HU</td>\n      <td>Mobile phone line</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>Not mentioned</td>\n      <td>...</td>\n      <td>0.276740</td>\n      <td>0.243226</td>\n      <td>0.000000</td>\n      <td>0.271453</td>\n      <td>0.000000</td>\n      <td>0.249232</td>\n      <td>0.000000</td>\n      <td>0.208992</td>\n      <td>0.228837</td>\n      <td>3611.134521</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 160 columns</p>\n</div>"
+      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x2353d9238b0>"
      },
-     "execution_count": 20,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 561.5x360 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "data.sample(10)"
+    "sns.displot(data, x='age_number', binwidth = 5, hue='q6', element='step')\\\n",
+    "    .set(title='Welche Altersklasse würde MHDs auf schwer verderblichen Lebensmitteln vermissen?', xlabel='Alter', ylabel='Anzahl von Antworten')"
    ],
    "metadata": {
     "collapsed": false,
@@ -320,16 +1118,16 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
\ No newline at end of file
diff --git a/notebooks/data_penguin_clustering.ipynb b/notebooks/data_penguin_clustering.ipynb
new file mode 100644
index 0000000..4316126
--- /dev/null
+++ b/notebooks/data_penguin_clustering.ipynb
@@ -0,0 +1,1538 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Clustering Penguin Data and building prediction model."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Daten einlesen"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n92      Adelie   Dream            34.0           17.1              185.0   \n216     Gentoo  Biscoe            45.8           14.2              219.0   \n110     Adelie  Biscoe            38.1           16.5              198.0   \n310  Chinstrap   Dream            49.7           18.6              195.0   \n175     Gentoo  Biscoe            46.3           15.8              215.0   \n\n     body_mass_g     sex  year  \n92        3400.0  female  2008  \n216       4700.0  female  2008  \n110       3825.0  female  2009  \n310       3600.0    male  2008  \n175       5050.0    male  2007  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>species</th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>92</th>\n      <td>Adelie</td>\n      <td>Dream</td>\n      <td>34.0</td>\n      <td>17.1</td>\n      <td>185.0</td>\n      <td>3400.0</td>\n      <td>female</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>216</th>\n      <td>Gentoo</td>\n      <td>Biscoe</td>\n      <td>45.8</td>\n      <td>14.2</td>\n      <td>219.0</td>\n      <td>4700.0</td>\n      <td>female</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>110</th>\n      <td>Adelie</td>\n      <td>Biscoe</td>\n      <td>38.1</td>\n      <td>16.5</td>\n      <td>198.0</td>\n      <td>3825.0</td>\n      <td>female</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>310</th>\n      <td>Chinstrap</td>\n      <td>Dream</td>\n      <td>49.7</td>\n      <td>18.6</td>\n      <td>195.0</td>\n      <td>3600.0</td>\n      <td>male</td>\n      <td>2008</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>Gentoo</td>\n      <td>Biscoe</td>\n      <td>46.3</td>\n      <td>15.8</td>\n      <td>215.0</td>\n      <td>5050.0</td>\n      <td>male</td>\n      <td>2007</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data = pd.read_csv('../data/external/penguins.csv')\n",
+    "data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Kategorische Variablen durch numerische ersetzen"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array(['Adelie', 'Gentoo', 'Chinstrap'], dtype=object)"
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['species'].unique()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "outputs": [],
+   "source": [
+    "species_replace_num = {\n",
+    "    'Adelie':0,\n",
+    "    'Gentoo':1,\n",
+    "    'Chinstrap':2\n",
+    "}\n",
+    "data['species'] = data['species'].replace(species_replace_num)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array(['Torgersen', 'Biscoe', 'Dream'], dtype=object)"
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.island.unique()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "outputs": [],
+   "source": [
+    "island_replace_num = {\n",
+    "    'Torgersen':0,\n",
+    "    'Biscoe':1,\n",
+    "    'Dream':2,\n",
+    "}\n",
+    "data['island'] = data.island.replace(island_replace_num)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array(['male', 'female', nan], dtype=object)"
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.sex.unique()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "outputs": [],
+   "source": [
+    "age_replace_num = {\n",
+    "    'male':0,\n",
+    "    'female':1\n",
+    "}\n",
+    "data['sex'] = data.sex.replace(age_replace_num)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "     species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n0          0       0            39.1           18.7              181.0   \n1          0       0            39.5           17.4              186.0   \n2          0       0            40.3           18.0              195.0   \n3          0       0             NaN            NaN                NaN   \n4          0       0            36.7           19.3              193.0   \n..       ...     ...             ...            ...                ...   \n339        2       2            55.8           19.8              207.0   \n340        2       2            43.5           18.1              202.0   \n341        2       2            49.6           18.2              193.0   \n342        2       2            50.8           19.0              210.0   \n343        2       2            50.2           18.7              198.0   \n\n     body_mass_g  sex  year  \n0         3750.0  0.0  2007  \n1         3800.0  1.0  2007  \n2         3250.0  1.0  2007  \n3            NaN  NaN  2007  \n4         3450.0  1.0  2007  \n..           ...  ...   ...  \n339       4000.0  0.0  2009  \n340       3400.0  1.0  2009  \n341       3775.0  0.0  2009  \n342       4100.0  0.0  2009  \n343       3775.0  1.0  2009  \n\n[344 rows x 8 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>species</th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>39.1</td>\n      <td>18.7</td>\n      <td>181.0</td>\n      <td>3750.0</td>\n      <td>0.0</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>39.5</td>\n      <td>17.4</td>\n      <td>186.0</td>\n      <td>3800.0</td>\n      <td>1.0</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>40.3</td>\n      <td>18.0</td>\n      <td>195.0</td>\n      <td>3250.0</td>\n      <td>1.0</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>36.7</td>\n      <td>19.3</td>\n      <td>193.0</td>\n      <td>3450.0</td>\n      <td>1.0</td>\n      <td>2007</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>339</th>\n      <td>2</td>\n      <td>2</td>\n      <td>55.8</td>\n      <td>19.8</td>\n      <td>207.0</td>\n      <td>4000.0</td>\n      <td>0.0</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>340</th>\n      <td>2</td>\n      <td>2</td>\n      <td>43.5</td>\n      <td>18.1</td>\n      <td>202.0</td>\n      <td>3400.0</td>\n      <td>1.0</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>341</th>\n      <td>2</td>\n      <td>2</td>\n      <td>49.6</td>\n      <td>18.2</td>\n      <td>193.0</td>\n      <td>3775.0</td>\n      <td>0.0</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>342</th>\n      <td>2</td>\n      <td>2</td>\n      <td>50.8</td>\n      <td>19.0</td>\n      <td>210.0</td>\n      <td>4100.0</td>\n      <td>0.0</td>\n      <td>2009</td>\n    </tr>\n    <tr>\n      <th>343</th>\n      <td>2</td>\n      <td>2</td>\n      <td>50.2</td>\n      <td>18.7</td>\n      <td>198.0</td>\n      <td>3775.0</td>\n      <td>1.0</td>\n      <td>2009</td>\n    </tr>\n  </tbody>\n</table>\n<p>344 rows × 8 columns</p>\n</div>"
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Datenbereinigung: Unbrauchbare Spalten droppen & unvollständige Daten entfernen."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "outputs": [],
+   "source": [
+    "data = data.drop(columns='year')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "species               0\nisland                0\nbill_length_mm        2\nbill_depth_mm         2\nflipper_length_mm     2\nbody_mass_g           2\nsex                  11\ndtype: int64"
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.9680232558139535"
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data.dropna())/len(data)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "species              0\nisland               0\nbill_length_mm       0\nbill_depth_mm        0\nflipper_length_mm    0\nbody_mass_g          0\nsex                  0\ndtype: int64"
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.dropna()\n",
+    "data.isna().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0"
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.duplicated().sum()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "outputs": [],
+   "source": [
+    "labels = data['species']\n",
+    "\n",
+    "# Lösche die Spalte aus dem Datensatz\n",
+    "data = data.drop(columns='species')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Korrelationsmatrix berechnen:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\nisland             1.000000        0.212038       0.189636          -0.162739   \nbill_length_mm     0.212038        1.000000      -0.228626           0.653096   \nbill_depth_mm      0.189636       -0.228626       1.000000          -0.577792   \nflipper_length_mm -0.162739        0.653096      -0.577792           1.000000   \nbody_mass_g       -0.201966        0.589451      -0.472016           0.872979   \nsex               -0.005834       -0.344078      -0.372673          -0.255169   \n\n                   body_mass_g       sex  \nisland               -0.201966 -0.005834  \nbill_length_mm        0.589451 -0.344078  \nbill_depth_mm        -0.472016 -0.372673  \nflipper_length_mm     0.872979 -0.255169  \nbody_mass_g           1.000000 -0.424987  \nsex                  -0.424987  1.000000  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>island</th>\n      <td>1.000000</td>\n      <td>0.212038</td>\n      <td>0.189636</td>\n      <td>-0.162739</td>\n      <td>-0.201966</td>\n      <td>-0.005834</td>\n    </tr>\n    <tr>\n      <th>bill_length_mm</th>\n      <td>0.212038</td>\n      <td>1.000000</td>\n      <td>-0.228626</td>\n      <td>0.653096</td>\n      <td>0.589451</td>\n      <td>-0.344078</td>\n    </tr>\n    <tr>\n      <th>bill_depth_mm</th>\n      <td>0.189636</td>\n      <td>-0.228626</td>\n      <td>1.000000</td>\n      <td>-0.577792</td>\n      <td>-0.472016</td>\n      <td>-0.372673</td>\n    </tr>\n    <tr>\n      <th>flipper_length_mm</th>\n      <td>-0.162739</td>\n      <td>0.653096</td>\n      <td>-0.577792</td>\n      <td>1.000000</td>\n      <td>0.872979</td>\n      <td>-0.255169</td>\n    </tr>\n    <tr>\n      <th>body_mass_g</th>\n      <td>-0.201966</td>\n      <td>0.589451</td>\n      <td>-0.472016</td>\n      <td>0.872979</td>\n      <td>1.000000</td>\n      <td>-0.424987</td>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <td>-0.005834</td>\n      <td>-0.344078</td>\n      <td>-0.372673</td>\n      <td>-0.255169</td>\n      <td>-0.424987</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr = data.corr()\n",
+    "corr"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                   island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\nisland               1.00            0.21           0.19              -0.16   \nbill_length_mm       0.21            1.00          -0.23               0.65   \nbill_depth_mm        0.19           -0.23           1.00              -0.58   \nflipper_length_mm   -0.16            0.65          -0.58               1.00   \nbody_mass_g         -0.20            0.59          -0.47               0.87   \nsex                 -0.01           -0.34          -0.37              -0.26   \n\n                   body_mass_g   sex  \nisland                   -0.20 -0.01  \nbill_length_mm            0.59 -0.34  \nbill_depth_mm            -0.47 -0.37  \nflipper_length_mm         0.87 -0.26  \nbody_mass_g               1.00 -0.42  \nsex                      -0.42  1.00  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>island</th>\n      <td>1.00</td>\n      <td>0.21</td>\n      <td>0.19</td>\n      <td>-0.16</td>\n      <td>-0.20</td>\n      <td>-0.01</td>\n    </tr>\n    <tr>\n      <th>bill_length_mm</th>\n      <td>0.21</td>\n      <td>1.00</td>\n      <td>-0.23</td>\n      <td>0.65</td>\n      <td>0.59</td>\n      <td>-0.34</td>\n    </tr>\n    <tr>\n      <th>bill_depth_mm</th>\n      <td>0.19</td>\n      <td>-0.23</td>\n      <td>1.00</td>\n      <td>-0.58</td>\n      <td>-0.47</td>\n      <td>-0.37</td>\n    </tr>\n    <tr>\n      <th>flipper_length_mm</th>\n      <td>-0.16</td>\n      <td>0.65</td>\n      <td>-0.58</td>\n      <td>1.00</td>\n      <td>0.87</td>\n      <td>-0.26</td>\n    </tr>\n    <tr>\n      <th>body_mass_g</th>\n      <td>-0.20</td>\n      <td>0.59</td>\n      <td>-0.47</td>\n      <td>0.87</td>\n      <td>1.00</td>\n      <td>-0.42</td>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <td>-0.01</td>\n      <td>-0.34</td>\n      <td>-0.37</td>\n      <td>-0.26</td>\n      <td>-0.42</td>\n      <td>1.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr.round(2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "ax = sns.heatmap(\n",
+    "    corr,\n",
+    "    vmin=-1, vmax=1, center=0,\n",
+    "    cmap='RdYlGn',\n",
+    "    square=True,\n",
+    "    annot=True\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Regression von Körpergewicht und Flipperlänge:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='body_mass_g', ylabel='flipper_length_mm'>"
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(data=data, x='body_mass_g', y='flipper_length_mm')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ergebnis: Körpergröße und Flipperlänge korrelieren positiv miteinander."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='body_mass_g', ylabel='island_cat'>"
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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4hnd7ETKZ3C11N2S/zRYTthz9CZXmcqSdXI3+HR8B4Hi/Nx/5EccL9sBXFYDhXV+GSql2OPbx/D04lLsJgAzNQuLRLCShwXU7en9vPbYcpZV5KNcVOt3v9BOrcTB3E+RQ4rqqFASoQy7UbSiH0ay31n25NXS/yZZbjpwd2bt3L0aPHu3JKbxazZGPRZihrSpFXvlJ6I3aJl6VtGgNZRCoPq2oqSpBifYcynWFDRqj9hGmu4827dEZNDBbTC7PV3Mkp6ly3rdHwlAkRHVBr8S7Eeofc0nrrM1g0sNg1ru8jpo+lYYy6I1a5JWdRGVVuU3dZZUFKK10fFaodt16owb55aegrSp1eb/NFhN0Ro11PuvanHxdZayE0VLlUn2AsDnSrl235fzpbkf7Xfv9XWkot1u3MzV9LDChylQJwHHdl1tDX+dkyy1HzvZOWXz88cdYuXIl/Pz83DGFVzJZjNDqSxHsF2n3o4EubQbAaNYjyDcCp4sOIStvBwLUIbgr5V+oNFYg2C8Sclnjfr5yNvfFynQFCPAJgVLhY20zmg2oslTU6autKoNCroCvKtDaJoQFZbpCBPmGQyF3/rLT6EugUqqhVvpb2ywWM8r1RTZ1tw7vgE6xN0FXVYFQ/2j8vPs9yGRyDLp2PKKDW7tUd7uYbiirzIdZmJHcvKfTtRnNBugM5Qj2i3Ta117d0cFtkNrmdhRrz6Jb3CCndd/U/gFkntuODi1ucDhPzXN++OwWHC/YjZySTLSOuAZ+PoEO667Pxfsd7BeBnol342zpUXSNG2h37tr73SfpXuzP/hvxUSlYvW8hKvRFiI+8zlp36/COWLHrXQgI9O/4CFqFJ9dZQ03dbWO64udd70NnrEBy8xsc7revKgB+qkDrEa+P0hd9ku7FniMb0bvWkb6j/b6h3T3YdWotWoUl21zhbG+/r2nRB9qqUqgUvjZXa9fUnRid6nS/a7+/W4Yl1an74v3WG7UwmY0I9A21tnWPvwOAHFVlcoQHNLep+2TBfnRudYvNGDpDBQREnSu4Hb2/7b3OXX1/1/c6t8fe+7umboOl0unjPc1e3Z7klnB+8skn67S1bt0aH3zwAV5++WV3TOF1LMKC1XsXoFibi6Rm1+OGtsPq9MkuzsTxgr1QK/0QoA4BUP0C+ePwl8gvP4lW4R3Rv+PDHpm7tp0nVuFgzkaE+EVjaJeJUMpVMJqrsHL3PFRUFcHvlBFd2tx6fs0ZWH/oKyjkCgzuPAHhgS0AABuzfsDxgt2ICmqNIdc9Ve98R/PSsenIj1Ar/TA0ZSICfcMAAOsOfY6zpUdt6i7XFyHz7DaYzAaYzh/tVP9FkV8nnB3VXazNRVbeDgghEB/ZGc1DEx2uzVq3vgjXtepvrdsRe3VXGipwKHcz9EYNmoXEI7l5r3rrPpi70foZa4vQtnXmqP2cRwbGAgCqTJWoNJTBzyfQbft9MGcjKvRFCPWPcbrfGWe34UzxIVQaylGhLwYAFGmzcbbsGPRGDaovWao+sizRnrMbzjV1G81V0BmrfwgsrDiNMl1Bnf0+mLsRpwr3w98nBHd1eQ5qlT8swoID2f+g3FK9v5FB9e/3kbw0nC46iLLKAsRHp9i+zi/a77zyEziatwsKuQJto1Pr1K03ahB3/rNoR/td+/3dLqZbvftdpivAr3s+hMlswE3J9yMusjMA4GThPpwq2g+VzA9Vxkqbuou1ufD1CbTWXVBxGr/tXwwhBG695nFr3fW+vy+quyHvb0d1O2Lv/V1Tt9FcheaFIda6LzdHdXuS08Ou5ORkdOjQwfrftddei5SUFHTo0AHdu3cHAAwePLjO426//XYolfxI2xGjSY9ibS4A4FzZcbt98spPABCoMlUiMaoLWoV3wPUJd6Gw4ky9j3PH3LXV9CnT5UNvqD6trq0qQ4W+qM4YeeUnIWCByWK0uWK4pk9BxRmYLMZ656tdd4n27IX2shN15ivW5MJoroKAgI/SH4lRXZDcvCcSolJcrrug/DTMFhMswoz8ivovCnNUtyP26i6rzD8fUBc/d67XXVvt57xZaFu0DGuPlNYDEBHYst66na3ZHftdpMlBz8S70Cq8A5Kb3WCt2yIsSGrWA22juzo8W1FTd5EmB93iBqFlaBLaRHSyu98mc/Xn65WGC+ts6H576nXujv2u/TrPKztZa77qvkahu2LqdqS+uqu/f9Lewy4LR3V7ktP0zMjIAFB9E5LU1FTceeedkMlk+P3337Fx40aPL9BbqVX+6Bo3CGeKDuHaVv3s9ukUexM0+hIE+oahQ4veuCa2LwBAIVcg69zOek9zXurctXWLG4Tdp9ahRVg76ym1UP9oXBt7M47lHkBq3O3WvsnNe6FYe7bOzRl6JNyBgzkbER91HZRyVb3z1a67ZVh7a3vPxLtwJC/N5ifwVhEdkRDVBTpDBTq36ocg3/AG150Y0xXnyo5XB0ZMj3rXVlN3XvlJm7odsVd3TEg82jfridLKPFzXqn+j6q6t9nOuUqiRU5KFCn0ROra4ET5KX7fu97myEw3e7+TmPZHcvCcswoIyXQFKtOfQpfWtTi9wrKm7XUx3HM7djJLKc4gJjkdCVBfojRqb/S6syIbJXIWIwJaIOH/2oKbuQ6e2o3v8EOu4iTFdkVd+os5+11f3xfud3LwXijS58FH6On2d1+x3ifZcnf2u0BUjyC+8zn5f/P6ueZ3rjRp0bHmjtT2ldfWpcZNWVafu04UH0bn1hdPa7qrb1fe3o9e5I/W9v/OLcm3qvtwc1e1JMuHiNe72bjRy9913Y8WKFQ4fk52djeeffx7ff/99vWNXVVXhwIED6NSpE9Rqx1dIXonS09PRtWtXt465+cgyHMlLQ4fmvXB94p1uHbuhPFFfQ1RWlWPN/oXQGTXo3+GRek9JO1JWWYDf9i+GRZhxe6cn6pyyauoaG2Ldwc+RU5IJABhy3VOICqr7ufvFXKnPbDFh7YFPkF9+Gr3a3o2kZvX/EGOPwaTHmn0LUaYrxE3t77P5FaT6lGjz8PPudwEAMcFxLt9UQwiBDYeX4EzxIaS2uR2dXfjBxFUFFaex7sBnUCrUGNR5XL0/FDqqO7skE38d/hr+6lAM7lz9mTnQuPd37T28UPdhpLa5za11N5Ur6T3oKme55/LVRH5+fli2bBkqKyuh0WjwzTffICQkxK2LJdcczU8HIM7//+p2rvw4KvTFMJkNOFV0oFFjZJdkQGesQJWpEmeKD7t5hZfXtbE3IdQ/BonRqdYjKXco1xVZT+0dzd/VqDGKNNkoqTwHizDheMEelx8X6h+NpGY9EOofXecCp/pU7+chANWf57vTqaKDMJj1qDSUIbf0SL19HdV9smAfTBYjynUFyC8/aW2/1Pf3hbqF2+umy8flcJ47dy7WrVuH3r17o2/fvti2bRvmzJlT72NiY2OdHjVTw3Vq2RdqpT+uadmnqZfS5FqEtkNkYCwC1KFoG924n6zbRFyLUP8YBPtFIi6qaS44cZdmIQm4O3US+iTd2+gr+e0J8Y9Em4hr4KsKQIfmjfs4JSqoNZqHJMJPFYT2za93+XEymQw3tL0Hd6c+b3NVszO+qgC0i+kOBXzcfko0MaoLgnzDERbQHK3CO9Tb11Hd7Zp1h79PMKKD26BZyIUzPpf6/q6pW630b9JTwXRpXL5iq2XLlli4cKEn10Iu6ho3sM6vs1ytfFUBuCPlmUsaI9A3FHenTnLTiryTXKZAvw6Xds8CpcIHt1871k0rck3vdsPhWx6H5ObuPSUaFtAMw7u59psojuqOCY7DvT2m1ml3x/tbrfSDj9IPKoV3fUx4NXE5nDdu3Ij33nsPZWVlNr/XvH79eo8sjIiIGk5v1OJAzj8AgL1n1iMxuksTr4gaw+VwfuONNzB58mS0a9eO99ImIpIotdIPMcHxyCs/gdbh1zT1cqiRXA7nsLAw9Ot35V/1R0TkzWQyOQZeOxYGkx5qlb/zB5AkuRzOXbt2xezZs9GnTx+by75rbkRCRETSIJPJGcxXOJfDed++fQCAQ4cOWdtkMhm++uor96+KiIjoKuZyOC9ZssST6yAiIqLzXA7nPXv2YNGiRaisrIQQAhaLBbm5udiwYYMn10dERHTVcfkuBVOnTsWAAQNgNpvx4IMPIiYmBgMGDPDk2oiIiK5KLh85+/j4YPjw4cjJyUFwcDDmzJmDoUOHenJtREREVyWXj5zVajVKS0sRHx+PvXv3QqFQwGw2e3JtREREVyWXw/nRRx/FpEmT0K9fP/z8888YMmQIOnVy7V+VISIiIte5fFp70KBBGDhwIGQyGZYtW4aTJ0+iQ4f6b/hOREREDec0nKdMmVLv92fPnu22xRAREZEL4dyjR8P/UXUiIiJqPKfhPGzYMOvX+fn5iI6ORlpaGjIzMzF8+HCPLo6IiOhq5PIFYTNmzMB7772Ho0eP4oUXXsDBgwcxbdo0T66NiIjoquRyOO/fvx9vvvkm1qxZgxEjRmDWrFk4ceKEJ9dGRER0VXI5nM1mMywWC9avX4++fftCp9NBp9N5cm1ERERXJZfDediwYejTpw9atmyJ6667DiNGjMCoUaM8uTYiIqKrktNwnj59OgBg/fr1iI+PR3l5OR5++GHIZDKsX7/e4wskIiK62ji9Wrvm6HjixIkeXwwRERG5EM41t+jk7zsTERFdHi5/5kxERESXB8OZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZiIhIYhjOREREEsNwJiIikhiGMxERkcQwnImIiCSG4UxERCQxDGciIiKJYTh7Oc3O7Th0S2+cmjQRwmJp6uXYECYTTjwzDocH9EHlvj2XZc7cubNxsG9PlPy60tpmKi5G1si7kDVsCIx55y7LOoiI6sNw9nK5b70B7Y7tyP94AbS70pp6OTYqNm9E4RefQrNtK3LnvuXx+UzFxcj5v+mo3JWG7OlTrO2F//saZWtWoWzd7yj48jOPr4OIyBmGs5cL7tcfAODTug18E9s18Wps+SV3hKpZcwBA8M23NOixuXNnI2vYEGh3p7v8GEVICPxTu9WZL7BnL8j9/CBTqxHYq3eD1kFE5AkMZw8xlZXh1KSJsCyaD4vR6PH5hMWCnJkzcHLiBJiKiqztoUPuROhd9yBq7Hgow8IaNXblgX04NuZhFH79ZaPXV7F1M449PholK1dY21QxMYh6YhzCho1AyO2DrO3GvHM48fSTyH3rDQgh6oyly8pEzv9NR9m633Fm6ssur0GmUCBmwkSEDB6KiIcetrb7X3MtIh58GBH3PYSA8+Fdn/zPPsbxJx6BLjPD2laz32denWKz3/bqbqjStWtw7PHRKP9zvdO++hPHcfzJx3Bu/jyb9qIfluLY46OhSd/Z6HUQ0WUkJECv14u0tDSh1+ubeiluk/3Ga2JHgELsCFCIgm++8vh8xT8vt853avIL1vas+4Zb27V7dzdq7IO39K4eI1ApqnJzbL6Xlpbm0hh7O7UTOwIUYmeorzCf3+fyrZutazv6+Ghr3xPPPmVtL133W52xjKWlYndSG7EjQCFO//tll+swlpaKHcE+YkeAQuzvkWJtz1u8wDpf7rtz6zyudo26o0esfTPuuM3a7mi/7dXdUGnRwWJHgELsatPMaV97++2obnv1eSNvr08I76/RG+tzlnseOXI2Go146aWX8MADD2DEiBFYv975T/zeRp3YtvoLuRzquHiPz+fTujVkSiUA2Jy+9k1MBAAogoNhLCrCqReec+kIzFRSgjPTXkHeoo+s4ymjoqE7dBCnXngOmrQdTscwnDuL0688j8L/fQ11QuL5dbaBzMen+utmzSH39wcAqOPikTtnFnJmvQ51mzYAAJlaDZ+WreqMqwwJQct/z0DYiPsQ/eRTTtdRQ+7nB58WLavnO7+e6q8TAJkMAKCKbmat2x5lWDiU4RHnH9f2whgO9tte3Q1VM486MdFJT9v9VkbHVC/poroLvvgUp6e8CGNBQaPWQ0Sep/TEoCtXrkRoaCjmzp2LkpISDBs2DP379/fEVHYVLf0Wxvw8RD/5FORqtdvGNZw7i8IvPkVg7z4I7nNTvX3Dh98L7a505Ot0CLrhRmu7/vgxFH33DUIHDrY5hVryy8/QZ2Ug+smnoAgKAgCYKyuRv/gjqFu1Qfjwkda+lQf2oWTlCoTfMxJ+yR0AAAEpqWg5YyYMZ04jcvSj1r7NJr0MY2EhgnrfiNPPT4T+SBYKv/oM7ZavRsXGvxD50CNQt2oNABBCoODzTyCMRugzDyN/8QIAQMJnSwBhQeiQO3HskfthLi1FycrliF/8GcSK5TC2bg1VVBQAwGI0In/xAiiCg1G29jeULP8RAJD47Q9QRkYi8sFHIDsfhOq4eLSaNRfa3elQRkbhzEv/AgC0nDkbkY+PhV9SMvw6dKxTd2DPG3Dy6ScBIWApL0XEqAdc2m+5jw/avPshin5cihaTp1nbg2+5Fc1enAxhMECbtt1atyoqCvqsTIioGKBrVwCAMjwcrf7zHsr/WIsWU6bX2W9lWJjNfreaNRdn33nbpu6GavPOPOR/shAxTz3ntG/t/fY5/3l+7bpDh9yJ46NHAai+QC7w+l4Qx49BpKRAplAAcPw6L/vzD2h3bEfU409a99sdHL3Oia5qnjhc12g0oqKiQgghRHFxsbjlllvq7e/O09qlf/xuPa2XPXPGJY9XW8bQ26tPUYb7C0NBQb19c9+da11H0fJl1vb9PVLEjgCFSG8RLswGgxBCCO3e3da+JyZOsPY9NeVFa3v55o3W9l3xLcSOAIXYe01ba5ujuo+Nebi6PUglDtzYvfr0aGKs9VTpwX43WPsWfLvEOkbm8Durvw72EQdvvsFad81p2n1drxU7Q9RiR4BCZI0aZrfujLsHVz8uIkDs69Kx3roz7hp0Ye5hd9Rb957keJEWEyJ2BCjE4Ttuc3m/TRpNo+reEeZn3e+q7DNO63a23w1lb78dqb3fuqzMOnXv79lF7Az1ra71ngvPc96ij6xj2HudO6rbHRy9zt3FG0+JXszba/TG+prktHZAQAACAwOh0Wjw7LPP4l//+pcnprFLprpw6rCxpxEdjn1+PJlCYT3KcGUdch+VnTGUkMmrn36ZUgXUfO1T+3H2a5GpVHXmcFS3dT65HC1nvIGIBx5C3EcfW0+Byx2METr4DiR+8wM6bNgEZUSEte52P/yMhE+/QuKX3wLnnwNH64h+7AkkfPEtOv61FfKAwHrr9m2bhPa/rkXSilXw79y53rrlvn6Im/8xIh56BNFPTrDb1y6ZrFF1Qy6/sN8KhdO6ne13Q9nbb4d9a+03ztdau25lcAg6/LUFiV99h4hRD9Z5nO2aFU7rdgdHr3Oiq5qnfirIzc0Vw4YNEz/88IPTvu6+IKxk9S8i/4tPhcVodMt4NYxFReLc/HlCs8v5T3EWs1kUfP2l2PmO7QVGVTnZ4uyH74vKQwdt2sv+XC/yFi8Q5spKa5u5qkrkfbq4zkVRuqNHxNkP3xf6kyds2s++/19x8oXnbOo2lpSI4xOeEAVffykO9LneemGRZs9ucW7+PGE4d85mjKIfvxeF330jLBbLhTVnnxHHxj4min9ZYdO38MelYseIu0RVXl6duotW/GTTt/LwQXHsiUdF2Z/rbdrzPl0sTjwzTpjOn2kRQgiTTidOPPe0OLfgA7t1V+zcLnZGBFRfSPbw/XbrdqRk7RpxbOyjQnf0qE179szXxOlXp9jUXbPfO79ZUqfuY+PGuFS3o/1uCEf7bY9JoxHnFs4X5Zv+sWnX7t/rcL93zn7Dbt0Xv84rdmwT5xZ8KIylpY2uxR5Hr3N38cajrot5e43eWJ+z3PNIOBcUFIiBAweKLVu2uNTfG6/WrnG5XlQV27eKHYFKsSNAIc5+8K61/eTzz4odAQqRFhko9qWcP7XcPKxBp1iPPDSq+nGxkcJUViaEEMKQny/SmoWKHQEKcXzc407HOHzbzWJHgELsTmojLGazEEII3ZEssTPMr/qq6+mTrX1z3nrDempZu29PnbH0Z05bT7EeuvUmu3XbY9brraeIM4cNsbaXrFppPa1auPR/dR5Xew8bWveVwBv/4qvN2+sTwvtr9Mb6nOWeRy4IW7hwIcrLy/HRRx/ho4+qr3r9+OOP4evr64npCICpsAA4/zvBxvw8a3vN1xadDrGz5qAyfSdCBg6GXKWyO449Nbe0NJeXw6LTQREcDItWA4tGY/P9esc4vw5zcRGEyQSZjw9MpSUQBsP5MWqvOb/6C7MZpsLCOmOpY1uh3bKV0OzYDnVcPDRbNtWp2x5RVQVzaUnd+Wzmrr+WhtZNRNQYHgnnadOmYdq0ac47ktuEDLoDrWbPhTE/Dy1evHBrytZz3oGqWXMEpHRB2KAhCBs0pMFjxy/4BHnz5yHopn5QxVT/eo46Lh4Jn32N4ytXoM2bzm+9mfDFNyj84jOEDr3T+hljYLceiPtgIXQZh9D8hcnWvi2nvQaztgK+Ce2sdzirU2//2xDS/zYIIWAqLKhTtz2K4GC0/d+PKPttDaLHjre2R45+FMb8PAijEdFjJ9QzwoW6Ndu2oNlzzzutm4ioMWRC2LkF02VWVVWFAwcOoFOnTlC78VefpCA9PR1dz/8ajjfyRH0Fn3+CkxPHQxEcjI6bdsI3wfnv93oS9/DK5u31Ad5fozfW5yz3ePtOkpyaG5yYy8uhz8pw0puIyPt45LQ20aVo8dIUGPPzoG4dh5ABtzf1coiILjuGM0mOOi4eST/83NTLICJqMjytTUREJDEMZyIiIolhOBMREUkMw5mIiEhiGM5EREQSw3AmIiKSGIYzERGRxDCciYiIJIbhTEREJDEMZyIiIolhOBMREUkMw5mIiEhiGM5EREQSw3AmIiKSGIYzERGRxDCciYiIJIbhTEREJDEMZyIiIolhOBMREUkMw5mIiEhiGM5EREQSw3AmIiKSGIYzERGRxDCciYiIJIbhTEREJDEMZyIiIolhOBMREUkMw5mIiEhiGM5EREQSw3AmIiKSGIYzERGRxDCciYiIJIbhTEREJDEMZyIiIolhOBMREUkMw5mIiEhiGM5EREQSw3AmIiKSGIYzERGRxDCciYiIJEbZ1AsAACEEAMBgMDTxSjyjqqqqqZfgUd5eH+D9NbK+K5+31+ht9dXkXU3+XUwmHH3nMqqoqEBWVlZTL4OIiOiySkpKQlBQUJ12SYSzxWKBVquFSqWCTCZr6uUQERF5lBACRqMRAQEBkMvrfsIsiXAmIiKiC3hBGBERkcQwnImIiCSG4UxERCQxDGciIiKJkcTvOV9pzGYzpk2bhhMnTkChUGD27NkQQmDy5MmQyWRo164dZsyYAblcju+//x7fffcdlEolJkyYgH79+kGv1+Oll15CUVERAgIC8PbbbyM8PLypy7KyV19FRQXGjx+PuLg4AMD999+PwYMHX5H11VZUVIR77rkHn332GZRKpdfsYY3a9en1eq/bw7vvvtv6ayixsbEYP368V+3hxfU99NBDXrWHixYtwoYNG2A0GnH//fejR48eXrV/l0RQg61bt05MnjxZCCHEtm3bxPjx48W4cePEtm3bhBBCTJ8+Xaxdu1bk5+eLO+64Q1RVVYny8nLr15999pmYN2+eEEKIX3/9VcycObPJarHHXn3ff/+9+PTTT236Xan11TAYDOKpp54St912mzh69KhX7aEQdevztj3U6/Xirrvusmnzpj20V5837eG2bdvEuHHjhNlsFhqNRsybN8+r9u9S8bR2IwwYMAAzZ84EAOTm5iIyMhIHDx5Ejx49AAB9+/bFli1bsG/fPnTp0gU+Pj4ICgpC69atkZGRgfT0dPTp08fad+vWrU1Wiz326jtw4AD++usvPPjgg5g6dSo0Gs0VW1+Nt99+G/fddx+io6MBwKv2EKhbn7ftYUZGBnQ6HR5//HE8/PDD2LNnj1ftob36vGkPN23ahKSkJDz99NMYP348br75Zq/av0vF09qNpFQq8corr2DdunWYN28e/vzzT+sNVAICAlBRUQGNRmNz55eAgABoNBqb9pq+UnNxfXl5eRg5ciQ6deqEBQsWYP78+UhOTr5i6/vpp58QHh6OPn36YPHixQCqbwrgLXtor77OnTt71R76+vpizJgxGDlyJE6ePImxY8d61R7aq+/JJ5/0mj0sKSlBbm4uFi5ciOzsbEyYMMGr9u9S8cj5Erz99tv4/fffMX36dJv7vmq1WgQHByMwMBBardamPSgoyKa9pq8U1a7vxhtvRKdOnQAAt956Kw4dOnRF17ds2TJs2bIFo0ePxuHDh/HKK6+guLjY+v0rfQ/t1de3b1+v2sP4+HjceeedkMlkiI+PR2hoKIqKiqzfv9L30F59ffr08Zo9DA0NxY033ggfHx8kJCRArVbbBOyVvn+XiuHcCCtWrMCiRYsAAH5+fpDJZOjUqRO2b98OAPjnn3/QrVs3dO7cGenp6aiqqkJFRQWOHTuGpKQkpKam4u+//7b27dq1a5PVYo+9+p555hns27cPALB161Zcc801V2x9APDNN9/g66+/xpIlS9ChQwe8/fbb6Nu3r9fsob36nnrqKa/awx9//BFvvfUWACAvLw8ajQa9e/f2mj20V9/TTz/tNXvYtWtXbNy4EUII5OXlQafToVevXl6zf5eKt+9shMrKSkyZMgWFhYUwmUwYO3YsEhMTMX36dBiNRiQkJOCNN96AQqHA999/j6VLl0IIgXHjxuH222+HTqfDK6+8goKCAqhUKvz3v/9FVFRUU5dlZa++5s2bY+bMmVCpVIiMjMTMmTMRGBh4RdZ3sdGjR+O1116DXC73mj2sraY+vV7vVXtoMBgwZcoU5ObmQiaT4cUXX0RYWJjX7KG9+tRqtVft4Zw5c7B9+3YIITBp0iTExsZ6zf5dKoYzERGRxPC0NhERkcQwnImIiCSG4UxERCQxDGciIiKJYTgTERFJDMOZqAlt374do0ePvuyPJSJpYzgTERFJDO+tTdTESkpKMGbMGOTn56Nz586YMWMGNm/ejPfeew8WiwWtWrXC66+/jsjISGzatAmzZ8+GWq1GfHw8AODUqVN45JFHsGHDBsjlcmzfvh0ff/wxPvnkE7vzZWdn4+mnn0ZCQgKOHj2Kjh07okuXLli+fDnKysowf/58JCYmYs2aNfj888+h1+thMBgwa9YspKam4vPPP8fy5cshl8vRuXNnvP7668jIyMCrr74Kk8kEtVqN2bNnW/9ZQ3u2b99uvcFESkoKjh07hiVLlnji6SW6IvHImaiJZWdnY/r06Vi5ciW0Wi0WL16MV199FfPnz8cvv/yC1NRUvP766zAYDJg8eTLmzZuHn376Cb6+vgCANm3aIDY21nrbwxUrVuCee+6pd87MzEyMHTsWP//8M3bt2oWcnBwsXboUd9xxB5YuXQqLxYLvvvsOCxcuxMqVK/HEE09g8eLFMJvNWLRoEZYtW4affvoJRqMReXl5+PLLL/HYY4/hp59+wr333os9e/Y4nNtoNOLll1/G3LlzsWLFCiiVPEYguhjDmaiJdevWDXFxcZDJZBg6dCi+/PJLdO7cGbGxsQCAUaNGYdu2bcjMzER0dDQSExMBAMOGDbOOMXz4cKxcuRI6nQ7btm1D//79650zMjISHTt2hFwuR7NmzdCrVy8AQIsWLVBeXg65XI758+dj06ZNeP/997F8+XJotVooFAp06dIFI0aMwIcffojHHnsMMTExuOmmmzBz5kxMnToVQUFBGDp0qMO5s7KyEBERgeTkZADAiBEjLun5I/JGDGeiJlb7yLH2P5lXu81kMkEmk6H23XYVCoX164EDB2Lz5s34/fff0bdvX6jV6nrn9PHxsflz7bGA6n/lZ8SIEcjOzkb37t1tLjz76KOP8Nprr0EIgSeeeAI7duzAwIEDsXz5cnTu3BlffPEFZsyY4XBuhUIBi8VS7/qIrnYMZ6Imlp6ejtzcXFgsFqxYsQJPPPEE9u7di+zsbADA0qVLcf3116N9+/YoLCxERkYGAGDVqlXWMfz8/NC3b1+88847Tk9pu+LkyZOQyWQYP348rr/+eqxbtw5msxnFxcUYPHgwkpKS8Nxzz6F3797IzMzEv/71L+zfvx/33XcfnnvuORw6dMjh2AkJCSgvL0dmZiYA4Jdffrnk9RJ5G37YQ9TE2rZti6lTp6KgoAA9e/bEmDFj0LZtWzzzzDMwGo1o0aIF3nzzTahUKrzzzjt46aWXoFQq0bFjR5txhgwZgl27duG666675DUlJyejQ4cOGDRoEGQyGW688Uakp6cjPDwco0aNwogRI+Dn54f4+HgMHz4c3bt3x7///W/Mnz8fKpUKr732msOxfXx8MGfOHLzyyiuQy+WIj4+3fn5ORNX4r1IReQGz2Yx3330XEREReOyxx5p6OfWyWCz4z3/+g2eeeQb+/v74/PPPkZeXh8mTJzf10ogkg0fORF5g+PDhCAsLw4IFCwAAp0+fxsSJE+32feONN3Dttdd6fE2jR49GeXl5nfb77rsPoaGhGDFiBFQqFVq2bIk333zT4+shupLwyJmIiEhieEEYERGRxDCciYiIJIbhTEREJDEMZyIiIolhOBMREUkMw5mIiEhi/h9Sl1PHd/+h/AAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_tmp = data.copy()\n",
+    "df_tmp['island_cat'] = df_tmp['island'].astype('category')\n",
+    "sns.swarmplot(data=df_tmp.sample(200),\n",
+    "              x='body_mass_g',\n",
+    "              y='island_cat',\n",
+    "              size=3)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Clustern der Daten mit KMeans"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "island                  1.228228\nbill_length_mm         43.992793\nbill_depth_mm          17.164865\nflipper_length_mm     200.966967\nbody_mass_g          4207.057057\nsex                     0.495495\ndtype: float64"
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.mean()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "island                 0.678088\nbill_length_mm         5.468668\nbill_depth_mm          1.969235\nflipper_length_mm     14.015765\nbody_mass_g          805.215802\nsex                    0.500732\ndtype: float64"
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.std()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-1.8140371 , -0.89604189,  0.7807321 , -1.42675157, -0.56847478,\n        -0.99103121],\n       [-1.8140371 , -0.82278787,  0.11958397, -1.06947358, -0.50628618,\n         1.00904996],\n       [-1.8140371 , -0.67627982,  0.42472926, -0.42637319, -1.1903608 ,\n         1.00904996],\n       ...,\n       [ 1.13987173,  1.02687621,  0.52644436, -0.56928439, -0.53738048,\n        -0.99103121],\n       [ 1.13987173,  1.24663828,  0.93330475,  0.64546078, -0.13315457,\n        -0.99103121],\n       [ 1.13987173,  1.13675725,  0.7807321 , -0.2120064 , -0.53738048,\n         1.00904996]])"
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "scaler = preprocessing.StandardScaler()\n",
+    "X = scaler.fit_transform(data)\n",
+    "X"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([ 1.06688098e-16, -1.02420575e-15, -1.28025718e-15,  2.56051436e-16,\n       -1.28025718e-16, -3.20064295e-17])"
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.mean(axis=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([1., 1., 1., 1., 1., 1.])"
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.std(axis=0)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g  \\\n0   -1.814037       -0.896042       0.780732          -1.426752    -0.568475   \n1   -1.814037       -0.822788       0.119584          -1.069474    -0.506286   \n2   -1.814037       -0.676280       0.424729          -0.426373    -1.190361   \n4   -1.814037       -1.335566       1.085877          -0.569284    -0.941606   \n5   -1.814037       -0.859415       1.747026          -0.783651    -0.692852   \n..        ...             ...            ...                ...          ...   \n339  1.139872        2.162314       1.340165           0.431094    -0.257532   \n340  1.139872       -0.090248       0.475587           0.073816    -1.003795   \n341  1.139872        1.026876       0.526444          -0.569284    -0.537380   \n342  1.139872        1.246638       0.933305           0.645461    -0.133155   \n343  1.139872        1.136757       0.780732          -0.212006    -0.537380   \n\n          sex  \n0   -0.991031  \n1    1.009050  \n2    1.009050  \n4    1.009050  \n5   -0.991031  \n..        ...  \n339 -0.991031  \n340  1.009050  \n341 -0.991031  \n342 -0.991031  \n343  1.009050  \n\n[333 rows x 6 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1.814037</td>\n      <td>-0.896042</td>\n      <td>0.780732</td>\n      <td>-1.426752</td>\n      <td>-0.568475</td>\n      <td>-0.991031</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.814037</td>\n      <td>-0.822788</td>\n      <td>0.119584</td>\n      <td>-1.069474</td>\n      <td>-0.506286</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.814037</td>\n      <td>-0.676280</td>\n      <td>0.424729</td>\n      <td>-0.426373</td>\n      <td>-1.190361</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1.814037</td>\n      <td>-1.335566</td>\n      <td>1.085877</td>\n      <td>-0.569284</td>\n      <td>-0.941606</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>-1.814037</td>\n      <td>-0.859415</td>\n      <td>1.747026</td>\n      <td>-0.783651</td>\n      <td>-0.692852</td>\n      <td>-0.991031</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>339</th>\n      <td>1.139872</td>\n      <td>2.162314</td>\n      <td>1.340165</td>\n      <td>0.431094</td>\n      <td>-0.257532</td>\n      <td>-0.991031</td>\n    </tr>\n    <tr>\n      <th>340</th>\n      <td>1.139872</td>\n      <td>-0.090248</td>\n      <td>0.475587</td>\n      <td>0.073816</td>\n      <td>-1.003795</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>341</th>\n      <td>1.139872</td>\n      <td>1.026876</td>\n      <td>0.526444</td>\n      <td>-0.569284</td>\n      <td>-0.537380</td>\n      <td>-0.991031</td>\n    </tr>\n    <tr>\n      <th>342</th>\n      <td>1.139872</td>\n      <td>1.246638</td>\n      <td>0.933305</td>\n      <td>0.645461</td>\n      <td>-0.133155</td>\n      <td>-0.991031</td>\n    </tr>\n    <tr>\n      <th>343</th>\n      <td>1.139872</td>\n      <td>1.136757</td>\n      <td>0.780732</td>\n      <td>-0.212006</td>\n      <td>-0.537380</td>\n      <td>1.009050</td>\n    </tr>\n  </tbody>\n</table>\n<p>333 rows × 6 columns</p>\n</div>"
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scaled = pd.DataFrame(X, index=data.index, columns=data.columns)\n",
+    "data_scaled"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-1.8140371 , -0.89604189,  0.7807321 , -1.42675157, -0.56847478,\n        -0.99103121],\n       [-1.8140371 , -0.82278787,  0.11958397, -1.06947358, -0.50628618,\n         1.00904996],\n       [-1.8140371 , -0.67627982,  0.42472926, -0.42637319, -1.1903608 ,\n         1.00904996],\n       ...,\n       [ 1.13987173,  1.02687621,  0.52644436, -0.56928439, -0.53738048,\n        -0.99103121],\n       [ 1.13987173,  1.24663828,  0.93330475,  0.64546078, -0.13315457,\n        -0.99103121],\n       [ 1.13987173,  1.13675725,  0.7807321 , -0.2120064 , -0.53738048,\n         1.00904996]])"
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_scaled_numpy = data_scaled.values\n",
+    "data_scaled_numpy"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "numpy.ndarray"
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data_scaled_numpy)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KMeans(n_clusters=2)"
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "model = KMeans(n_clusters=2)\n",
+    "model.fit(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0])"
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions = model.labels_\n",
+    "predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "outputs": [],
+   "source": [
+    "data_with_predictions = data.copy()\n",
+    "data_with_predictions['cluster'] = predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "     island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g  \\\n0         0            39.1           18.7              181.0       3750.0   \n1         0            39.5           17.4              186.0       3800.0   \n2         0            40.3           18.0              195.0       3250.0   \n4         0            36.7           19.3              193.0       3450.0   \n5         0            39.3           20.6              190.0       3650.0   \n..      ...             ...            ...                ...          ...   \n339       2            55.8           19.8              207.0       4000.0   \n340       2            43.5           18.1              202.0       3400.0   \n341       2            49.6           18.2              193.0       3775.0   \n342       2            50.8           19.0              210.0       4100.0   \n343       2            50.2           18.7              198.0       3775.0   \n\n     sex  cluster  \n0    0.0        0  \n1    1.0        0  \n2    1.0        0  \n4    1.0        0  \n5    0.0        0  \n..   ...      ...  \n339  0.0        0  \n340  1.0        0  \n341  0.0        0  \n342  0.0        0  \n343  1.0        0  \n\n[333 rows x 7 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n      <th>cluster</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>39.1</td>\n      <td>18.7</td>\n      <td>181.0</td>\n      <td>3750.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>39.5</td>\n      <td>17.4</td>\n      <td>186.0</td>\n      <td>3800.0</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>40.3</td>\n      <td>18.0</td>\n      <td>195.0</td>\n      <td>3250.0</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>36.7</td>\n      <td>19.3</td>\n      <td>193.0</td>\n      <td>3450.0</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>39.3</td>\n      <td>20.6</td>\n      <td>190.0</td>\n      <td>3650.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>339</th>\n      <td>2</td>\n      <td>55.8</td>\n      <td>19.8</td>\n      <td>207.0</td>\n      <td>4000.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>340</th>\n      <td>2</td>\n      <td>43.5</td>\n      <td>18.1</td>\n      <td>202.0</td>\n      <td>3400.0</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>341</th>\n      <td>2</td>\n      <td>49.6</td>\n      <td>18.2</td>\n      <td>193.0</td>\n      <td>3775.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>342</th>\n      <td>2</td>\n      <td>50.8</td>\n      <td>19.0</td>\n      <td>210.0</td>\n      <td>4100.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>343</th>\n      <td>2</td>\n      <td>50.2</td>\n      <td>18.7</td>\n      <td>198.0</td>\n      <td>3775.0</td>\n      <td>1.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>333 rows × 7 columns</p>\n</div>"
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Cluster analysieren"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "outputs": [],
+   "source": [
+    "sns.set()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:xlabel='body_mass_g', ylabel='flipper_length_mm'>"
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=data_with_predictions,\n",
+    "                x='body_mass_g',\n",
+    "                y='flipper_length_mm',\n",
+    "                alpha=0.5,\n",
+    "                hue='cluster')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ergebnis: Die 2 Cluster teilen sich in schwere Pinguine mit langen Flippern und leichten Pinguinen mit kleineren Flippern auf."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "          island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\ncluster                                                              \n0        1.35514       42.004673      18.370561         191.920561   \n1        1.00000       47.568067      14.996639         217.235294   \n\n         body_mass_g       sex  \ncluster                         \n0        3714.719626  0.500000  \n1        5092.436975  0.487395  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n    </tr>\n    <tr>\n      <th>cluster</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.35514</td>\n      <td>42.004673</td>\n      <td>18.370561</td>\n      <td>191.920561</td>\n      <td>3714.719626</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.00000</td>\n      <td>47.568067</td>\n      <td>14.996639</td>\n      <td>217.235294</td>\n      <td>5092.436975</td>\n      <td>0.487395</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_predictions.groupby('cluster').mean()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_with_predictions.groupby('cluster').mean().T.plot.bar()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "outputs": [],
+   "source": [
+    "data_scaled_with_predictions = data_scaled.copy()\n",
+    "data_scaled_with_predictions['cluster'] = predictions"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_scaled_with_predictions.groupby('cluster').mean().T.plot.bar()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from yellowbrick.cluster.silhouette import SilhouetteVisualizer\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "for k in [2,3,4]:\n",
+    "    sil = SilhouetteVisualizer(\n",
+    "        KMeans(n_clusters=k, random_state=42))\n",
+    "    sil.fit(X)\n",
+    "    sil.finalize()\n",
+    "    plt.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:>"
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "model = KMeans(n_clusters=3, random_state=42)\n",
+    "model.fit(X)\n",
+    "predictions = model.labels_\n",
+    "\n",
+    "data_scaled_with_predictions = data_scaled.copy()\n",
+    "data_scaled_with_predictions['cluster'] = predictions\n",
+    "\n",
+    "data_scaled_with_predictions.groupby('cluster').mean().T.plot.bar()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Cluster 0 = Eher von Insel 0, höhere bill Length, niedrigere bill depth, höhere flipper length und höheres Gewicht\n",
+    "Cluster 1 = Eher von Insel 1, höhere bill depth, niedrigere flipper length und niedrigeres Körpergewicht\n",
+    "Cluster 2 = Eher von Insel 2, niedrigere bill length, höhere bill depth, niedrigere flipper length und niedrigeres Gewicht"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "         island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\ncluster                                                             \n0           119             119            119                119   \n1           123             123            123                123   \n2            91              91             91                 91   \n\n         body_mass_g  sex  \ncluster                    \n0                119  119  \n1                123  123  \n2                 91   91  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n    </tr>\n    <tr>\n      <th>cluster</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>119</td>\n      <td>119</td>\n      <td>119</td>\n      <td>119</td>\n      <td>119</td>\n      <td>119</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>123</td>\n      <td>123</td>\n      <td>123</td>\n      <td>123</td>\n      <td>123</td>\n      <td>123</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>91</td>\n      <td>91</td>\n      <td>91</td>\n      <td>91</td>\n      <td>91</td>\n      <td>91</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows_per_cluster_per_column = data_scaled_with_predictions.groupby('cluster').count()\n",
+    "rows_per_cluster_per_column"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAFaCAYAAAD2CZ+nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfL0lEQVR4nO3deXRU9eH+8WeSSUIwoIAzLIHCqUYQquBxwQhNWusJYcmxBI8sEsomylHUVEBkSYoWiUIbtZYKGsElLIGyhIhxIZqKodqmHhYJUIVQFEyG0FACSYDM/P7g51i+NkzIbJkP79c5njP33pl7n5iPebx37mJxuVwuAQAAI4UFOwAAAPAfih4AAINR9AAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMGswQ7gL//+9yk5ndwi4GI6dIhRVVVNsGPAIIwp+BpjyrOwMIvatbui0eXGFr3T6aLom4B/R/A1xhR8jTHlHQ7dAwBgMIoeAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg1H0AAAYzNh73QOma9M2Wq2iWt5/wjZbm2BHuEBd/Tmd/E9tsGMAQdPy/koAaJJWUValPL4p2DFavM2/u1sngx0CCCIO3QMAYDC/Fn1NTY2GDRumr7/+WpK0Zs0aDRs2TCkpKXryySd15swZSVJZWZlSU1M1aNAgzZkzR+fOnfNnLAAALht+K/odO3Zo9OjRKi8vlyQdPHhQOTk5Wr16tfLz8+V0OrVy5UpJ0owZM5SRkaF3331XLpdLeXl5/ooFAMBlxW9Fn5eXp8zMTNntdklSZGSkMjMzFRMTI4vFouuuu05HjhzRN998o7q6OvXr10+SlJqaqsLCQn/FAgDgsuK3k/EWLFhwwXRsbKxiY2MlScePH1dubq4WLlyoyspK2Ww29/tsNpsqKir8FQsAgMtKwM+6r6io0OTJkzVixAj1799fpaWlslgs7uUul+uC6ebq0CHG63VcDlrapVCAPzDOQxu/P+8EtOi/+uorTZ48WWlpaZo4caIkqVOnTnI4HO73HDt2zH243xtVVTVyOl1er8dkNlsbORxceBSq+OPXdIzz0MXfKc/CwiwX3bkNWNHX1NRo0qRJeuyxx/TLX/7SPT82NlZRUVEqLS3VzTffrE2bNikhISFQsQKGm5s0DTc3AQDfCljzrFu3TseOHdPy5cu1fPlySdKdd96pRx99VIsXL9bcuXNVU1OjPn36aNy4cYGKFTDc3KRpuLkJAPiW34u+qKhIkjR+/HiNHz/+f76nV69eWrdunb+jAABw2eHOeAAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg1H0AAAYjKIHAMBgFD0AAAaj6AEAMBhFDwCAwSh6AAAMRtEDAGAwih4AAINR9AAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAzm16KvqanRsGHD9PXXX0uSSkpKlJKSoqSkJGVnZ7vfV1ZWptTUVA0aNEhz5szRuXPn/BkLAIDLht+KfseOHRo9erTKy8slSXV1dZo9e7aWLFmiLVu2aPfu3SouLpYkzZgxQxkZGXr33XflcrmUl5fnr1gAAFxW/Fb0eXl5yszMlN1ulyTt3LlT3bt3V7du3WS1WpWSkqLCwkJ98803qqurU79+/SRJqampKiws9FcsAAAuK1Z/rXjBggUXTFdWVspms7mn7Xa7KioqfjDfZrOpoqLC6+136BDj9ToQHDZbm2BHgGEYU6GN3593/Fb0/5fT6ZTFYnFPu1wuWSyWRud7q6qqRk6ny+v1+AoDtekcjpPBjhASGFNNx5gKXTZbG35/HoSFWS66cxuws+47deokh8PhnnY4HLLb7T+Yf+zYMffhfgAA4J2AFX3fvn118OBBHTp0SA0NDSooKFBCQoJiY2MVFRWl0tJSSdKmTZuUkJAQqFgAABgtYIfuo6KilJWVpWnTpqm+vl6JiYlKTk6WJC1evFhz585VTU2N+vTpo3HjxgUqFgAARvN70RcVFblfx8fHKz8//wfv6dWrl9atW+fvKAAAXHa4Mx4AAAaj6AEAMBhFDwCAwSh6AAAMRtEDAGAwih4AAINR9AAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDUfQAABjM78+jBwC0fG3aRqtVVMusBJutTbAjXKCu/pxO/qc22DGarGX+VgEAAdUqyqqUxzcFO0ZI2Py7u3Uy2CEuAYfuAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg3k86/6DDz7QM888oxMnTsjlcsnlcslisegf//hHIPIBAAAveCz6RYsWadasWerdu7csFksgMgEAAB/xWPRt27ZVUlJSILIAAAAf8/gdfd++fVVcXByILAAAwMc87tEXFxfrrbfeUkREhCIiIviOHgCAEOKx6FesWBGAGAAAwB88HrqPjY3Vrl27lJeXp/bt2+vzzz9XbGxsILIBAAAveSz6ZcuWadWqVSosLFRdXZ1eeukl/fGPfwxENgAA4CWPRf/222/rlVdeUXR0tNq1a6e8vDwVFBQEIhsAAPCSx6K3Wq2KjIx0T7dt21ZWK0+3BQAgFHhs7M6dO+ujjz6SxWLRmTNnlJOTw3f0AACECI9FP2/ePM2cOVP79u1Tv3791LdvX/3ud78LRDYAAOClJh2Df/3111VbW6uGhgbFxMToyy+/9HcuAADgA41+R19dXa3q6mrdf//9OnHihOrr69XQ0KBjx47p4YcfDmRGAADQTI3u0T/++OP65JNPJEn9+/f//gNWqwYNGuT/ZAAAwGuNFn1OTo4k6cknn9TChQt9utFNmzZp2bJlkqSEhAQ98cQTKikp0cKFC1VfX6/BgwcrPT3dp9sEAOBy5PE7+tTUVP3tb39zT1ssFrVq1Uo9evRQTEzMJW+wtrZWCxYsUGFhodq2bavRo0erqKhITz31lN5880117txZDzzwgIqLi5WYmHjJ6wcAAN/zWPQLFy7U3r17de211yo8PFz79++XzWZzF/Zdd911SRtsaGiQ0+lUbW2tWrdurXPnzikmJkbdu3dXt27dJEkpKSkqLCyk6AEA8JLHou/SpYueeOIJ9/f0O3fu1IoVKzRz5kxNnTr1kos+JiZGjz76qAYPHqzo6GjdeuutqqyslM1mc7/HbreroqLiEn+UC3XocOlHG9Ay2Gxtgh0BhmFMwddCaUx5LPrDhw9fcDLejTfeqPLycnXq1KlZG9y7d6/+/Oc/68MPP1SbNm00ffp0lZeXy2KxuN/z3aNwvVFVVSOn0+XVOnwplAZFsDkcJ4MdISQwppqOMeUZ4+nStKQxFRZmuejObZNugbtt2zb39LZt2xQREaHjx4/r3Llzlxxo27Ztio+PV4cOHRQZGanU1FR9+umncjgc7vc4HA7Z7fZLXjcAALiQxz36zMxMPfLII7JYLHI6nYqKitKLL76oV199VaNGjbrkDfbq1UuLFi3S6dOnFR0draKiIvXt21ebN2/WoUOH1LVrVxUUFGjEiBHN+oEAAMD3PBb9jTfeqK1bt2r//v0KDw/XNddco/DwcPXq1atZGxw4cKD27Nmj1NRURURE6IYbbtC0adM0YMAATZs2TfX19UpMTFRycnKz1g8AAL7nseiPHz+u/Px8nTp1Si6XS06nU4cOHfLqfvdTpkzRlClTLpgXHx+v/Pz8Zq8TAAD8kMeif+yxx9SqVSt9+eWXuuOOO1RSUqKbb745ENkAAICXPJ6Md+TIES1btkwJCQkaO3asVq1apQMHDgQiGwAA8JLHor/66qslST169ND+/fvVsWPHZp1tDwAAAs/jofsOHTro1VdfVb9+/fSHP/xBMTExqqurC0Q2AADgJY979E899ZQiIyN1yy236Cc/+YlefPFFTZ8+PRDZAACAlzzu0T/77LN67rnnJEkzZszQjBkz/B4KAAD4hsc9+rKyMrlcLedWsgAAoOk87tHb7XYNHTpUffv21RVXXOGeP3fuXL8GAwAA3mu06M+cOaPIyEjddNNNuummmwKZCQAA+EijRT927Fjl5eWptraW7+UBAAhRjRb9sWPH9PLLL6ugoMB9Lf1/mzBhgl+DAQAA7zVa9E8//bTefvtt1dXVaf/+/YHMBAAAfKTRoh8wYIAGDBignJwcTZo0KZCZAACAj3i8vG7kyJGaP3++fvWrX6m6uloZGRk6depUILIBAAAveSz6BQsWqG3btqqqqlJUVJRqamqUkZERiGwAAMBLTbphTnp6uqxWq6Kjo7V48WKVlZUFIhsAAPCSx6IPC7vwLQ0NDT+YBwAAWiaPd8a79dZbtWjRItXV1enjjz9Wbm6u+vfvH4hsAADASx53zadPn67WrVurTZs2ys7OVs+ePTVz5sxAZAMAAF7yuEcfERGhhx56SA899FAg8gAAAB9qdI/+9OnTWrp0qdavX6+amhpNmjRJ/fr107hx43T06NFAZgQAAM3UaNFnZmbq888/16ZNmzRmzBjFxsZq9erVio+P1/z58wOZEQAANFOjh+737NmjgoIC1dbWKiEhQRs2bFB4eLh69eqlIUOGBDIjAABopkb36K1WqywWi1q3bq0uXbooPDzcvSwqKiog4QAAgHcaLfr/vlb+v0seAACEjkYP3X/77bf67W9/+4PXklRRUeH/ZAAAwGuNFv199933P19L0pgxY/yXCAAA+EyjRf/www8HMgcAAPADbloPAIDBKHoAAAzmsei/+OKLQOQAAAB+0KSH2gAAgNDkseh79uypzZs368iRI6qurnb/AwAAWj6PT6/bunWrCgsLL5hnsVhUVlbmt1AAAMA3PBb9rl27ApEDAAD4gcdD906nUzk5OZo1a5Zqamq0dOlSNTQ0BCIbAADwkseif+6557Rv3z7t2LFDLpdLH3/8sRYuXBiIbAAAwEsei3779u3KyspSVFSU2rRpo9dee02ffPKJVxstKipSamqqBg8e7L6HfklJiVJSUpSUlKTs7Gyv1g8AAM7zWPRWq/WCJ9lFRkbKavX41X6jDh8+rMzMTC1ZskT5+fnas2ePiouLNXv2bC1ZskRbtmzR7t27VVxc3OxtAACA8zwW/XXXXafc3Fw1NDTowIEDysjIUK9evZq9wffff19DhgxRp06dFBERoezsbEVHR6t79+7q1q2brFarUlJSfnCmPwAAuHQei37OnDn64osvVFVVpdGjR+vUqVOaPXt2szd46NAhNTQ06MEHH9Tdd9+tlStXqrKyUjabzf0eu93Oo3ABAPABj8fgY2Ji9Mwzz/hsgw0NDfr73/+uN998U61bt9bUqVPVqlUrWSwW93tcLtcF083RoUOMt1ERJDZbm2BHgGEYU/C1UBpTjRb9k08+edEPNvfM+6uvvlrx8fFq3769JOmuu+5SYWGhwsPD3e9xOByy2+3NWv93qqpq5HS6vFqHL4XSoAg2h+NksCOEBMZU0zGmPGM8XZqWNKbCwiwX3blt9NB9XFyc4uLidPLkSe3bt089e/ZU7969VV5e7tV19D//+c+1bds2/ec//1FDQ4M+/vhjJScn6+DBg+7D+gUFBUpISGj2NgAAwHmN7tFPnDhR0vmT53JzcxUdHS1JuvfeezVu3Lhmb7Bv376aPHmyxowZo7Nnz2rAgAEaPXq0fvzjH2vatGmqr69XYmKikpOTm70NAABwnsfv6KuqqhQZGemetlgs+ve//+3VRu+55x7dc889F8yLj49Xfn6+V+sFAAAX8lj08fHxmjx5soYNGyaXy6VNmzbpzjvvDEQ2AADgJY9FP2/ePK1cuVLvv/++JGnw4MEaNWqU34MBAADveSx6q9WqcePGefW9PAAACA6PN8wBAAChi6IHAMBgFD0AAAZr9Dv6Bx988KIffPnll30eBgAA+FajRT9o0KBA5gAAAH7QaNEPHz7c/bqhoUE1NTVyuVrOveMBAIBnHi+vW7lypbKysnT27FlJ3z9ZrqyszO/hAACAdzwWfU5OjtasWaPrr78+EHkAAIAPeTzr/sorr6TkAQAIUY0WfXV1taqrq9WvXz+tWLFCx44dc8+rrq4OYEQAANBcjR66v/3222WxWNwn4GVlZbmX8R09AAChodGi37t3byBzAAAAP/B4Mt6ZM2dUXFysU6dOSTp/qd2//vUvpaen+z0cAADwjseiT09P1+HDh+VwONS7d2/t2LFDt912WyCyAQAAL3k8676srEzr16/XL37xC82ePVurVq3SiRMnApENAAB4yWPR2+12Wa1W9ejRQ/v371dcXJxOnjwZiGwAAMBLHou+devW2rx5s3r16qV33nlH+/bt0+nTpwORDQAAeMlj0WdkZKisrEwDBgxQWFiY0tLSNGnSpEBkAwAAXvJ4Ml5YWJhmzpwpSXr++eclSR9++KFfQwEAAN/wuEefnJysnJycC+a9+OKLfgsEAAB8x2PRd+3aVR999JF+85vfuO+Sx+NqAQAIDR6L/oorrlBOTo6OHz+uqVOnqra2VmFhHj8GAABagCY1dmRkpF544QXFxsYqLS1N9fX1/s4FAAB8wGPRt2/fXtL5B9nMmzdPSUlJOnDggN+DAQAA73ks+v97It6UKVNUXFzst0AAAMB3PF5e9/nnn+v3v/+9Tpw4ccFJeJs3b/ZrMAAA4D2PRZ+RkaHU1FT17t1bFoslEJkAAICPeCx6q9WqCRMmBCILAADwMY/f0cfFxWnfvn2ByAIAAHzM4x794cOHNWLECHXp0kVRUVGSzp+Bn5+f7/dwAADAOx6LPj093f363LlzOn78uFasWOHPTAAAwEc8Fv1tt92mEydOaM2aNcrNzdXp06eVlpYWiGwAAMBLFy36AwcO6PXXX1d+fr5iY2NVV1enoqIitWnTJlD5AACAFxo9GW/KlCkaO3asIiIi9MYbb6igoEBXXHEFJQ8AQAhptOj37NmjPn36KC4uTt27d5ckrqMHACDENFr0H330kYYPH66CggINHDhQjzzyiM8fZvPss89q1qxZkqSSkhKlpKQoKSlJ2dnZPt0OAACXq0aL3mq1asiQIXrzzTe1fv162e121dfXKykpSatWrfJ6w9u3b9eGDRskSXV1dZo9e7aWLFmiLVu2aPfu3dxPHwAAH2jSY2qvvfZazZ07V3/5y180adIk5eXlebXR6upqZWdn68EHH5Qk7dy5U927d1e3bt1ktVqVkpKiwsJCr7YBAACacHndf4uOjtbIkSM1cuRIrzaakZGh9PR0HT16VJJUWVkpm83mXm6321VRUeHVNjp0iPHq8wgem40TPuFbjCn4WiiNqUsqel9Yu3atOnfurPj4eK1fv16S5HQ6LzjRz+VyeX3iX1VVjZxOl+c3BkgoDYpgczhOBjtCSGBMNR1jyjPG06VpSWMqLMxy0Z3bgBf9li1b5HA4dPfdd+vEiRM6ffq0vvnmG4WHh7vf43A4ZLfbAx0NAADjBLzoly9f7n69fv16ffbZZ5o/f76SkpJ06NAhde3aVQUFBRoxYkSgowEAYJyAF/3/EhUVpaysLE2bNk319fVKTExUcnJysGMBABDyglr0qampSk1NlSTFx8fzRDwAAHysSZfXAQCA0ETRAwBgMIoeAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg1H0AAAYjKIHAMBgFD0AAAaj6AEAMBhFDwCAwSh6AAAMRtEDAGAwih4AAINR9AAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg1H0AAAYjKIHAMBgQSn6l156SUOHDtXQoUP13HPPSZJKSkqUkpKipKQkZWdnByMWAADGCXjRl5SUaNu2bdqwYYM2btyoL774QgUFBZo9e7aWLFmiLVu2aPfu3SouLg50NAAAjBPworfZbJo1a5YiIyMVERGha665RuXl5erevbu6desmq9WqlJQUFRYWBjoaAADGsQZ6g3Fxce7X5eXleueddzR27FjZbDb3fLvdroqKCq+206FDjFefR/DYbG2CHQGGYUzB10JpTAW86L/zz3/+Uw888IBmzpyp8PBwlZeXu5e5XC5ZLBav1l9VVSOn0+VlSt8JpUERbA7HyWBHCAmMqaZjTHnGeLo0LWlMhYVZLrpzG5ST8UpLSzV+/Hg9/vjjGj58uDp16iSHw+Fe7nA4ZLfbgxENAACjBLzojx49qoceekiLFy/W0KFDJUl9+/bVwYMHdejQITU0NKigoEAJCQmBjgYAgHECfug+JydH9fX1ysrKcs8bNWqUsrKyNG3aNNXX1ysxMVHJycmBjgYAgHECXvRz587V3Llz/+ey/Pz8AKcBAMBs3BkPAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg1H0AAAYjKIHAMBgFD0AAAaj6AEAMBhFDwCAwSh6AAAMRtEDAGAwih4AAINR9AAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDUfQAABiMogcAwGAUPQAABqPoAQAwGEUPAIDBKHoAAAxG0QMAYDCKHgAAg1H0AAAYjKIHAMBgFD0AAAaj6AEAMFiLKvrNmzdryJAhSkpKUm5ubrDjAAAQ8qzBDvCdiooKZWdna/369YqMjNSoUaPUv39/XXvttcGOBgBAyGoxe/QlJSW6/fbbddVVV6l169YaNGiQCgsLgx0LAICQ1mL26CsrK2Wz2dzTdrtdO3fubPb6wsIsvojlU/Z20cGOEBJa4u+upWJMNQ1jqmkYT03XksaUpywtpuidTqcslu/DulyuC6YvVbt2V/gilk/lzE0KdoSQ0KFDTLAjhAzGVNMwppqG8dR0oTSmWsyh+06dOsnhcLinHQ6H7HZ7EBMBABD6WkzR33HHHdq+fbuOHz+u2tpavffee0pISAh2LAAAQlqLOXTfsWNHpaena9y4cTp79qzuuece3XjjjcGOBQBASLO4XC5XsEMAAAD/aDGH7gEAgO9R9AAAGIyiBwDAYBQ9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDtZhb4ML/vvrqK7377rv69ttvFRYWJrvdrp/+9Ke64YYbgh0NAPTBBx/o6NGjSkxM1I9+9CP3/DVr1mjkyJFBTBba2KO/TOTm5urXv/61JOmGG25Qnz59JEnz5s3Ta6+9FsxoAKDFixfrrbfeUnl5uUaPHq1Nmza5l61evTqIyUIfe/SXiTfeeEMbN25UdHT0BfMnTJig4cOHa+LEiUFKhlB15MiRiy7v0qVLgJLABMXFxdqwYYOsVqvS0tI0ceJERUZGavDgweKRLN6h6C8TVqtV586d+8H8uro6RUREBCERQt0DDzyg8vJy2e32H/whtlgs2rp1a5CSIRS5XC5ZLBZJUo8ePbR06VJNmDBB7du3d89H8/D0usvE5s2b9fzzzys+Pl42m00Wi0WVlZX661//qvT0dA0dOjTYERFiampqNGbMGGVmZurmm28OdhyEuJdeekklJSWaNWuW+xHlpaWlevjhh3XmzBmVlpYGOWHoougvIxUVFdq+fbsqKyvldDrVqVMnxcfHq2PHjsGOhhC1c+dOrV27Vk8//XSwo8AA27dvl91u1zXXXOOed/ToUb322muaM2dOEJOFNooeAACDcdY9AAAGo+gBADAYRQ+gUZ9++qmGDRvW7M+vXbtWubm5PkwE4FJR9AD8prS0VHV1dcGOAVzWuI4egNu6deu0fPlyhYWFqV27dkpNTXUvmzVrluLi4jRp0qQfTK9cuVKrV69WRESEoqKi9NRTT+ngwYMqKirSJ598olatWum+++7Tn/70J7333ntyOp2KjY1VZmamOnbsqLS0NF155ZU6cOCARo8erbS0tGD9KwCMQ9EDkCTt3btXixcv1oYNG9S5c2etWLFCL7/8sqzWi/+ZaGho0DPPPKOioiLZ7XZt3LhRpaWlGjlypLZu3aq4uDjdd9992rhxo/bv36+1a9fKarVqzZo1mjt3rl555RVJUtu2bbVly5ZA/KjAZYWiByDp/DXMAwcOVOfOnSVJ48eP1/XXX+/xGvnw8HAlJydr1KhR+tnPfqaBAwcqMTHxB+/78MMPtWvXLo0YMUKS5HQ6VVtb615+yy23+PCnAfAdih6ApPOF/d+3Gq2rq9OBAwfc0xaL5YJb3Z49e9b9evHixdq/f79KSkq0bNkybdq0SS+88MIF63c6nZo8ebLGjBkjSTpz5oxOnDjhXt66dWuf/0wAOBkPwP/Xv39/950TpfNPDFu0aJF7ebt27bR7925J5++y+Nlnn0mSjh8/rsTERF111VUaP368HnvsMe3atUvS+f95+O4ZCwMHDtS6detUU1MjSXrhhRc0c+bMgP18wOWKPXoAkqSePXtqxowZmjx5siTJZrNp/vz5Wrp0qSQpLS1N06dP16BBg9S1a1fdfvvtkqT27dtr6tSpGj9+vFq1aqXw8HD99re/lSQlJCQoKytLknT//feroqJC9957rywWizp37uxeBsB/uAUuAAAG49A9AAAGo+gBADAYRQ8AgMEoegAADEbRAwBgMIoeAACDUfQAABiMogcAwGD/D9RQUO++N4bYAAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rows_per_cluster = rows_per_cluster_per_column.iloc[:,0]\n",
+    "rows_per_cluster.plot.bar(ylabel='Anzahl der Befragten')\n",
+    "plt.savefig('barp.png', dpi=150)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 1000x500 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:title={'center':'Principal Component Plot'}, xlabel='$PC_1$', ylabel='$PC_2$'>"
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from yellowbrick.features import PCA\n",
+    "\n",
+    "classes = ['0', '1', '2']\n",
+    "\n",
+    "visualizer = PCA(classes=classes,\n",
+    "                 proj_features=True,\n",
+    "                 alpha=0.2,\n",
+    "                 colormap='sns_muted',\n",
+    "                 size=(1000,500))\n",
+    "\n",
+    "visualizer.fit_transform(data_scaled, predictions)\n",
+    "visualizer.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=6)\n",
+    "pca_fit = pca.fit(data_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:label='c77ac7f8-704a-4dbd-a366-ac61ead24806'>"
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 2 Axes>",
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.Series(pca.explained_variance_ratio_).plot.bar(ylabel='Explained Variance')\n",
+    "pd.Series(pca.explained_variance_ratio_).cumsum().plot.line(\n",
+    "    secondary_y=True,\n",
+    "    color='orange')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Klassifizierung"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    146\n1    119\n2     68\nName: species, dtype: int64"
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "KNeighborsClassifier()"
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "model = KNeighborsClassifier()\n",
+    "model.fit(data_scaled, labels)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g  \\\n24  -0.337083       -0.950982       0.017869          -1.498207    -0.506286   \n176 -0.337083       -0.200129      -2.067291           1.002739     0.986240   \n249 -0.337083        0.532412      -1.304427           1.502928     0.830769   \n94   1.139872       -1.427134       0.068726          -0.998018    -1.128172   \n154 -0.337083        0.862055      -1.558715           0.645461     0.302166   \n\n          sex  \n24  -0.991031  \n176  1.009050  \n249  1.009050  \n94   1.009050  \n154  1.009050  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>island</th>\n      <th>bill_length_mm</th>\n      <th>bill_depth_mm</th>\n      <th>flipper_length_mm</th>\n      <th>body_mass_g</th>\n      <th>sex</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>24</th>\n      <td>-0.337083</td>\n      <td>-0.950982</td>\n      <td>0.017869</td>\n      <td>-1.498207</td>\n      <td>-0.506286</td>\n      <td>-0.991031</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>-0.337083</td>\n      <td>-0.200129</td>\n      <td>-2.067291</td>\n      <td>1.002739</td>\n      <td>0.986240</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>-0.337083</td>\n      <td>0.532412</td>\n      <td>-1.304427</td>\n      <td>1.502928</td>\n      <td>0.830769</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>1.139872</td>\n      <td>-1.427134</td>\n      <td>0.068726</td>\n      <td>-0.998018</td>\n      <td>-1.128172</td>\n      <td>1.009050</td>\n    </tr>\n    <tr>\n      <th>154</th>\n      <td>-0.337083</td>\n      <td>0.862055</td>\n      <td>-1.558715</td>\n      <td>0.645461</td>\n      <td>0.302166</td>\n      <td>1.009050</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_to_predict = data_scaled.sample(5)\n",
+    "data_to_predict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "24     0\n176    1\n249    1\n94     0\n154    1\nName: species, dtype: int64"
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels_to_predict = labels[data_to_predict.index]\n",
+    "labels_to_predict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 1, 1, 0, 1], dtype=int64)"
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(data_to_predict)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ergebnis: Alle richtig vorhergesehen."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "outputs": [],
+   "source": [
+    "all_predictions = model.predict(data_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "True     0.996997\nFalse    0.003003\nName: species, dtype: float64"
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "correct_predictions = all_predictions==labels\n",
+    "correct_predictions.value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Beispieldaten ausdenken und Modell vorhersagen lassen"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "island                  1\nbill_length_mm         50\nbill_depth_mm          15\nflipper_length_mm     200\nbody_mass_g          4000\nsex                     1\ndtype: int64"
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data_to_predict = pd.Series({\n",
+    "    'island':1,\n",
+    "    'bill_length_mm':50,\n",
+    "    'bill_depth_mm':15,\n",
+    "    'flipper_length_mm':200,\n",
+    "    'body_mass_g':4000,\n",
+    "    'sex':1\n",
+    "})\n",
+    "new_data_to_predict"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[   1,   50,   15,  200, 4000,    1]], dtype=int64)"
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data_to_predict_for_scaler = new_data_to_predict.values.reshape(1, -1)\n",
+    "new_data_to_predict_for_scaler"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([[-0.33708269,  1.10013024, -1.10099721, -0.0690952 , -0.25753178,\n         1.00904996]])"
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data_to_predict_scaled = scaler.transform(new_data_to_predict_for_scaler)\n",
+    "new_data_to_predict_scaled"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([1], dtype=int64)"
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(new_data_to_predict_scaled)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/notebooks/spaghetti_classification.ipynb b/notebooks/spaghetti_classification.ipynb
new file mode 100644
index 0000000..9229998
--- /dev/null
+++ b/notebooks/spaghetti_classification.ipynb
@@ -0,0 +1,634 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Erstellen, trainieren und optimieren eines Modells zur Klassifizierung"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Daten einlesen"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0                2.0   64          13.0             2.0   50.8333       4.0   \n1                4.0   87          15.0             1.0   50.8333       4.0   \n2                4.0   73          30.0             1.0   50.8333       4.0   \n3                4.0   55          14.0             2.0   50.8333       4.0   \n4                4.0   74          24.0             1.0   50.8333       4.0   \n...              ...  ...           ...             ...       ...       ...   \n26596            1.0   42          18.0             1.0   45.1667      15.5   \n26597            4.0   37          19.0             4.0   45.1667      15.5   \n26598            4.0   61          25.0             4.0   45.1667      15.5   \n26599            4.0   57          23.0             2.0   45.1667      15.5   \n26600            1.0   60          25.0             2.0   45.1667      15.5   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                          2.0                   1.0         0.0   \n1                          1.0                   0.0         0.0   \n2                          1.0                   1.0         0.0   \n3                          NaN                   NaN         0.0   \n4                          0.0                   0.0         0.0   \n...                        ...                   ...         ...   \n26596                      1.0                   1.0         0.0   \n26597                      0.0                   0.0         2.0   \n26598                      2.0                   NaN         2.0   \n26599                      0.0                   2.0         0.0   \n26600                      0.0                   0.0         2.0   \n\n       population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n0                     0.0  0.1139                             0              1  \n1                     1.0  0.1139                             0              1  \n2                     1.0  0.1139                             0              0  \n3                     2.0  0.0000                             1              1  \n4                     1.0  0.1139                             1              1  \n...                   ...     ...                           ...            ...  \n26596                 2.0  0.0000                             0              1  \n26597                 1.0  0.3200                             1              0  \n26598                 1.0  0.3645                             0              0  \n26599                 1.0  0.1139                             1              1  \n26600                 2.0  0.3645                             1              0  \n\n[26601 rows x 13 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>64</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>55</td>\n      <td>14.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>1.0</td>\n      <td>42</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>4.0</td>\n      <td>37</td>\n      <td>19.0</td>\n      <td>4.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26598</th>\n      <td>4.0</td>\n      <td>61</td>\n      <td>25.0</td>\n      <td>4.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3645</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>4.0</td>\n      <td>57</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>1.0</td>\n      <td>60</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.3645</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>26601 rows × 13 columns</p>\n</div>"
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data = pd.read_parquet('../data/interim/data_numeric.parquet')\n",
+    "\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zeilen mit leeren Werten (NaN) entfernen"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0                2.0   64          13.0             2.0   50.8333       4.0   \n1                4.0   87          15.0             1.0   50.8333       4.0   \n2                4.0   73          30.0             1.0   50.8333       4.0   \n4                4.0   74          24.0             1.0   50.8333       4.0   \n5                4.0   21          18.0             2.0   50.8333       4.0   \n...              ...  ...           ...             ...       ...       ...   \n26594            4.0   28          18.0             1.0   45.1667      15.5   \n26596            1.0   42          18.0             1.0   45.1667      15.5   \n26597            4.0   37          19.0             4.0   45.1667      15.5   \n26599            4.0   57          23.0             2.0   45.1667      15.5   \n26600            1.0   60          25.0             2.0   45.1667      15.5   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                          2.0                   1.0         0.0   \n1                          1.0                   0.0         0.0   \n2                          1.0                   1.0         0.0   \n4                          0.0                   0.0         0.0   \n5                          0.0                   0.0         1.0   \n...                        ...                   ...         ...   \n26594                      0.0                   0.0         2.0   \n26596                      1.0                   1.0         0.0   \n26597                      0.0                   0.0         2.0   \n26599                      0.0                   2.0         0.0   \n26600                      0.0                   0.0         2.0   \n\n       population_density  salary  no_date_spaghetti_throw_away  gender_Female  \n0                     0.0  0.1139                             0              1  \n1                     1.0  0.1139                             0              1  \n2                     1.0  0.1139                             0              0  \n4                     1.0  0.1139                             1              1  \n5                     1.0  0.2850                             0              1  \n...                   ...     ...                           ...            ...  \n26594                 1.0  0.3645                             0              1  \n26596                 2.0  0.0000                             0              1  \n26597                 1.0  0.3200                             1              0  \n26599                 1.0  0.1139                             1              1  \n26600                 2.0  0.3645                             1              0  \n\n[22636 rows x 13 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>no_date_spaghetti_throw_away</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>64</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4.0</td>\n      <td>21</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.2850</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26594</th>\n      <td>4.0</td>\n      <td>28</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3645</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>1.0</td>\n      <td>42</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>4.0</td>\n      <td>37</td>\n      <td>19.0</td>\n      <td>4.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>4.0</td>\n      <td>57</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>1.0</td>\n      <td>60</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.3645</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>22636 rows × 13 columns</p>\n</div>"
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.dropna()\n",
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Klassifizierungsvariable entfernen"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0        0\n1        0\n2        0\n4        1\n5        0\n        ..\n26594    0\n26596    0\n26597    1\n26599    1\n26600    1\nName: no_date_spaghetti_throw_away, Length: 22636, dtype: uint8"
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels = data['no_date_spaghetti_throw_away']\n",
+    "labels"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "       look_at_dates  age  age_stop_edu  household_size  cntrylat  cntrylon  \\\n0                2.0   64          13.0             2.0   50.8333       4.0   \n1                4.0   87          15.0             1.0   50.8333       4.0   \n2                4.0   73          30.0             1.0   50.8333       4.0   \n4                4.0   74          24.0             1.0   50.8333       4.0   \n5                4.0   21          18.0             2.0   50.8333       4.0   \n...              ...  ...           ...             ...       ...       ...   \n26594            4.0   28          18.0             1.0   45.1667      15.5   \n26596            1.0   42          18.0             1.0   45.1667      15.5   \n26597            4.0   37          19.0             4.0   45.1667      15.5   \n26599            4.0   57          23.0             2.0   45.1667      15.5   \n26600            1.0   60          25.0             2.0   45.1667      15.5   \n\n       best_before_meaning_map  validity_meaning_map  work_scale  \\\n0                          2.0                   1.0         0.0   \n1                          1.0                   0.0         0.0   \n2                          1.0                   1.0         0.0   \n4                          0.0                   0.0         0.0   \n5                          0.0                   0.0         1.0   \n...                        ...                   ...         ...   \n26594                      0.0                   0.0         2.0   \n26596                      1.0                   1.0         0.0   \n26597                      0.0                   0.0         2.0   \n26599                      0.0                   2.0         0.0   \n26600                      0.0                   0.0         2.0   \n\n       population_density  salary  gender_Female  \n0                     0.0  0.1139              1  \n1                     1.0  0.1139              1  \n2                     1.0  0.1139              0  \n4                     1.0  0.1139              1  \n5                     1.0  0.2850              1  \n...                   ...     ...            ...  \n26594                 1.0  0.3645              1  \n26596                 2.0  0.0000              1  \n26597                 1.0  0.3200              0  \n26599                 1.0  0.1139              1  \n26600                 2.0  0.3645              0  \n\n[22636 rows x 12 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>look_at_dates</th>\n      <th>age</th>\n      <th>age_stop_edu</th>\n      <th>household_size</th>\n      <th>cntrylat</th>\n      <th>cntrylon</th>\n      <th>best_before_meaning_map</th>\n      <th>validity_meaning_map</th>\n      <th>work_scale</th>\n      <th>population_density</th>\n      <th>salary</th>\n      <th>gender_Female</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2.0</td>\n      <td>64</td>\n      <td>13.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>87</td>\n      <td>15.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>73</td>\n      <td>30.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>74</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4.0</td>\n      <td>21</td>\n      <td>18.0</td>\n      <td>2.0</td>\n      <td>50.8333</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.2850</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>26594</th>\n      <td>4.0</td>\n      <td>28</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3645</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26596</th>\n      <td>1.0</td>\n      <td>42</td>\n      <td>18.0</td>\n      <td>1.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26597</th>\n      <td>4.0</td>\n      <td>37</td>\n      <td>19.0</td>\n      <td>4.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.3200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26599</th>\n      <td>4.0</td>\n      <td>57</td>\n      <td>23.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.1139</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>26600</th>\n      <td>1.0</td>\n      <td>60</td>\n      <td>25.0</td>\n      <td>2.0</td>\n      <td>45.1667</td>\n      <td>15.5</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.3645</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>22636 rows × 12 columns</p>\n</div>"
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.drop(columns='no_date_spaghetti_throw_away')\n",
+    "data"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    16728\n1     5908\nName: no_date_spaghetti_throw_away, dtype: int64"
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.value_counts()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Daten skalieren"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Labels in eine NumPy Variable schreiben"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "array([0, 0, 0, ..., 1, 1, 1], dtype=uint8)"
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = labels.values\n",
+    "y"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Train-Test-Splitting"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.7999646580667963"
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "\n",
+    "len(X_train)/len(X)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    0.73901\n1    0.26099\ndtype: float64"
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(y_train).value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0    0.738958\n1    0.261042\ndtype: float64"
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(y_test).value_counts(normalize=True)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Entscheidungsbaummodell"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "DecisionTreeClassifier(random_state=42)"
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "model_dt = DecisionTreeClassifier(random_state=42)\n",
+    "model_dt.fit(X_train, y_train)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[Text(697.5, 815.4, 'best_before_meaning_map <= -0.668\\ngini = 0.386\\nsamples = 18108\\nvalue = [13382, 4726]\\nclass = nicht wegwerfen'),\n Text(348.75, 634.2, 'validity_meaning_map <= -0.437\\ngini = 0.492\\nsamples = 5071\\nvalue = [2865, 2206]\\nclass = nicht wegwerfen'),\n Text(174.375, 453.0, 'cntrylat <= -0.405\\ngini = 0.5\\nsamples = 3496\\nvalue = [1782, 1714]\\nclass = nicht wegwerfen'),\n Text(87.1875, 271.80000000000007, 'cntrylon <= 0.501\\ngini = 0.496\\nsamples = 1806\\nvalue = [824, 982]\\nclass = wegwerfen'),\n Text(43.59375, 90.60000000000002, '\\n  (...)  \\n'),\n Text(130.78125, 90.60000000000002, '\\n  (...)  \\n'),\n Text(261.5625, 271.80000000000007, 'cntrylon <= -0.806\\ngini = 0.491\\nsamples = 1690\\nvalue = [958, 732]\\nclass = nicht wegwerfen'),\n Text(217.96875, 90.60000000000002, '\\n  (...)  \\n'),\n Text(305.15625, 90.60000000000002, '\\n  (...)  \\n'),\n Text(523.125, 453.0, 'cntrylat <= -0.31\\ngini = 0.43\\nsamples = 1575\\nvalue = [1083, 492]\\nclass = nicht wegwerfen'),\n Text(435.9375, 271.80000000000007, 'cntrylon <= 0.406\\ngini = 0.464\\nsamples = 760\\nvalue = [482, 278]\\nclass = nicht wegwerfen'),\n Text(392.34375, 90.60000000000002, '\\n  (...)  \\n'),\n Text(479.53125, 90.60000000000002, '\\n  (...)  \\n'),\n Text(610.3125, 271.80000000000007, 'cntrylon <= -0.704\\ngini = 0.387\\nsamples = 815\\nvalue = [601, 214]\\nclass = nicht wegwerfen'),\n Text(566.71875, 90.60000000000002, '\\n  (...)  \\n'),\n Text(653.90625, 90.60000000000002, '\\n  (...)  \\n'),\n Text(1046.25, 634.2, 'cntrylat <= -0.536\\ngini = 0.312\\nsamples = 13037\\nvalue = [10517, 2520]\\nclass = nicht wegwerfen'),\n Text(871.875, 453.0, 'cntrylon <= 0.501\\ngini = 0.409\\nsamples = 2907\\nvalue = [2075, 832]\\nclass = nicht wegwerfen'),\n Text(784.6875, 271.80000000000007, 'validity_meaning_map <= -0.437\\ngini = 0.376\\nsamples = 2168\\nvalue = [1623, 545]\\nclass = nicht wegwerfen'),\n Text(741.09375, 90.60000000000002, '\\n  (...)  \\n'),\n Text(828.28125, 90.60000000000002, '\\n  (...)  \\n'),\n Text(959.0625, 271.80000000000007, 'age <= -1.235\\ngini = 0.475\\nsamples = 739\\nvalue = [452, 287]\\nclass = nicht wegwerfen'),\n Text(915.46875, 90.60000000000002, '\\n  (...)  \\n'),\n Text(1002.65625, 90.60000000000002, '\\n  (...)  \\n'),\n Text(1220.625, 453.0, 'look_at_dates <= 0.166\\ngini = 0.278\\nsamples = 10130\\nvalue = [8442, 1688]\\nclass = nicht wegwerfen'),\n Text(1133.4375, 271.80000000000007, 'validity_meaning_map <= -0.437\\ngini = 0.212\\nsamples = 4241\\nvalue = [3730, 511]\\nclass = nicht wegwerfen'),\n Text(1089.84375, 90.60000000000002, '\\n  (...)  \\n'),\n Text(1177.03125, 90.60000000000002, '\\n  (...)  \\n'),\n Text(1307.8125, 271.80000000000007, 'cntrylon <= -1.265\\ngini = 0.32\\nsamples = 5889\\nvalue = [4712, 1177]\\nclass = nicht wegwerfen'),\n Text(1264.21875, 90.60000000000002, '\\n  (...)  \\n'),\n Text(1351.40625, 90.60000000000002, '\\n  (...)  \\n')]"
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 1800x1200 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import plot_tree\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "# Größe des Plots festlegen\n",
+    "plt.figure(dpi=300)\n",
+    "\n",
+    "plot_tree(\n",
+    "    model_dt,\n",
+    "    max_depth=3,\n",
+    "    feature_names = data.columns,\n",
+    "    class_names = ['nicht wegwerfen','wegwerfen'],\n",
+    "    rounded=True,\n",
+    "    filled=True,\n",
+    "    fontsize=4\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.77      0.75      0.76      3346\n",
+      "           1       0.35      0.38      0.36      1182\n",
+      "\n",
+      "    accuracy                           0.65      4528\n",
+      "   macro avg       0.56      0.56      0.56      4528\n",
+      "weighted avg       0.66      0.65      0.66      4528\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "y_predicted_test_dt = model_dt.predict(X_test)\n",
+    "print(classification_report(y_test,y_predicted_test_dt))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.77      0.91      0.84      3346\n",
+      "           1       0.48      0.23      0.31      1182\n",
+      "\n",
+      "    accuracy                           0.74      4528\n",
+      "   macro avg       0.63      0.57      0.57      4528\n",
+      "weighted avg       0.70      0.74      0.70      4528\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "model_rf = RandomForestClassifier()\n",
+    "model_rf.fit(X_train, y_train)\n",
+    "y_predicted_test_rf = model_rf.predict(X_test)\n",
+    "print(classification_report(y_test,y_predicted_test_rf))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "age                        0.216537\nage_stop_edu               0.159424\ncntrylat                   0.094575\ncntrylon                   0.084097\nhousehold_size             0.083676\nsalary                     0.082515\nbest_before_meaning_map    0.068256\npopulation_density         0.050221\nvalidity_meaning_map       0.049476\nlook_at_dates              0.044679\ngender_Female              0.034008\nwork_scale                 0.032536\ndtype: float64"
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(model_rf.feature_importances_, index=data.columns).sort_values(ascending=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zeilen in Training und Testing:  18108 4528\n",
+      "Zeilen in Training und Testing:  18109 4527\n",
+      "Zeilen in Training und Testing:  18109 4527\n",
+      "Zeilen in Training und Testing:  18109 4527\n",
+      "Zeilen in Training und Testing:  18109 4527\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import StratifiedKFold\n",
+    "\n",
+    "cv_splitter = StratifiedKFold(shuffle=True, random_state=42)\n",
+    "splits = cv_splitter.split(X, y)\n",
+    "for train_index, test_index in splits:\n",
+    "    print('Zeilen in Training und Testing: ', len(train_index), len(test_index))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'fit_time': array([2.76217985, 2.03631592, 2.44614553, 3.00046968, 2.60314894]),\n 'score_time': array([0.1627748 , 0.16268897, 0.15410042, 0.16924095, 0.12227607]),\n 'test_f1': array([0.30990783, 0.31766055, 0.3308007 , 0.33258679, 0.32747875]),\n 'test_precision': array([0.48555957, 0.4920071 , 0.53396226, 0.49172185, 0.49571184]),\n 'test_recall': array([0.22758037, 0.23454699, 0.23962743, 0.25126904, 0.24450085])}"
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_validate\n",
+    "\n",
+    "model = RandomForestClassifier()\n",
+    "scores = cross_validate(model, X, y, cv=cv_splitter, scoring=['f1','precision','recall'])\n",
+    "\n",
+    "scores"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.32368692511192787"
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores['test_f1'].mean()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.27751399599004234"
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = RandomForestClassifier(max_depth=10)\n",
+    "scores = cross_validate(model, X, y, cv=cv_splitter, scoring=['f1','precision','recall'])\n",
+    "scores['test_f1'].mean()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 576x396 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<AxesSubplot:title={'center':'Validation Curve for RandomForestClassifier'}, xlabel='max_depth', ylabel='score'>"
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from yellowbrick.model_selection import ValidationCurve\n",
+    "\n",
+    "viz = ValidationCurve(\n",
+    "    model,\n",
+    "    param_name=\"max_depth\",\n",
+    "    param_range=range(1,25),\n",
+    "    # range(1,10) heißt 1-9 x)\n",
+    "    cv=cv_splitter,\n",
+    "    scoring=\"f1\"\n",
+    ")\n",
+    "viz.fit(X, y)\n",
+    "viz.show()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file