attachment_EDA-Python-01 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://shwetkm.github.io/upxlogo.png\"></img>\n",
    "\n",
    "# Exploratory Data Analysis using Python(Part 01) - Data Science Foundation\n",
    "\n",
    "\n",
    "**SET THE WORKING DIRECTORY SO THAT WE DO NOT HAVE TO THINK ABOUT PATH PROBLEMS**\n",
    "\n",
    "Please ensure all your folder names <font color='red'><b>do not have a space & use forward slash (/)</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "your_local_path=\"C:/sandbox/python\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploratory Data Analysis**\n",
    "\n",
    "<b>Exploratory Data Analysis (EDA)</b> is a crucial step of the data analytics process. It involves exploring the data and identifying important features about the data as well as asking interesting questions from the data by using statistical and visualization tools studied in earlier classes such as descriptive statistics and basic plotting. Entire aim of exploratory data analysis is to be able to <b>get familiar with the data, perform data munging and manipulation, and ask questions before trying to forecast and run Machine Learning models on the data</b>.\n",
    "\n",
    "Dataset: In the first example, the dataset that we consider is <b>data on countries and their territories</b>. Specifically, we would using data files for <b>TB Deaths, spread of TB, and number of new cases of TB to answer some important questions</b>. \n",
    "\n",
    "In any data analysis process, there is one or more questions we want to answer. That is the most basic and important step in the whole process, <b>to define these questions</b>. Since we are going to perform some Exploratory Data Analysis in our TB dataset, these are <u>the questions we want to answer</u>:\n",
    "\n",
    "<ul><i>\n",
    "<li>Which are the countries with the highest and infectious TB incidence?</li>\n",
    "<li>What is the general world tendency in the period from 1990 to 2007?</li>\n",
    "<li>What countries don't follow that tendency?</li>\n",
    "<li>What other facts about the disease do we know that we can check with our data?</li>\n",
    "</ul></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "\n",
    "# In real world we need to fetch data from different source systems and put it in a single place\n",
    "\n",
    "tb_deaths_url_csv = 'https://docs.google.com/spreadsheets/d/12uWVH_IlmzJX_75bJ3IH5E-Gqx6-zfbDKNvZqYjUuso/pub?gid=0&output=CSV'\n",
    "tb_existing_url_csv = 'https://docs.google.com/spreadsheets/d/1X5Jp7Q8pTs3KLJ5JBWKhncVACGsg5v4xu6badNs4C7I/pub?gid=0&output=csv'\n",
    "tb_new_url_csv = 'https://docs.google.com/spreadsheets/d/1Pl51PcEGlO9Hp4Uh0x2_QM0xVb53p2UDBMPwcnSjFTk/pub?gid=0&output=csv'\n",
    "\n",
    "local_tb_deaths_file = your_local_path+'tb_deaths_100.csv'\n",
    "local_tb_existing_file = your_local_path+'tb_existing_100.csv'\n",
    "local_tb_new_file = your_local_path+'tb_new_100.csv'\n",
    "\n",
    "deaths_f = urllib.request.urlretrieve(tb_deaths_url_csv, local_tb_deaths_file)\n",
    "existing_f = urllib.request.urlretrieve(tb_existing_url_csv, local_tb_existing_file)\n",
    "new_f = urllib.request.urlretrieve(tb_new_url_csv, local_tb_new_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Let us load the data into our Python environment for getting started with EDA\n",
    "\n",
    "tb_deaths_file = your_local_path+'tb_deaths_100.csv'\n",
    "tb_existing_file = your_local_path+'tb_existing_100.csv'\n",
    "tb_new_file = your_local_path+'tb_new_100.csv'\n",
    "\n",
    "deaths_df = pd.read_csv(tb_deaths_file, index_col = 0, thousands  = ',').T\n",
    "existing_df = pd.read_csv(tb_existing_file, index_col = 0, thousands  = ',').T\n",
    "new_df = pd.read_csv(tb_new_file, index_col = 0, thousands  = ',').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>TB prevalence, all forms (per 100 000 population per year)</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Anguilla</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Wallis et Futuna</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>353.333333</td>\n",
       "      <td>36.944444</td>\n",
       "      <td>47.388889</td>\n",
       "      <td>12.277778</td>\n",
       "      <td>25.277778</td>\n",
       "      <td>413.444444</td>\n",
       "      <td>35.611111</td>\n",
       "      <td>10.833333</td>\n",
       "      <td>61.222222</td>\n",
       "      <td>74.944444</td>\n",
       "      <td>...</td>\n",
       "      <td>28.055556</td>\n",
       "      <td>128.888889</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>40.888889</td>\n",
       "      <td>282.666667</td>\n",
       "      <td>126.222222</td>\n",
       "      <td>43.388889</td>\n",
       "      <td>194.333333</td>\n",
       "      <td>535.277778</td>\n",
       "      <td>512.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>64.708396</td>\n",
       "      <td>6.915220</td>\n",
       "      <td>4.487091</td>\n",
       "      <td>9.886447</td>\n",
       "      <td>7.274497</td>\n",
       "      <td>97.751318</td>\n",
       "      <td>1.243283</td>\n",
       "      <td>2.812786</td>\n",
       "      <td>20.232634</td>\n",
       "      <td>16.129885</td>\n",
       "      <td>...</td>\n",
       "      <td>3.717561</td>\n",
       "      <td>15.911109</td>\n",
       "      <td>62.027508</td>\n",
       "      <td>2.422660</td>\n",
       "      <td>57.322616</td>\n",
       "      <td>86.784083</td>\n",
       "      <td>8.332353</td>\n",
       "      <td>52.158131</td>\n",
       "      <td>91.975576</td>\n",
       "      <td>113.411925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>238.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>305.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>321.250000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>128.750000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>234.250000</td>\n",
       "      <td>63.250000</td>\n",
       "      <td>36.250000</td>\n",
       "      <td>146.750000</td>\n",
       "      <td>459.000000</td>\n",
       "      <td>420.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>373.500000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>131.500000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>257.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>184.500000</td>\n",
       "      <td>521.500000</td>\n",
       "      <td>466.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>404.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>85.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.750000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>165.750000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>248.500000</td>\n",
       "      <td>620.000000</td>\n",
       "      <td>616.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>436.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>278.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>352.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>680.000000</td>\n",
       "      <td>714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 207 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "TB prevalence, all forms (per 100 000 population per year)  Afghanistan  \\\n",
       "count                                                         18.000000   \n",
       "mean                                                         353.333333   \n",
       "std                                                           64.708396   \n",
       "min                                                          238.000000   \n",
       "25%                                                          305.000000   \n",
       "50%                                                          373.500000   \n",
       "75%                                                          404.500000   \n",
       "max                                                          436.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Albania  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        36.944444   \n",
       "std                                                          6.915220   \n",
       "min                                                         22.000000   \n",
       "25%                                                         32.000000   \n",
       "50%                                                         40.500000   \n",
       "75%                                                         42.000000   \n",
       "max                                                         44.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Algeria  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        47.388889   \n",
       "std                                                          4.487091   \n",
       "min                                                         42.000000   \n",
       "25%                                                         44.000000   \n",
       "50%                                                         45.500000   \n",
       "75%                                                         50.750000   \n",
       "max                                                         56.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  American Samoa  \\\n",
       "count                                                            18.000000   \n",
       "mean                                                             12.277778   \n",
       "std                                                               9.886447   \n",
       "min                                                               0.000000   \n",
       "25%                                                               6.000000   \n",
       "50%                                                               9.000000   \n",
       "75%                                                              16.250000   \n",
       "max                                                              42.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Andorra  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        25.277778   \n",
       "std                                                          7.274497   \n",
       "min                                                         17.000000   \n",
       "25%                                                         19.250000   \n",
       "50%                                                         22.500000   \n",
       "75%                                                         31.500000   \n",
       "max                                                         39.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)      Angola  \\\n",
       "count                                                        18.000000   \n",
       "mean                                                        413.444444   \n",
       "std                                                          97.751318   \n",
       "min                                                         281.000000   \n",
       "25%                                                         321.250000   \n",
       "50%                                                         399.000000   \n",
       "75%                                                         512.000000   \n",
       "max                                                         530.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Anguilla  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        35.611111   \n",
       "std                                                          1.243283   \n",
       "min                                                         34.000000   \n",
       "25%                                                         35.000000   \n",
       "50%                                                         35.000000   \n",
       "75%                                                         36.000000   \n",
       "max                                                         38.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Antigua and Barbuda  \\\n",
       "count                                                                 18.000000   \n",
       "mean                                                                  10.833333   \n",
       "std                                                                    2.812786   \n",
       "min                                                                    7.000000   \n",
       "25%                                                                    9.000000   \n",
       "50%                                                                   10.000000   \n",
       "75%                                                                   12.750000   \n",
       "max                                                                   16.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Argentina  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        61.222222   \n",
       "std                                                         20.232634   \n",
       "min                                                         35.000000   \n",
       "25%                                                         41.250000   \n",
       "50%                                                         60.500000   \n",
       "75%                                                         77.000000   \n",
       "max                                                         96.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Armenia  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        74.944444   \n",
       "std                                                         16.129885   \n",
       "min                                                         49.000000   \n",
       "25%                                                         62.000000   \n",
       "50%                                                         77.000000   \n",
       "75%                                                         85.750000   \n",
       "max                                                         99.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)     ...      \\\n",
       "count                                                          ...       \n",
       "mean                                                           ...       \n",
       "std                                                            ...       \n",
       "min                                                            ...       \n",
       "25%                                                            ...       \n",
       "50%                                                            ...       \n",
       "75%                                                            ...       \n",
       "max                                                            ...       \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Uruguay  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        28.055556   \n",
       "std                                                          3.717561   \n",
       "min                                                         23.000000   \n",
       "25%                                                         25.000000   \n",
       "50%                                                         27.500000   \n",
       "75%                                                         30.750000   \n",
       "max                                                         35.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Uzbekistan  \\\n",
       "count                                                        18.000000   \n",
       "mean                                                        128.888889   \n",
       "std                                                          15.911109   \n",
       "min                                                         102.000000   \n",
       "25%                                                         116.500000   \n",
       "50%                                                         131.500000   \n",
       "75%                                                         143.000000   \n",
       "max                                                         152.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)     Vanuatu  \\\n",
       "count                                                        18.000000   \n",
       "mean                                                        186.000000   \n",
       "std                                                          62.027508   \n",
       "min                                                         102.000000   \n",
       "25%                                                         128.750000   \n",
       "50%                                                         185.000000   \n",
       "75%                                                         240.000000   \n",
       "max                                                         278.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Venezuela  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        40.888889   \n",
       "std                                                          2.422660   \n",
       "min                                                         38.000000   \n",
       "25%                                                         39.000000   \n",
       "50%                                                         41.000000   \n",
       "75%                                                         42.000000   \n",
       "max                                                         46.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Viet Nam  \\\n",
       "count                                                        18.000000   \n",
       "mean                                                        282.666667   \n",
       "std                                                          57.322616   \n",
       "min                                                         220.000000   \n",
       "25%                                                         234.250000   \n",
       "50%                                                         257.000000   \n",
       "75%                                                         349.000000   \n",
       "max                                                         365.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Wallis et Futuna  \\\n",
       "count                                                              18.000000   \n",
       "mean                                                              126.222222   \n",
       "std                                                                86.784083   \n",
       "min                                                                13.000000   \n",
       "25%                                                                63.250000   \n",
       "50%                                                               106.000000   \n",
       "75%                                                               165.750000   \n",
       "max                                                               352.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  West Bank and Gaza  \\\n",
       "count                                                                18.000000   \n",
       "mean                                                                 43.388889   \n",
       "std                                                                   8.332353   \n",
       "min                                                                  31.000000   \n",
       "25%                                                                  36.250000   \n",
       "50%                                                                  43.000000   \n",
       "75%                                                                  51.500000   \n",
       "max                                                                  55.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)       Yemen  \\\n",
       "count                                                        18.000000   \n",
       "mean                                                        194.333333   \n",
       "std                                                          52.158131   \n",
       "min                                                         130.000000   \n",
       "25%                                                         146.750000   \n",
       "50%                                                         184.500000   \n",
       "75%                                                         248.500000   \n",
       "max                                                         265.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)      Zambia  \\\n",
       "count                                                        18.000000   \n",
       "mean                                                        535.277778   \n",
       "std                                                          91.975576   \n",
       "min                                                         387.000000   \n",
       "25%                                                         459.000000   \n",
       "50%                                                         521.500000   \n",
       "75%                                                         620.000000   \n",
       "max                                                         680.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Zimbabwe  \n",
       "count                                                        18.000000  \n",
       "mean                                                        512.833333  \n",
       "std                                                         113.411925  \n",
       "min                                                         392.000000  \n",
       "25%                                                         420.750000  \n",
       "50%                                                         466.000000  \n",
       "75%                                                         616.750000  \n",
       "max                                                         714.000000  \n",
       "\n",
       "[8 rows x 207 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First step that we always do in EDA is that we examine the data \n",
    "#(first few row to understand what kind of data we are dealing with)\n",
    "\n",
    "df_summary = existing_df.describe()\n",
    "df_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>TB prevalence, all forms (per 100 000 population per year)</th>\n",
       "      <th>Spain</th>\n",
       "      <th>United Kingdom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.666667</td>\n",
       "      <td>9.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.677442</td>\n",
       "      <td>0.916444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.750000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TB prevalence, all forms (per 100 000 population per year)      Spain  \\\n",
       "count                                                       18.000000   \n",
       "mean                                                        30.666667   \n",
       "std                                                          6.677442   \n",
       "min                                                         23.000000   \n",
       "25%                                                         25.250000   \n",
       "50%                                                         29.000000   \n",
       "75%                                                         34.750000   \n",
       "max                                                         44.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  United Kingdom  \n",
       "count                                                            18.000000  \n",
       "mean                                                              9.611111  \n",
       "std                                                               0.916444  \n",
       "min                                                               9.000000  \n",
       "25%                                                               9.000000  \n",
       "50%                                                               9.000000  \n",
       "75%                                                              10.000000  \n",
       "max                                                              12.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In order to access individual columns\n",
    "\n",
    "#df_summary[['Afghanistan','Zambia','Zimbabwe']]\n",
    "df_summary[['Spain','United Kingdom']]\n",
    "\n",
    "#What can you infer from the data above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>TB prevalence, all forms (per 100 000 population per year)</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Anguilla</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Wallis et Futuna</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>-0.016055</td>\n",
       "      <td>-0.047619</td>\n",
       "      <td>-0.022222</td>\n",
       "      <td>-0.666667</td>\n",
       "      <td>-0.051282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>-0.052083</td>\n",
       "      <td>-0.057692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028571</td>\n",
       "      <td>-0.078947</td>\n",
       "      <td>-0.035971</td>\n",
       "      <td>-0.021739</td>\n",
       "      <td>-0.010959</td>\n",
       "      <td>1.793651</td>\n",
       "      <td>-0.018182</td>\n",
       "      <td>-0.015094</td>\n",
       "      <td>0.045872</td>\n",
       "      <td>0.019560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>-0.016317</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>-0.054054</td>\n",
       "      <td>-0.001946</td>\n",
       "      <td>-0.026316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.054945</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029412</td>\n",
       "      <td>-0.028571</td>\n",
       "      <td>-0.033582</td>\n",
       "      <td>-0.022222</td>\n",
       "      <td>-0.008310</td>\n",
       "      <td>-0.818182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>-0.004796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>-0.016588</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>-0.057143</td>\n",
       "      <td>-0.001949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>-0.046512</td>\n",
       "      <td>0.078431</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030303</td>\n",
       "      <td>0.156863</td>\n",
       "      <td>-0.034749</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>-0.011173</td>\n",
       "      <td>1.718750</td>\n",
       "      <td>-0.037037</td>\n",
       "      <td>-0.038023</td>\n",
       "      <td>0.064777</td>\n",
       "      <td>0.009639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>-0.019277</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.030303</td>\n",
       "      <td>-0.003906</td>\n",
       "      <td>-0.027027</td>\n",
       "      <td>-0.071429</td>\n",
       "      <td>-0.048780</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031250</td>\n",
       "      <td>-0.016949</td>\n",
       "      <td>-0.032000</td>\n",
       "      <td>-0.023256</td>\n",
       "      <td>-0.011299</td>\n",
       "      <td>-0.011494</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011858</td>\n",
       "      <td>0.057034</td>\n",
       "      <td>0.016706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-0.024570</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>-0.023256</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>-0.003922</td>\n",
       "      <td>-0.027778</td>\n",
       "      <td>-0.076923</td>\n",
       "      <td>-0.051282</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032258</td>\n",
       "      <td>0.025862</td>\n",
       "      <td>-0.033058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011429</td>\n",
       "      <td>-0.459302</td>\n",
       "      <td>-0.038462</td>\n",
       "      <td>-0.024000</td>\n",
       "      <td>0.052158</td>\n",
       "      <td>0.030516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.023256</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.040541</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>-0.067227</td>\n",
       "      <td>-0.034188</td>\n",
       "      <td>-0.023810</td>\n",
       "      <td>-0.098266</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>-0.045082</td>\n",
       "      <td>0.029060</td>\n",
       "      <td>0.031891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-0.025189</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>inf</td>\n",
       "      <td>-0.178571</td>\n",
       "      <td>-0.291016</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.056338</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035714</td>\n",
       "      <td>0.099099</td>\n",
       "      <td>-0.035398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>-0.061224</td>\n",
       "      <td>-0.111588</td>\n",
       "      <td>0.039867</td>\n",
       "      <td>0.061810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-0.033592</td>\n",
       "      <td>-0.022727</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>-0.520000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.140496</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.059701</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.057377</td>\n",
       "      <td>-0.032110</td>\n",
       "      <td>-0.024390</td>\n",
       "      <td>-0.043956</td>\n",
       "      <td>-0.497653</td>\n",
       "      <td>-0.043478</td>\n",
       "      <td>-0.062802</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>-0.185031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-0.002674</td>\n",
       "      <td>-0.023256</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>-0.072464</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.181818</td>\n",
       "      <td>-0.079365</td>\n",
       "      <td>0.162162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038760</td>\n",
       "      <td>-0.246445</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>-0.030651</td>\n",
       "      <td>-0.018692</td>\n",
       "      <td>-0.045455</td>\n",
       "      <td>-0.097938</td>\n",
       "      <td>0.036278</td>\n",
       "      <td>0.096939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>-0.072386</td>\n",
       "      <td>-0.047619</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.090909</td>\n",
       "      <td>0.380208</td>\n",
       "      <td>-0.027778</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>-0.103448</td>\n",
       "      <td>0.093023</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035714</td>\n",
       "      <td>0.037313</td>\n",
       "      <td>-0.100629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.019763</td>\n",
       "      <td>-0.019048</td>\n",
       "      <td>-0.047619</td>\n",
       "      <td>-0.062857</td>\n",
       "      <td>0.001522</td>\n",
       "      <td>0.113953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-0.057803</td>\n",
       "      <td>-0.150000</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.367925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>0.053191</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.074074</td>\n",
       "      <td>0.064748</td>\n",
       "      <td>-0.104895</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>-0.020161</td>\n",
       "      <td>-0.873786</td>\n",
       "      <td>-0.025000</td>\n",
       "      <td>-0.060976</td>\n",
       "      <td>0.033435</td>\n",
       "      <td>0.091858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>-0.067485</td>\n",
       "      <td>-0.058824</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>-0.083582</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.222222</td>\n",
       "      <td>-0.176471</td>\n",
       "      <td>-0.020202</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>-0.027027</td>\n",
       "      <td>0.164062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.032922</td>\n",
       "      <td>20.153846</td>\n",
       "      <td>-0.051282</td>\n",
       "      <td>-0.032468</td>\n",
       "      <td>-0.239706</td>\n",
       "      <td>0.091778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>-0.084691</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>-0.023810</td>\n",
       "      <td>-0.061856</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.074074</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>-0.140940</td>\n",
       "      <td>-0.048780</td>\n",
       "      <td>-0.004255</td>\n",
       "      <td>-0.465455</td>\n",
       "      <td>-0.027027</td>\n",
       "      <td>-0.020134</td>\n",
       "      <td>-0.075435</td>\n",
       "      <td>0.106830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>-0.081169</td>\n",
       "      <td>-0.093750</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.131673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>-0.048780</td>\n",
       "      <td>-0.065934</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080000</td>\n",
       "      <td>-0.019737</td>\n",
       "      <td>-0.078125</td>\n",
       "      <td>-0.025641</td>\n",
       "      <td>-0.034188</td>\n",
       "      <td>-0.571429</td>\n",
       "      <td>-0.027778</td>\n",
       "      <td>-0.054795</td>\n",
       "      <td>-0.020921</td>\n",
       "      <td>0.031646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>-0.056537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>-0.052632</td>\n",
       "      <td>0.040881</td>\n",
       "      <td>-0.028571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.070588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>-0.033557</td>\n",
       "      <td>0.110169</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>-0.095238</td>\n",
       "      <td>-0.057143</td>\n",
       "      <td>-0.007246</td>\n",
       "      <td>-0.032051</td>\n",
       "      <td>0.042945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>-0.059925</td>\n",
       "      <td>-0.103448</td>\n",
       "      <td>0.037736</td>\n",
       "      <td>-0.181818</td>\n",
       "      <td>-0.055556</td>\n",
       "      <td>-0.087613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>-0.051282</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>-0.069444</td>\n",
       "      <td>-0.206107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.022026</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.030303</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>-0.068433</td>\n",
       "      <td>0.027941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>-0.051793</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>-0.444444</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>-0.026490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.054054</td>\n",
       "      <td>0.025316</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080000</td>\n",
       "      <td>0.044776</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>-0.009009</td>\n",
       "      <td>-0.583333</td>\n",
       "      <td>-0.031250</td>\n",
       "      <td>-0.037037</td>\n",
       "      <td>-0.082938</td>\n",
       "      <td>0.021459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 207 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "TB prevalence, all forms (per 100 000 population per year)  Afghanistan  \\\n",
       "1990                                                                NaN   \n",
       "1991                                                          -0.016055   \n",
       "1992                                                          -0.016317   \n",
       "1993                                                          -0.016588   \n",
       "1994                                                          -0.019277   \n",
       "1995                                                          -0.024570   \n",
       "1996                                                           0.000000   \n",
       "1997                                                          -0.025189   \n",
       "1998                                                          -0.033592   \n",
       "1999                                                          -0.002674   \n",
       "2000                                                          -0.072386   \n",
       "2001                                                          -0.057803   \n",
       "2002                                                          -0.067485   \n",
       "2003                                                           0.013158   \n",
       "2004                                                          -0.081169   \n",
       "2005                                                          -0.056537   \n",
       "2006                                                          -0.059925   \n",
       "2007                                                          -0.051793   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Albania  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.047619   \n",
       "1992                                                        0.025000   \n",
       "1993                                                        0.024390   \n",
       "1994                                                        0.000000   \n",
       "1995                                                        0.023810   \n",
       "1996                                                       -0.023256   \n",
       "1997                                                        0.047619   \n",
       "1998                                                       -0.022727   \n",
       "1999                                                       -0.023256   \n",
       "2000                                                       -0.047619   \n",
       "2001                                                       -0.150000   \n",
       "2002                                                       -0.058824   \n",
       "2003                                                        0.000000   \n",
       "2004                                                       -0.093750   \n",
       "2005                                                        0.000000   \n",
       "2006                                                       -0.103448   \n",
       "2007                                                       -0.153846   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Algeria  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.022222   \n",
       "1992                                                        0.000000   \n",
       "1993                                                       -0.022727   \n",
       "1994                                                        0.000000   \n",
       "1995                                                       -0.023256   \n",
       "1996                                                        0.023810   \n",
       "1997                                                        0.023256   \n",
       "1998                                                        0.022727   \n",
       "1999                                                        0.022222   \n",
       "2000                                                        0.043478   \n",
       "2001                                                        0.020833   \n",
       "2002                                                        0.020408   \n",
       "2003                                                        0.020000   \n",
       "2004                                                        0.019608   \n",
       "2005                                                        0.019231   \n",
       "2006                                                        0.037736   \n",
       "2007                                                        0.018182   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  American Samoa  \\\n",
       "1990                                                                   NaN   \n",
       "1991                                                             -0.666667   \n",
       "1992                                                             -0.714286   \n",
       "1993                                                              3.500000   \n",
       "1994                                                             -0.055556   \n",
       "1995                                                              0.294118   \n",
       "1996                                                             -1.000000   \n",
       "1997                                                                   inf   \n",
       "1998                                                             -0.520000   \n",
       "1999                                                             -0.333333   \n",
       "2000                                                              0.000000   \n",
       "2001                                                             -0.250000   \n",
       "2002                                                             -0.166667   \n",
       "2003                                                              0.200000   \n",
       "2004                                                              0.500000   \n",
       "2005                                                              0.222222   \n",
       "2006                                                             -0.181818   \n",
       "2007                                                             -0.444444   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Andorra  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.051282   \n",
       "1992                                                       -0.054054   \n",
       "1993                                                       -0.057143   \n",
       "1994                                                       -0.030303   \n",
       "1995                                                       -0.062500   \n",
       "1996                                                       -0.066667   \n",
       "1997                                                       -0.178571   \n",
       "1998                                                        0.043478   \n",
       "1999                                                       -0.083333   \n",
       "2000                                                       -0.090909   \n",
       "2001                                                        0.000000   \n",
       "2002                                                        0.050000   \n",
       "2003                                                       -0.142857   \n",
       "2004                                                        0.055556   \n",
       "2005                                                       -0.052632   \n",
       "2006                                                       -0.055556   \n",
       "2007                                                        0.117647   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Angola  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                        0.000000   \n",
       "1992                                                       -0.001946   \n",
       "1993                                                       -0.001949   \n",
       "1994                                                       -0.003906   \n",
       "1995                                                       -0.003922   \n",
       "1996                                                        0.007874   \n",
       "1997                                                       -0.291016   \n",
       "1998                                                        0.140496   \n",
       "1999                                                       -0.072464   \n",
       "2000                                                        0.380208   \n",
       "2001                                                       -0.367925   \n",
       "2002                                                       -0.083582   \n",
       "2003                                                       -0.084691   \n",
       "2004                                                        0.131673   \n",
       "2005                                                        0.040881   \n",
       "2006                                                       -0.087613   \n",
       "2007                                                       -0.026490   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Anguilla  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                        0.000000   \n",
       "1992                                                       -0.026316   \n",
       "1993                                                        0.000000   \n",
       "1994                                                       -0.027027   \n",
       "1995                                                       -0.027778   \n",
       "1996                                                        0.000000   \n",
       "1997                                                        0.028571   \n",
       "1998                                                        0.000000   \n",
       "1999                                                        0.000000   \n",
       "2000                                                       -0.027778   \n",
       "2001                                                        0.000000   \n",
       "2002                                                        0.000000   \n",
       "2003                                                        0.000000   \n",
       "2004                                                        0.000000   \n",
       "2005                                                       -0.028571   \n",
       "2006                                                        0.000000   \n",
       "2007                                                        0.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Antigua and Barbuda  \\\n",
       "1990                                                                        NaN   \n",
       "1991                                                                  -0.062500   \n",
       "1992                                                                   0.000000   \n",
       "1993                                                                  -0.066667   \n",
       "1994                                                                  -0.071429   \n",
       "1995                                                                  -0.076923   \n",
       "1996                                                                   0.000000   \n",
       "1997                                                                  -0.083333   \n",
       "1998                                                                   0.000000   \n",
       "1999                                                                  -0.181818   \n",
       "2000                                                                  -0.111111   \n",
       "2001                                                                   0.125000   \n",
       "2002                                                                  -0.222222   \n",
       "2003                                                                   0.285714   \n",
       "2004                                                                  -0.111111   \n",
       "2005                                                                   0.000000   \n",
       "2006                                                                   0.125000   \n",
       "2007                                                                   0.000000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Argentina  \\\n",
       "1990                                                              NaN   \n",
       "1991                                                        -0.052083   \n",
       "1992                                                        -0.054945   \n",
       "1993                                                        -0.046512   \n",
       "1994                                                        -0.048780   \n",
       "1995                                                        -0.051282   \n",
       "1996                                                        -0.040541   \n",
       "1997                                                        -0.056338   \n",
       "1998                                                        -0.059701   \n",
       "1999                                                        -0.079365   \n",
       "2000                                                        -0.103448   \n",
       "2001                                                        -0.019231   \n",
       "2002                                                        -0.176471   \n",
       "2003                                                        -0.023810   \n",
       "2004                                                        -0.048780   \n",
       "2005                                                         0.000000   \n",
       "2006                                                        -0.051282   \n",
       "2007                                                        -0.054054   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Armenia  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.057692   \n",
       "1992                                                        0.040816   \n",
       "1993                                                        0.078431   \n",
       "1994                                                        0.090909   \n",
       "1995                                                        0.133333   \n",
       "1996                                                        0.088235   \n",
       "1997                                                        0.013514   \n",
       "1998                                                       -0.013333   \n",
       "1999                                                        0.162162   \n",
       "2000                                                        0.093023   \n",
       "2001                                                        0.053191   \n",
       "2002                                                       -0.020202   \n",
       "2003                                                       -0.061856   \n",
       "2004                                                       -0.065934   \n",
       "2005                                                       -0.070588   \n",
       "2006                                                        0.000000   \n",
       "2007                                                        0.025316   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    ...     \\\n",
       "1990                                                          ...      \n",
       "1991                                                          ...      \n",
       "1992                                                          ...      \n",
       "1993                                                          ...      \n",
       "1994                                                          ...      \n",
       "1995                                                          ...      \n",
       "1996                                                          ...      \n",
       "1997                                                          ...      \n",
       "1998                                                          ...      \n",
       "1999                                                          ...      \n",
       "2000                                                          ...      \n",
       "2001                                                          ...      \n",
       "2002                                                          ...      \n",
       "2003                                                          ...      \n",
       "2004                                                          ...      \n",
       "2005                                                          ...      \n",
       "2006                                                          ...      \n",
       "2007                                                          ...      \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Uruguay  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.028571   \n",
       "1992                                                       -0.029412   \n",
       "1993                                                       -0.030303   \n",
       "1994                                                       -0.031250   \n",
       "1995                                                       -0.032258   \n",
       "1996                                                       -0.066667   \n",
       "1997                                                       -0.035714   \n",
       "1998                                                        0.037037   \n",
       "1999                                                        0.000000   \n",
       "2000                                                       -0.035714   \n",
       "2001                                                       -0.074074   \n",
       "2002                                                        0.080000   \n",
       "2003                                                       -0.074074   \n",
       "2004                                                       -0.080000   \n",
       "2005                                                        0.043478   \n",
       "2006                                                        0.041667   \n",
       "2007                                                       -0.080000   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Uzbekistan  \\\n",
       "1990                                                               NaN   \n",
       "1991                                                         -0.078947   \n",
       "1992                                                         -0.028571   \n",
       "1993                                                          0.156863   \n",
       "1994                                                         -0.016949   \n",
       "1995                                                          0.025862   \n",
       "1996                                                         -0.067227   \n",
       "1997                                                          0.099099   \n",
       "1998                                                          0.057377   \n",
       "1999                                                          0.038760   \n",
       "2000                                                          0.037313   \n",
       "2001                                                          0.064748   \n",
       "2002                                                         -0.027027   \n",
       "2003                                                          0.055556   \n",
       "2004                                                         -0.019737   \n",
       "2005                                                         -0.033557   \n",
       "2006                                                         -0.069444   \n",
       "2007                                                          0.044776   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)   Vanuatu  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.035971   \n",
       "1992                                                       -0.033582   \n",
       "1993                                                       -0.034749   \n",
       "1994                                                       -0.032000   \n",
       "1995                                                       -0.033058   \n",
       "1996                                                       -0.034188   \n",
       "1997                                                       -0.035398   \n",
       "1998                                                       -0.032110   \n",
       "1999                                                       -0.246445   \n",
       "2000                                                       -0.100629   \n",
       "2001                                                       -0.104895   \n",
       "2002                                                        0.164062   \n",
       "2003                                                       -0.140940   \n",
       "2004                                                       -0.078125   \n",
       "2005                                                        0.110169   \n",
       "2006                                                       -0.206107   \n",
       "2007                                                       -0.019231   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Venezuela  \\\n",
       "1990                                                              NaN   \n",
       "1991                                                        -0.021739   \n",
       "1992                                                        -0.022222   \n",
       "1993                                                        -0.022727   \n",
       "1994                                                        -0.023256   \n",
       "1995                                                         0.000000   \n",
       "1996                                                        -0.023810   \n",
       "1997                                                         0.000000   \n",
       "1998                                                        -0.024390   \n",
       "1999                                                        -0.025000   \n",
       "2000                                                         0.000000   \n",
       "2001                                                         0.051282   \n",
       "2002                                                         0.000000   \n",
       "2003                                                        -0.048780   \n",
       "2004                                                        -0.025641   \n",
       "2005                                                         0.000000   \n",
       "2006                                                         0.000000   \n",
       "2007                                                         0.026316   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Viet Nam  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.010959   \n",
       "1992                                                       -0.008310   \n",
       "1993                                                       -0.011173   \n",
       "1994                                                       -0.011299   \n",
       "1995                                                       -0.011429   \n",
       "1996                                                       -0.098266   \n",
       "1997                                                       -0.125000   \n",
       "1998                                                       -0.043956   \n",
       "1999                                                       -0.030651   \n",
       "2000                                                       -0.019763   \n",
       "2001                                                       -0.020161   \n",
       "2002                                                       -0.032922   \n",
       "2003                                                       -0.004255   \n",
       "2004                                                       -0.034188   \n",
       "2005                                                        0.004425   \n",
       "2006                                                       -0.022026   \n",
       "2007                                                       -0.009009   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  Wallis et Futuna  \\\n",
       "1990                                                                     NaN   \n",
       "1991                                                                1.793651   \n",
       "1992                                                               -0.818182   \n",
       "1993                                                                1.718750   \n",
       "1994                                                               -0.011494   \n",
       "1995                                                               -0.459302   \n",
       "1996                                                                0.322581   \n",
       "1997                                                                0.731707   \n",
       "1998                                                               -0.497653   \n",
       "1999                                                               -0.018692   \n",
       "2000                                                               -0.019048   \n",
       "2001                                                               -0.873786   \n",
       "2002                                                               20.153846   \n",
       "2003                                                               -0.465455   \n",
       "2004                                                               -0.571429   \n",
       "2005                                                               -0.095238   \n",
       "2006                                                                0.052632   \n",
       "2007                                                               -0.583333   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)  West Bank and Gaza  \\\n",
       "1990                                                                       NaN   \n",
       "1991                                                                 -0.018182   \n",
       "1992                                                                  0.000000   \n",
       "1993                                                                 -0.037037   \n",
       "1994                                                                  0.000000   \n",
       "1995                                                                 -0.038462   \n",
       "1996                                                                 -0.020000   \n",
       "1997                                                                 -0.061224   \n",
       "1998                                                                 -0.043478   \n",
       "1999                                                                 -0.045455   \n",
       "2000                                                                 -0.047619   \n",
       "2001                                                                 -0.025000   \n",
       "2002                                                                 -0.051282   \n",
       "2003                                                                 -0.027027   \n",
       "2004                                                                 -0.027778   \n",
       "2005                                                                 -0.057143   \n",
       "2006                                                                 -0.030303   \n",
       "2007                                                                 -0.031250   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)     Yemen  \\\n",
       "1990                                                             NaN   \n",
       "1991                                                       -0.015094   \n",
       "1992                                                        0.007663   \n",
       "1993                                                       -0.038023   \n",
       "1994                                                       -0.011858   \n",
       "1995                                                       -0.024000   \n",
       "1996                                                       -0.045082   \n",
       "1997                                                       -0.111588   \n",
       "1998                                                       -0.062802   \n",
       "1999                                                       -0.097938   \n",
       "2000                                                       -0.062857   \n",
       "2001                                                       -0.060976   \n",
       "2002                                                       -0.032468   \n",
       "2003                                                       -0.020134   \n",
       "2004                                                       -0.054795   \n",
       "2005                                                       -0.007246   \n",
       "2006                                                       -0.014599   \n",
       "2007                                                       -0.037037   \n",
       "\n",
       "TB prevalence, all forms (per 100 000 population per year)    Zambia  Zimbabwe  \n",
       "1990                                                             NaN       NaN  \n",
       "1991                                                        0.045872  0.019560  \n",
       "1992                                                        0.083333 -0.004796  \n",
       "1993                                                        0.064777  0.009639  \n",
       "1994                                                        0.057034  0.016706  \n",
       "1995                                                        0.052158  0.030516  \n",
       "1996                                                        0.029060  0.031891  \n",
       "1997                                                        0.039867  0.061810  \n",
       "1998                                                        0.012780 -0.185031  \n",
       "1999                                                        0.036278  0.096939  \n",
       "2000                                                        0.001522  0.113953  \n",
       "2001                                                        0.033435  0.091858  \n",
       "2002                                                       -0.239706  0.091778  \n",
       "2003                                                       -0.075435  0.106830  \n",
       "2004                                                       -0.020921  0.031646  \n",
       "2005                                                       -0.032051  0.042945  \n",
       "2006                                                       -0.068433  0.027941  \n",
       "2007                                                       -0.082938  0.021459  \n",
       "\n",
       "[18 rows x 207 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If you want to check percentage change in exsiting cases over the years\n",
    "tb_pct_change = existing_df.pct_change()\n",
    "tb_pct_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990         NaN\n",
       "1991   -0.045455\n",
       "1992   -0.047619\n",
       "1993   -0.075000\n",
       "1994   -0.054054\n",
       "1995   -0.028571\n",
       "1996   -0.029412\n",
       "1997   -0.090909\n",
       "1998    0.000000\n",
       "1999   -0.066667\n",
       "2000   -0.035714\n",
       "2001   -0.037037\n",
       "2002    0.000000\n",
       "2003   -0.038462\n",
       "2004   -0.040000\n",
       "2005    0.000000\n",
       "2006    0.000000\n",
       "2007   -0.041667\n",
       "Name: Spain, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us look at curious case of Spain. What do you infer?\n",
    "\n",
    "tb_pct_change_spain = existing_df.Spain.pct_change()\n",
    "tb_pct_change_spain\n",
    "\n",
    "#existing_df.Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990         NaN\n",
       "1991    0.000000\n",
       "1992   -0.166667\n",
       "1993    0.000000\n",
       "1994    0.000000\n",
       "1995   -0.200000\n",
       "1996    0.000000\n",
       "1997    0.000000\n",
       "1998    0.000000\n",
       "1999    0.000000\n",
       "2000    0.000000\n",
       "2001   -0.250000\n",
       "2002    0.000000\n",
       "2003    0.000000\n",
       "2004    0.000000\n",
       "2005    0.000000\n",
       "2006    0.000000\n",
       "2007    0.000000\n",
       "Name: Spain, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_pct_change_spain = deaths_df.Spain.pct_change()\n",
    "tb_pct_change_spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_pct_change_spain.max()         # It always decreased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990     9\n",
       "1991     9\n",
       "1992    10\n",
       "1993    10\n",
       "1994     9\n",
       "1995     9\n",
       "1996     9\n",
       "1997     9\n",
       "1998     9\n",
       "1999     9\n",
       "2000     9\n",
       "2001     9\n",
       "2002     9\n",
       "2003    10\n",
       "2004    10\n",
       "2005    11\n",
       "2006    11\n",
       "2007    12\n",
       "Name: United Kingdom, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "existing_df['United Kingdom']                               # 1992, 2003, 2005, 2007 shows increase\n",
    "#existing_df['United Kingdom'].pct_change()        \n",
    "#existing_df['United Kingdom'].pct_change().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1992'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "existing_df['United Kingdom'].pct_change().argmax()         # Returns the indices of the maximum values along an axis\n",
    "#existing_df['United Kingdom'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f792f78080>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HDlBYWMi5c+eIjo4udTvOF2QKCAggPT29Gp6NUpVTrj5tETkrIi1E5LTTtJMi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\nYEK3CSTGJNKnVR/tPlFKVQs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f792eaadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let us go ahead and do some plotting\n",
    "%matplotlib inline\n",
    "existing_df[['United Kingdom', 'Spain', 'Colombia']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1f793f9fc88>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793facfd0>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fc29b0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1f793fa4f60>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fa9eb8>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fb6fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fbd828>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fcf940>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fcfa58>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1f793facf28>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fc2898>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fd4ac8>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1f793fa9fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fbd940>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fd42b0>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1f793f9fe10>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fa4e48>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fb1f60>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fb67b8>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fc98d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1f793fc99e8>]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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amj8XbTOzhLinbcmqQj+5CjFYPbmnbWZWAy7aJTW1r1YHzl3ampo/F20zs4S4p23J0pi/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f792f42b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How about box-plots\n",
    "existing_df[['United Kingdom', 'Spain', 'Colombia']].boxplot()\n",
    "#existing_df[['Spain']].sort_values([\"Spain\"])                  # Lets discuss spread of Spain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990            Djibouti\n",
       "1991            Djibouti\n",
       "1992            Djibouti\n",
       "1993            Djibouti\n",
       "1994            Djibouti\n",
       "1995            Djibouti\n",
       "1996            Kiribati\n",
       "1997            Kiribati\n",
       "1998            Cambodia\n",
       "1999    Korea, Dem. Rep.\n",
       "2000            Djibouti\n",
       "2001           Swaziland\n",
       "2002            Djibouti\n",
       "2003            Djibouti\n",
       "2004            Djibouti\n",
       "2005            Djibouti\n",
       "2006            Djibouti\n",
       "2007            Djibouti\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let us ask some questions to the data\n",
    "#Which country has the highest number of existing and new TB cases Year wise.\n",
    "\n",
    "# Solution: Get a Series data with index values as Years & Country with max existing cases\n",
    "existing_df.apply(pd.Series.argmax, axis=1)\n",
    "\n",
    "# Info: Djibouti country located in the Horn of Africa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#What about world trends? \n",
    "#What is following code doing?\n",
    "deaths_df.head()\n",
    "deaths_df.sum(axis=1)\n",
    "deaths_total_per_year_df = deaths_df.sum(axis=1)\n",
    "existing_total_per_year_df = existing_df.sum(axis=1)\n",
    "new_total_per_year_df = new_df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f793feaf28>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EjhkKs02rqqoiMjKSnJwcOnbs2Oz206ZN48CBA8ybN++Ij+WX/GzxD4cKs/r1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f793fea588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "world_trends_df = pd.DataFrame({\n",
    "           'Total deaths per 100K' : deaths_total_per_year_df, \n",
    "           'Total existing cases per 100K' : existing_total_per_year_df, \n",
    "           'Total new cases per 100K' : new_total_per_year_df}, \n",
    "       index=deaths_total_per_year_df.index)\n",
    "\n",
    "world_trends_df.plot(figsize=(12,6)).legend(\n",
    "    loc='center left', \n",
    "    bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "#What inferences can we derive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#What about specific countries?\n",
    "deaths_by_country_mean = deaths_df.mean()\n",
    "deaths_by_country_mean_summary = deaths_by_country_mean.describe()\n",
    "existing_by_country_mean = existing_df.mean()\n",
    "existing_by_country_mean_summary = existing_by_country_mean.describe()\n",
    "new_by_country_mean = new_df.mean()\n",
    "new_by_country_mean_summary = new_by_country_mean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'v'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-db034d21374f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdeaths_by_country_mean\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m24\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\User\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   2670\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2671\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2672\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2673\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2674\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'v'"
     ]
    }
   ],
   "source": [
    "deaths_by_country_mean.v().plot(kind='bar', figsize=(24,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Let us think about outlier countries  -- Get all data datapoints that are more than 1.5 times the median of all the countries\n",
    "print(deaths_by_country_mean.describe())\n",
    "\n",
    "deaths_outlier = deaths_by_country_mean_summary['50%']*1.5\n",
    "existing_outlier = existing_by_country_mean_summary['50%']*1.5\n",
    "new_outlier = new_by_country_mean_summary['50%']*1.5\n",
    "\n",
    "outlier_countries_by_deaths_index = deaths_by_country_mean > deaths_outlier\n",
    "outlier_countries_by_existing_index =  existing_by_country_mean > existing_outlier\n",
    "outlier_countries_by_new_index = new_by_country_mean > new_outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Proportions of countries as outliers\n",
    "\n",
    "print ('Consider countries whose Mean TB Deaths are greater than: ',deaths_outlier)\n",
    "print ('Consider countries whose Mean Existing Cases are greater than: ',existing_outlier)\n",
    "print ('Consider countries whose Mean New Cases are greater than: ',new_outlier)\n",
    "\n",
    "print ('--------------------------------------------------------------------------------')\n",
    "num_countries = len(deaths_df.T)\n",
    "print ('Number of countries: ',num_countries)\n",
    "print ('Number (Mean TB Deaths) of outlier countries: ',sum(outlier_countries_by_deaths_index))\n",
    "print ('Number (Mean Existing Cases) of outlier countries: ',sum(outlier_countries_by_existing_index))\n",
    "print ('Number (Mean New Cases) of outlier countries: ',sum(outlier_countries_by_new_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What if you change outlier criteria? ~ As a Data Scientist always keep questioning the data and find ways to get more insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outlier_deaths_df = deaths_df.T[ outlier_countries_by_deaths_index ].T\n",
    "outlier_existing_df = existing_df.T[ outlier_countries_by_existing_index ].T\n",
    "outlier_new_df = new_df.T[ outlier_countries_by_new_index ].T\n",
    "\n",
    "#Filter the data frame\n",
    "print(outlier_deaths_df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outlier_new_df.plot(figsize=(12,4)).legend(loc='center left', bbox_to_anchor=(1, 0.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#What do you infer from above dataset? Can we somehow combine all of that information?\n",
    "\n",
    "#print(outlier_new_df.head())\n",
    "average_outlier_country = outlier_new_df.mean(axis=1)\n",
    "average_outlier_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Compare this with rest of world\n",
    "\n",
    "#print(new_df.T[ - outlier_countries_by_new_index ].T.head())     # Gets all the left over Countries that are not outlier\n",
    "avearge_better_world_country = new_df.T[ - outlier_countries_by_new_index ].T.mean(axis=1)\n",
    "avearge_better_world_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'avearge_better_world_country' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-f389905bd65b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m two_world_df = pd.DataFrame({ \n\u001b[1;32m----> 4\u001b[1;33m             \u001b[1;34m'Average Better World Country'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mavearge_better_world_country\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m             'Average Outlier Country' : average_outlier_country},\n\u001b[0;32m      6\u001b[0m         index = new_df.index)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'avearge_better_world_country' is not defined"
     ]
    }
   ],
   "source": [
    "# Let us plot the 2 data set and see how it behaves for Outliers Countries and Better World Countries\n",
    "\n",
    "two_world_df = pd.DataFrame({ \n",
    "            'Average Better World Country': avearge_better_world_country,\n",
    "            'Average Outlier Country' : average_outlier_country},\n",
    "        index = new_df.index)\n",
    "two_world_df\n",
    "#two_world_df.plot(title=\"Estimated new TB cases per 100K\",figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fdcb0267f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uVShhIUGNbgKY+fPnc8UVV5R7bOrUqaVdQCZMmMCyZcsYP348YWFhgC0xSE1N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\n6vYeM29ud361urWrj7tWfkt4gA9P/2y8U6YN6bVq5o+KYG1+LVY3OTP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nDy5OAwmHFCDP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\nAAAgIUI1AAAAkND/Ayd+HVihEYf2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fdcb0432e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_world_df.pct_change().plot(title=\"Percentage change in estimated new TB cases\", figsize=(12,8))\n",
    "#What do you infer here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndeath_china_study = deaths_df[['China']]\\nexist_china_study = existing_df[['China']]\\nnew_china_study = new_df[['China']]\\nchina_study = pd.concat([death_china_study,exist_china_study,new_china_study],axis=1)\\nchina_study.columns = ['Death','Existing','New']\\nprint(china_study)\\n\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6tmFI1zZsyD/Aixk5vLVkK5t3H+KVawfh8diFV6GgRjV6EYnCSfKvq+p7bnEu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fdc942acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# China: DOTS strategy\n",
    "\n",
    "existing_df.China.plot(title=\"Estimated existing TB cases in China\")\n",
    "\n",
    "\n",
    "#print(new_df[['China']].index)\n",
    "'''\n",
    "death_china_study = deaths_df[['China']]\n",
    "exist_china_study = existing_df[['China']]\n",
    "new_china_study = new_df[['China']]\n",
    "china_study = pd.concat([death_china_study,exist_china_study,new_china_study],axis=1)\n",
    "china_study.columns = ['Death','Existing','New']\n",
    "print(china_study)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Swaziland'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.apply(pd.Series.argmax, axis=1)['2007']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Another example**\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}