census-python-practice (2).ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Load libraries and dataframe","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\ndf = pd.read_csv(\"../input/adult-census-income/adult.csv\")\ndf","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-08T15:08:48.718792Z","iopub.execute_input":"2021-08-08T15:08:48.719300Z","iopub.status.idle":"2021-08-08T15:08:48.830624Z","shell.execute_reply.started":"2021-08-08T15:08:48.719267Z","shell.execute_reply":"2021-08-08T15:08:48.829649Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"/kaggle/input/adult-census-income/adult.csv\n","output_type":"stream"},{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"       age workclass  fnlwgt     education  education.num      marital.status  \\\n0       90         ?   77053       HS-grad              9             Widowed   \n1       82   Private  132870       HS-grad              9             Widowed   \n2       66         ?  186061  Some-college             10             Widowed   \n3       54   Private  140359       7th-8th              4            Divorced   \n4       41   Private  264663  Some-college             10           Separated   \n...    ...       ...     ...           ...            ...                 ...   \n32556   22   Private  310152  Some-college             10       Never-married   \n32557   27   Private  257302    Assoc-acdm             12  Married-civ-spouse   \n32558   40   Private  154374       HS-grad              9  Married-civ-spouse   \n32559   58   Private  151910       HS-grad              9             Widowed   \n32560   22   Private  201490       HS-grad              9       Never-married   \n\n              occupation   relationship   race     sex  capital.gain  \\\n0                      ?  Not-in-family  White  Female             0   \n1        Exec-managerial  Not-in-family  White  Female             0   \n2                      ?      Unmarried  Black  Female             0   \n3      Machine-op-inspct      Unmarried  White  Female             0   \n4         Prof-specialty      Own-child  White  Female             0   \n...                  ...            ...    ...     ...           ...   \n32556    Protective-serv  Not-in-family  White    Male             0   \n32557       Tech-support           Wife  White  Female             0   \n32558  Machine-op-inspct        Husband  White    Male             0   \n32559       Adm-clerical      Unmarried  White  Female             0   \n32560       Adm-clerical      Own-child  White    Male             0   \n\n       capital.loss  hours.per.week native.country income  \n0              4356              40  United-States  <=50K  \n1              4356              18  United-States  <=50K  \n2              4356              40  United-States  <=50K  \n3              3900              40  United-States  <=50K  \n4              3900              40  United-States  <=50K  \n...             ...             ...            ...    ...  \n32556             0              40  United-States  <=50K  \n32557             0              38  United-States  <=50K  \n32558             0              40  United-States   >50K  \n32559             0              40  United-States  <=50K  \n32560             0              20  United-States  <=50K  \n\n[32561 rows x 15 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>education</th>\n      <th>education.num</th>\n      <th>marital.status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capital.gain</th>\n      <th>capital.loss</th>\n      <th>hours.per.week</th>\n      <th>native.country</th>\n      <th>income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>90</td>\n      <td>?</td>\n      <td>77053</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>?</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>82</td>\n      <td>Private</td>\n      <td>132870</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Exec-managerial</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>18</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>66</td>\n      <td>?</td>\n      <td>186061</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Widowed</td>\n      <td>?</td>\n      <td>Unmarried</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>54</td>\n      <td>Private</td>\n      <td>140359</td>\n      <td>7th-8th</td>\n      <td>4</td>\n      <td>Divorced</td>\n      <td>Machine-op-inspct</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>41</td>\n      <td>Private</td>\n      <td>264663</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Separated</td>\n      <td>Prof-specialty</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32556</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>310152</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Never-married</td>\n      <td>Protective-serv</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32557</th>\n      <td>27</td>\n      <td>Private</td>\n      <td>257302</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Tech-support</td>\n      <td>Wife</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32558</th>\n      <td>40</td>\n      <td>Private</td>\n      <td>154374</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>32559</th>\n      <td>58</td>\n      <td>Private</td>\n      <td>151910</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Adm-clerical</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32560</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>201490</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n<p>32561 rows × 15 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Initial exploration of the data\n","metadata":{}},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:48.832106Z","iopub.execute_input":"2021-08-08T15:08:48.832373Z","iopub.status.idle":"2021-08-08T15:08:48.869190Z","shell.execute_reply.started":"2021-08-08T15:08:48.832347Z","shell.execute_reply":"2021-08-08T15:08:48.868173Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"                age        fnlwgt  education.num  capital.gain  capital.loss  \\\ncount  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \nmean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \nstd       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \nmin       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \nmax       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n\n       hours.per.week  \ncount    32561.000000  \nmean        40.437456  \nstd         12.347429  \nmin          1.000000  \n25%         40.000000  \n50%         40.000000  \n75%         45.000000  \nmax         99.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>fnlwgt</th>\n      <th>education.num</th>\n      <th>capital.gain</th>\n      <th>capital.loss</th>\n      <th>hours.per.week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32561.000000</td>\n      <td>3.256100e+04</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n      <td>32561.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>38.581647</td>\n      <td>1.897784e+05</td>\n      <td>10.080679</td>\n      <td>1077.648844</td>\n      <td>87.303830</td>\n      <td>40.437456</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13.640433</td>\n      <td>1.055500e+05</td>\n      <td>2.572720</td>\n      <td>7385.292085</td>\n      <td>402.960219</td>\n      <td>12.347429</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>1.228500e+04</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>28.000000</td>\n      <td>1.178270e+05</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.000000</td>\n      <td>1.783560e+05</td>\n      <td>10.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>48.000000</td>\n      <td>2.370510e+05</td>\n      <td>12.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>90.000000</td>\n      <td>1.484705e+06</td>\n      <td>16.000000</td>\n      <td>99999.000000</td>\n      <td>4356.000000</td>\n      <td>99.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:48.871001Z","iopub.execute_input":"2021-08-08T15:08:48.871267Z","iopub.status.idle":"2021-08-08T15:08:48.891008Z","shell.execute_reply.started":"2021-08-08T15:08:48.871242Z","shell.execute_reply":"2021-08-08T15:08:48.889788Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"   age workclass  fnlwgt     education  education.num marital.status  \\\n0   90         ?   77053       HS-grad              9        Widowed   \n1   82   Private  132870       HS-grad              9        Widowed   \n2   66         ?  186061  Some-college             10        Widowed   \n3   54   Private  140359       7th-8th              4       Divorced   \n4   41   Private  264663  Some-college             10      Separated   \n\n          occupation   relationship   race     sex  capital.gain  \\\n0                  ?  Not-in-family  White  Female             0   \n1    Exec-managerial  Not-in-family  White  Female             0   \n2                  ?      Unmarried  Black  Female             0   \n3  Machine-op-inspct      Unmarried  White  Female             0   \n4     Prof-specialty      Own-child  White  Female             0   \n\n   capital.loss  hours.per.week native.country income  \n0          4356              40  United-States  <=50K  \n1          4356              18  United-States  <=50K  \n2          4356              40  United-States  <=50K  \n3          3900              40  United-States  <=50K  \n4          3900              40  United-States  <=50K  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>education</th>\n      <th>education.num</th>\n      <th>marital.status</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capital.gain</th>\n      <th>capital.loss</th>\n      <th>hours.per.week</th>\n      <th>native.country</th>\n      <th>income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>90</td>\n      <td>?</td>\n      <td>77053</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>?</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>82</td>\n      <td>Private</td>\n      <td>132870</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Exec-managerial</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>18</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>66</td>\n      <td>?</td>\n      <td>186061</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Widowed</td>\n      <td>?</td>\n      <td>Unmarried</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>54</td>\n      <td>Private</td>\n      <td>140359</td>\n      <td>7th-8th</td>\n      <td>4</td>\n      <td>Divorced</td>\n      <td>Machine-op-inspct</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>41</td>\n      <td>Private</td>\n      <td>264663</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Separated</td>\n      <td>Prof-specialty</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Cleaning: Remove rows with question marks","metadata":{}},{"cell_type":"code","source":"df = df[~((df['age'] == '?' ) | (df['workclass'] == '?' ) | (df['education'] == '?' ) | (df['education.num'] == '?' ) | (df['marital.status'] == '?' ) | (df['occupation'] == '?' ) | (df['relationship'] == '?' )  | (df['race'] == '?' ) | (df['sex'] == '?' ) | (df['capital.gain'] == '?' ) | (df['capital.loss'] == '?' ) | (df['hours.per.week'] == '?' )  | (df['native.country'] == '?' ) | (df['income'] == '?' ))]\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:48.893606Z","iopub.execute_input":"2021-08-08T15:08:48.894245Z","iopub.status.idle":"2021-08-08T15:08:48.950823Z","shell.execute_reply.started":"2021-08-08T15:08:48.894201Z","shell.execute_reply":"2021-08-08T15:08:48.950006Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"# Data Cleaning: Correcting column names to be more appropriate for Pandas","metadata":{}},{"cell_type":"code","source":"df.columns = ['age', 'workclass', 'fnlwgt', 'edu', 'edu_num', 'marital', 'occupation', 'relationship', 'race', 'sex', 'capgain', 'caploss', 'hourspw', 'country', 'income']\ndf","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:48.951986Z","iopub.execute_input":"2021-08-08T15:08:48.952255Z","iopub.status.idle":"2021-08-08T15:08:48.981762Z","shell.execute_reply.started":"2021-08-08T15:08:48.952230Z","shell.execute_reply":"2021-08-08T15:08:48.980680Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"       age workclass  fnlwgt           edu  edu_num             marital  \\\n1       82   Private  132870       HS-grad        9             Widowed   \n3       54   Private  140359       7th-8th        4            Divorced   \n4       41   Private  264663  Some-college       10           Separated   \n5       34   Private  216864       HS-grad        9            Divorced   \n6       38   Private  150601          10th        6           Separated   \n...    ...       ...     ...           ...      ...                 ...   \n32556   22   Private  310152  Some-college       10       Never-married   \n32557   27   Private  257302    Assoc-acdm       12  Married-civ-spouse   \n32558   40   Private  154374       HS-grad        9  Married-civ-spouse   \n32559   58   Private  151910       HS-grad        9             Widowed   \n32560   22   Private  201490       HS-grad        9       Never-married   \n\n              occupation   relationship   race     sex  capgain  caploss  \\\n1        Exec-managerial  Not-in-family  White  Female        0     4356   \n3      Machine-op-inspct      Unmarried  White  Female        0     3900   \n4         Prof-specialty      Own-child  White  Female        0     3900   \n5          Other-service      Unmarried  White  Female        0     3770   \n6           Adm-clerical      Unmarried  White    Male        0     3770   \n...                  ...            ...    ...     ...      ...      ...   \n32556    Protective-serv  Not-in-family  White    Male        0        0   \n32557       Tech-support           Wife  White  Female        0        0   \n32558  Machine-op-inspct        Husband  White    Male        0        0   \n32559       Adm-clerical      Unmarried  White  Female        0        0   \n32560       Adm-clerical      Own-child  White    Male        0        0   \n\n       hourspw        country income  \n1           18  United-States  <=50K  \n3           40  United-States  <=50K  \n4           40  United-States  <=50K  \n5           45  United-States  <=50K  \n6           40  United-States  <=50K  \n...        ...            ...    ...  \n32556       40  United-States  <=50K  \n32557       38  United-States  <=50K  \n32558       40  United-States   >50K  \n32559       40  United-States  <=50K  \n32560       20  United-States  <=50K  \n\n[30162 rows x 15 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>edu</th>\n      <th>edu_num</th>\n      <th>marital</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capgain</th>\n      <th>caploss</th>\n      <th>hourspw</th>\n      <th>country</th>\n      <th>income</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>82</td>\n      <td>Private</td>\n      <td>132870</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Exec-managerial</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>18</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>54</td>\n      <td>Private</td>\n      <td>140359</td>\n      <td>7th-8th</td>\n      <td>4</td>\n      <td>Divorced</td>\n      <td>Machine-op-inspct</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>41</td>\n      <td>Private</td>\n      <td>264663</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Separated</td>\n      <td>Prof-specialty</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>34</td>\n      <td>Private</td>\n      <td>216864</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Other-service</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3770</td>\n      <td>45</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>150601</td>\n      <td>10th</td>\n      <td>6</td>\n      <td>Separated</td>\n      <td>Adm-clerical</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>3770</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32556</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>310152</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Never-married</td>\n      <td>Protective-serv</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32557</th>\n      <td>27</td>\n      <td>Private</td>\n      <td>257302</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Tech-support</td>\n      <td>Wife</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32558</th>\n      <td>40</td>\n      <td>Private</td>\n      <td>154374</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n    </tr>\n    <tr>\n      <th>32559</th>\n      <td>58</td>\n      <td>Private</td>\n      <td>151910</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Adm-clerical</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n    <tr>\n      <th>32560</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>201490</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n    </tr>\n  </tbody>\n</table>\n<p>30162 rows × 15 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Cleaning: Making a new column that is the difference of capgain and caploss","metadata":{}},{"cell_type":"code","source":"# df['capital.agg'] = df['capital.gain'] - df['capital.loss']\n# df = df.assign(capital.agg = df['capital.gain'] - df['capital.loss'])\n\npd.set_option('mode.chained_assignment', None)\n\ndf.loc[:,\"capagg\"] = df.capgain - df.caploss\ndf","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:48.983390Z","iopub.execute_input":"2021-08-08T15:08:48.983822Z","iopub.status.idle":"2021-08-08T15:08:49.018535Z","shell.execute_reply.started":"2021-08-08T15:08:48.983776Z","shell.execute_reply":"2021-08-08T15:08:49.017486Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"       age workclass  fnlwgt           edu  edu_num             marital  \\\n1       82   Private  132870       HS-grad        9             Widowed   \n3       54   Private  140359       7th-8th        4            Divorced   \n4       41   Private  264663  Some-college       10           Separated   \n5       34   Private  216864       HS-grad        9            Divorced   \n6       38   Private  150601          10th        6           Separated   \n...    ...       ...     ...           ...      ...                 ...   \n32556   22   Private  310152  Some-college       10       Never-married   \n32557   27   Private  257302    Assoc-acdm       12  Married-civ-spouse   \n32558   40   Private  154374       HS-grad        9  Married-civ-spouse   \n32559   58   Private  151910       HS-grad        9             Widowed   \n32560   22   Private  201490       HS-grad        9       Never-married   \n\n              occupation   relationship   race     sex  capgain  caploss  \\\n1        Exec-managerial  Not-in-family  White  Female        0     4356   \n3      Machine-op-inspct      Unmarried  White  Female        0     3900   \n4         Prof-specialty      Own-child  White  Female        0     3900   \n5          Other-service      Unmarried  White  Female        0     3770   \n6           Adm-clerical      Unmarried  White    Male        0     3770   \n...                  ...            ...    ...     ...      ...      ...   \n32556    Protective-serv  Not-in-family  White    Male        0        0   \n32557       Tech-support           Wife  White  Female        0        0   \n32558  Machine-op-inspct        Husband  White    Male        0        0   \n32559       Adm-clerical      Unmarried  White  Female        0        0   \n32560       Adm-clerical      Own-child  White    Male        0        0   \n\n       hourspw        country income  capagg  \n1           18  United-States  <=50K   -4356  \n3           40  United-States  <=50K   -3900  \n4           40  United-States  <=50K   -3900  \n5           45  United-States  <=50K   -3770  \n6           40  United-States  <=50K   -3770  \n...        ...            ...    ...     ...  \n32556       40  United-States  <=50K       0  \n32557       38  United-States  <=50K       0  \n32558       40  United-States   >50K       0  \n32559       40  United-States  <=50K       0  \n32560       20  United-States  <=50K       0  \n\n[30162 rows x 16 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>edu</th>\n      <th>edu_num</th>\n      <th>marital</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capgain</th>\n      <th>caploss</th>\n      <th>hourspw</th>\n      <th>country</th>\n      <th>income</th>\n      <th>capagg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>82</td>\n      <td>Private</td>\n      <td>132870</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Exec-managerial</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>4356</td>\n      <td>18</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>-4356</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>54</td>\n      <td>Private</td>\n      <td>140359</td>\n      <td>7th-8th</td>\n      <td>4</td>\n      <td>Divorced</td>\n      <td>Machine-op-inspct</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>-3900</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>41</td>\n      <td>Private</td>\n      <td>264663</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Separated</td>\n      <td>Prof-specialty</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3900</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>-3900</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>34</td>\n      <td>Private</td>\n      <td>216864</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Divorced</td>\n      <td>Other-service</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>3770</td>\n      <td>45</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>-3770</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>38</td>\n      <td>Private</td>\n      <td>150601</td>\n      <td>10th</td>\n      <td>6</td>\n      <td>Separated</td>\n      <td>Adm-clerical</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>3770</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>-3770</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32556</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>310152</td>\n      <td>Some-college</td>\n      <td>10</td>\n      <td>Never-married</td>\n      <td>Protective-serv</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32557</th>\n      <td>27</td>\n      <td>Private</td>\n      <td>257302</td>\n      <td>Assoc-acdm</td>\n      <td>12</td>\n      <td>Married-civ-spouse</td>\n      <td>Tech-support</td>\n      <td>Wife</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32558</th>\n      <td>40</td>\n      <td>Private</td>\n      <td>154374</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&gt;50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32559</th>\n      <td>58</td>\n      <td>Private</td>\n      <td>151910</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Widowed</td>\n      <td>Adm-clerical</td>\n      <td>Unmarried</td>\n      <td>White</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32560</th>\n      <td>22</td>\n      <td>Private</td>\n      <td>201490</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Never-married</td>\n      <td>Adm-clerical</td>\n      <td>Own-child</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>United-States</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>30162 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Cleaning: Making a subset that only has people born outside of US ","metadata":{}},{"cell_type":"code","source":"foreignborn = df.loc[df['country'] != \"United-States\"]\nforeignborn","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:49.020183Z","iopub.execute_input":"2021-08-08T15:08:49.020649Z","iopub.status.idle":"2021-08-08T15:08:49.058661Z","shell.execute_reply.started":"2021-08-08T15:08:49.020604Z","shell.execute_reply":"2021-08-08T15:08:49.057620Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"       age         workclass  fnlwgt          edu  edu_num  \\\n26      33           Private  228696      1st-4th        2   \n27      49           Private  122066      5th-6th        3   \n47      73  Self-emp-not-inc  233882      HS-grad        9   \n63      51  Self-emp-not-inc  160724    Bachelors       13   \n67      55      Self-emp-inc  124137  Prof-school       15   \n...    ...               ...     ...          ...      ...   \n32468   85           Private   98611    Bachelors       13   \n32513   45           Private  155093         10th        6   \n32536   54           Private  337992    Bachelors       13   \n32549   43           Private  260761      HS-grad        9   \n32554   32           Private  116138      Masters       14   \n\n                  marital         occupation   relationship  \\\n26     Married-civ-spouse       Craft-repair  Not-in-family   \n27     Married-civ-spouse      Other-service        Husband   \n47     Married-civ-spouse    Farming-fishing        Husband   \n63     Married-civ-spouse              Sales        Husband   \n67     Married-civ-spouse     Prof-specialty        Husband   \n...                   ...                ...            ...   \n32468  Married-civ-spouse    Exec-managerial        Husband   \n32513            Divorced      Other-service  Not-in-family   \n32536  Married-civ-spouse    Exec-managerial        Husband   \n32549  Married-civ-spouse  Machine-op-inspct        Husband   \n32554       Never-married       Tech-support  Not-in-family   \n\n                     race     sex  capgain  caploss  hourspw  \\\n26                  White    Male        0     2603       32   \n27                  White    Male        0     2603       40   \n47     Asian-Pac-Islander    Male        0     2457       40   \n63     Asian-Pac-Islander    Male        0     2415       40   \n67                  White    Male        0     2415       35   \n...                   ...     ...      ...      ...      ...   \n32468               White    Male        0        0        3   \n32513               Black  Female        0        0       38   \n32536  Asian-Pac-Islander    Male        0        0       50   \n32549               White    Male        0        0       40   \n32554  Asian-Pac-Islander    Male        0        0       11   \n\n                  country income  capagg  \n26                 Mexico  <=50K   -2603  \n27                 Greece  <=50K   -2603  \n47                Vietnam  <=50K   -2457  \n63                  China   >50K   -2415  \n67                 Greece   >50K   -2415  \n...                   ...    ...     ...  \n32468              Poland  <=50K       0  \n32513  Dominican-Republic  <=50K       0  \n32536               Japan   >50K       0  \n32549              Mexico  <=50K       0  \n32554              Taiwan  <=50K       0  \n\n[2658 rows x 16 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>workclass</th>\n      <th>fnlwgt</th>\n      <th>edu</th>\n      <th>edu_num</th>\n      <th>marital</th>\n      <th>occupation</th>\n      <th>relationship</th>\n      <th>race</th>\n      <th>sex</th>\n      <th>capgain</th>\n      <th>caploss</th>\n      <th>hourspw</th>\n      <th>country</th>\n      <th>income</th>\n      <th>capagg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>26</th>\n      <td>33</td>\n      <td>Private</td>\n      <td>228696</td>\n      <td>1st-4th</td>\n      <td>2</td>\n      <td>Married-civ-spouse</td>\n      <td>Craft-repair</td>\n      <td>Not-in-family</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>2603</td>\n      <td>32</td>\n      <td>Mexico</td>\n      <td>&lt;=50K</td>\n      <td>-2603</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>49</td>\n      <td>Private</td>\n      <td>122066</td>\n      <td>5th-6th</td>\n      <td>3</td>\n      <td>Married-civ-spouse</td>\n      <td>Other-service</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>2603</td>\n      <td>40</td>\n      <td>Greece</td>\n      <td>&lt;=50K</td>\n      <td>-2603</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>73</td>\n      <td>Self-emp-not-inc</td>\n      <td>233882</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Farming-fishing</td>\n      <td>Husband</td>\n      <td>Asian-Pac-Islander</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>2457</td>\n      <td>40</td>\n      <td>Vietnam</td>\n      <td>&lt;=50K</td>\n      <td>-2457</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>51</td>\n      <td>Self-emp-not-inc</td>\n      <td>160724</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Sales</td>\n      <td>Husband</td>\n      <td>Asian-Pac-Islander</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>2415</td>\n      <td>40</td>\n      <td>China</td>\n      <td>&gt;50K</td>\n      <td>-2415</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>55</td>\n      <td>Self-emp-inc</td>\n      <td>124137</td>\n      <td>Prof-school</td>\n      <td>15</td>\n      <td>Married-civ-spouse</td>\n      <td>Prof-specialty</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>2415</td>\n      <td>35</td>\n      <td>Greece</td>\n      <td>&gt;50K</td>\n      <td>-2415</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>32468</th>\n      <td>85</td>\n      <td>Private</td>\n      <td>98611</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>Poland</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32513</th>\n      <td>45</td>\n      <td>Private</td>\n      <td>155093</td>\n      <td>10th</td>\n      <td>6</td>\n      <td>Divorced</td>\n      <td>Other-service</td>\n      <td>Not-in-family</td>\n      <td>Black</td>\n      <td>Female</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>Dominican-Republic</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32536</th>\n      <td>54</td>\n      <td>Private</td>\n      <td>337992</td>\n      <td>Bachelors</td>\n      <td>13</td>\n      <td>Married-civ-spouse</td>\n      <td>Exec-managerial</td>\n      <td>Husband</td>\n      <td>Asian-Pac-Islander</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>50</td>\n      <td>Japan</td>\n      <td>&gt;50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32549</th>\n      <td>43</td>\n      <td>Private</td>\n      <td>260761</td>\n      <td>HS-grad</td>\n      <td>9</td>\n      <td>Married-civ-spouse</td>\n      <td>Machine-op-inspct</td>\n      <td>Husband</td>\n      <td>White</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>Mexico</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>32554</th>\n      <td>32</td>\n      <td>Private</td>\n      <td>116138</td>\n      <td>Masters</td>\n      <td>14</td>\n      <td>Never-married</td>\n      <td>Tech-support</td>\n      <td>Not-in-family</td>\n      <td>Asian-Pac-Islander</td>\n      <td>Male</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>Taiwan</td>\n      <td>&lt;=50K</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2658 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Summarizing some aspect of the data\n","metadata":{}},{"cell_type":"code","source":"print(\"Descriptive statistics of entire data base\")\ndf_summary = df.describe()\ndf_summary","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:49.061622Z","iopub.execute_input":"2021-08-08T15:08:49.061925Z","iopub.status.idle":"2021-08-08T15:08:49.102170Z","shell.execute_reply.started":"2021-08-08T15:08:49.061897Z","shell.execute_reply":"2021-08-08T15:08:49.101185Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"Descriptive statistics of entire data base\n","output_type":"stream"},{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"                age        fnlwgt       edu_num       capgain       caploss  \\\ncount  30162.000000  3.016200e+04  30162.000000  30162.000000  30162.000000   \nmean      38.437902  1.897938e+05     10.121312   1092.007858     88.372489   \nstd       13.134665  1.056530e+05      2.549995   7406.346497    404.298370   \nmin       17.000000  1.376900e+04      1.000000      0.000000      0.000000   \n25%       28.000000  1.176272e+05      9.000000      0.000000      0.000000   \n50%       37.000000  1.784250e+05     10.000000      0.000000      0.000000   \n75%       47.000000  2.376285e+05     13.000000      0.000000      0.000000   \nmax       90.000000  1.484705e+06     16.000000  99999.000000   4356.000000   \n\n            hourspw        capagg  \ncount  30162.000000  30162.000000  \nmean      40.931238   1003.635369  \nstd       11.979984   7430.372730  \nmin        1.000000  -4356.000000  \n25%       40.000000      0.000000  \n50%       40.000000      0.000000  \n75%       45.000000      0.000000  \nmax       99.000000  99999.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>fnlwgt</th>\n      <th>edu_num</th>\n      <th>capgain</th>\n      <th>caploss</th>\n      <th>hourspw</th>\n      <th>capagg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30162.000000</td>\n      <td>3.016200e+04</td>\n      <td>30162.000000</td>\n      <td>30162.000000</td>\n      <td>30162.000000</td>\n      <td>30162.000000</td>\n      <td>30162.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>38.437902</td>\n      <td>1.897938e+05</td>\n      <td>10.121312</td>\n      <td>1092.007858</td>\n      <td>88.372489</td>\n      <td>40.931238</td>\n      <td>1003.635369</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>13.134665</td>\n      <td>1.056530e+05</td>\n      <td>2.549995</td>\n      <td>7406.346497</td>\n      <td>404.298370</td>\n      <td>11.979984</td>\n      <td>7430.372730</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>1.376900e+04</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>-4356.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>28.000000</td>\n      <td>1.176272e+05</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.000000</td>\n      <td>1.784250e+05</td>\n      <td>10.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>47.000000</td>\n      <td>2.376285e+05</td>\n      <td>13.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>45.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>90.000000</td>\n      <td>1.484705e+06</td>\n      <td>16.000000</td>\n      <td>99999.000000</td>\n      <td>4356.000000</td>\n      <td>99.000000</td>\n      <td>99999.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(\"Descriptive statistics of immigrants subset\")\nfb_summary = foreignborn.describe()\nfb_summary","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:49.104680Z","iopub.execute_input":"2021-08-08T15:08:49.104978Z","iopub.status.idle":"2021-08-08T15:08:49.143201Z","shell.execute_reply.started":"2021-08-08T15:08:49.104947Z","shell.execute_reply":"2021-08-08T15:08:49.142236Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"Descriptive statistics of immigrants subset\n","output_type":"stream"},{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"               age        fnlwgt      edu_num       capgain      caploss  \\\ncount  2658.000000  2.658000e+03  2658.000000   2658.000000  2658.000000   \nmean     37.750941  2.181343e+05     9.077878    797.183973    68.709556   \nstd      12.590824  1.109478e+05     3.747431   6236.080589   357.771715   \nmin      17.000000  1.487800e+04     1.000000      0.000000     0.000000   \n25%      28.000000  1.426922e+05     7.000000      0.000000     0.000000   \n50%      36.000000  2.000890e+05     9.000000      0.000000     0.000000   \n75%      46.000000  2.705170e+05    13.000000      0.000000     0.000000   \nmax      90.000000  1.161363e+06    16.000000  99999.000000  2603.000000   \n\n          hourspw        capagg  \ncount  2658.00000   2658.000000  \nmean     40.51994    728.474417  \nstd      11.32198   6255.101219  \nmin       1.00000  -2603.000000  \n25%      40.00000      0.000000  \n50%      40.00000      0.000000  \n75%      40.00000      0.000000  \nmax      99.00000  99999.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>fnlwgt</th>\n      <th>edu_num</th>\n      <th>capgain</th>\n      <th>caploss</th>\n      <th>hourspw</th>\n      <th>capagg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2658.000000</td>\n      <td>2.658000e+03</td>\n      <td>2658.000000</td>\n      <td>2658.000000</td>\n      <td>2658.000000</td>\n      <td>2658.00000</td>\n      <td>2658.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>37.750941</td>\n      <td>2.181343e+05</td>\n      <td>9.077878</td>\n      <td>797.183973</td>\n      <td>68.709556</td>\n      <td>40.51994</td>\n      <td>728.474417</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>12.590824</td>\n      <td>1.109478e+05</td>\n      <td>3.747431</td>\n      <td>6236.080589</td>\n      <td>357.771715</td>\n      <td>11.32198</td>\n      <td>6255.101219</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>1.487800e+04</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.00000</td>\n      <td>-2603.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>28.000000</td>\n      <td>1.426922e+05</td>\n      <td>7.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.00000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>36.000000</td>\n      <td>2.000890e+05</td>\n      <td>9.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.00000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>46.000000</td>\n      <td>2.705170e+05</td>\n      <td>13.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>40.00000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>90.000000</td>\n      <td>1.161363e+06</td>\n      <td>16.000000</td>\n      <td>99999.000000</td>\n      <td>2603.000000</td>\n      <td>99.00000</td>\n      <td>99999.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Summarizing some aspect of the data: What are the differences beteween the general df and the immigrants df?","metadata":{}},{"cell_type":"code","source":"ne = df_summary - fb_summary\nne","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:49.144737Z","iopub.execute_input":"2021-08-08T15:08:49.145011Z","iopub.status.idle":"2021-08-08T15:08:49.161718Z","shell.execute_reply.started":"2021-08-08T15:08:49.144986Z","shell.execute_reply":"2021-08-08T15:08:49.160611Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"                age         fnlwgt       edu_num       capgain       caploss  \\\ncount  27504.000000   27504.000000  27504.000000  27504.000000  27504.000000   \nmean       0.686961  -28340.444851      1.043433    294.823885     19.662933   \nstd        0.543841   -5294.870263     -1.197436   1170.265907     46.526656   \nmin        0.000000   -1109.000000      0.000000      0.000000      0.000000   \n25%        0.000000  -25065.000000      2.000000      0.000000      0.000000   \n50%        1.000000  -21664.000000      1.000000      0.000000      0.000000   \n75%        1.000000  -32888.500000      0.000000      0.000000      0.000000   \nmax        0.000000  323342.000000      0.000000      0.000000   1753.000000   \n\n            hourspw        capagg  \ncount  27504.000000  27504.000000  \nmean       0.411298    275.160952  \nstd        0.658005   1175.271511  \nmin        0.000000  -1753.000000  \n25%        0.000000      0.000000  \n50%        0.000000      0.000000  \n75%        5.000000      0.000000  \nmax        0.000000      0.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>fnlwgt</th>\n      <th>edu_num</th>\n      <th>capgain</th>\n      <th>caploss</th>\n      <th>hourspw</th>\n      <th>capagg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>27504.000000</td>\n      <td>27504.000000</td>\n      <td>27504.000000</td>\n      <td>27504.000000</td>\n      <td>27504.000000</td>\n      <td>27504.000000</td>\n      <td>27504.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.686961</td>\n      <td>-28340.444851</td>\n      <td>1.043433</td>\n      <td>294.823885</td>\n      <td>19.662933</td>\n      <td>0.411298</td>\n      <td>275.160952</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.543841</td>\n      <td>-5294.870263</td>\n      <td>-1.197436</td>\n      <td>1170.265907</td>\n      <td>46.526656</td>\n      <td>0.658005</td>\n      <td>1175.271511</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>-1109.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-1753.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>-25065.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>-21664.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>-32888.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.000000</td>\n      <td>323342.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1753.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# (ggplot(mpg)\n#  + aes(x='displ', y='hwy', color='class')\n#  + geom_point()\n#  + labs(title='Engine Displacement vs. Highway Miles per Gallon', x='Engine Displacement, in Litres', y='Highway Miles per Gallon')\n# )\n\nfrom pandas.api.types import CategoricalDtype\nfrom plotnine import *\nfrom plotnine.data import mpg\n%matplotlib inline\n(ggplot(foreignborn)\n+ aes(x = 'country', fill = 'income') \n+ geom_bar(position = \"fill\") \n+ coord_flip()\n+ labs(y = \"Proportion\"))","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:49.162877Z","iopub.execute_input":"2021-08-08T15:08:49.163154Z","iopub.status.idle":"2021-08-08T15:08:50.765837Z","shell.execute_reply.started":"2021-08-08T15:08:49.163127Z","shell.execute_reply":"2021-08-08T15:08:50.764769Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"<ggplot: 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Axes>","image/png":"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\n"},"metadata":{}},{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"<ggplot: 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\n"},"metadata":{}},{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<ggplot: (8747017121569)>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Visualization: Box plots of education and income vs relationship roles ","metadata":{}},{"cell_type":"code","source":"ax = sns.catplot(x=\"edu_num\", y=\"relationship\", hue=\"income\",  kind=\"box\", data=df)\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:54.203209Z","iopub.execute_input":"2021-08-08T15:08:54.203484Z","iopub.status.idle":"2021-08-08T15:08:54.813850Z","shell.execute_reply.started":"2021-08-08T15:08:54.203457Z","shell.execute_reply":"2021-08-08T15:08:54.812810Z"},"trusted":true},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 431.75x360 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAasAAAFvCAYAAAD0aOTPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAA4wElEQVR4nO3dfXwcZbn/8c/VlEJoQJ609hRLxFQRxR8PERGUEyogtAfwCYXTAlVbfnhEIooHfqWHlr5Kj6gcLVFB2oOl0gOKgjxYELD0gDxpeKwgyqJtIRaQ1tKmDWmTXL8/ZrZstptkZpPdncl+369XXtl7du6ZK9mdvfaeuee+zd0RERFJshGVDkBERGQgSlYiIpJ4SlYiIpJ4SlYiIpJ4SlYiIpJ4IysdgMAJJ5zgd911V6XDEJHysEoHkEZqWSXAa6+9VukQREQSTclKREQST8lKREQST8lKREQSTx0sRBKopaWFTCZTVN22tjYAxo0bN5QhlUVDQwNf+cpXKh2GJJCSlUgCZTIZnvzDH+neda/YdWu2vA7Ay53pOrxrtqyvdAiSYOl6N4tUke5d96LjgEmx69U+twygqLqVlI1bpBBdsxIRkcRTshIRkcRTspJIWlpaaGlpqXQYIomi46J8dM1KIim2Z5rIcKbjonzUshIRkcRTyyplPv3pT7Nu3Tre9ra38bOf/SxW3aampu2PV6xYMbSBiYiUkFpWKbNu3ToAXn311QpHIiJSPkpWKfLpT3+6V/mzn/1s5Lq5rapCZRGRJNNpwBTJtqqyytm6amtro6Ojg+bm5rLts5plMhlGbPVKh1FWI97YSCazKVXvsUwmQ21tbaXDqApqWYmISOKpZSWRZAdFXbBgQYUjqQ7Nzc089pdXKh1GWfXssjsN+49J1XssTa3AtFPLKkX23nvvXuW3ve1tFYpERKS8lKxS5Be/+EWvcpyu6/ld1dV1XUTSRMkqZbKtK7WqRKSa6JpVyuS3ruIYTGuqoaGh6Loiw5WOi/JRspJINHuryI50XJRP2U4Dmpmb2RU55QvMbM4AdT5hZgf28/xDRcTxbTN7xsy+HbdugW0tysZnZqvMbJ/BblNERHZUzmtWncCnYn6gfwLoM1m5+5FFxHE28AF3/0YRdfP3P93dnx3sdkREpH/lPA3YBVwDnA9cnPuEmdUD1wL7AH8HPg/sC5wM/LOZzQI+7e4v5NVrd/c6M2sC5gCvAe8HHgOmurvnrX8bUAc8Zmb/CWwBZgGjgHXAFHd/JWzxvRPYHxgfxnwEcCLQBpzk7tvMbAVwgbu35uxjLrDe3b8Xli8DXnX39Nw8IolQs2V9UVO917S/Cu7UPf6TEkRVQt3beOaZ14q+d6mhoUGn5Yaxcl+z+gHwtJl9K295C3Cdu19nZl8ArnT3T4TJ5Q53/3mEbR8CvA/4G/AgcBTw29wV3P3kMMEdDGBmewJHuLub2XTg34Gvh6u/CziGoGX3MEGy/HczuwWYDPyyjziuBW4GvmdmI4DTgMPzVzKzswlaeYwfPz7CnyfVZDAX7p955jXo3kbDbluGMKLSW72pBnwrnatbB145z5r2mhJEJElS1mTl7hvNbAlwHtCR89SHgU+Fj38C5CezKH7n7i8BmNmTQD15yaqAfYGfmtlYgtbVX3OeuzNsPa0EaoC7wuUrw20X5O6rzGydmR0CjAGecPd1Bda7hqClSWNjY3UNAicDGkwLobm5mc7Vrcw8dOMQRlR68x/fHaCouLN1ZfiqxH1W3wO+CIyOU8nM3mFmT4Y/5xRYpTPncTcw0sw+lFPn5AJ1WoDvu/tBwP8Fdsnfnrv3ANtyTin2MHCSXwRMIzidee1Af5uIiPSv7F3X3X29mf2MIGFlP8gfIjhd9hNgCvBAuHwTsFtY70Xg4Jj7enSAOm8huAYFcFacbQ/gFmAusBPwr0O4XRGRqlSpESyuIOhMkfUV4PNm9jRwBpC9wnoj8A0ze8LM3lWCOOYAN5nZYwSdM4aEu28F7gN+5u7dQ7VdEZFqVbaWlbvX5Tx+Bdg1p7wamFigzoP033W9Lvy9AliRs/zciHHcCtxaYJ05/dSZk/O4KedxffZx2LHiCODUvuKoJi0tLYBuoJThR+/t8tEIFkMsvEn4DuAWd3++0vEkQSaTqXQIIiWh93b5KFkNsfAm4f0rHYeIyHCiUddFRCTxlKxERCTxdBpQSq6trY2Ojg5NAV4mmUyGUduq63voK1tGsDWTKft7LJPJUFtbW9Z9VqvqekeLiEgqqWUlJTdu3DgAFizQWL7lkB1uqZqM2bWHnfdrKPt7TGcLykctKxERSTwlKxERSTwlKxERSTxds5KSG8zcTCJJpvd2+ShZSclp3DQZrvTeLh+dBhQRkcRTshIRkcTTaUCRYWhNe03qpnpfvakGKG6K+jXtNUwY6oAkUZSsRIaZtF70H90WTNq9c3gTeRwTSO/fLdGYu1c6hqrX2Njora3VNeKASBWzSgeQRrpmJSIiiadkJSIiiadkJSIiiadkJSIiiadkJSIiiadkJSIiiadkJSIiiadkJSIiiadkJSIiiafhlkQSaPr06WzYsIFxRQw9BMHQQ5q+QoYTJSuRBFq7di3tm7fwcmf8Q7Rmy/oSRCRSWUpWIklVM5KOAybFrlb73LISBCNSWbpmJSIiiadkJSIiiadkJVICLS0ttLS0VDqM2NIatwx/umYlUgKZTKbSIRQlrXHL8KdklTKZTIbm5mYWLFgQe2bUqVOn8tJLL1FfX8/ixYtj1W1qatr+eMWKFWWrW8l9DzbuapPW1/mCCy6gtbWVI444gm9+85ux6l5++eXceeednHTSSXz961+PVVfi0WnAlJk3bx6bN29m3rx5seu+9NJLAKxatWqIoxJJr+ws3Y888kjsunfeeScAt99++5DGJDtSskqRTCazPdGsWrUq1imbqVOn9ipPmzYtct3cb62FyqWqW8l9DzbuapPW1/mCCy7oVb7ooosi17388st7la+44orIdSU+nQZMkfzW1Lx58yKfzsu2qrLUuiqttrY2Ojo6aG5uLqp+R0cHeHH7HvHGRjKZTUXtO5PJUFtbW9yOUyjbqsqK07rKtqqybr/9dp0KLCG1rFIkP8Eo4YhItVDLKkXq6+t7Jaj6+vqKxSL9y47pt2DBgqLqT548mfY3thZVt2eX3WnYf0xR+y62JShSampZpcisWbP6Lfdn33337VVWohOBxsbGXuUjjjgict0TTzyxV/mkk04akpikMCWrFGloaNieZOrr62N1Xb/++ut7leN0Xc/vChyna/Bg6lZy34ONu9qk9XX+zne+06scp+v6hRde2Kus61WlpWSVMrNmzWL06NGxWlVZ2daVWlUib8q2ruK0qrKyrSu1qkrP3IvsciRDprGx0fN7JUm6ZYcsKnZOqew1q/ZDz4hdt/a5ZRxW5DWrwcYtkVilA0gjdbAQKYG0ftinNW4Z/nQaUEREEi+xycrM6s3sD3nL5pjZBX3VKUNMJ5tZ9FvcgzqrzGyfUsUkIlINdBqwD2Y20t278sq3AbdVMCwRkaqUymRlZiuAR4FjgD2AL7r7A2Y2DfgEMBqYAHwHGAWcAXQCk9x9vZnNAM4On8sAZ7j7FjNbDLwBHAI8aGZ75ZWfBhrd/VwzeytwNTA+DOur7v6gme0N3ACMAx5GF1OlWN1dRU1RX9P+atHDLVVSW1sb8OYN1XE1NDTomtswlspkFRrp7oeb2SRgNnBsuPz9BMllF4JEdKG7H2Jm3wXOBL4H3OzuCwHMbB7wRSA749y+wJHu3h0mr9zytJz9LwC+6+6/NbPxwK+B94ax/Nbd55rZ5HDbOzCzswkSJuPHjy+0ilSxsWPHsmHDBsaNGxO7biazCe/cROfqdPUw3bypBoDOrrWx665prxnqcCRhkpys+upTn11+c/j7MaA+5/n73H0TsMnMXgeyY/evBD4QPn5/mKT2AOoIEk3WTe7e3U8561jgQLPtDafdzawOOBr4FIC7/8rM/lHwj3C/BrgGgq7rffytUqUWLVpUdN3m5mY6V7cy89CNQxhR6c1/fHeAouLO1pXhK8nJah2wZ96yvYC/ho87w9/d9P47OnMe9+SUe3LWWwx8wt2fCltLTTl1NuftM7+cNQI4wt3fyF2Yk7xERGSIJLY3oLu3A2vNbCJAeP3oBOC3Q7D53cJt7wRMKXIbdwPbT5Cb2cHhw/uBfw2XnciOCVdERGJKbLIKnQn8h5k9CSwHLnX3F4Zgu/9B0EHjQeC5IrdxHtBoZk+b2bPAOeHyS4GjzewZgtOBawYbbLVraWnZPrKCSJLovVk+ST4NiLs/S9DjL395U87j1wivWbn7YoJTfNnn6nMeb3/O3a8Criqw3WkDlHO38RrwuQLbWAcc3+cfJbHFmRFZpJz03iyfpLesRERElKxERCT5lKxERCTxlKxERCTxEt3BQgSCYXg6OjpSN3xQpWQyGUZtq67voa9sGcHWTKbs75FMJkNtbW1Z91mtqusdLSIiqaSWlSRedmDTYma+rUbZ4ZaqyZhde9h5v4ayv0fU2i8ftaxERCTxlKxERCTxlKxERCTxdM1KEq+hoaHSIYgUpPdm+ShZSeJp9ldJKr03y0enAUVEJPGUrEREJPF0GlBkGFrTXpO6qd5Xb6oBipuifk17DROGOiBJFCUrkWEmrRf9R7e1AbBzeBN4HBNI798t0Zi7VzqGqtfY2OitrdU14oBIFbNKB5BGumYlIiKJp2QlIiKJp2QlIiKJp2QlIiKJp2QlIiKJp2QlIiKJp2QlIiKJp2QlIiKJpxEsRBJo+vTpbNiwgXFFjOYAwWgOGhFchhMlK5EEWrt2Le2bt/ByZ/xDtGbL+hJEJFJZSlYiSVUzko4DJsWuVvvcshIEI1JZumYlIiKJp2QlIiKJp2QlUgItLS20tLRUOozY0hq3DH+6ZiVSAplMptIhFCWtccvwp5aViIgknpJVyixdupSmpiZuvPHG2HWvvPJKmpqa+MEPfhC77pe+9CWampqKunenqalp+08xBlO/UnWrUVpf55kzZ9LU1MQll1wSu+5gjkeJR8kqZRYuXAjA1VdfHbvuzTffDMBNN90Uu+4f//hHAFauXBm7rkiSPfTQQwDcf//9sesO5niUeJSsUmTp0qW9ynG+zV155ZW9ynFaV1/60pd6leO0rvK/6cb95juY+pWqW43S+jrPnDmzVzlO62owx6PEpw4WKZL9Fpd19dVXc9ppp0Wqm21VZd100018+ctfjlQ326rKUutqYG1tbXR0dNDc3FxU/Y6ODvDi9j3ijY1kMpuK2ncmk6G2tra4HadQtlWVFad1NZjjUeJTy0pERBJPLSuREsgOQLtgwYKi6k+ePJn2N7YWVbdnl91p2H9MUfsutiUoUmpqWaXIjBkzepXPOeecyHU/9alP9Sqfeuqpkeu+973v7VU+6KCDItcVSbIjjzyyV/noo4+OXHcwx6PEp2SVIlOmTOlVjnN+/LzzzutVjnq9CuCqq67qVY4zwsGKFSv6LZeyfqXqVqO0vs7z58/vVZ47d27kuoM5HiU+JauUyX6bK+ZbXLZ1FadVlZVtXalVJcNNtnUVp1WVNZjjUeIx9yK7HMmQaWxs9NbW1kqHIUMo2/osdgLE7DWr9kPPiF239rllHFbkNavBxi2RWKUDSCN1sBApgbR+2Kc1bhn+UnMa0Mz2NbNbzex5M3vBzBaY2agy7HeFmTUWWN5oZleGj6eZ2ff7qN9e6hhFRIa7VCQrMzPgZuCX7j4BeDdQB1xWqZjcvdXdzxt4TRERGay0nAacCLzh7j8GcPduMzsf+GvY6ml296fN7AngFnefa2ZzgReB54E5wGvA+4HHgKle4GKdmV0ITAV6gDvd/aLwqVPN7IfAHsAX3f0BM2sCLnD3f8nbxjuB/yFIprcO4f9Aqk13V1FT1NdsWQ+MGfp4RCoocrIys7cDhxMMAvN7d3+5ZFHt6H0ESWY7d99oZmuA+4CPmtlqoAs4Klzlo8A5wFjgkHAbfwMeDNf5be72zOxE4BTgQ+6+xcz2ynl6pLsfbmaTgNnAsf3EugC4yt2XmFmf/cPN7GzgbIDx48f397dLFRo7diwbNmxg3LiBk052aKftdtmJtra2om7wbWtrA968qbncGhoadN1MCoqUrMxsOnAJsJygJ0uLmc1192tLGVxE/wv8G/BX4FfAcWa2K/BOd/+TmY0FfufuLwGY2ZNAPXnJiiAB/djdtwC4+/qc57ID6z0W1u3PUcCnw8c/AS4vtJK7XwNcA0FvwAG2KVVm0aJFkddtbm7m+WeeYHxd95sLuzbQuXpt7P1u3lQDQGdX/LqDtaa9puz7lPSI2rL6BnCIu68DMLO9gYeAciWrZ4HP5C4ws92B8cATQCPwF+AeYB9gBr1bYp05j7uBkWb2IeBH4bKBhlrO1u8m2v9MyUfKanxdNzMP3Tjo7cx/fHeAIdlWsfsWKSRqB4t1wKac8qZwWbn8BtjVzM4EMLMa4ApgsbtvJLg2dSrwMPAAcAHQ7/DJ7v6oux8c/txGkOg+H7bKyDsNGMeDQPZW9in9rSgiItFETVYZ4FEzm2Nms4FHgD+b2dfM7GulCy8Qdob4JEFHh+eBPwNvANnJaB4AXnX3jvDxvuHvOPu4C7gNaA1PFV5QZLjNwJfNbCVQmRP/IiLDTNTTgC+EP1nZXm67DW04fXP3F4GT+njuP4D/CB//jZw7xN19BbAip3xuP/v4JvDNvGVNOY9fI7xmlbtdd18MLA4f/xX4cM4mZvX3d8nANKqCJJXem+UTKVm5+6WlDkSkL5lMptIhiBSk92b59JuszOx77v5VM7udAp0G3P3kkkUmIiISGqhl9ZPw93dKHYiIiEhf+k1W7v5Y+Pt/yxOOiIjIjqLeFHwUwZBF+4V1jKCT3v6lC00kkB2hQVOuF5bJZBi1LRXDfPbrlS0j2JrJpOp1zmQy1NbWVjqMqhC1N+B/A+cT3GjbPcC6IiIiQypqsnrd3e8saSQifciOU1fMZILVoLm5mc7V6Z+8c8yuPey8X0OqXuc0tQLTbqDegIeGD+8zs28TjJG3fegid3+8hLGJiIgAA7esrsgr505C6ARTd4iIiJTUQL0BjylXICIiIn2J2huwGfgxwQC2C4FDgYvc/e4SxiYCBHMciSSR3pvlE7WDxRfcfYGZfRzYGziD4IZhJSspOY27Jkml92b5RL05Izsw7CRgibs/k7NMRESkpKImq8fM7G6CZPVrM9sN6CldWCIiIm+Kehrwi8DBwF/cfUs4U/DnSxaViIhIjqhThPSY2SvAgWYWNcGJSJmsaa8ZkmnhV2+qASozxfya9homlH2vkhZRewNeDnwOeJY3h1tyBpg6XkRKbyh7pI1uawNg53Hln+R6AupdJ32zYMb4AVYy+xPwAXfvHHBlia2xsdFbW9M/XI6IRKLOaUWI2sHiL8BOpQxERETiM7OHKh1DOUS9/rQFeNLMfkPvsQHPK0lUIiISibsfWekYyiFqsrot/BERkQQxs3Z3rzOzJoJ5B18D3k8wpdNUd3cz+yCwABhN0OD4GLANuIpgzNcu4Gvufp+ZTQM+Ea47gWCm+FEEg0F0ApPcfb2ZvQv4AfBWggbNDHd/rlR/Z9TegNeZ2Sjg3eGiP7n7tlIFJSIiRTkEeB/wN+BB4Cgz+x3wU+Bz7v57M9sd6ACaCSbRPcjMDgDuNrPsZ/z7w23tAmSAC939EDP7LnAm8D3gGuAcd3/ezD4E/JASDm4etTdgE3AdsIrg4uA7zOwsd1dvQBGR5Pidu78EYGZPAvXA68Bad/89gLtvDJ//CNASLnvOzFbzZoPkPnffBGwys9eB28PlK4EPmFkdcCRwk9n2/iI7l/IPi3oa8ArgeHf/E0CYfW8ADitVYCIiEltuj+1uon/G97ednpxyT7jNEcAGdz+4yO3HFrU34E7ZRAXg7n9GvQNFRNLgT8DY8LoVZrZbOLjDA8CUcNm7gfHhugMKW2d/NbNTw/pmZv+nFMFnRU1WrWa2yMyawp+FgG4MEhFJOHffSjCoQ4uZPQXcQ3At6ofACDNbSXBNa1rMe2mnAF8Mt/kMcMrQRt5b1JuCdwa+DHwkXPQA8EPdJDw0dFPw8NPS0kImkym6fls4ksS4IkeSaGho0PQVyaWbgosQtTdgJ/Bf4Y+IDCCTyfDkH/5I9657FVW/ZsvrALzcGf+SQ82W9UXtUyTJ+j0SzOxn7v7ZsJm4QxPM3T9QsshEUq57173oOGBSUXVrn1sGUFT9bF2R4WSgr23N4e9/KXUgIiIifem3g4W7rw0f/pu7r879Af6t9OGJiIhE7w14XIFlJw5lICJ9aWlpoaWlpdJhSInpdZb+DHTN6ksELaj9zezpnKd2IxjKQ6TkBtOrTtJDr7P0Z6BrVv8D3An8J3BRzvJN7q4uRxWwfPly5s6dy+zZsznmmGNi1Z02bRqrVq2ioaGBRYsWxarb1NS0/fGKFSti1T322GPp6upip5124p577olVVySKpUuXsnDhQs455xxOO+20WHUHc0wNlaOPm3Q/I0aOH7IN9nStuf+eZUcP2fYKCIfhuxX4a7joZnefGz53AsHAuTXAInf/Zrh8BXCBu7ea2TuBu4Fz3f3XA+2v32Tl7q8TjCt1erijtxHcTFZnZnXuvibuHyiDM3/+fAAuu+yy2AfWqlWrgPJ/g+3q6gJg2zaNfSylsXDhQgCuvvrq2MlqMMfUkBkxcvzmD3xmv6Ha3Oinf15UvXDA8p3cfXPEKg+4e68OeGZWQzAa+3HAS8Dvzew2d382Z519gbuAr0dJVBDxmpWZnWRmzxNk0P8lGND2zih1ZegsX758+wd/V1cX9913X+S606ZN61WePn165Lq5rapC5f4ce+yxvcrHHVfo8qdI8ZYuXdqrfOONN0auO5hjajgxs/ea2RUEwy29e6D1B3A4kHH3v4SjZ9xI79EtxhK0qC5298hTT0W943AecARwbzhM/DHA1Kg7kaGR/QaYFeebYLZVlVWu1lX2gyCrmNZVW1sbHR0dNDc3D7xyQmQyGUZsHXh0mFIY8cZGMplNqfp/QfA/q62tjV0v26rKitO6GswxlXZmNhr4LPDFcNGPgTnhaOuE04EU+mfcmD2tB3w4HG7pbwSn954BxgEv5qz/EvChnPJ1wCx3j9X8i5qstrn7OjMbYWYjwgm6vhdnRzJ4+R/8+WURiafKj6m1wNPA9EKTJrr7+QPUfxzYz93bzWwS8EuCyRoHci8w1cwWu/uWqMFGTVYbwvlL7geWmtmrQNRzmjJERo4c2etgGjmy2NH/0yU7Pt6CBQsqHEl0zc3NPPaXVyqy755ddqdh/zGp+n8BFWkJVusxFfoMQavqZjO7EbguvIcWGLhllZ0XC8Ddl5nZD81sH6ANeEfO+vuGy7K+RTDr8E1mdoq7R/qGEPU+q1MIZpY8n+Ci2AvASRHryhCZOXNmr/LFF18cuW59fX2vckNDw1CENKD8g3+nnTSzjAytGTNm9Cqfc845kesO5phKO3e/290/B3yUoCPdrWZ2r5nVh8+f7+4HF/jJ9ux7u4UzL5rZ4QT5ZB3we2CCmb0z7LBxGpB/beqrwEbgv7PbGEjUgWxzW1HXRakjQ2/ixInMnz+frq4uRo4cGevc+uLFi3t1jIjTdX3FihVFd12/9957e9VV13UZalOmTOl13SpOb8DBHFNDqqdrTbE9+PraXtRV3X0dQTfzBWHS6Y5Y9TPAl8ysi6Axc5oH03h0mdm5wK8Juq5fG17Lyt2nm9lZwB0ELa1vDLSzgW4K3kTvAWwtLFu4v90j/lEyRGbOnMncuXOL+gZYX1+//T6rcsqealGrSkplxowZ2++zimswx9RQKfU9UVG5++9irPt94Pt9PLcM2GFEZXdvynm8FTg+6v4Gus9qt6gbkvKYOHEiEydOLKru4sWLi95v3BuBc917771F1xWJYsqUKUyZMqWouoM5pqR8Il9NNLOPABPc/cfhRbTd3P2vA9UTGaxytwSlMvQ6S38iJSszmw00Au8h6Is/CrgeOKp0oYkENONtddDrLP2J2hvwk8DJhN3V3f1vBIPZ9svM9jWzW83seTN7wcwWmNkoMzs47JefXW+OmV1QzB8wGGa2wswaB1jnq2a2a055mZntUfLgRERku6jJamvYy8Nh+53P/Qq7I94M/NLdJxAM4VEHXAYcDBQ3hWrhfdUU81xEXwW2Jyt3n+TuGwa5TRERiWHA04Bh0rnDzH4E7GFmM4AvAAv7r8lE4A13/zGAu3eb2fnAamBbuOmPEIzoDnBgOCLveOB77n5luP+pwHkEpx4fJZgIstvM2oEfAccCXwZ+mxPzKuCnBAMpfsvM1gOXAjsT3CP2eXdvz/s7rwI+CNQCP3f32WZ2HvBPwH1m9pq7HxNuuxG4AHjR3X8Q1p8DtLv7d8zsGwTDmOwM3OLuswf6P8vwU7NlfdFTzNe0vwru1D3+k/iVu7fxzDOvpW64JQiuW+l0oBQyYLIK+8OfCnyN4Cau9wCXuPtAN8y8D3gsb1sbww/7HwPvdvdzYfsH/QEEd0vvBvwpTB4NwOeAo9x9m5n9EJgCLAFGA4+6+9f72P86dz807AxyM3Csu282swvDv2Vu3voXu/v6sCX2GzP7gLtfaWZfA45x99fy1v8p8D2C0YUhSE4fN7PjCYYcOZygi/9tZna0u9+fW9nMzgbOBhg/fuhmBpBkGGxngWeeeQ26t9GwW+TRaLZbvakGfCudq1sHFUO5rWkf7EmQ9Prk8R+9f1QNQ/ZBsLWbNbfc/cCgusOb2WLgnwluGAaY5u5Phg2YBQRnx7aEyx8Pbya+w93fH9afAZxD8Nn7j8HEAtF7Az4ObHD3AW/cGoRfuXsn0BkO5zQG+BhwGMEQ8xC0el4N1+8GftHP9n4a/j4COBB4MNzGKODhAut/NkwgIwlGBT6QYNysgtz9CTN7m5n9E/BW4B/u/qKZNRPcO/BEuGodQfK6P6/+NcA1AI2NjZUZ8VRKZrCtg+bmZjpXtzLz0I0Dr5xn/uPB7Y/F1K2kbNzVaFQN47/94Q1DNkXINx7eY8B1zGzPCEnkGwUGnD2R4DNtAsEAtVfRe6BazOwM4CvAxKFIVBA9WX0ImGJmq8kZE9DdP9BPnWcJ7nDezsx2JzjNV2gsqM6cx91hbEYwXtX/K7D+G+7eHW731wTJrdXds3NfZOM04B53P72vQMNJwC4APuju/wi/UezSz9+WdRPB3/h23kyOBvynu/8oQn0RkUppNbNHgP8G7gv7JURxCrAkXP8RM9vDzMZmnzSzzxJM1vuxAmekiha1g8XHgXcRXIc6KeenP78BdjWzM2F7R4crgMXAK0ToTRhu4zPhpI+Y2V5mtsO3D3f/eDhmVaFJmh4BjjKzhnAbo80sf76W3QmS2+tmNobgm0PWpn5i/SnBuFefIUhcEAwx8oVw4F/MbFw2fhGRBHk3cANwLvCsmc0MzxTluszMnjaz75rZzuGyQlOAjAsf70cwqsXx7v7yUAYbKVm5++pCPwPUcYIu76daMHHjn4E3gJnAfQQdKp40s8/1s41ngVnA3Wb2NHAPwSm6yNz978A04IZwGw8TXB/LXecpgtN2zwH/AzyY8/Q1wF1mtsOsbOF4V7sBbe6+Nlx2d7iNh81sJfBzoiXmRGtpaaGlpaXSYYgkSpqPC3fvdvc73P1TwNHA/sCacHxAgP9H8Fn5QWAv4MIIm/07sIbgGv6QKul4+O7+IoVbYJ0E/4C+6r0/5/FPefMUW+46df3Ur88rLy+0v7xxqqb1sa0WoCWnnL/tgwrUWUBwAXLYKNdkjSJpkvbjwszeQnB2aBqwlaCn99MA2S/gBP0IfkxwqQT6ngJkZ4IOF5OAB8zsVXfvPY3zIEQ9DSgiIsOImV1P0HnuncCZ7v7P7r7E3d8Inx8b/jbgE8Afwqq3AWda4Ajg9ZzEhru/CpwAzDezjw9VvFU105iISBJt7WZNlB58cbYXYbWfEXQ772vyw6Vm9laCTmNPEnRDh2A09UlAhqAl9fn8iu7+VzM7GVhmZp+MM5p7X5SsREQqbLD3RBXD3fMnRMx/vuBQ9GF/hC8XWL4KyL2E8xRvdrwYNJ0GFBGRxFPLSiJpa2ujo6MjlUP4pFEmk2HUtur6LvnKlhFszWRS9R7LZDLU1tZWOoyqUF1Hg4iIpJJaVhLJuHHBqecFC4ZVj/zEyg63VE3G7NrDzvs1pOo9lqZWYNqpZSUiIomnZCUiIomnZCUiIomna1YSyWDnZxIZjnRclI+SlUSi2VtFdqTjonx0GlBERBJPyUpERBJPpwFFEmpNe01RU72v3lQDpG+a+DXtNUyodBCSWEpWIgk0mAv3o9vaANh53JCNIVoWE1CHBembBQPoSiU1NjZ6a2t1jVYgUsWs0gGkka5ZiYhI4ilZiYhI4ilZiYhI4ilZiYhI4ilZiYhI4ilZiYhI4ilZiYhI4ilZiYhI4mkEC5EEmj59Ohs2bGBckaNQNDQ0aERwGVaUrEQSaO3atbRv3sLLnfEP0Zot60sQkUhlKVmJJFXNSDoOmBS7Wu1zy0oQjEhl6ZqViIgknpKViIgknpKViIgknpKVSAm0tLTQ0tJS6TBiS2vcMvypg4VICWQymUqHUJS0xi3Dn5JVysycOZOHHnqIo48+mrlz58aq29TUtP3xihUrUlG3kvsebNzVphpfZ71HykenAVPmoYceAuD++++vcCQiIuWjZJUiM2fO7FW+5JJLItfN/QZYqJzEupXc92DjrjbV+DrrPVJeOg2YItlWVZZaV8nV1tZGR0cHzc3NRdXv6OgAL27fI97YSCazqah9ZzIZamtri9uxSAmpZSUiIomnlpVICWQHoF2wYEFR9SdPnkz7G1uLqtuzy+407D+mqH0X2xIUKTW1rFLkyCOP7FU++uijKxSJiEh5KVmlyPz583uV43Rdz+9WG6ebbaXqVnLfg4272lTj66z3SHkpWaVMtnWlVpWIVBNzL7LLkQyZxsZGb21trXQYMoSyQxYVOwFi9ppV+6FnxK5b+9wyDivymtVg45ZIrNIBpJE6WIiUQFo/7NMatwx/qT4NaGbteeVpZvb9odjWUDGzxWb2mVJsW0SkWqQ6WYmISHUYtqcBzWwxcIe7/zwst7t7nZmNBX4K7E7w93/J3R8I1/kucDzwMnCau//dzGYAZwOjgAxwhrtvCbe/EWgE3g78u7v/3MwMaAGOA14EirtZRqS7q6gp6mu2rAfGDH08IhWU9mRVa2ZP5pT3Am4boM6/Ar9298vMrAbYNVw+Gmh19/PN7BJgNnAucLO7LwQws3nAFwmSEcBY4CPAAeF+fw58EngPcCDBJ8azwLWD+SOl+owcOZKdRtZQ17MpfuVddqKtra3sN/i2tbUBb94QXYyGhgZdN5OC0p6sOtz94GzBzKYRtHT683vgWjPbCfiluz8ZLu8haHEBXA/cHD5+f5ik9gDqgF/nbOuX7t4DPGtm2a+yRwM3uHs38DczW14oCDM7m6DFxvjx4wcIWapNfX09zz/zBO/YqaO4DXRtoHP12qENagCbN9UA0NlV3H7XtNcMZTgyzKQ9WfWni/CanJmNIDiNh7vfb2ZHA5OBxWb2X+6+pED9bJ/+xcAn3P2pMBk25azTmfM4VndUd78GuAaCrutx6kp1GF/XzcxDN1Y6jMjmP747QNExZ+uLFDKcO1isAg4LH58M7ARgZvsBr4Sn9hYBh4brjACyvfb+Ffht+Hg3YG3YEpsSYb/3A58zs5rw+tgxg/w7RESq3nBuWS0EbjWzp4C7gM3h8ibgG2a2DWgHzgyXbwYON7NZwKvA58Ll/wE8Cvw9/L3bAPu9BZhIcK1qDfDwUPwxIiLVLNXJyt3r8sqLCU7b4e6vAEfkPH1huPw64LqBtpWz/CrgqgLLpxWq78GQIOdG/iNSQiMbiOxIx0X5pDpZSflkMplKhyCSODouymc4X7OSPEuXLqWpqYkbb7yx0qGIiMSiZFVFFi5cCMDVV19d4UhEROJRsqoSS5cu7VVW60pE0kTXrKpEtlWVdfXVV3PaaadFrt/W1kZHR4emPS+TTCbDqG3V9V3ylS0j2JrJpOo9lslkqK2trXQYVaG6jgYREUkltawkkux4b8VM6CfxNTc307m6uibkHLNrDzvv15Cq91iaWoFpp5ZVlZgxY0av8jnnnFOhSERE4lOyqhJTpvQeKSrO9SoRkUpTsqoi2daVWlUikja6ZlVFpkyZskMLS0QkDZSsJJKGhoZKhyCSODouykfJSiLRQJ0iO9JxUT66ZiUiIomnZCUiIomn04AiCbWmvSZVU72v3lQDFD89/Zr2GiYMZUAyrChZiSRQGi/cj25rA2DncLSTuCaQzr9bysOCiW2lkhobG721tbqG1hGpYlbpANJI16xERCTxlKxERCTxlKxERCTxlKxERCTxlKxERCTxlKxERCTxlKxERCTxlKxERCTxNIKFSAJNnz6dDRs2MK7I0SAaGho0IrgMK0pWIgm0du1a2jdv4eXO+IdozZb1JYhIpLKUrESSqmYkHQdMil2t9rllJQhGpLJ0zUpERBJPyUpERBJPyUqkBFpaWmhpaal0GLGlNW4Z/nTNSqQEMplMpUMoSlrjluFPLauUyWQyTJ48uagPleXLl9PU1MR9990Xu+7ChQtpamri2muvjV136tSpNDU1MW3atNh1AaZNm0ZTUxPTp0+PXfekk06iqamJU045JXbdU045haamJj75yU/GrluNjjnmGJqampg4cWJR9Zuamrb/xHX66afT1NTE1KlTY9dtbW1l4sSJPPbYY7HrDuZ4lHiUrFJm3rx5bN68mXnz5sWuO3/+fAAuu+yy2HWXLl0KwJIlS2LXfemllwBYtWpV7Lq59Yr5QNi0aRMAr7/+euy62Tr/+Mc/YtetRtmJXHt6esq+77Vr1wJvvtfimDNnDj09PcyePTt23cEcjxKPklWKZDKZ7R/cq1ativXhvXz5crq6ugDo6uqK1bpauHBhr3Kc1lX+N924rav89eO0rk466aRe5Titq/x11brq3zHHHNOrHLd1ld+aitO6Ov3003uV47SuWltbaW9vB6C9vT1W62owx6PEp2tWKZL/7W3evHksXrw4Ut1sqyrrsssu2+EDpi/ZVlXWkiVL+MIXvhCpbv433bitq/z143wgZFtVWXFaV/nrxm1dtbW10dHRQXNzc6x6WR0dHeBFVWXEGxvJZDYVte9MJkNtbW3setlWVVY5W1fZVlVWnNbVnDlzepVnz57NHXfcEanuYI5HiU8tqxTJ/+CO88GfbVX1VRapRtlWVV/l/gzmeJT41LJKkfr6+l4HRH19feS6I0eO7JWgRo7US19K2TH9FixYUFT9yZMn0/7G1qLq9uyyOw37jylq38W2BNOqrq6uV4Kqq6uLXHcwx6PEp5ZVisyaNavfcn9mzpzZq3zxxRdHrjtlypRe5TPPPDNy3X333bdXOe4Bnb9+Q0ND5Lq77bZbr/Jb3vKWyHXz191zzz0j161GZtarPGJE+T5axo4d26uc/57rT/5pwEsvvTRy3cEcjxKfklWKNDQ0bP/wrq+vj/XBPXHixO2tqZEjR0a+XgUwY8aMXuWo16sArr/++l7luOf089dftGhR5Lq33357r/Ktt94auW7+urfcckvkutUov8PO8uXLY9VfsWJFv+X+3HDDDb3K+e+5/jQ2Nm5vTdXV1XHYYYdFrjuY41HiU7JKmVmzZjF69OiivsVlW1dxWlVZ2dZVnFZVVvabbrGnSbL1ivkwyLau4rSqsrJ11KqKJtu6KmerKivbuorTqsqaM2cOI0aMiNWqyhrM8SjxWH4vHim/xsZGb21trXQYMoSy134Ge82q/dAzYtetfW4Zhw3ymlWxcUskNvAqkk9X2UVKIK2nhNIatwx/SlYiJZDWWXrTGrcMf7pmlcfMvmtmX80p/9rMFuWUrzCzS8zsorD8VjN71MyeMLOPViBkEZFhT8lqRw8CRwKY2QhgH+B9Oc8fCdzt7t8Myx8DVrr7Ie7+QFkjFRGpEjoNuKOHgO+Gj98H/AEYa2Z7AluA9wIfMLOpwCLgW0CtmTUCHwY+ClwK7Ay8AHze3aPfFi+S1d1V1BT1Ne2vFj3c0mC0tbUBb94QXYyGhgadipSClKzyuPvfzKzLzMYTtKIeBsYRJKLXgZXA1nDdJ83sEqDR3c81s32AWcCx7r7ZzC4EvgbMzd+PmZ0NnA0wfvz4MvxlkiZjx45lw4YNjBs3JnbdTGYT3rmJztXl7WG6eVMNAJ1dawdYs7A17TVDGY4MM0pWhT1EkKiOBP6LIFkdSZCsHuyn3hHAgcCD4T0nowiS3Q7c/RrgGgi6rg9V4DI8xLn5OV9zczOdq1uZeejGIYxoYPMf3x2g6P1m64sUomRVWPa61UEEpwFfBL4ObAR+DOzVRz0D7nH30/t4XkREiqAOFoU9BPwLsN7du919PbAHwanAh/qp9whwlJk1AJjZaDN7d6mDFREZ7pSsCltJ0Avwkbxlr7v7a31Vcve/A9OAG8zsaYJTgAeUME4Rkaqg04AFuHs3sHvesmk5jxcDi/Mfh+XlwAdLHqRE0tLSAuhmVykNvb/KR8lKhjVNNS6lpPdX+eg0oESSyWSYPHlyUQfnunXrOO+881i3bl1R+25tbWXixIk89thjRdUXkfRTspJI5s2bx+bNm5k3b17sutdddx0rV65kyZIlRe17zpw59PT0MHv27KLqi0j6KVnJgDKZzPbpu1etWhWrdbVu3Truuusu3J277rorduuqtbV1+7Tj7e3tal2JVClds5IB5bem5s2bF3nG3+uuu46enh4Auru7WbJkCeeff37kfedPOz579mzuuOOOyPXb2tro6Ogo+9BDlZTJZBi1LX3fQ1/ZMoKtmUyqXqtMJkNtbW2lw6gK6XtHS9llW1V9lftz77330tXVBUBXVxf33HNPrH1nW1V9lUWkOqhlJQOqr6/vlaDiTE9/7LHHsmzZMrq6uhg5ciTHHXdcrH3X1dX1SlB1dXWx6mcHVa2mmW+zwy2lzZhde9h5v4ZUvVZpagWmnVpWMqBZs2b1W+7PWWedxYgRwduspqaGM888M9a+808DXnrppbHqi8jwoGQlA2poaNjemqqvr4819fnee+/NCSecgJlxwgknsPfee8fad2Nj4/bWVF1dHYcddlis+iIyPChZSSSzZs1i9OjRsVpVWWeddRYHHXRQ7FZV1pw5cxgxYoRaVSJVTNesJJKGhgZ+9atfFVV377335sorryx6342NjSxfvryounFagSJx6f1VPkpWMqxpzDYpJb2/ykenAUVEJPGUrEREJPF0GlBkGFrTXlP2aeJXb6oBip+efk17DROGMiAZVpSsRIaZSl30H93WBsDO4Y3YcU1AHRakb+bulY6h6jU2Nnpra/pGHBCRolilA0gjXbMSEZHEU7ISEZHEU7ISEZHEU7ISEZHEUweLBDCzvwOrKx1HaB/gtUoHUYDiikdxxVPOuF5z9xPKtK9hQ8lKejGzVndvrHQc+RRXPIornqTGJW/SaUAREUk8JSsREUk8JSvJd02lA+iD4opHccWT1LgkpGtWIiKSeGpZiYhI4ilZiYhI4ilZVRkze4eZ3Wdmz5rZM2bWXGCdJjN73cyeDH8uKWN8q8xsZbjfHUb3tcCVZpYxs6fN7NAyxPSenP/Fk2a20cy+mrdOWf5nZnatmb1qZn/IWbaXmd1jZs+Hv/fso+5Z4TrPm9lZZYjr22b2XPg63WJme/RRt9/XvARxzTGztpzXalIfdU8wsz+F77WLhjIuKYK766eKfoCxwKHh492APwMH5q3TBNxRofhWAfv08/wk4E6CkauPAB4tc3w1wMvAfpX4nwFHA4cCf8hZ9i3govDxRcDlBertBfwl/L1n+HjPEsd1PDAyfHx5obiivOYliGsOcEGE1/kFYH9gFPBU/nGin/L+qGVVZdx9rbs/Hj7eBPwRKG4Coso4BVjigUeAPcxsbBn3/zHgBXevyIgj7n4/sD5v8SnAdeHj64BPFKj6ceAed1/v7v8A7gGGbBSFQnG5+93u3hUWHwH2Har9DSauiA4HMu7+F3ffCtxI8H+WClGyqmJmVg8cAjxa4OkPm9lTZnanmb2vjGE5cLeZPWZmZxd4fhzwYk75JcqbbE8DbujjuUr9z8a4+9rw8cvAmALrVPr/9gWCFnEhA73mpXBueHry2j5Om1b6/yV5lKyqlJnVAb8AvuruG/OefpzgNNf/AVqAX5YxtI+4+6HAicCXzezoMu67X2Y2CjgZuKnA05X8n23nwTmsRN2PYmYXA13A0j5WKfdrfhXwLuBgYC1wRYn3J0NAyaoKmdlOBIlqqbvfnP+8u2909/bw8TJgJzPbpxyxuXtb+PtV4BaC0zG52oB35JT3DZeVw4nA4+7+Sv4TlfyfAa9kT4WGv18tsE5F/m9mNg34F2BKmEh3EOE1H1Lu/oq7d7t7D7Cwj/1V8n0mBShZVRkzM+C/gT+6+3/1sc7bw/Uws8MJ3ifryhDbaDPbLfuY4AL9H/JWuw04M+wVeATwes4psFI7nT5OAVbqfxa6Dcj27jsLuLXAOr8GjjezPcPTXseHy0rGzE4A/h042d239LFOlNd8qOPKvcb5yT7293tggpm9M2xRn0bwf5ZKqXQPD/2U9wf4CMFpoqeBJ8OfScA5wDnhOucCzxD0gHoEOLJMse0f7vOpcP8Xh8tzYzPgBwQ9tVYCjWWKbTRB8nlLzrKy/88IkuVaYBvBdZQvAnsDvwGeB+4F9grXbQQW5dT9ApAJfz5fhrgyBNd9su+zq8N1/wlY1t9rXuK4fhK+d54mSEBj8+MKy5MIesu+MNRx6Sf+j4ZbEhGRxNNpQBERSTwlKxERSTwlKxERSTwlKxERSTwlKxERSTwlKxERSTwlK5FBMrNpZvb9SschMpwpWYmISOIpWYkMwMymmtnvwon6fmRmNWb2eTP7s5n9DjgqZ93FZvaZnHJ7P9ttMrMVZvbzcJLCpTlDNq3Kji1oZo1mtiJ8PMfMrjOzB8xstZl9ysy+FU5eeFc47qPIsKNkJdIPM3sv8DngKHc/GOgGpgKXEiSpjwAHDmIXhwBfDbexPzmJrx/vAiYSjAB/PXCfux8EdACTBxGLSGKNrHQAIgn3MeAw4Pdho6cWOBJY4e5/BzCznwLvLnL7v3P3l8LtPAnUA78doM6d7r7NzFYSzGh7V7h8ZVhfZNhRy0qkfwZc5+4Hhz/vIZgWvS9dhMeVmY0gmBK9P505j7t58wvk9u0AuxSq48EUF9v8zQE+e9AXUBmmlKxE+vcb4DNm9jYAM9sLeAL4ZzPbO7xGdGrO+qsIWmIQnKYr9hpS7nY+XeQ2RIYNJSuRfrj7s8AsgmnXnwbuAcYStK4eBh4E/phTZSFBInsK+DCwuchdXwosMLNWghaXSFXTFCEiIpJ4almJiEji6WKsSImZ2UEEs9Pm6nT3D1UiHpE00mlAERFJPJ0GFBGRxFOyEhGRxFOyEhGRxFOyEhGRxPv/jltLD8K7msQAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Box plots of education level and race","metadata":{}},{"cell_type":"code","source":"sns.boxplot(y=df[\"race\"], x=df[\"edu_num\"])\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:54.815488Z","iopub.execute_input":"2021-08-08T15:08:54.815908Z","iopub.status.idle":"2021-08-08T15:08:55.050624Z","shell.execute_reply.started":"2021-08-08T15:08:54.815863Z","shell.execute_reply":"2021-08-08T15:08:55.049497Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='edu_num', ylabel='race'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Boxplots of education level and race for immigrants","metadata":{}},{"cell_type":"code","source":"sns.boxplot(y=foreignborn[\"race\"], x=foreignborn[\"edu_num\"])\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:55.051919Z","iopub.execute_input":"2021-08-08T15:08:55.052202Z","iopub.status.idle":"2021-08-08T15:08:55.438870Z","shell.execute_reply.started":"2021-08-08T15:08:55.052175Z","shell.execute_reply":"2021-08-08T15:08:55.437805Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='edu_num', ylabel='race'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Education level, sex vs race","metadata":{}},{"cell_type":"code","source":"ax = sns.catplot(x=\"edu_num\", y=\"race\", hue=\"sex\",  kind=\"box\", data=df)\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:55.440476Z","iopub.execute_input":"2021-08-08T15:08:55.440901Z","iopub.status.idle":"2021-08-08T15:08:55.988842Z","shell.execute_reply.started":"2021-08-08T15:08:55.440858Z","shell.execute_reply":"2021-08-08T15:08:55.987722Z"},"trusted":true},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432.125x360 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAaUAAAFvCAYAAADqodR8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3UklEQVR4nO3dfXxcdZn//9eVpEDaUJS4snyrJV82KN7AIs16g3fT2LKFgNXfdn/Coy5FVpBdbQPKrmzbh02/23ZF1y4le1MpX6R1+1tw8ab0hkpr23VRQdMutiiooxY0IkhqoWlD2yTX749zJkzSZObMdE7mZPJ+Ph7z6JyZ8zmfKzOnc831OZ85x9wdERGRJKgqdwAiIiIZSkoiIpIYSkoiIpIYSkoiIpIYSkoiIpIYSkrj3KxZsxzQTTfdSnuTIikpjXPPP/98uUMQERmgpCQiIomhpCQiIomhpCQiIomhpCQiIolRU+4AROTktLe3k06nC2rT2dkJwJQpU+IIaZDGxkbmz58fez9SGZSURMa4dDrNY48/Qd/EMyO3qT7yAgC/PRrvR0D1kQOxbl8qj5KSSAXom3gmPedfHnn92ie3ABTUphiZfkSi0jElERFJDCUlERFJDCUlERFJDCUlGXfa29tpb28vdxgyBmnfiZ8mOsi4U+j0aZEM7TvxU6UkIiKJoaQkIiKJoeE7yWvp0qXs3LmTmTNnsmjRokhtZs6cyfHjxznllFN46KGHIrVJpVID93ft2hVbGxFJLlVKktfOnTsB2LZtW+Q2x48fB+DYsWOxxCQilUlJSXJaunTpoOXly5fnbTNz5sxBy5deemneNtkVz3DLpWojIsmm4TvJKVMlZWzbti3vEF6mSspIWrXU2dlJT08Pra2t5Q6lJNLpNFXHknkF7qqXXiSdPlRRr3VtbW25w6hoqpRERCQxVCnJuJO5XMOqVavKHElptLa2svsXz5Y7jGH1nzaZxnPPqqjXWuKlSklymj59+qDloceLhjNhwoRBy6ecckpJYxKRyqWkJDktWbJk0HKUKeFDZ+lFmRI+dDp3lOndxbQRkWRTUpK8MtVSlCopI1MtqUoSkULomJLktWTJkhMqpnwK+U1TRjGVjqojkcqipCTjTmNjY7lDkDFK+078lJRk3Jk/f365Q5AxSvtO/HRMSUREEkNJqQzM7J/M7Kas5W+a2V1Zy18ws0+a2aYR2t9lZm8M7y+MPWARkVGipFQe3wEuATCzKuBVwJuynr8EGHHamrt/1N1/HC4qKYlIxdAxpfL4LvBP4f03AY8DZ5vZK4EjwBuAJcAsM7sfeDOwG/iwu7uZ7QJuAeYAtWb2GPAjd59rZh8GFhAktUeBv3b3vlH7y6Qsqo8coPbJLQWs3wVQUJtiVB85AJwVax9SWZSUysDdf2NmvWY2laAq+h4wBXgH8AKwDzgGvIUgaf2GoLp6J/Bw1nZuNbNPuPtFAGb2BuBDwDvd/biZ/SswF1g3Wn+bjL7Dhw9Td9oE6D8Uuc2R/l4A6gpoU5TTJtDZ2Vnw6XkaGxs1qWCcUlIqn+8SJKRLgJUESekSgqT0nXCd77v7rwHCaqiBrKQ0jPcB04AfmBlALfDc0JXM7AbgBoCpU6ee9B8i5TVp0iT86CGm1kUviJ+qqgbgtRMOxhRVlt6DHH3qmcirP91dHWMwknRKSuWTOa50AcHw3a+ATwEvAl8K1zmatX4f+d8vA9a6+9/lWsnd7wTuBGhqakrmNQ+kIFPr+lh48YuR11+xZzJAQW1GSyY2GZ800aF8vgtcARxw9z53PwC8gmAI77sFbOe4mWXOgPotYI6ZvRrAzM40s3NKGLOISKyUlMpnH8Gsu0eGPPaCuz9fwHbuBPaa2fpwRt5i4CEz2wtsA84uVcAiInHT8F2ZhDPiJg957Nqs+7uAXVnLn8i6n8q6/2ng01nL9wH3lT5iEZH4qVKSxGhvb6e9vb3cYYiMSPto/FQpSWKk0+lyhyCSk/bR+KlSEhGRxFClJHldeeWVHDp0iDPOOIMNGzZEapNKpQbu65pHIhKVKiXJ69Ch4Ff/L7zwQpkjEZFKp6QkOV155ZWDlmfPnp23TXaVNNyyiMhINHwnOWWqpIw4q6XOzk56enoKPk/aeJdOpznleOV8v3z2SBXH0ulE7gfpdJra2tpyh1HRKmdPFhGRMU+VkiTGlClTAFi1alWZIxlbWltbOfpUR7nDKJmzJvZz6jmNidwPkli9VRpVSpLT6aefPmj5jDPOKFMkIjIeKClJThs3bhy0HGVK+NAp4JoSLiJRKSlJXplqSVWSiMRNx5Qkr6HVUhSqjkSkGKqUREQkMVQpSWI0NjaWOwSRnLSPxk9JSRJj/vz55Q5BJCfto/HT8J2IiCSGkpKIiCSGkpKIiCSGjimJVICnu6tZsWdy5PWfOlQNUFCb0fJ0dzXnlTsIKRslJZExrpgZYZM6OwE4NTzfYJKch2a5jWfm7uWOQcqoqanJOzoq52SeIglh5Q5grNIxJRERSQwlJRERSQwlJRERSQwlJRERSQwlJRERSQwlJRERSQwlJRERSQwlJRERSQwlJRERSQwlJRERSQyd+05kjPvoRz/KwYMHmVLgeewaGxt10TpJHCUlkTHumWeeofvwEX57NPp/5+ojB2KMSKR4SkoilaC6hp7zL4+8eu2TW2IMRqR4OqYkIiKJoaQkIiKJoaQkIiKJoaQkIiKJoaQkkiDt7e20t7eXO4xhJTk2qRyafSeSIOl0utwhjCjJsUnlUKUkIiKJoaQkeXV1dbFgwQK6uroit1mzZg2pVIq77747cpv169eTSqW49957I7e57LLLSKVStLS0RG4zY8YMUqkUM2fOjNwmlUoN3OJsU2lG63XLrD99+vTIbTZs2EAqlWLjxo2R2+zYsYNUKsXOnTsjt5HCKClJXmvXrmXfvn2sW7cucpv169cDFNRmzZo1AKxevTpym56eHgAOHz4cuU1vby8Ax48fj9xGxgZ3j7zu7bffDsDKlSsjt1mxYgUAy5cvLyguiU5JSXLq6upi69atuDtbt26NVC1lkktGlGopk8QyolRLl1122aDlKNXSjBkzBi1HqZaGfmOP8g2+mDaVZrRet6HrRKmWNmzYMJDA3D1StbRjx46BLzS9vb2qlmKiiQ6S09q1a+nv7wegr6+PdevWcfPNN+dsMzTBrFu3juuuuy5nm6GJbPXq1Vx11VU522SqpIwo1VLmQyUjadVSZ2cnPT09tLa2Rm7T09MD0QsEAKpeepF0+lBB/aTTaWprawvrqAyiVEuZKilj5cqVXHnllTnbZKqkjOXLlxc0XCjRqFKSnLZv3z7o2+G2bdvKHJHIyRuauKIksqFfaIYuS2moUpKcZsyYwZYtW+jt7aWmpqagyQFSuMzlJ1atWhW5TUtLC90vHSuon/7TJtN47lkF9VNIVZV0ZjYoEZlZ3jY1NTWDElFNjT4+46BKSXKaN28eVVXBblJdXc0111yTt83cuXMHLUdpc/311w9avvHGG/O2GTqUNGnSpLxthn6QTJgwIW8bGVuiJJibbrpp0PInP/nJvG0WLlw4aHnRokUFxSXRKClJTvX19cyaNQszY9asWdTX1+dtMzTB5DueBCcmsnzHkwAefPDBQcubN2/O22b79u2DlqMMR+7atSvncqnaVJrRet2GrhNlAsLs2bMHkpeZ5T2eBNDc3DzwpaampkbHk2KipCR5zZs3jwsuuCBSxZORSTKFtMkksyhVUkamWopSJWVkPlhUJVWeKFVSRqZailIlZWSqJVVJ8bFC5vVL5WlqavKOjo5yhyGhzHGbYo4pdV/8F5Hb1D65hWlFHlMqpM04Fj07yiA6UieSII2NjeUOYURJjk0qh5KSSILMnz+/3CGMKMmxSeXQMSUREUkMJSUREUkMJaUyMrM+M3vMzH5oZnvM7JLw8QYze7zIbe4ys6bSRioiMjp0TKm8etz9IgAz+1PgH4D3ljUiEZEyUlJKjsnA74c+aGYNwJeBzA9xPuHu3w2f+zTwYaAfeNDdb81qVwXcDfza3RfHG7qU05EjR8Cduj1fjt6or5fOTp27TZJHSam8as3sMeA04GygeZh1ngNmuvtLZnYe8B9Ak5ldBswG3ubuR8zszKw2NcB64HF3P+HCL2Z2A3ADwNSpU0v590iZGM75px+JvP7T3dUxRiNSPCWl8soevnsHsM7M3jxknQnAP5vZRUAf8Lrw8RnAl9z9CIC7H8hq80XgK8MlpHDdO4E7IfjxbGn+FCmXiRMn0v/SIRZe/GLkNiv2TObU8OSvIkmiiQ4J4e7fA14F/MGQp24GngX+GGgCTomwue8C083stJIGKSISMyWlhDCz84FqYOilXc8AnnH3fuAvwnUAtgEfMbOJYfvs4bv/C2wBvmJmqoZFZMxQUiqv2nBK+GPAfcA8d+8bss6/AvPM7IfA+cBhAHffCjwAdITtb8lu5O4rgf8BvhxOehARSTx9iy4jdx/2aLO77wfeHN7/GXBh1tOfzlrvs8Bnh7RNZd1fUrpoZTS0t7cDyTylT5Jjk8qhpCSSIOl0utwhjCjJsUnl0LCOiIgkhpKSiIgkhpKS5NXR0UFzczO7d++O3Gb9+vWkUinuvffeyG1uueUWUqkUt956a/6VQy0tLaRSqUiXs85IpVIDt6S1qTRJfq2XLl1KKpVi+fJhf843rB07dpBKpSJdcl2Ko6QkebW1tdHf38+SJdHnTaxZswaA1atXR26TuQLuI488ErnN4cOHATh06FDkNiLAQGLZtm1b5DYrVqwAKCiRSWGUlCSnjo4Ouru7Aeju7o5ULa1fv37QcpRq6ZZbBs1oj1QttbS0DFqOUi0N/SYd5Zv1aLWpNEl+rZcuXTpoOUqS2bFjB729wfkCe3t7VS3FRLPvJKe2trZBy0uWLGHTpk0522SqpIzVq1dz1VVX5WyTqZIyolRLmSopoxKqpc7OTnp6emhtbY3cpqenhyq3gvp59kgVx9LpgvpJp9PU1tYW1E9SDU0o27ZtY9GiRTnbZKqkjOXLlzN9+vSSxzbeqVKSnDJV0kjLIuNFpkoaaVlKQ5WS5FRXVzcoEdXV1ZUxmso3JTxJ6qpVqyK3aWlpof+lwqrEsyb2c+o5jQX1U0hVVYlqamoGJaKaGn18xkGVkuQ0dPhu6Fj8cK6//vpByzfeeGPeNk1Ngy+W+/a3vz1vm0mTJg1aPv300/O2EQFOGHabOXNm3jYLFy4ctJxvuE+Ko6QkOTU1NQ1UR3V1dUybNi1vm7lz5w5aznc8CeAf//EfBy1/9rOfHWHNl23evHnQ8saNG/O22bVrV87lcrapNEl+rYfOJI2SYJqbmweqo5qaGh1PiomSkuTV1tZGVVVVpCopI1MtRamSMjLVUpQqKSNTLalKkkJlkkqUKikjUy2pSoqPuesab+NZU1OTD535JuWTOW5TzDGlL77395HbrNgzmVPPaSrqmFIhbcaxwqZDygAdqRNJkMbGxnKHMKIkxyaVQ0lJJEGSfFmIJMcmlUPHlEREJDGUlEREJDGUlEREJDF0TEmkAhztM1bsmRx5/ae7qzkvxnhEiqWkJDLGnX322Rw8eJBTw1MURXEemk0nyaSkJDLG3XXXXeUOQaRkdExJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ+e+Exnj2tvbSafTBbXp7OwEYEoBJ3EdTY2NjbrS7TilpCQyxqXTaR57/An6Jp4ZuU31kRcA+O3R5H0EVB85UO4QpIySt0eKSMH6Jp5Jz/mXR16/9sktAAW1GS2Z2GR80jElERFJDCUlERFJDCUlERFJDCUlSYz29nba29vLHYaME9rfkkkTHSQxCp3WLHIytL8lkyolERFJDCUlyaujo4Pm5mZ2794duU06naalpUXfRkWkIEpKkldbWxv9/f0sWbIkcptly5Zx+PBhli1bFmNkIlJplJQkp46ODrq7uwHo7u6OVC2l02n2798PwP79+1UtiUhkmuggObW1tQ1aXrJkCZs2bcrZZmh1tGzZMu655568fXV2dtLT00Nra2uhYY5r6XSaqmNe7jBKpuqlF0mnD8W+H6TTaWpra2PtQwqnSklyylRJIy0PJ1MljbQsIjISVUqSU11d3aBEVFdXl7dNQ0PDoETU0NAQqa/MGatXrVpVUIzjXWtrK7t/8Wy5wyiZ/tMm03juWbHvB6rIk0mVkuQ0dPhu6dKledssXrw457KIyEiUlCSnpqamgeqorq6OadOm5W3T2Ng4UB01NDTQ2NgYZ4giUkGUlCSvtrY2qqqqIlVJGYsXL2bSpEmqkkSkIJGPKZnZOcB57r7dzGqBGnc/FF9okhRNTU3s2LGjoDaNjY1s3rw5pohEpFJFqpTM7HrgfuCL4UOvAb4RU0wiIjJORa2UPg68FXgUwN1/Zmavji0qGZd07ElGk/a3ZIqalI66+zEzA8DMaoDK+bWeJML8+fPLHYKMI9rfkinqRIf/MrOFQK2ZzQT+E9gYX1giIjIeRU1KtwK/A/YBHwO2ACc9rcrMPmBmbmbn51lvi5m94mT7C7e138z2mdleM3vIzP7wJLfXYGaP51kn/2kQStSXiMhYFjUp1QJ3u/ufu/sc4O7wsZN1NfBw+O+I3P1ydz9Ygv4yprv7hUAHsLCE202UcJhVRGTMiPqh9S1gBpD5xl8LPARcUmzHZlYHvAuYTjAUuMTMzgbuAyaHsf2Vu/+3me0Hmtz9eTP7BvBa4DRglbvfGW6vG1gFXAH0ALPdPd+5V74NLDCzt4ZtTwvbfsTdf2Jm1cBtwCygH1jj7iNeP9nM3gR8CTiFIOH/mbv/bMjfvAF4JTABWOzuG8ysAXiQIEFfAnSG8feY2TSCLwEQvOaZbVUDnwVSwKnAv7j7F80sBfw98HvgfOB1eV4DGeM6OzupPtRF3Z4vR2/U1wtQWJv+vuDfquoCoitCXy+dnb3x9lGhzGwS8BWCGdLVBJ8FaWAlUAc8D1wLHAG+D7w//Kz7D2CHu68pR9zZoial09x9YAjK3bvNbOJJ9j0b2OruPzWzrvDDNwV8092Xhx+6w/VxnbsfCH8r9QMz+6q7dwGTgEfcfZGZfQ64Hsh3MZ8rCIYknwTe7e69ZjYDWAH8GXAD0ABcFD53Zp7t3UiQKNeb2SkEO0W2l4APuvuLZvYq4BEzeyB87jzgane/3sy+Evb/7wRJ7hPu/m0z+3zWtv4SeMHd/8TMTgW+Y2aZpHUx8GZ3/+VwQZrZDeHfxtSpU/P8STIWTKrpY2rdscjrP3skGCQ5a2L0Nk8dCnbnc04/WlhwBXq6O+akV9lmAb9x9xYAMzuD4AvvbHf/nZl9CFju7teZ2SeAe8xsFfDKJCQkiJ6UDpvZxe6+ByBMID0n2ffVBNUJwL3h8gPA3WY2AfiGuz82TLsFZvbB8P5rCT7Mu4BjQOaaCruBmTn63mlmfcBegmNjZwBrzew8glmFE8L1ZgCr3b0XwN0P5PmbvgcsMrPXAF/LrpJCBqwws/cQVF5TgLPC536Z9ffuBhrC42ivcPdvh49/GbgsvH8pcKGZzQmXzwhfi2PA90dKSOHfcSdwJ0BTU5NmUY5xU6ZM4WjvMyy8+MVY+1mxZzLAqPRzanhyXinYPuALZnYbwefh74E3A9vC2dPVwDMA7r7NzP4c+Bfgj8sT7omiJqWbgP80s98QfLD+IfChYjsNK45m4AIzc4IXyoG/Ad4DtBBk8JXuvi6rXYogUbzD3Y+Y2S6CITeA4+6e+YDtA2rCaitzVboH3P0z4f3p7v581nZvB3a6+wfDobRdOWJ/Gy//iPgzBIkNAHf//8zs0TD+LWb2MXfPPhXCXOAPgGnufjwclszEn/31s4/8x+wMmO/u3xwSXwo4nKetiFSgcOTpYuBygpGiHcCP3P0dQ9c1syrgDQRDea8Efj2asY4kUlJy9x+EM+ReHz70E3c/fhL9zgG+7O4fyzxgZv9FkJAedvc14ZDUxcC6rHZnAL8PE9L5wNvzxN0HXBQhnjMIjuNAMN6asQ34mJntzAzfufuj2dsMk1jm/rnAL9z9DjObClxIsFNk9/NcmJCmA+fkif+gmR00s3e5+8MESS3jm8BfmdmOcHuvy/obRGQcMrP/BRxw9383s4PAXwN/YGbvcPfvhaNQr3P3HwE3A08QTPb6UrjOyXyul0Qhs7NeD7yR4Jv9xWZGdhVToKsJJhBk+ypwD8FQ4XGCSRXXDFlnK3CjmT0B/AR4pMj+h/ocwfDdYiD7hG13EUwU2BvGtAb45xzb+X+BvwjX/S3Bsals64GNZraPYObfkxFi+wjBkKaTNdEhjK0B2GNBXf474AMRticilesC4PNm1g8cB/4K6AXuCI8v1QC3m1kv8FHgre5+yMy+TXAoY0mZ4h5gL4945VjJbAnBJIQ3EvxG6TKCimZOrnaSfE1NTd7R0VHuMOQktLa2cvSpjso6pnRO01i/2KOVO4CxKurvlOYA7wN+6+4fITgodkZsUcm41N7eTnv7iDPuRcpO+2j8og7fveTu/WbWa2aTgecIZr6JlEw6nS53CCI5aR+NX96kFB6v2BtOT15DMJutm2D6s4iISMnkHb4Lp1m/1d0Puvtqgt//zAuH8WQc6OjooLm5md27d+dfOdTV1cWCBQvo6uqKMTIRqTRRjyntMbM/AXD3/e6+N18DqRxtbW309/ezZEn0iTlr165l3759rFtX7ARNERmPoialtwHfM7Ofh2fX3mdmSkzjQEdHB93dwRmmuru7I1VLXV1dbN26FXdn69atqpZEJLKoEx3+NNYoJLHa2toGLS9ZsoRNmzYNv3Jo7dq19Pf3A9DX18e6deu4+eab8/bV2dlJT08Pra2tRcc7HqXTaU45HvX7ZfI9e6SKY+l0IveDdDpNbW3xF0h4z8zLv01VTelOONnf+/S3t215T65VwlOq7ct66APuvr9kMQzuaz/hybOL3UbUMzo8VWwHMrZlqqSRloezfft2enuDszz39vaybdu2SElJpOJV1Uw9fOGcnGdyKcSkvfdHWa3H3S8qVZ9x0/V2JKe6urpBiaiuri5vmxkzZrBlyxZ6e3upqalh5sxc58Z92ZTwJJxj/EeToy7z49lKcdbEfk49pzGR+0ESq7dihCfVHnQ5C3d/Jjyf6P8A7ya48sI1wN8RnCniPndfHLb/BsNcQmhIHx8GFhBcyudR4K/DU7/lVDk1v8Ri6PDd0qVL87aZN28eVVXBrlVdXc011ww9W5SIjKJaM3ssvH09PP9dOzDH3TPXa1uetf4xd28CVhNc/+3jBGcav9bM6sN1rgvbNhFcuaE+qz1m9gaCk3a/M6zS+hh87s4RqVKSnJqamgaqpbq6OqZNm5a3TX19PbNmzWLjxo3MmjWL+vr6vG1EJDaDhu/M7M2McDmLUOYab/sIzjD+TNjuFwTVURcjX0Io433ANIJr3kFw1YPnogSrpCR5tbW18bd/+7eRqqSMefPmsX//flVJIsljjHA5i1DmMjr9DL6kTj/BJYFSjHwJoew+1rr73xUanIbvJK+mpiZ27NgRqUrKqK+v54477lCVJJI8PyG8nAWAmU0wszcV0D7KJYS+Bcwxs1eHfZxpZpEmeKhSksRobGwsdwgiOZ30Ptrf+3TEGXORt1doE3c/Fl6xetDlLIAfRdxE3ksIufuPw0sBPRReTPA4wbGpvDO5lZQkMebPn1/uEERyOtl9NN9viuLg7idMmXX3xwguqjr08VTW/V1kXYU7+zmCyxcN11dD1v37gPsKjVfDdyIikhhKSiIikhhKSiIikhhKSiIikhia6CBSAZ7urmbFnsmx9vHUoWqA2Pt5urua82LtQZJMSUlkjButqfSTOjsBODU8R2FczkM/DxjPLLiwrIxXTU1N3tFROSfzFEkIG+7BD1767m+fUk3JLl1xrI+nv/7Qf+e7dIUD6939w+FyDcFphR519ytytEsBt+RaJw6qlERERskp1Uz9/DsOluzSFX/zvVdEWe0w8GYzq3X3HmAm0FmqGEpNEx1ERCrfFqAlvH818B+ZJ8zsrWb2PTP7HzP7rpm9fmhjM5tkZneb2ffD9WbHFaiSkohI5bsXuMrMTgMuJLi+UcaTwLvd/S3AZ4AVw7RfBOxw97cC04HPm9mkOALV8J2ISIVz971m1kBQJW0Z8vQZwFozOw9wYMIwm7gUeL+Z3RIunwZMBZ4odaxKSiIi48MDwD8CKSD79P1/D+x09w+GiWvXMG0N+DN3/0nMMWr4TkRknLgbWOru+4Y8fgYvT3y4doS23wTmW3jFPjN7SywRokpJRGTUHOvj6Ygz5iJvL+q67v5r4I5hnvocwfDdYmDzCM3/nuDyFnvDS1H8Eohlqrh+pzTO6XdKIrEY9ndKkp+G70REJDGUlEREJDF0TEkkJu3t7aTT6dj76QzPSTelwHPSNTY26mq/kjhKSiIxSafTPPb4E/RNPDPWfqqPvADAb49G/+9cfeRAXOGInBQlJZEY9U08k57zL4+1j9ong99CFtJPpo1I0uiYkoiIJIaSkoiIJIaSkoiIJIaSkiRGe3s77e3t5Q5DxiDtO5VDEx0kMUZj+rRUJu07lUOVkoiIJIaSkoiIJIaSksSio6OD5uZmdu/eXe5QRGQMUVKSWLS1tdHf38+SJUvKHYqIjCFKSlJyHR0ddHd3A9Dd3a1qSUQi0+w7Kbm2trZBy0uWLGHTpk1523V2dtLT00Nra2tMkY2udDpN1bFkXq+s6qUXSacPVdRrXVtbW+4wpARUKUnJZaqkkZZFREaiSklKrq6ublAiqquri9Quc+mFVatWxRLXaGttbWX3L54tdxjD6j9tMo3nnlVRr7VUBlVKUnJDh++WLl1ankBEZMxRUpKSa2pqGqiO6urqmDZtWpkjEpGxQklJYtHW1kZVVZWqJBEpiI4pSSyamprYsWNHucMQkTFGSUkSo7GxsdwhyBilfadyKClJYsyfP7/cIcgYpX2ncuiYkoiIJIaSUhmZ2WvMbIOZ/czMfm5mq8zsFDO7yMwuz1qvzcxuKWesIiKjQUmpTMzMgK8B33D384DXAXXAcuAi4PKRWxfcV3WptiUiEicdUyqfZuAld/8SgLv3mdnNwFPAcYK89S7gH8L132hmu4CpwO3ufgfBSh8GFgCnAI8Cfx1uqxv4IjAD+Djw8Kj9ZTKg+sgBap/cEnMfXQAF9VN95ABwVkwRiRRPSal83gQMOn22u79oZvuBLwGvc/dPQDB8B5wPTAdOB35iZv8GNAIfAt7p7sfN7F+BucA6YBLwqLt/amjHZnYDcAPA1KlTY/njBA4fPkzdaROg/1Cs/Rzp7wWgrpB+TpvA4cOHY4pIpHhKSmPHZnc/Chw1s+cIvua+D5gG/CAYDaQWeC5cvw/46nAbcvc7gTsBmpqaknka6wowadIk/Oghptb1xdrPU1XB6OxrJxyM3Obp7momTdI0akkeJaXy+TEwJ/sBM5tMMDzXO8z6R7Pu9xG8dwasdfe/G2b9l9w93k9DyWtqXR8LL34x1j5W7JkMUFA/mTYiSaOJDuXzLWCimV0DA5MRvgDcAzxLMEwXZRtzzOzV4TbONLNz4glXRCR+Skpl4u4OfBD4czP7GfBT4CVgIbCTYGLDY2b2oRzb+DGwGHjIzPYC24CzYw9eRCQmGr4rI3f/FXDlME8dBf4kR7s3Z92/D7hvmHWiXcRIRCRBVClJYrS3t9Pe3l7uMGQM0r5TOVQpSWKk0+lyhyBjlPadyqFKSUREEkNJSfJKp9O0tLQU9G10/fr1pFIp7r333hgjEynejh07SKVS7Ny5M3Kbrq4uFixYQFdXV4yRjW9KSpLXsmXLOHz4MMuWLYvcZs2aNQCsXr06rrBETsqKFSsAWL58eeQ2a9euZd++faxbty6usMY9JSXJKZ1Os3//fgD2798fqVpav379oGVVS5I0O3bsoLc3+I16b29vpGqpq6uLrVu34u5s3bpV1VJMLPi5jIxXTU1N3tHRMeLz11577UBSAmhoaOCee+7Juc1UKnXCY7t27coby5w5c+jp6amYq4im02lOOf4iq951MNZ+ijmjQ+vDr+DYhMkV9VrX1tZy//33R1p/xowZA0kJoKamhu3bt+dss3LlSrZs2UJvby81NTW0tLRw8803j7S6RQxdhlClJDllJ6ThlkXGouyENNzycLZv3z6outq2bVsssY13mhIuOTU0NJxQKcVlypQpAKxatSq2PkZTa2srR58auQotp7Mm9nPqOY0V9VoXoqam5oRKKZ8ZM2YMqpRmzpxZcJySnyolyWnx4sU5l4dz/fXXD1q+8cYbSxqTyMlauHDhoOVFixblbTNv3jyqqoKPzOrqaq655ppYYhvvlJQkp8bGxoHqqKGhIdIxiLlz5w5avuqqq+IITaRozc3NA9VRTU0N06dPz9umvr6eWbNmYWbMmjWL+vr6uMMcl5SUJK/FixczadKkSFVSRqZaUpUkSZWplqJUSRnz5s3jggsuUJUUIx1TkrwaGxvZvHlzQW3mzp17QsUkkiTNzc00NzcX1Ka+vp477rgjpogEVCmJiEiCqFKSxKiU38zI6NO+UzmUlCQx5s+fX+4QZIzSvlM5NHwnIiKJoaQkIiKJoaQkIiKJoWNKIjF6urt64ISpcXnqUDVAQf083V3NeXEFJHISlJREYjJaM8ImdXYCcGp47sAozkMz1iSZdOmKcS7fpStEpCi6dEWRdExJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ0lJREQSQ+e+Exnj2tvbSafTBbXpDM+XN2WY8+U1NjbqonlSNkpKImNcOp3mscefoG/imZHbVB95AYDfHq0Z8viBksYmUiglJZEK0DfxTHrOvzzy+rVPbgE4oU3mcZFy0TElERFJDCUlERFJDCUlERFJDCUlERFJDCUlkZi0t7fT3t5e7jDKSq+BFEqz70RiUuhvhyqRXgMplColERFJDCUliUU6naalpSX2b8odHR00Nzeze/fuWPvZsGEDqVSKjRs3xtqPwJo1a0ilUtx9992R2xSzv3V1dbFgwQK6urpi7UcKo6QksVi2bBmHDx9m2bJlsfbT1tZGf38/S5YsibWf22+/HYCVK1fG2o/A+vXrAVi3bl3kNsXsb2vXrmXfvn2x9yOFUVKSkkun0+zfvx+A/fv3x/atsqOjg+7ubgC6u7tjq5Y2bNiAuwPg7qqWYrRmzZpBy1GqpWL2t66uLrZu3Yq7s3Xr1kjV0mjt1+OdZf6zyfjU1NTkHR0dJd3mtddeO/CfF6ChoYF77rmnpH0AXHHFFQNJCaCuro5NmzaVvJ/p06eT/f/EzNi5c2fednPmzKGnp4fGxsaSx5QtnU5z6Jhz+KKrIrcZ6TRDkx67l9NPsZLFnE6nqa2t5f7774+0fiqVOuGxXbt25WxTzP62cuVKtmzZQm9vLzU1NbS0tHDzzTeXsh/LuTEZkSolKbns/7jDLZdKdkIabrlUhn5x0xe5ZClmf9u+fTu9vb0A9Pb2sm3btlj6kcJpSriUXENDwwnfKONQV1d3QqUUBzM7oVKKInNZiFWrVsUSV0Zrayu7f/FsSbbVf9pkGs89q2Qxt7a2lmQ7uRSzv82YMWNQpTRz5sxY+pHCqVKSklu8eHHO5VJpa2sbtLx06dJY+rnpppsGLX/yk5+MpR+BuXPnDlq+5ppr8rYpZn+bN28eVVXBx191dXVs/UjhlJSk5BobGwe+RTY0NMR2TKWpqWmgOqqrq2PatGmx9DN79uyB6sjMuPLKK2PpR+D6668ftHzdddflbVPM/lZfX8+sWbMwM2bNmkV9fX0s/UjhlJQkFosXL2bSpEmxf5tsa2ujqqoqtiopI1MtqUqKX6ZailK9ZBSzv82bN48LLrgg9n6kMDqmJLFobGxk8+bNsffT1NTEjh07Yu9n9uzZzJ49O/Z+JKiWhlZM+RSzv9XX13PHHXfE3o8URklJJCYa3tFrIIVTUhKJyfz588sdQtnpNZBC6ZiSiIgkRqxJycw+YGZuZufH2U/YV8rMCvo5v5m1mdkt4f3/Y2YzShTLPWb2SzN7LLx9N8e6w8ZtZu83s1tLEY+IyFgR9/Dd1cDD4b8lPWOmmdW4e2+ptufunynVtkJ/4+7Rzq0yDHd/AHighPGIiCRebEnJzOqAdwHTgY3AEjNLAUuBg8AFwFeAfUArUAt8wN1/bmZ/AKwGpoabu8ndv2NmbcAfAecCTxMku+H6bgvbnhv+e7u73xE+twiYBzwH/ArYHT5+D7DJ3e83s88AV4YxfRf4mLu7me0CHg3/plcAf+nu/13Aa/JeIPNTeQfeM+T5PwHuBOYA7waa3P0TYWw9wFuAVwPXAdcA7wAedfdrw/ZXAwsJzru12d0/HTU2GduqjxwYOJ9dtPWDE5AObVN95ABwVilDEylInJXSbGCru//UzLrMLPPLxj8G3gAcAH4B3OXubzWzVmA+cBPBB/c/ufvDZjYV+GbYBuCNwLvcvSdP/+cTJI/TgZ+Y2b8BFwJXARcR/O17CJPSEP/s7v8HwMy+DFxBkFgBasJ4Lyeo/kYa8vu8mWV+zPAjd58L3AJ8PEywdcBLmZXN7BKgHZjt7k+b2buHbO+VBEno/QQV1DuBjwI/MLOLCJLsbcA04PfAQ2b2AXf/Rs5XSca8/fv3M8H6qes/FLnNUXNqamp4/blDE9BZmjEnZRVnUrqal6uCe8PlTcAP3P0ZADP7OfBQuM4+giQCwQf9G7POMTY5/BAHeCBCQoKgUjgKHDWz5wi+/r0b+Lq7Hwn7H2l4bLqZ/S0wETgT+BEvJ6Wvhf/uBhpy9D/c8N13gJVmth74mrv/Ovwb30BQIV3q7r8ZYXsbw2ptH/Csu+8L/4YfhXGcA+xy99+Fj68nqMS+MXRDZnYDcAPA1KlThz4tY0xvby/9vcd5bV2U/xaBp49Wc97r3xT7eflEChVLUjKzM4Fm4AIzc6CaYLhqM3A0a9X+rOX+rHiqgLe7+0tZ62ZO9XI4vP9BXj5O9dFhwsjup4+If6uZnQb8K8HQ2a/CocDThtnuwDbN7EsEQ2u/cffB1wLI4u6fNbPNwOXAd8zsT8Onngn7eAswUlLKfp2GvoY1wPEof18Yx50ESZCmpiad8roCnFrtLLz4xcjrr9gzOcZoRIoX1+y7OcCX3f0cd29w99cCvySoVKJ4iGAoD4BweGoQd/+6u18U3qJeEOjbwAfMrNbMTic4bjRUJgE9H1Znc/Jt1N0/EsYxYkICMLM/cvd97n4b8AOCIUYIjrG1AP8QHncrxveB95rZq8ysmqAy/a8ityUiUhZxJaWrga8PeeyrjDAxYRgLgCYz22tmPwZuLEVQ7r4HuA/4IfAgQWIYus5BYA3wOMGxrBPWiejzWVPCHzOzU4CbzOxxM9tLUNk8mNXvswTHrv7FzN5WaGfhkOitwE6Cv2+3u28oMnYRkbLQlWfHuTiuPCujq6Wlhf6XDvHF9/4+cpsVeyZz6jlNOqYUH115tkg6o4NIgrS3t9Pe3l7uMIaV5NikcujcdyIJkk6nyx3CiJIcm1QOVUoiIpIYSkoiIpIYSkoSi66uLhYsWEBXV1fkNul0mpaWloKGiXbs2EEqlWLnzp2R23R0dNDc3Mzu3cOdzGN4t912G6lUii984QuR2yxdupRUKsXy5csjt6k0xbynCxYsIJVKFXSV32Le02L2UYmfkpLEYu3atezbt49169ZFbrNs2TIOHz7MsmXLIrdZsWIFQEEf/G1tbfT397NkSfRzBD/4YDB7f+PGjXnWfFkmUW7bti1ym0pTzHu6d+9eAPbs2RO5TTHvaTH7qMRPSUlKrquri61bt+LubN26NdI30XQ6zf79+4HgXG5Rvlnv2LGD3t7gRPG9vb2RqqWOjg66u7sB6O7ujvTN+rbbbhu0HKVaWrp06aDl8VgtFfOeLliwYNBylGqpmPe0mH1URod+pzTOxfE7pZUrV7JlyxZ6e3upqamhpaWFm2++OWeba6+9duADDKChoYF77rknZ5sZM2YMJCWAmpoatm/fnrPNFVdcMfABBlBXV8emTbkvw5VKpU54bNeuXSVvAzBnzhx6enoKOinqvn37qPI+7p5+IHKb1odfwbEJkwvqJ51OU1tby/33R7siSzHvaTGvWzHvaTH7aIH0O6UiqVKSktu+ffugCibK8FX2h9dwy8PJTkjDLQ8n+8NruGUpnWLe02IU854Ws4/K6NDvlKTkZsyYMehb6MyZM/O2aWhoOOFbdT41NTUnVEr51NXVnfCtOkmmTJkCUNCZFjJndCjEWRP7OfWcxoL6aW1tLaiPYt7TYhTznhazj8roUKUkJTdv3jyqqoJdq7q6mmuuuSZvm8WLF+dcHs7ChQsHLS9atChvm7a2tkHLQ4/9DOeyyy4btHzllcOdx3ew6dOnD1oejx96xbynF1544aDliy++OG+bYt7TYvZRGR1KSlJy9fX1zJo1CzNj1qxZ1NfX523T2Ng48E26oaEh0rGO5ubmgeqopqbmhEQwnKampoFv0nV1dUybNi1PC/j0pwdfwPdTn/pU3jZDZ4FFSZiVppj39I477hi0vHLlyrxtinlPi9lHZXQoKUks5s2bxwUXXFDQN9DFixczadKkSN+oMzLVUiEf+m1tbVRVVUX6Rp2RqZaiVEkZmSQ5HqukjGLe00y1FKVKyijmPS1mH5X4afbdOKezhCdL5rhNMceU4j5LeDGxjWOafVckTXQQSZBCpmiPtiTHJpVDSUkkQebPn59/pTJJcmxSOXRMSUREEkNJSUREEkNJSUREEkPHlEQqwNE+Y8WeyZHXf7q7mvNijEekWEpKImPc2WefzcGDBzk1PEVRFOeh2XSSTEpKImPcXXfdVe4QREpGx5RERCQxlJRERCQxlJRERCQxlJRERCQxlJRERCQxdJbwcc7Mfgc8VYauXwU8X4Z+1X9yYih3/3HG8Ly7z4phuxVPSUnKwsw63L1J/ZdPuWMod/9JiUEG0/CdiIgkhpKSiIgkhpKSlMud6r/syh1DufuHZMQgWXRMSUREEkOVkoiIJIaSkoiIJIaSksTCzF5rZjvN7Mdm9iMzax1mnZSZvWBmj4W3z8QQx34z2xduv2OY583M7jCztJntNbOLS9j367P+tsfM7EUzu2nIOiV/DczsbjN7zswez3rsTDPbZmY/C/995Qht54Xr/MzM5pWw/8+b2ZPha/x1M3vFCG1zvl8nGUObmXVmvdaXj9B2lpn9JNwnbi02BimSu+umW8lvwNnAxeH904GfAm8csk4K2BRzHPuBV+V4/nLgQcCAtwOPxhRHNfBb4Jy4XwPgPcDFwONZj30OuDW8fytw2zDtzgR+Ef77yvD+K0vU/6VATXj/tuH6j/J+nWQMbcAtEd6nnwPnAqcAPxy63+oW702VksTC3Z9x9z3h/UPAE0D0q9CNntnAOg88ArzCzM6OoZ/3AT9399jPnuHu3wYODHl4NrA2vL8W+MAwTf8U2ObuB9z998A2oOCzEgzXv7s/5O694eIjwGsK3e7JxhDRW4G0u//C3Y8B9xK8djJKlJQkdmbWALwFeHSYp99hZj80swfN7E0xdO/AQ2a228xuGOb5KcCvspZ/TTzJ8yrgP0Z4Lu7XAOAsd38mvP9b4Kxh1hmt1+I6gup0OPner5P1iXAI8e4RhjBH6zWQESgpSazMrA74KnCTu7845Ok9BMNZfwy0A9+IIYR3ufvFwGXAx83sPTH0kZOZnQK8H/jPYZ4ejddgEA/GqcryWxAzWwT0AutHWCXO9+vfgD8CLgKeAb5Qwm1LiSgpSWzMbAJBQlrv7l8b+ry7v+ju3eH9LcAEM3tVKWNw987w3+eArxMMz2TrBF6btfya8LFSugzY4+7PDhNf7K9B6NnMsGT473PDrBPra2Fm1wJXAHPDxHiCCO9X0dz9WXfvc/d+YM0I2x6N/UFyUFKSWJiZAf8XeMLdV46wzh+G62FmbyXYH7tKGMMkMzs9c5/gYPvjQ1Z7ALgmnIX3duCFrGGuUrmaEYbu4n4NsjwAZGbTzQM2DLPON4FLzeyV4dDWpeFjJ83MZgF/C7zf3Y+MsE6U9+tkYsg+VvjBEbb9A+A8M/vfYYV7FcFrJ6Ol3DMtdKvMG/AugiGivcBj4e1y4EbgxnCdTwA/Ipjh9AhwSYljODfc9g/DfhaFj2fHYMC/EMy42gc0lTiGSQRJ5oysx2J9DQgS4DPAcYJjIn8J1APfAn4GbAfODNdtAu7KansdkA5vHylh/2mCYzWZfWF1uO7/Arbker9KGMOXw/d4L0GiOXtoDOHy5QSzRX9+MjHoVtxNpxkSEZHE0PCdiIgkhpKSiIgkhpKSiIgkhpKSiIgkhpKSiIgkhpKSiIgkhpKSSBmY2bVm9s/ljkMkaZSUREQkMZSURGJgZh82s++HF5P7oplVm9lHzOynZvZ94J1Z695jZnOylrtzbDdlZrvM7P7wonnrs05TtD9z3jwzazKzXeH9NjNba2b/bWZPmdn/Y2afCy+mtzU8R6FIIigpiZSYmb0B+BDwTne/COgDPgwsJUhG7wLeeBJdvAW4KdzGuWQluBz+CGgmOFv5vwM73f0CoAdoOYlYREqqptwBiFSg9wHTgB+ERUwtcAmwy91/B2Bm9wGvK3L733f3X4fbeQxoAB7O0+ZBdz9uZvsIrq66NXx8X9heJBFUKYmUngFr3f2i8PZ6gktxj6SX8P+imVURXIY7l6NZ9/t4+cvlwHaA04Zr48FlG477yye97EdfTiVBlJRESu9bwBwzezWAmZ0J/A/wXjOrD4/h/HnW+vsJKisIhteKPcaTvZ0/K3IbImWlpCRSYu7+Y2AxwWW99wLbgLMJqqXvAd8BnshqsoYgYf0QeAdwuMiulwKrzKyDoIISGXN06QoREUkMVUoiIpIYOsApkkBmdgHBlVKzHXX3t5UjHpHRouE7ERFJDA3fiYhIYigpiYhIYigpiYhIYigpiYhIYvz/t04FD98SGJQAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Marital status, sex vs education level","metadata":{}},{"cell_type":"code","source":"ax = sns.catplot(x=\"edu_num\", y=\"marital\", hue=\"sex\",  kind=\"box\", data=df)\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:55.990401Z","iopub.execute_input":"2021-08-08T15:08:55.990802Z","iopub.status.idle":"2021-08-08T15:08:56.625962Z","shell.execute_reply.started":"2021-08-08T15:08:55.990759Z","shell.execute_reply":"2021-08-08T15:08:56.624907Z"},"trusted":true},"execution_count":44,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432.125x360 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Marital status, sex vs hours of work per week","metadata":{}},{"cell_type":"code","source":"ax = sns.boxplot(x=\"hourspw\", y=\"marital\", hue=\"sex\", data=df)\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:56.627224Z","iopub.execute_input":"2021-08-08T15:08:56.627528Z","iopub.status.idle":"2021-08-08T15:08:57.360747Z","shell.execute_reply.started":"2021-08-08T15:08:56.627498Z","shell.execute_reply":"2021-08-08T15:08:57.359757Z"},"trusted":true},"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Marital Status, income, vs hours of work per week","metadata":{}},{"cell_type":"code","source":"ax = sns.boxplot(x=\"hourspw\", y=\"marital\", hue=\"income\", data=df)\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:57.362410Z","iopub.execute_input":"2021-08-08T15:08:57.362930Z","iopub.status.idle":"2021-08-08T15:08:58.081794Z","shell.execute_reply.started":"2021-08-08T15:08:57.362883Z","shell.execute_reply":"2021-08-08T15:08:58.080757Z"},"trusted":true},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAeQAAAEGCAYAAAC0IuZwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAABeyUlEQVR4nO29e3gV1bn4/3mTcI+CglIKhdgT6xVEpNZ6qmfDQQsEQtV6q1bQqkdbIWo9rV/kp9iDVlulauqpFYqAUi/1Uu4oiGhb6wVURMWWXY0WDl6IVQkil+T9/bFntrMne3Zm7+xbkvfzPHmyZ81a633XzJp555215l2iqhiGYRiGUVhKCq2AYRiGYRhmkA3DMAyjKDCDbBiGYRhFgBlkwzAMwygCzCAbhmEYRhFQVmgFjLZLnz59tKKiotBqGIZhtCnWrVu3TVUP8KebQTYypqKigrVr1xZaDcMwjDaFiLyTLN1eWRuGYRhGEWAesmEUGbW1tUSj0UKr0Sq2bNlCr169mD17dqFVMYw2gxlkwygyotEor7y2kcbu+xdalYwp3V7Pzp07C62GYbQpzCAbRhHS2H1/dh46ttBqZEz5S/cWWgXDaHPYGLKRd2pra6mtrS20GoaRdVrbt6PRKFVVVUSjUerr65kyZQr19fWBv4PKZkJQvZno3t7JVVvNIBt5JxqNdoiL1uh4tLZvz5gxgx07djBjxgzmzZvHhg0bmD9/fuDvoLKZEFRvJrq3d3LVVjPIhmEYRUA0GqWurg6Auro6li1bhqqyfPlyVqxYEf+9fPlyVJUVK1bEvVl/2XQfCurr6+MyvPVmqnt7fuDOZVttDNnIO1u2bGHnzp3U1NQUWpWiJBqNUrK7jS+L2tTYIc9xNBqlW7duGZX1e1t79+4FYM+ePfE07+/Gxkbmz5/PFVdc0azsjBkzmDt3bmjZ8+bNo6mpqVm9meqervy2RC7bah6yYRhGEeB6XX5UFXfdeu/vvXv3snLlyqRlg+oKYtWqVfEHAG+9YWmt/LZELttqHrKRd/r37w/A7bffXmBNipOamhrWvfV+odVoHSWldOvaucOd49a8EaioqEh6cxcRIGaMvb/Lyso46aSTkpZNN6TtqFGjWLZsGXv37k2oN1Pd23NI3Vy21TxkwzCMImDatGkJ22VlMX+pU6dOdOrUKf7bTS8tLeW8885LWta/3RITJ06kpKSkWb2Z6p6u/LZELttqBjmPiMivRORyz/bjIjLbs32riFwrIlcHlG/Ig5qIyCQR+XWu6q+srKSysjJX1RtGwWhN366srIx7WxUVFYwdOxYRYcyYMYwePTr+e8yYMYgIo0ePpnfv3knLpqtD79694zK89Waqe3u+vnPZVntlnV/+ApwB3CYiJUAfYF/P/uOBK1T1uUIoly8mT55caBUMIye0tm9PmzaNmpoapk2bxn777UddXV3cWw36naxsJkycODFpvZno3t7JVVvFnSBg5B4R+TLwvKp+RUQGA1cB/YAzgc+A94GfAENU9TIROQj4PVAOLAQuV9VyiQ0k/QIYAygwQ1UfFJE7gcdVdZGIPAb8S1UvEJELgH9T1WtE5FxgCtAZeB74oao2isj5wP8DPgbWA7tU9bJU7Rk+fLjaak/Zp6amph2EznwPAY466qiCyK+srLQHP6NoEZF1qjrcn24ech5R1f8Tkb0iMpCYN/xXoD/wTeATYAOw21PkduA3qjpfRH7kST8VGAocRczLflFEngH+BJwALHLq7efkPwF4QEQOI2b8/11V94jI/wLniMhK4HrgGEePp4CXk7VBRC4GLgYYOHBgK46GEUR7eN23fv17gLLrnfw/sL3bUJp3mYaRDcwg559niRnj44GZxAzn8cQM4V98ef8dOM35fS9ws/P7W8D9qtoIvC8iTwNfJ2aQLxeRw4E3gP1EpB8xgz8FmEjM6L7ozNbsBnwAfANYo6ofAojIg8DXkimvqncDd0PMQ874KBiBtAfPrqqqiqbPtzN12Kd5l33jS/u2nMkwihCb1JV//kLMAA8GXgOeI2YwjydmrP2ENnqqugXoBYwGXI/5DKBBVbcDAsxT1aHO3yGqOj3zprSeTOLnBpVJNz0f+hZjfF+LJV685OrcZPMaKKSM9o4Z5PzzLDAO+EhVG1X1I2JG9Js0N8h/Ac5yfp/jSf8TcKaIlIrIAcCJwAvOvueAy/nCIF/l/Ad4EviuiBwIICL7i8ggYmPJ/yEivUWkE3B6ltraIpnEzw0qk256PvQtxvi+Fku8eMnVucnmNVBIGe0dM8j5ZwOxcd/nfGmfqOo2X94a4EcisoHYq22Xx4BXiU2+Wg38RFXfc/b9CShT1SjwErC/k4aqvgFMA54QkVeBlUA/Vd0KTCc2pv0XYGN2mpqaTOLnBpVJNz0f+nak+L5G8ZLNa6CQMjoCNoacZ5xx3319aZM8v+cCc53fbxPznF2mOekK/Lfz56//d8DvnN97gB6+/Q8CDyYpdw9wT9oNagWZxM8NKpNuej70Ldb4vvmIJb5z505KVHJWfyre/6yE3dFom4yj3ZpY2EFk8xoopIyOgHnIRsHIJH5uUJl00/Ohb0eK72sUL9m8BgopoyNgHrJRMDKJnxtUJt30fOhbrPF98xFL3J1lXQj6dm+iy6DKNhlHOxdefTavgULK6AiYh2wUjEzi5waVSTc9H/p2pPi+RvGSzWugkDI6AmaQjYKRSfzcoDLppudD32KN72uxxIuXXJybbF4DhZTREbBX1kZBySR+blCZdNPzoW8xxvdtD4FH2iu5OjfZvAYKKaO9Y7GsjYyxWNZGEFVVVezc0cDXeu3Nu+x3G0o5+Iij2+QYstExsFjWhmHkjX79+vHxxx/TpX//ljNnmYNpH/HAjY6HGWTDMLLO7NmzW85kGEYCNqnLMAzDMIoAM8iGYRiGUQSYQTYMwzCMIsAMsmEYhmEUATapyzCMrHPhhRfy8ccfx8N0+qmsrLTvoQ3DhxlkwzCyztatW2nY8Rnv7Wp+iyn97KMCaGQYxY8ZZMMwckNpGTsPHdssudubywqgjGEUPzaGbOSd+vp6pkyZknIR87Vr1zJy5EjWrVsHxNaJraqqIhqNsnr1aiKRCE899VRCXbNmzSISiTBnzpyE/BdeeCGRSIRLLrkkQcYdd9xBJBLhzjvvTMjvl3faaacRiUQ444wzmDRpEpFIhAsvvDBBdlCbIpFI/M9b58KFC4lEIixevLiZ7KqqKiKRCOPHj08oHyTv0ksvJRKJMHny5IS6gsp60/3HwXtsvfm8f7W1tdTW1marOyTFK2PKlClEIhGuvPLKhHPs1SlIbz9nnHEGkUiEs88+O7DveMt78wf1waBj7sfbp71lvOmnnHIKkUiE0047LfDYBPW1MWPGEIlEqKqqalZmwYIFRCIRHnjggcDy3mOQCd5rw9u+IHnea6C1hLmnZJPWHqsgLHSmkTGZhs6cOXMmixcvprq6OnAR83HjxtHQ0EB5eTlLlixh0qRJ1NXVUVFRwebNm+PLvI0dOzZe18KFC+Pl3aUP/UsgrlmzJv7be9P05p87d26CvKB1jKurq+OyVTVpm4JkvPPOO6gqIsKgQYMSZCe7maeSF6bd3rLe/GvWrEmQV1ZWFj+27vq2fo466igg9fKNVVVVNHy+m4Zh32+2r9ubyzjmq31TlneXIbz99tsDj4eXIL295xsSz0fQMQnCe2y9fXDAgAEt9jVI7NN9+vSJl9m2bVs8vaGhIbC8S9D1421b2HaHLR+GoL4+ZMiQpPJGjBgRvwaeeuqptOV5CXNPySatPVZBoTPNQzbySn19PStWrEBVWbFiRdIn2rVr18ZvTA0NDSxevDh+o6urq0tYCH3p0qWoKosWLUqow5vfi+sl33HHHYH5V69eHVjey6JFi1BVli9fzvLly5u1yW9IvHW6D8KqmpA+ZsyYUPLcY+g3JEF6u2X9+f06eo9tEOvXrw/cl22mTJkSKl+Q3t72nXHGGQn7go5JEEF9MOiYe2X7+7S3jDfdSzIvOej68fcbr5e8YMGChH2LFy9uVn7WrFkJedL1/CZNmpSw7W3fsmXLmslbuHBhwjXQGi85zD0lm7T2WKXCPGQjYzLxkGfOnJmwkHlVVVWzJ1rXk3AREbLZT/1eoZ9U3mEyRASI3Vi8bQrj2WWCV16h6NGjR8p40Rs2bKBRoWH4pOZlX3mAfTpLyvLRaJRu3bqxbdu2VuvqejC5Oh9hZPv7dLrlXYKun2Rta6ndYcuHIexbDFee6x27tMZLDnNPySatPVZgHrJRJKxatSrBu1i5cmWzPP4bV74NTzrGGGL6uToGtSmbeOUZbYNMjHEywlw/YclHXw2S5++/renP2TwmhcZmWRt5ZdSoUQlPsyeddFKzPP6xtGx7yC3RWg85WZuySTF4yJWVlaHGkJPR1HVfKkOOIWfDQy4G/H06U8JcP2HJR18Nkue/pt0+nQnZPCaFxjxkI69MnDiRkpJYtystLU26mPn06dMTtq+88srA+kpLS4HwF/Shhx4KwKmnnhqYZ+rUqaHqcunUqRNlZWVxfVqzQHu3bt1CyevUqVPGMtoSQ4YMyVpdBx54YNbqShd/nw5D7969m6UFXT/+ftOjR4/474suuihhn3uteMufc845CXnS7cMVFRWB+5JdG5dffnlCnlTXeEuEuadkk9Yeq1SYQS4AItIoIq+IyOsisl5EfiwiJc6+4SJyR0t15EHH6SJyVbbr7d27N6NHj0ZEGD16dNKbzvDhwykvLwdinsX48ePjF3xFRUX8AnfHi0SE6urqhDq8+b3cddddQPPJQt78I0eODCzvpbq6GhFhzJgxjBkzplmb/ONK3jrdm6KIJKQvX748lDz3GE6YMCFUu92y/vx+Hb3HNojTTjst5+sNV1ZWUllZ2WzyXRBBenvb99BDDyXsCzomQQT1waBj7pXt79PeMt50L4888kgzHYKuH3+/Wbp0afy334CMHz++WXm/0b7ggguayU7F3LlzE7a97Rs7dmwzeRMmTEi4BsaPH5+WPC9h7inZpLXHKhVmkAvDTlUdqqpHACcBY4DrAFR1raqGm1qaAhEp2uGIiRMnMnjw4JRPltOnT6ekpITrr78egGnTptGjRw+mTZsW92CvueaahLrcG895552XkN81Hq537OJ6yaeffnpCfr889wI/8MAD4zeaysrKBNlh2uSt0/UQrrzyymayXe9mn332CTxu3t+HHXYYAIMHD25WV5hj7j0O3mMbxOTJk3Me9tIrw/WShw0blnCOvYTRG77wkvv16xfYd4LyB/XBoGPux9unvWW86fvttx+Q3Dt2CTqXrpfs9Y5dXCNyySWXBJYPOgZh8V4b3vYFyfNeA60lzPWXTVp7rIKwWdYFQEQaVLXcs/1V4EWgD/AfwFVANfAWMFRVP3bybQK+BXQD5jj5PwTOV9V3RWQu8DlwNPAX4H+Bu4ADgEbgdFX9h4j8N3AG0AV4TFWvc+q/BpgIfAD8E1inqrcEtSPT75CN9s+oUaPYs3cvlCZ5td64l/Ie3VN62Vu2bAEIjIXdEhYr2yhmgmZZF60X1ZFQ1bdEpBQ40JPWJCILgVOAe0TkG8A7qvq+iCwG5qnqPBG5ALgD+I5TdABwvKo2isjzwE2q+piIdAVKRORk4GDgWECARSJyIrADOAsYSqxfvASs8+sqIhcDFwMMHDgw24fCaCeUlZVB4x4q9/ksIMdudr0T/DC3Y3tsbsCuvVvTlv1uQ2naZQyjGDCDXNw8CFwL3EPMWD7opH8TcGcl3Qv8wlPmD44x3gfor6qPAajq5wCOQT4ZeNnJX07MQO9DzFv+zMmXGGnDQVXvBu6GmIechTYa7ZBDDjmEXe+sZeqwTzMqf+NL+wJkVN4taxhtDRtDLgKcV9aNxF4Ve/krUCkiBxDzgB8NUd2OlsQBP3fGsIeqaqWq/i5dnfNNmFi16cazzUf820xkeMvkI260UTyEOd9Bfermm28mEolw6623ZiQ7TGz2VP3ZH38+G/LC4o8Hn2tyJc8McoFxjO1dwK/VN6DvbD8GzAQ2qqrbW58l5jEDnAP8yV+vqm4HNovIdxw5XUSkO/A4cIGIlDvp/UXkQOAZ4Dsi0s3xrjOf9pgD5s2bx4YNG5g/f36r8rQmfyZkIsNbJhqN5u0mYxSeMOc7qE+5M60zDUPprTdIRqr+PH36dJqamrjuuuuyJi8sM2bMYMeOHcyYMSOj8sUizwxyYejmfvYErAKeAK4PyPsgcC5fvK4GmAycLyKvAt8HagLKfh+Y4uR7FviSqj4B/B74q4hsAB4G9lHVlxwZ64HlxCaZFQVhYtWmG882H/FvM5HhL7Nnz56s62W0XYL61M0335yQL10v2VtvUGz2VP3ZH6u7JS85jLywRKPRhNjZuX6AzaU8G0MuAKoaOOtEVdcAazzba4m9ZvbmeQcYmaTsJN/2poB8twPNwiSp6g3ADS2on3fmzZtHU1MTAI2NjcyfP79ZrNoweVqTP1d6t1TmH//4B2VlZfHIVW2FaDRK5z2Fed5//7MSdkejbe6YwRcxvIMI6lP+75AXL17Mj3/849ByvfV6HwK9MlL1Z3/gk+uuu44lS5a0Sl5Y/F7qjBkzmn0XnU1yKc88ZKPoCROrNt14tvmIf5uJDH+Z3buTh580Oia56rfeeoNis6eS7Q8L2lKY0DDywuJfZSvVCm3ZIJfyzEM2ip4wsWrTjWebj/i3mcjwl+nZsycDBgxIGfe5GKmpqUn5WVMu6du9iS6DUsfaLlZa8upz1W+99QbFZk8l2x+r2x91LBN5YfGvQ50qul42yKU885CNoidMrNp049nmI/5tJjL8Zfr27Zt1vYy2S1Cf8q+HnG4oSm+9QbHZU/Vn/ytrN8Jea+SFxR8hraWIaa0ll/LMIBtFT5hYtenGs81H/NtMZPjLHHbYYTmPG20UD24M7yCC+tRPf/rThHzpjB/76w2KzZ6qP/tjdR9zzDGtlheWysrKhNjZ+Yiznit59sraaBNMnDiRurq6lE/PYfK0Jn8mZCLDWybXgfKN4iJMuM+gPjVmzBiWL1+e8UIN/nqTyUjVn6dPn85PfvKTFr3jdOSFZdq0adTU1OTcO861PItlbWSMxbI2gqipqWHT6y8zsLwxo/LvOKEzB+2Tfvl3G0o5+Iij2+QYstExsFjWhmHkjda+xuvhLC7RJYPFJQ7OgnzDKARmkA3DyDq20pJhpI9N6jIMwzCMIsAMsmEYhmEUAWaQDcMwDKMIMINsGIZhGEWATeoyDCMtamtrW1zhZsuWLfTq1YvZs2fnSSvDaPuYQTYMIy2i0SivvLaRxu77B+Yp3V7Pzp0786iVYbR9zCAbhpE2jd33Z+ehYwP3l790bx61MYz2gY0hGwWlvr6eKVOmpL0oebLy0WiUqqqqZq9TV69eTSQS4amnngqV7q934cKFRCIRFi9ezIIFC4hEIjzwwAOhdK+qqiISiTB+/PgE/bxl/fV4t0855RQikQinnXZagr7ePEHpkUgk/ufl0ksvJRKJxL8Vrq2tpba2NpPDn1W8enz7298mEokwevTohOO2du1aRo4cybp167j++uuJRCLccEPiEt5B7QZCnYNZs2YRiUSYM2dOgrxU58zFe778hOkvQX04DF698y3bL6O113W6tFb3dMlV+yx0ppEx2QidOXPmTBYvXkx1dXVai5InK79+/Xrq6uqoqKhIWDB81KhR8SXjVq1a1WK6v95FixahqogI3uulurq6Rd29RsFdtq2iooIhQ4bEy6pqQj1e2QsXLoyXLysri+s7duzYeJ6lS5cmTfeWXbNmTVKd1qxZE1/yL2yoyZqaGta99X6LHnJ5184sXbo0VJ1uva4eQcdt27ZtNDQ0NFvuL1X7vEyaNKnFc+A9bq6c8vJyRo4cGXjOwsgO09e9+qW76H0hZftlBB2fXNFa3dOltfetoNCZ5iEbBaO+vp4VK1agqqxYsSLtp01v+WXLlsXXKK2rq4s/Ka9evTphUXXXGw5K99e7ZMmSuBH2P7y6+4J0r6qqStj26rd8+XJUleXLl8d/r1ixgmg0GpftNQyunu5/V/aSJUsS0pctW5a0rHuzvvTSSxPSizGi1re//e2Ebe9xc42w1xgDcS/Z7xV7t6PRaOA5cI/54sWLE8p75bnH1n/O3HN/yimnJJT1eslh+rpfv3S8vVmzZiVse73kXMv2y/Aez0yu63Rpre7p0tr7VirMQzYyprUe8syZMxMWPK+qqkrradNb3o/7pOx6wS6uNxyU3lK9yQjSPdkrUz/+xdkHDBjA5s2bQ8tOhzVr1iTVqU+fPuzcuTN0/OdoNMr23cqOoWcF5ilfO5dSgcGDB4fWLxqN0q1bN7Zt2xa6jJeg9rneoutF+fGegzD4z5l77lPJDtPX/fql4+0VUrZfRtDxyRWt1T1dWnvfAvOQjSJk1apVCd7dypUrMy7vx71A/fu98pKlt1RvMjLR3UVV44Zg79691NXV5cQYGyQ1xpB4DsLgP2dhzn2Yvu7XL0jfdMmHbK+MTI5Pa8jVcQuitfetVNgsa6NgjBo1KuFJ86STTsq4vB93AXF33NWlrKwsZXpL9SYjE91d8ukhB9HfWVEp3THklJSU0q1r57SWQHTHkDP1kFvCHYv201oPOcy5D9PX/fq5fbi15EO2V0Ymx6c15Oq4BdHa+1YqzEM2CsbEiRMpKYl1wdLS0rQXJ/eW79SpU8I+d+HwqVOnJqRfc801KdOT6RVES7r36NEjsKyrb6dOneIPA6WlpUybNi1ebyqC9PM+WCTjsMMOS9hO55VyvujSpUvaZcLcFP2LyXvPgfs71bF3j63/nLnnfr/99kvI37t37/jvMH3dr59/OxXnnHNOwra3/lzL9svwHs9Mrut0aa3u6dLa+1YqzCDnCBG5RkReF5FXReQVEflGAXToJSI/zKDcdBG5Khc6eenduzejR49GRBg9enTCDSzd8mPHjo0/GVdUVMTHQ0eOHJngFY8YMSJlur/ecePGxZ/43f8u7r4g3f0zjL36jRkzBhFhzJgx8d+jR4+msrIyLnvChAkJ5b36urLHjRuXkD527NikZd3xxN/85jcJ6bW1tVRWVhbF+sGuHo8//nhCuve4lZeXA8T/u7gPVP7Zxd7tysrKwHPgHvPx48cnlPfKc4+t/5y55/6xxx5LKPvII4/Ef4fp63790jknF110UcL2BRdckDfZfhne45nJdZ0urdU9XVp730qFGeQcICLfBMYBw1R1CDAK+GeOZKVyiXoBaRvkfDJx4kQGDx6c8VOmt/y0adPo0aNHsydk1xv2esGp0v31Xn755QBceeWV8RvfJZdcEkp310veZ599EvTzlvXX4912va7evXsn6OvNE5QehOslu97x5MmTi2K2tVcP10vu2rVrwnGbPn06JSUlXH/99fGHqHReGYY5B663ed555yXIS3XOXLzny0+YcxPUh8Pg1Tvfsv0yWntdp0trdU+XXLXPZlnnABE5FThfVcf70o8BZgLlwDZgkqpuFZE1wHrgP4iN61+gqi+IyLHA7UBXYKdT599EZBJwqlNPKVAFLAT2AzoB01R1oYg8AEwA/gasVNX/FpH/Bs4AugCPqep1jm7XABOBD4g9PKxT1VtStTMb3yEbbY+ampoQoTPfo7SkJONX4lu2bAG+GN9Ol8rKyqJ4yDCMZATNsrZJXbnhCeBaEfk7sAp4EHgWqAUmqOqHInImcAPgvlvqrqpDReREYA5wJPAmcIKq7hWRUcCNgPtx4zBgiKp+5HjJp6jqpyLSB3hORBYBVwNHqupQABE5GTgYOBYQYJEjbwdwFjCUWJ94CViXrGEicjFwMcDAgQNbf6SMNkeYV4Kvv74NGvew653MHth2bI+Nje/auzXtsu82BI/7G0YxYwY5B6hqg+MNnwCMIGaQZxAzsiudschSwHu3ud8p+4yI7CsivYB9gHkicjCgxLxfl5Wq+pHzW4AbHePaBPQH+iZR7WTn72Vnu5yYgd6HmLf8GYBjzIPadjdwN8Q85BYPhtHuCON51tTUsOudtUwd9mlGMm58aV+AjMq7ZQ2jrWFjyDlCVRtVdY3zSvgyYp7t66o61PkbrKone4v4qwD+B3hKVY8ExhN7de2yw/P7HOAA4BjHG37fl9dFgJ97dKhU1d+1pp1G9sl1HOBiiV3dHinksc13/Ohik98eMIOcA0TkEMerdRkKbAQOcCZ8ISKdROQIT54znfRvAZ+o6idAT2CLs39SCpE9gQ9UdY+IjAAGOenbiXm/Lo8DF4hIuSOrv4gcCDwDfEdEuonIPsSMv1Eg5s2bx4YNG5g/f35O6o9Go3kLwt/RKOSxzXW/KXb57QEzyLmhnNir5jdE5FXgcOBa4LvAzSKyHngFON5T5nMReRm4C/iBk/YL4OdOeqrhhQXAcBHZAJxHbOwZVa0H/iIir4nIL1X1CeD3wF+dvA8D+6jqS8Req68HlgMvtvoIGBmRyzi5Rvul0P2m0PLbCzaGnANUdR2JxtZlG3BiQLH7VPVyXz1/Bb7mSZrmpM8F5nrybQO+GaDL93zbtxObue3PdwOxSWZGAZk3bx5NTU0ANDY2Mn/+/KzHAd6yZQs7d+6MR8bKBdFolM57CvO8//5nJeyORnPaviDceNz5Jh/9ppjltxfMQzaMIiKXcXKN9kuh+02h5bcXzEMuAlQ1UmgdjOIgl3FyXdKNXZ0J7izrQtC3exNdBlXmtH1BFMIrh/z0m2KW314wD9kwiohcxsk12i+F7jeFlt9eMINsGEVELuPkuhRL7Or2SKGObT76TTHLby/YK2vDKDImTpxIXV1dzrwMCymZOwp5bHPdb4pdfnvAYlkbGWOxrI0gampq2PT6ywwsb8yo/DtO6MxB+6Rf/t2GUg4+4uiCjCEbRhgslrVhGHmjta9teziLS3TJYHGJg7Mg3zAKgRlkwzCyjr0WN4z0sUldhmEYhlEEmEE2DMMwjCLADLJhGIZhFAEpx5BFZDHNlwWMo6rVWdfIMAzDMDogLU3quiUvWhiGkXVqa2sLthTgli1b6NWrF7Nnzy6IfMNoi6Q0yKr6dL4UMQwju0SjUV55bSON3ffPu+zS7fXs3Lkz73INoy0T6rMnETkY+DmxdX27uumq+tUc6WUYRhZo7L4/Ow8dm3e55S/dm3eZhtHWCTup6x7gN8BeYAQwH7gvV0oZ7Zu1a9cycuRI1q1bx8KFC4lEIixevJirrrqKSCTC1VdfnfAb4NxzzyUSiTBp0qSE33fccQeRSIQ777yTSCQS//OW96bPmjWLSCTCnDlzEtK99fjl1dbWUltbW8hDZuQR7/meMmUKkUiEK6+8kvr6eqZMmUJ9fX1CH/bi7VN+vGW8dXmvgaC+GoZUsqdOnUokEuHaa68NLO9ta5De0WiUqqqqpEMhp5xyCpFIhNNOOy2UvqtXryYSifDUU08l/PYeG+/vVASdjzCEkeGv/9JLLyUSiWT9e/tQoTOdMF/HiMgGVR3sTcuqNkabItPQmePGjaOhoYHy8nJ27NiBqiIiBPXFNWvWJL3J5Aq/vKOOOgrI7XKFuaCmpoZ1b71fMA+5vGtnli5dmnfZrcVdQvH2229P6AfV1dUsXryY6upqnnzyyXgfXrJkSTyPN/+aNWsS6vX2+5EjR8brWrRoUahroCVSyU61r6U8Xr379OlDXV0dFRUVzJ07N20ZXkaNGhVfrhGI/x47dmz82Khq/PcVV1wRWJdXR+/5CMPMmTNblOGvP922+gkKnRnWQ94lIiXAJhG5TEROAcrT1sLo8Kxdu5aGhgYAGhoa4jegVA+GJ598cl50cxk7NtGAvfHGG3mVbxQHU6ZMSdh2DefSpUsT+rDrNfkfGr3b/n6/bNmyuLEJcw205CWnkj116tSEfcm8ZH9bXS/Zr3ddXR0AdXV1CV7yKaecklC+JS959erV7N27F4gZYu/vpUuXoqosX76c5cuXo6qsWLEi0IP165iOl1xfX8+KFStSyvDX7188I5teclgP+evARqAX8D/AvsAvVPX5rGlitDky8ZDdJ822Ro8ePdpcfORoNMr23cqOoWflXXb52rmUCgwePDjvsltLNBqlW7dubNu2LVT+ZF6Ti+s9ZaPfp/LEUslOta+l8qn09nrJYWR4cb3jVIgIEHtQKSsro6qqKqkH69cxHS955syZLFu2LO6dJ5MR5tyl6yW31kOuUNUGVd2squer6mnAwLQ0MAxok8bYMFIRpk+31X6fSm/XW86ElowxxAyx6zDu3buXlStXJs3n1zGdY71q1aoE7zyZjHyeu7CLS/w/4A8h0gwjJeXl5W3y5lRZWdlmx5ALQkkp3bp2bnPHDL4YQ07HQw6Tpy32+1R6V1RUZFxvWVlZ2h7ySSedFErHMOfDZdSoUQkecjIZ+Tx3KT1kERkjIrVAfxG5w/M3l9iMa8NIi+nTp6ddpnPnztlXJAXdu3dP2O7UqVNe5RvFwZAhQ5Kmu5OQXK6//voW6/L3e7eOkpJwLyn945bpcPzxxydsn3jiic3y+Ns6bNgwIPX1Om3atPjv/fbbL2Ff7969U+rkH9f2UloaWwu7U6dO8eNUWloaeAz8OoY5Hy4TJ06Mn4MgGf76Bw5MfDmczWGZlnrD/wFrgc+BdZ6/RcC3s6ZFgRERFZFbPdtXicj0AqqUVUSkWkSuTrNMnYj0ybYuw4cPjz/BlpeXx5+C3f/JeOKJJ7KtRkqWLVuWsF1dXd3mxo+NzKmsrKSyspI77rgjIb26uhoRoaqqKqEPH3NM7GMT/ziid9vf78eOHYuIMH78+FDXwAUXXJBS51Syb7zxxoR9P/vZz5qV97d15syZSfV2veKKioqEa+Kxxx5LKP/II4+k1HfkyJFxY1tWVpbwu6qqChFhzJgxjBkzBhFh9OjRgUber6N7PsLQu3dvRo8enVKGv/758+cn7M/mJ5EpDbKqrlfVecC/qeo8z9+jqvqvrGlReHYBp+bCACVDRHK2DrW/bhEpU9VFqnpTrmSmy/Tp0ykpKeH666/n8ssvB2KzOocPj81xOO644xJ+AwwYMACI3Qi8v0899VQATj/99AQZ/vIu55xzDtDc4/DX45UxefJkW9+3A+E9367nOGzYMCZOnMjgwYM577zzEvpwWLxlvHV5rwEvQX01E1wvOZl37OJta5De06ZNo0ePHgnesYvrJbfkHbu4XvI111yT8Nt7bLy/U5HJ+XAJI8Nf/2GHHQZkf9JiylnWIvKQqp4hIhtIssiEqiZ/p9PGEJEG4AagXFWvEZGrnN/TReQA4C6+mMR2OfBX4C1gqKp+7NSxCfgW0OTPr6p/cTzufwO+Cryrqmd75E8CvgP0AA4mFkO8M/B9Yg8LY1X1IxG5CLjY2RcFvq+qnzlDCJ8DRwN/Afb3bb8KDFfVy5K1x9GvN3A/0N9p30nAMaoaOJCW6XfIRn6oqakpYOjM9ygtKcn4hrVlyxYA+vfvn1H5yspKe4gyipagWdYteWo1zv9x2Vep6LgTeFVEfuFLvx34lar+WUQGAo+r6mEishA4BbhHRL4BvKOq74vI7/35gcOcug4HvqWqyYL8HknMgHYlZmx/qqpHi8ivgPOA24BHVXUWgIjMAH4AuO9LBgDHq2qjY6C925NStcfR7zrgz6r6MxGpcupuhohcTOyhoNlYilFcFPI1++uvb4PGPex6J7MHth3bY+OIu/ZuTbvsuw2lGck0jELT0uISW0WkFJirqiPypFNBUNVPRWQ+MAXwGsxRwOGe8Z19RaQceBC4llhY0bOc7VT5ARYFGGOAp1R1O7BdRD4BFjvpGwD3TcSRjiHuRSwwy+Oe8n9Q1cYU2y2150TgVOdYLBWRpEMSqno3cDfEPOSAthhFQCE9xJqaGna9s5apwz7NqPyNL+0LkFF5t6xhtDVanOLn3NSbRKRnHvQpNLcR8wx7eNJKgONUdajz119VG4i91q10XgF/B3i0hfwAOwBE5BQRecX5c19b7PLIbPJsN/HFg9Nc4DInfOn1eBb6cOtOsd1SezocqeLy5kJG2Li8YfO1hMXgbnu05pylin2dj77emnjSrSUf7csHYQODNAAbROR33s+fcqlYIVDVj4CHSHxd+wQQdzVEZKiTV4HHgJnARlWtT5XfJ+cxj0FM553ePsBWEekEnJNGOS9B+j0DfM9JGwPs16xkO2PGjBns2LGDGTNm5EXGvHnz2LBhQ7NZmn7C5muJaDTa5m9QHY3WnLMFCxYAJO03+ejr06dPp6mpieuuuy5nMoLIR/vyQViD/Cjw/xG7aXs/f2qP3Ap4Z1tPAYaLyKsi8gZwiWffg8C5fPG6uqX8reX/A54nNlHrzQzrCNLveuBEEXmd2Kvrd1urbDETjUYD4/LmSkaYuLxhYusahp9Zs2YlbHu95Hz09dbEk24t+WhfvggVy9owktGWZ1lPmjQpIfRfstVrsi3DJVVc3jCxdcPy3e9+l507dxZkclc0GqXznk+5/VsfZ1S+NWPINX/uxe5O+7bJb8fdONoPP/xwWuVSxZLOR19vTTzp1pKP9mWbVsWyFpGDReRhEXlDRN5y/7KvpmHkB7+hbE1c3rAyXFLF5Q0TW9cw0iEffb018aRbSz7aly/CBqi4h9hnMb8CRgDnE/51t2EUHRUVFc2eqnMtwyVVXN4wsXXD4n7DW4h40u4s60LQt3sTXQa1vdjj8EUc7WySj77emnjSrSUf7csXYY1qN1V9ktgr7ndUdTpQlTu1DCO3+CMNJYs8lG0ZbkzsVHF5w8TWNQw/blQvF2+/yUdfb0086daSj/bli7AGeZeIlACbROQyETmF2HewhtEmqaysDIzLmysZYeLyhomtm478tjiO2pHJ9JxddNFFCdve2Nf56OutiSfdWvLRvnwR1iDXAN2JzdA9htjMYnt0N9o0qeLy5kJG2Li8YfO1hMXgbnu05pylin2dj77emnjSrSUf7csHoWZZO8ErrgEGAe5adNpeYlkbmdGWZ1kbuaWmpoZNr7/MwPJkweJa5h0ndOagfdIv/25DKQcfcXSbHEM2OgaZxrJ2WQD8N7Ewjk3ZVMwwjPZHa18b9nAWl+iSweISB2dBvmEUgrAG+UNVXZRTTQzDaDfYq3LDSJ+wBvk6EZkNPIkn5rKqPhpcxDAMwzCMsIQ1yOcDhxIbP3ZfWStfLKhgGIZhGEYrCGuQv66qh+RUE8MwDMPowIT97OlZETk8p5oYhmEYRgcmrId8HPCKiLxNbAxZsM+eDMMI4MILL+Tjjz+Oh+/MhMrKSpscZnQowhrk0TnVwjCMdsXWrVtp2PEZ7+0Ke4tJpPSzj7KskWEUP6GuFlV9J9eKGIbRzigtY+ehYzMq2u3NZVlWxjCKH1uxycg79fX1TJkyhfr6embNmkUkEmHOnDkJv2+++WYikQi33norEAtWH4lEuOGGGxJ+r169mkgkwlNPPcX48eOJRCJMmDAhQV4kEon/eWWfe+65RCIRJk2aRDQapaqqKr64ubdeb/kRI0YQiUQYOXIkCxYsIBKJ8MADDwTq5C27cOFCIpEIixcvTmirN79fX+9fUBmv7DDt9qYD1NbWUltbm+OzXhx423rHHXcQiUS48847mTp1KpFIhGuvvTbh+Fx44YVEIhEuueSShPPn7Z+pjq2XoPMXVN7bv7x49fCSSvZpp51GJBLhjDPOSEj3yj7jjDOIRCKcffbZCenea8PfV72MHj2aSCTCmDFjQp2LSy+9lEgkwuTJkxPa5JXtxauHP4/3Gli7di0jR45k3bp1gXUFpQedF/8xP/nkk4lEInz7298O1dawhAqdaRjJyDR05syZM1m8eDHV1dUsXLiwxfxr1qxJepOB2FKG7lKF7jrCbhkXb9nq6uqkst0l3NzFzUeNGpW03jAE6SQiqGr8f7L8q1atCmxrLtq9Zs2a+JJ/2Qw1WVVVRcPnu2kY9v2Mynd7cxnHfLVv1sNfetsa5jh7CTp/qY6tF6887/kbO3Zsi9eDt64RI0bE9fAaRm/9qWR79wVdi942rV+/Pn5tbN68OaGvhpERhDe/99iOHz8+LvuKK66I55k0aVJcjyFDhiTk8dblLgVZXl7OyJEjk9blbbc33Xvde8/LokWLEo55um31ExQ60zxkI6/U19ezYsUKVLXZE34QZ511VuA+1xj5jabrJftvuu6F5b/xueup1tXV8fvf/z6w3jAElXVv4v6HYG/+sEYiSIbrJYdtd7pGqb1wxx13pF0m6PwtWbKkxWM7a9ashH3e8xdU3ovrJS9cuDBBD/ca8p9H7/Zpp52WsM/1kr3Xol+221+WLVuWcG149fY+DIwenTjNqCUv+dJLL03Y9rbJPR4rVqyIe7DRaDRBj+XLl8fzTJ06NaEud13mhoYGli1b1qwub7u96atXr05o39KlSxP0cfUbMWJEgrxsesmZzbgwjAyZN28eTU2x2DLu/5Z477330pbzySefpF3G5e677864bKFZuXIl11xzTVpltmzZws6dO+PeYzbYuXNnLHRQhpR8/inR6Pas6gSxG3u3bt1Yv3591uoM048XLFjQqvJ33XUXZ511FrfddltC+syZMxk/fnzKsv7Xsh988AGQeC0GsWfPnsB9N9xwQ9w4ff755wn7du7cmbLejRs3Bu5zdWpsbGT+/PlcccUVzJgxI6lejY2NPPvss4F1uQbWW5e33d70G2+8MaFsY2Njwn8X/wPZrl27yBbmIRt5ZdWqVRl5nYZhNDcGrRlybO21mOvreO/evaxcuRL44g1Wpjp46/K225teDPcl85CNvDJq1CiWLVtWFJ3fiOF+K5yLMeRMaeq6L5U5HEPetm1bVuvNF/7xaxHJuK7WXotlZbk1H2VlZZx00knAF3M8kuUJo7+3Lm+7vemZzBfJNuYhG3ll4sSJlJTEup37vyW+9KUvpS2nZ8+eaZdxufjiizMuW2jcm4uRmlNPPTVrdYXpx+ecc06ryl9yySUAXH755QnpV155ZYtle/funbB94IEHAonXYhCdOnUK3OcdGunatWvCvm7duqWs97DDDgvc5+pUWlrKeeedB8C0adOS6lVaWsrxxx8fWJf70OCty9tub7p/LLq0tDThv4v/IahLly6B8tOlzRhkEVERuc+zXSYiH4rIklbW+2UReTjNMnNF5Ltp5P+ZiIxKX7v2R+/evRk9enR8NmUY/J99eHEvOP/TujtJxT8Dsrq6GhFp9mlURUVF/P/3vve9wHrDEFTWvZD9F7Q3f9gZm0Ey3Jtk2HavWbOGysrKDrN+sNvWKVOmpF026PyNGzcu8Ni6XHTRRQn7vOcvqLwXd2LjhAkTEvRwryH/+fZuP/LIIwn7HnroISDxWvTLdvvL2LFjE64Nr97eyU0rVqxIKL98+fLAtgD85je/Sdj2tsk9HqNHj44/TFRWViboMWbMmHge/9hveXl5/P/YsWOb1eVttzd95MiRCe2rqqpK0MfVz//J1+OPP56yrenQZgwysAM4UkTcR6+TgC3pVCAiZf5tVf0/VQ1tXDNBVa9V1VUt5+wYTJw4kcGDB3PeeefFPQf/b3eWpnvDcS/+k046KeG3+1R7zTXXsM8++wCpvWOv7AEDBgCxC3zatGn06NEj/iTurdeLe2GWlJTEb7KXXHJJoE5eXO/myiuvTGhrUH4/QWW8ssO028/kyZM7TIhKb1tdL/n000+Pe1knnnhiQn73QeXQQw9NOH/e/pnq2HoJOn9B5b39y4tXj7C4Rsf1jl28st19/fr1S0j3Xhup+qrrJbfkHbu4XvLgwYMT2hR0PLx6+PN4r4Hp06dTUlLC9ddfH1hXUHrQefEf886dOwPZ9Y6hDX2HLCINwB3AS6r6sIjMB14HTlDVcSJyLHA70BXYCZyvqn8TkUnAqUA5UArc49ueCCxR1SNFpBS4CYgAXYA7VfW3ErsL1xJ7CPgnsBuYo6rNPGsR+SlwLrFlKper6tUiMhdYAjQAP1DV0528EeAqVR3nq+N04DqgEfhEVU902nEK0BPoD9ynqtc7+a8ELnCKz1bV20Skwm2Xk+cqoFxVp4vIFOASYC/whqqeJSI9nDYeSWyZzemqmvIj4Uy/QzbaPyNGjKBJFUqDX3mmpHEv5T26Z+y5Wxxso5gJ+g65rU3qegC41nlNPQSYA5zg7HuTmHHe67wevhFwP8AbBgxR1Y8cw+bdrvDU/wNiBvDrItIF+IuIPAEcDRwCHA70Bd5wZCcgImOACcA3VPUzEdnfl2UVcLeI9FDVHcCZTpv8XAt8W1W3iEgvT/qxxAzmZ8CLIrKU2Mcl5wPfILbox/Mi8jTwr+SHEICrgYNUdZen/muA1ap6gZP2goiscvT0tvFi4GKAgQMHphBhdHQE5dB9PmtFDbvZ9U76D3zvNpS2nMkwipA2ZZBV9VXHgJ4N+IPd9gTmicjBxIyU99F8pap+lGLb5WRgiGd8uCdwMHAicL+qNgL/JyKrA1QcBdyjqp85+ibIcB4WVgDjnXHrKuAnSer5CzBXRB4CHvXpXQ8gIo8C33La+phrOJ30E4BFAToCvAosEJE/An/0tL3a8aQh9qZhIJDwwaCq3g3cDTEPOYUMowPTvXt3mj7fztRhn+Zd9o0v7Zt3mYaRDdrSGLLLIuAW4H5f+v8ATzmvaMcTMyguO3x5/dsuAkxW1aHO30Gq+kSQIiLyDRF5xfmrDqn/A8AZwEhgrapuF5Eb3HoAVPUSYBrwFWCdiLjTJP0GMJVB3Evi+fUejyrgTmJvCl50xtYFOM3T9oGqGvz1fivwxogN+u2PLe2NJeuNVRsUZ9db11VXXUUkEuHqq69O0MMbK9gvz7vtjbkbFMPYG6fYize+sDe/tw1+zj77bCKRCOeee26HijNtJMba9vZBb38Jiift7Wt+vLGsg+I4B8Wy9hKU3pL8ZHjjhKe6HpLh1yMo7ne6dQX99usXFDe/tbQpD9lhDvCxqm5wxmBdevLFJK9JGdb9OHCpiKxW1T0i8jWnzmeA/xKRecCBwAjg96r6PDDULSwiu4m9Ul/gvrJO4ok/7bThIpzX1ap6DbFXxm49/+bU/bzzGvwrzq6TnNfgO4HvEBs3biLmTd9EzKieAnwfeB840DHmDcA4YIWIlABfUdWnROTPwFnExtMfByaLyGRVVRE5WlVfzvA4pmTevHls2LCB+fPno6pJf69fv54dO3YwY8YM5s6dG49QNHPmTHr06EFTUxPXXXcdffr0iefbvHkzEIsgNHbs2Hhd7jj3c889l6CHG87wrrvuoqKiIkHejBkz4tvu948bNmxgw4YNACxevBgRictwZ3XPnz+fCy64gGR42/3kk0/G27BkSeKHAlu3bgVg8+bNzT5ZMdo37gMhkNAHt23bFu8vblQsb6SslnAN1wcffJDQD71xnN0IXlu3bg3ME5Temra++eabTJ8+PfB6SIZfD++1nCrUbkt1Bd2P/Nfr9u3bgdZFBExGm/OQVXWzqiYLRPsL4Oci8jKZP2jMJjY+/JKIvAb81qnrMWCTs28+8NcA3VYQ8+DXOt7uVUnyNBKb4DXG+Z+MX4rIBkeHZwE3zt8LwCPEXjk/oqprVfUlYK6z73lik7peVtU9wM+c9JXExtghNpHtPhHZALwM3KGqHxN7w9AJeFVEXne2s443juzy5csTfrvxaf3xc2fNmpUQS9YbqzYozq4bw9Yfo9f1kv2hDL31rF69OmE7CDferz8mt+slB8WTXrp0aUIbvF7B2WefnVDm9ddfD5RvtF/8sZu9/SVZPOl0Ylm7/dAbx9m/ClSyPEExoFuSn4wLL7wwYTvoekiGXw9/nPB0vGT//ci9B3nvTf7r1R+3O5tecpuZZd3RcSajDVfVywqti0sms6xnzpwZj5LjfkLkrqLi/s41qVaPguxE7GlJhpfy8vK4V5CsTI8ePdrcd8IbNmygRBuZMyLZVI3cUvPnXuzutG+bO2bwRazt8vLylA+DLqlWCHO/RQ7qh+63tv7VkoLyeK9db3qQjFTf1Ke6NrzXQzL8eiS7VsN+z5+t+1G6Kz7Zak9GUeCNI6uqCZ5vsTwc5jt8nvv0bRguYYwxtK6veuM4h8kTFAM627R0Pfj1aA3Fdj9qi2PIHRJVnUvs1XSbxhtHtlAeckvkO6atG1koiMrKyqzHdM41VVVVNH2+vSCy+3ZvosugtnfM4ItY2+l4yJnijeMcJk9QDOhs09L14NejNddqsd2PzEM28oo3jmynTp3iMWk7deoUv7n44+emigMcRNCN6rjjjgOahzL04o9p2xL+eMAtRWzy63b99dfHf/fr1y9lXqNj4I/dHESYpTaDJgZ64zj7I3glyxMUAzoTUg0neK+HZPj18N8f/JHNwtblvwe59yH/NeiP292auPl+zCAbecUbR3bMmDEJv934tP74uRdddFFCLFlvrNqgOLtuDFv/hIubbroJaG7kvfWMHDkyYTsIN96vPya3O8s6KJ50VVVVQhuOOeaYeJ7770/8mm/ChAltcizUyAw31rY/drO3vySLJ51OLGu3H3rjOLvxrVPlCYoB3ZL8ZMyePTthO+h6SIZfD//DdTqzrP33I/ce5L03+a9Xf9xu/8TR1mAG2cg73hixQb/9saW9sWS9sWqD4ux66xo+PDZ3wvWOXbyxgv3yvNvemLtBMYy9cYrDtNvbBj+ulzxgwIAOFWfaSIy17e2D3v4SNva5F28s66A4zkGxrL2EjdsdBm+c8FTXQzL8egTF/U63rqDffv3CxM3PBJtlbWSMxbI2gqiqqmLnjga+1iv/68u+21DKwUcc3SbHkI2OQXuJZW0YRhugX79+fPzxx3Tp3z/vsg8m9RilYRQrZpANw8g6/jFCwzBaxsaQDcMwDKMIMINsGIZhGEWAGWTDMAzDKALMIBuGYRhGEWCTugzDyIja2tqE5QK9bNmyhV69etnkLsNIAzPIhmFkRDQa5ZXXNtLYff9m+0q317Nz584CaGUYbRczyIZhZExj9/3ZeejYZunlL91bAG0Mo21jY8hG0VBfX8+UKVMSFj5vTT6XaDRKVVVVs9erYevxlk+3rgsvvJBIJNIspN/atWsZOXJk0sXYvTJmzZpFJBJhzpw5CTKCfnu56qqriEQiXH311Ql5Vq9eTSQSiS9u76W2tpba2tqUxyObeOV5dbz00kuJRCIZhQ0999xziUQiTJo0KSOdFi5cSCQSYfHixWn3tZtvvplIJMKtt96akeyg/hWGVLqGaUdQnlxdl+nmz1ddhZRnBtkoGubNm8eGDRuYP39+VvK5zJgxgx07djBjxoyM6vGWT7cu98b65ptvJqRPnz6dpqYmrrvuupTyFixYAMD8+fMTZAT99uKGNX3uuecS8tx4440A3HDDDc1kuw8d+cIrz6vjxo0bAdiwYUPadW7evBkIv6awn9tuuw2ILV6fbl9bvnw5AIsXL85IdlD/CkMqXcO0IyhPrq7LdPPnq65CyjODbBQF9fX1rFixAlVlxYoVgU+eYfO5RKPR+I25rq4ufvMPW4+/fDp1XXjhhQl1uV7y2rVr44uwNzQ0JHjJfnleFi1ahKqyfPnyuDzvb6/sq666KmnZJUuWJCzunsxLLgTeY+hfPScdL/ncc89N2E7XS164cGHCIvVLliwJ3dduvvnmhO10veSgvhqGVP05TF8PypOr6zLd/Pmqq9DyzCAbRcG8efNoamoCoLGxMfDJM2w+F7+n4W6HrSeVp9JSXf4bquslT58+PSHd6yWH8Yz27NnDnj17mv32yg5a9KOxsTFh2+8lb9myhWg0Sk1NTYt/0WiUks8/Ta5kUyM7d+4MVceWLVsSjqGfdLxk1zt2SddLdr3jeDPS6Guud+ySrpcc1FfDkKo/h+nrQXlydV2mmz9fdRVank3qMoqCVatWJXhuK1eu5Iorrsg4n4v/huxuh60n1Q093bpcXO842XYYA+Jdoc37O4xsP67ehcZ7DAtJ0Op3mRzbdAnqq2FI1QfD9M+gPGHK7tmzhy9/+ctMnTo1vm55SUlJfNghGccccwxHH310fLul/KnIZl3Zlte1a1cGDBhAp06dQtVtBtkoCkaNGsWyZcvYu3cvZWVlnHTSSa3K51JRUZFwY3MXfQ9bj7+8f18mOpWXlycYYXfx85bkubg3PVVN+B1Gth93sXuX/s7qTGGWLqypqWHdW+8n31lSSreunVusp6amBoBBgwbFj2EhEZGkRjmTY5suQX01DKn6YJj+GZQnTNnNmzczfPhw9u7dG++PvXr1om/fvoH67rfffnzyySfx7ZbypyKbdWVTnqpSX1/P5s2bOeigg0LVba+sjaJg4sSJlJTEumNpaWngAuhh87lMmzYt6XbYevzl06nLvwTgoYceCjR/Ze1dlD2VPJdOnTrFn7i9v72yhw9vttRqPI+XdBa5zyXeY+hn8ODBoesZMGBAwnY6Rg3g8ssvT9hOp6+NGTMmYXv8+PFpyQ7qq2FI1Z/D9PWgPGHKfv755wwaNCieT0To3bt3Sn379OkTN95h8uerrmzKc/d9/vnnoevOmUEWERWR+zzbZSLyoYgsaWW9XxaRh9MsM1dEvtsaucWCiERaewxbqL9CRL6Xq/qD6N27N6NHj0ZEGD16dGAnD5vPpbKyMn5TrqioiBvJsPX4y6dTlz9K1V133QXEjKXrFZeXl3PMMccEyvNSXV2NiDBmzJi4PO9vr+xbbrkladlx48bFveKysjJGjBjRrL35XEvYlec9hhMmTEjIk85nWPfdd1/C9ty5c9PSZ8KECQk323HjxoXuaz/96U8Ttn/84x+nJTuor4YhVX8O09eD8oS9Tjp16sS+++4LQM+ePZu9efFTVlaWVv581ZVteW5fCksuPeQdwJEi0s3ZPgnYkk4FIlLm31bV/1PVdmFci5QKIO8GGWJP44MHD27REwmbz2XatGn06NGjmccRth5v+XTrcm+qrnfsMn36dEpKShK842TyzjnnHADOO++8BBlBv724XvJxxx2XkGfq1KlAcu948uTJGX37myleeV4dDzvsMCA979jF9ZLT9Y5dXC/5yiuvTLuvuV5yut6xS1D/CkMqXcO0IyhP2GPQp08funXrFtpDTTd/vuoqpDwJmsTQ6opFGoA7gJdU9WERmQ+8DpygquNE5FjgdqArsBM4X1X/JiKTgFOBcqAUuMe3PRFYoqpHikgpcBMQAboAd6rqbyX2WFJL7CHgn8BuYI6qJnjWItIPeBDYl9h4+qWq+idH91nAycB7wFmq+qGIDAXuAroD/wAuUNV/icga4CpVXSsifYC1qlohIkc4+ncm9vBzmqpuEpFzgSlO+vPAD1U1YfqriFQA9wI9nKTLVPVZEYkAPwO2A5XAU8APAQF+BwwH1Gnvr0Tk34A7gQOAz4CLVPVNEZkLfOrk/xLwE+c8PQccBrwNzFPVXwWd4+HDh2vQbF6j/VNTU5MidOZ7CHDUUUflX7EQVFZW5vXBoz2zcePG+ANUazn++ON59tlns1JXsZDs+IjIOlVtNq6U60ldDwDXOq9YhwBzgBOcfW8SM857RWQUcCNwmrNvGDBEVT9yDLR3u8JT/w+AT1T16yLSBfiLiDwBHA0cAhwO9AXecGT7+R7wuKre4Bj37k56D2JG9QoRuRa4DrgMmA9MVtWnReRnTvrlKdp/CXC7qi4Qkc5AqYgcBpwJ/Luq7hGR/wXOcer28gFwkqp+LiIHA/cTM54AxzptewdYQeyB5W2gv6oeCSAivZy8dwOXOA8C3wD+Fxjp7OsHfAs4FFgEPAxcTezhYlyyBonIxcDFAAMHDkzRdKO9k+qV6vr17wHKrneK74Ht3YbSljMZBaG9GeN0yalBVtVXHQN6NrDMt7snMM8xNgp454WvVNWPUmy7nAwM8YwP9wQOBk4E7ne8zv8TkdUBKr4IzBGRTsAfVfUVJ72JmOcMcB/wqIj0BHqp6tNO+jzgDwH1uvwVuEZEBgCPOkbxP4FjgBed8YVuxIyvn07Arx2vvBH4mmffC6r6FoCI3E/MqD4JfFVEaoGlwBMiUg4cD/zBM5bRxVPPH1W1CXhDREJNS1TVu4kZeYYPH56b1ytGmyCVh1lVVUXT59uZOizgO+UCcuNL+xZaBSMA9wuENWvWMH36dPr06cNrr73GMcccw3333YeI8OKLL1JTU8OOHTvo0qULTz75JJ06deLSSy9l7dq1lJWVMXPmTEaMGMHcuXP54x//yI4dO9i0aRNXXXUVu3fv5t5776VLly4sW7aM/fffn3/84x/86Ec/4sMPP6R79+7MmjWr2TBTPsjHLOtFwC3EPDwv/wM85Xh044m9unbZ4cvr33YRYh7rUOfvIFV9IkgREfmGiLzi/FWr6jPEjPcWYK6IBA2StGR49vLFsYy3Q1V/D1QTeyW/TERGOjrP8+h8iKpOF5FTPLoNB64A3geOIuYZd06hj6rqv5y8a4h55rMdnT72yBqqqt53J7u8h6eFNhpkL452WPIdW9pouxS6r+zdu5d33303a5+uvfzyy9x222288cYbvPXWW/zlL39h9+7dnHnmmdx+++2sX7+eVatW0a1bN+68805EhA0bNnD//fczceLE+Ozm1157jUcffZQXX3yRa665hu7du/Pyyy/zzW9+Mx7U4+KLL6a2tpZ169Zxyy238MMf/jArbUiXfBjkOcD1quoPt9OTLyZ5Tcqw7seBSx0PFxH5moj0AJ4BzhSRUmeceASAqj7vMUyLRGQQ8L6qziJmwIY59ZYArtf9PeDPqvoJ8C8RcV+5fx9wveU6Yl4vnnKIyFeBt1T1DmAhsdf2TwLfFZEDnTz7i8ggVX3Mo9ta5/hsdTzY7xMbP3c5VkQOEpESYq+//+yMXZeo6iPANGCYqn4KvC0ipzuyRERaGtTbDuzTQp4OS7biaIcl37GljbZLofvKtm3b2LlzZ9ZCSR577LEMGDCAkpIShg4dSl1dHX/729/o168fX//61wHYd999KSsr489//nM8bOqhhx7KoEGD+Pvf/w7AiBEj2GeffTjggAPo2bNnfMLd4MGDqauro6GhgWeffZbTTz+doUOH8l//9V9s3bo1K21Il5wbZFXd7BgkP78Afi4iL5P5q/PZxMaHXxKR14DfOnU9Bmxy9s0n9uo4GRFgvaPDmcQmmUHMIz/WqXMksUlUEJtQ9ksReRUY6km/hdiDwctAH0/9ZwCvicgrwJHAfFV9g5jBfMKpZyWxsVw//wtMFJH1xMZ4vW8JXgR+DWwkNnb8GNAfWOPIug/4f07ec4AfOPW8DiR+U9KcV4FGEVkvIrkLS9QGyVYcbcNob+zdu5dPP40NT3zyySdZ8ZK7dPlidK20tDTjOr31lJSUxLdLSkrYu3cvTU1N9OrVi1deeSX+l8tIX6nI2RiyqpYnSVtD7JUqqvpXEsdFpznpc4G5njL+7Tpixg3He5zq/Pm5LISO84iNBSfbd2WStFeA45Kkv0nM+3Vx23ITsVng/vwP8sUYdZBum3x1/tRJX0PsNbuf9Xzh4XvreRsYnSR9km+73Pm/hy8mfRkeksWwTRVKMVls4nS/i92yZUs8JnRbYufOnZRocY6CvP9ZCbudeN3tiWg0Srdu3VrOmAO2bduWsChHfX19TqJlHXLIIWzdupUXX3yRr3/962zfvp1u3bpxwgknsGDBAkaOHMnf//533n33XQ455BBeeumlFuvcd999Oeigg/jDH/7A6aefjqry6quvFuQLAYvUZRghSRbXNxWtiU1sGG2J7du3J2y73nK26dy5Mw8++CCTJ0/mqKOO4qSTTuLzzz/nhz/8IU1NTQwePJgzzzyTuXPnJnjGLbFgwQJ+97vfcdRRR3HEEUc0W3EsX+TsO2Sj/dPRvkOeOXNmQlzfqqqqlB7ypEmTmsUmTtdDdr24MLGliwl3lvVv/+NfhValGTe+tC9dBg1vc8e0JQrVVzZu3Jj3eNJtiXS+QzYP2TBCkq042obR3sh3POn2ihlkwwhJtuJop0O+Y0sbbZdC9pV8x5Nur9hRM4w0mDhxInV1dWnF0a6pqcnYO7bwjkZYCt1X+vTpw+7du807bgVmkA0jDXr37s0ddyT7ii85lZWVLF26NIcaGUZxUFZWZuF0W4kZZMMwcsKuRinKMJXvNpRycKGVMIwkmEE2DCPr9OvXj48//pgu/fsXWpVmHEzqhTEMo1CYQTYMI+vMnj270CoYBeKyK/+bD7YlWwsoMw7ssz+/nvnLrNWXjDVr1jBhwgQOOuggAE499VSuvfZaAFasWEFNTQ2NjY1ceOGFXH311QBEIhFuueUWhg8fzttvv83JJ5/Mr3/9a7797W9nrIcZZMMwDCNrfLDtI/7R9z+yV+H7T7ecJwm7d+9mz5499OjRo+XMwAknnMCSJUsS0hobG/nRj37EypUrGTBgAF//+teprq7m8MMPj+fZvHkzo0eP5tZbb22VMQb77MkwDMNoR2zcuJEf//jHHHLIIfEFJjLlhRdeoLKykq9+9at07tyZs846KyGK19atWzn55JO54YYbqK6ubq3qZpANwzCMts2OHTu45557+Na3vsVFF13E4YcfzquvvsrRRx8NwBVXXMHQoUOb/d100xdLDfz1r3/lqKOOYsyYMbz++utALJb8V77ylXieAQMGsGXLlvj2xIkTueyyy/jud+OL/LUKe2VtGB2I2travKw65d60+hfhpK5sUFlZWfDvfo0v6NevH0OGDGH27Nkceuihzfb/6le/Sll+2LBhvPPOO5SXl7Ns2TK+853vsGnTphbljho1ivvuu49JkybRvXv3jPV3MYNsGB2IaDTKK69tpLH7/jmVU/pZLK7xe7va3y2m9LPsTVgyssPDDz/M7373O0499VTOOussJk6cyKBBg+L7r7jiCp566qlm5c466yyuvvrqeJQxgLFjx/LDH/6Qbdu20b9/f/75z3/G923evDnhIfMnP/kJ9957L6effjoLFy5sdYSy9ne1GIaRksbu+7Pz0LE5ldHtzWUAOZdTCNy2GcXDySefzMknn0x9fT333XcfEyZMoE+fPsyePZuKiooWPeT33nuPvn37IiK88MILNDU10bt3b3r16sWmTZt4++236d+/Pw888AC///3vE8redtttfO973+MHP/gBc+fOjcf0zgQzyEa7p7a2Fih8aEHD6Agc2Gf/jGdGB9bXAu+//z4Affv2paamhpqaGl544QVKS0tDyXj44Yf5zW9+Q1lZGd26deOBBx5ARCgrK4t/ytTY2MgFF1zAEUcckVBWRJg3bx7jxo3jJz/5Cb/8ZeafaJlBNto9+RgzNQwjRq6/GU7Grl27mqUde+yxoctfdtllXHbZZUn3jR07lrFjm7/pWbNmTfx3586deeKJJ0LLC8JmWRuGYRhGEWAestHu2bJlCzt37owv4N6RiUajlOzWQqvRpin5/FOi0e3WnxzOOOMM3n333YLqsGvXrvha5W2Ztt8CwzAMw2gHmIdstHvczxRuv/32AmtSeGpqalj31vuFVqNN09R1Xyq/2tf6k8PGjRsLvuxioT30bGEesmEYhmEUAW3OIIuIish9nu0yEflQRJakKhei3i+LyMNplpkrIoEx00TkjyLynC9tuohsEZFXnL+bgsob2aGystKW2zOMdkyXLl3o0qVLodVoNW3xlfUO4EgR6aaqO4GTgC0tlElARMpUda9v+/+A7AQkjdXZCzgGaBCRr6rqW57dv1LVW7Ily0iNfX9sGPlj6o8v45Nt2RsW6dmnLzfe+uuUefr27Zty/6RJk3j66afp2bMnAHPnzmXo0KGoKjU1NSxbtozu3bszd+5chg0bRl1dHePGjeO1114DYNasWdx1112sWrWK/fbbLzsNS0JbNMgAy4Aq4GHgbOB+4AQAETkWuB3oCuwEzlfVv4nIJOBUoBwoFZF7fNsTgSWqeqSIlAI3ARGgC3Cnqv5WYiFYaok9BPwT2J1Cx1OBxcD7wFnAjWEbJyL9gAeBfYmdo0tV9U8i0gDMAk4G3gPOUtUPRWQocBfQHfgHcIGq/ktE1gBXqepaEekDrFXVChE5ArgH6EzsLclpqrpJRM4FpjjpzwM/VNXGsHobbYPSzz7KebSp0u3vAVD+0r2h8mtJJ5q67ttyxiIgFjoztQHoyHyy7X1++m9vZq2+m//Rcp5//etfLRrKX/7yl80WgVi+fDmbNm1i06ZNPP/881x66aU8//zzCXnuvfdeamtrWb16dU6NMbRdg/wAcK3zmnoIMAfHIANvAieo6l4RGUXMEJ7m7BsGDFHVjxwD7d2u8NT/A+ATVf26iHQB/iIiTwBHA4cAhxO7It9wZCfjbOBnxAzyIyQa5Csc4wfwU1V93Ff2e8DjqnqD83DgRi3vQcyoXiEi1wLXAZcB84HJqvq0iPzMSb886OABlwC3q+oCEelM7IHkMOBM4N9VdY+I/C9wjlN3HBG5GLgYKPhEDiN98vXqfv369wDl0H0+azHvuw2lSOfOVH61rRi5vjYEUmQMHz6c4447jh/84AeMGDEidPjKhQsXct555yEiHHfccXz88cds3bo1vv+hhx7ipptu4sknn6RPnz65Uj9OmzTIqvqqY0DPJuYte+kJzBORgwEFOnn2rVTVj1Jsu5wMDPGMD/cEDgZOBO53vMb/E5HVyfQTkb5O/j+rqorIHhE5UlVfc7K09Mr6RWCOiHQC/qiqrzjpTcQ8Z4D7gEdFpCfQS1XdWHXzgD+kqBvgr8A1IjIAeNTxjv+T2Cv2F53O3A34wF9QVe8G7gYYPny4fdDaxsjX6/uqqiqaPt/O1GGftpj3xpf2pcugSpu1bGTM3//+d5YvX86vf/1rfvSjH/H973+fSZMm8eUvfzme55prruFnP/sZ//mf/8lNN91Ely5dApdX7NOnD++88w6XXXYZL7/8Ml/60pfy0o42N6nLwyLgFmKvq738D/CUqh4JjCf26tplhy+vf9tFiHmcQ52/g1Q1MC6aiHzDM0mrGjgD2A94W0TqgApiDw+hyqvqM8SM/xZgroicF1C0JYO4ly/Ocfw4qOrvgWpir/SXichIp83zPG0+RFWnt1C/YSRl165d7GlKL8h+bW1tPO54a4hGo1RVVVnI1A5EaWkp48aN49FHH+WZZ57hrbfeYuDAgbzwwgsA/PznP+fNN9/kxRdf5KOPPuLmm29usc4DDjiAgQMH8tBDD+Va/Tht2SDPAa5X1Q2+9J58MclrUoZ1Pw5c6nioiMjXRKQH8AxwpoiUOuO8IwBU9XmPIVtEzPiOVtUKVa0g5nmeFSTMX15EBgHvq+osYDaxV+sQO1+u1/49Yh74J8C/RMR9Zf99wPWW6xzZeMohIl8F3lLVO4CFxF77Pwl8V0QOdPLs7+hhGGnT1NREU5rvT6LRaFaM6IwZM9ixYwczZsxodV1G2+GTTz7ht7/9LdXV1WzatIk5c+YwZMgQILZesojQpUsXzj///LihTrW8Yvfu3Vm2bBl33XUXCxYsyEsb2qxBVtXNjkHx8wvg5yLyMpm/kp9NbHz4JRF5DfitU9djwCZn33xir34TcF6lDwLinzup6tvAJyLyjZDyI8B6pw1nEpukBjGP/lhHp5HExqgBJgK/FJFXgaGe9FuIPVi8DHgHQM4AXhORV4Ajgfmq+gYwDXjCqWcl0C+kvoZRFESjUerq6gCoq6szL7mDcO655zJs2DDefvtt5s+fz9NPP815551H166xF4PuuLCq8sc//pEjjzwSgOrqaubPn4+q8txzz9GzZ0/69fvitnfggQeyYsUKpk6dyuOP+6f6ZB9RtWHAtoKINKhqeaH1cBk+fLiuXbu20GoYRcjIkSOhqZG5I5NN0Uik5s+92N0pNsO6W7duPPxwWuEAEpg0aVLcIANUVFQwd+7cjOszWmbjxo0cdthh8e1CfPa0aNEixo4dS1lZch9s5MiRfPjhh6gqQ4cO5a677qK8vBxV5bLLLmPFihV0796de+65h+HDhzf77Gn9+vWMHTuWxx57LK1VpKD58QEQkXWqOtyft01O6jIMw0iG1xgn2zZyT0vGMxdUV1en3L96ddL5t4gId955Z7P0ioqKuDEGOOqoo9iyJa1wFxlhBrkNUUzesWFki77dm+gyKDufEVVUVDTzkA2jrdBmx5ANwzD8TJs2LeW2YRQzZpANw2g3VFZWxr3iiooKC+CRJ2wuUnLSPS5mkA3DyDolJSWUpPcZctYWAZk2bRo9evQw7zhPdO3alfr6ejPKPlSV+vr6+EzvMNgYsmEYWadLly40fZ4q1HtzshVFrLKykqVLl2alLqNlBgwYwObNm/nwww8LrUrR0bVrVwYMGBA6vxlkwzBywq5G4caXWl4w4t2GUg7Ogz5GbujUqRMHHXRQodVoF5hBNgwj6/Tr14+PP/6YLk7Uo1QcTP4WvTCMYsYMsmEYWWf27NmFVsEw2hw2qcswDMMwigALnWlkjIh8CLyTRpE+wLYcqVPMWLs7Fh213dBx255uuwep6gH+RDPIRt4QkbXJ4re2d6zdHYuO2m7ouG3PVrvtlbVhGIZhFAFmkA3DMAyjCDCDbOSTuwutQIGwdncsOmq7oeO2PSvttjFkwzAMwygCzEM2DMMwjCLADLJhGIZhFAFmkI28ICKjReRvIhIVkasLrU+uEJGviMhTIvKGiLwuIjVO+v4islJENjn/9yu0rtlGREpF5GURWeJsHyQizzvn/EER6VxoHXOBiPQSkYdF5E0R2Sgi3+wg5/sKp4+/JiL3i0jX9njORWSOiHwgIq950pKeX4lxh9P+V0VkWDqyzCAbOUdESoE7gTHA4cDZInJ4YbXKGXuBH6vq4cBxwI+ctl4NPKmqBwNPOtvtjRpgo2f7ZuBXqloJ/Av4QUG0yj23AytU9VDgKGLHoF2fbxHpD0wBhqvqkUApcBbt85zPBUb70oLO7xhi4dkPBi4GfpOOIDPIRj44Foiq6luquht4AJhQYJ1ygqpuVdWXnN/bid2c+xNr7zwn2zzgOwVRMEeIyACgCpjtbAswEnjYydLu2gwgIj2BE4HfAajqblX9mHZ+vh3KgG4iUgZ0B7bSDs+5qj4DfORLDjq/E4D5GuM5oJeI9AsrywyykQ/6A//0bG920to1IlIBHA08D/RV1a3OrveAvoXSK0fcBvwEaHK2ewMfq+peZ7u9nvODgA+Be5zX9bNFpAft/Hyr6hbgFuBdYob4E2AdHeOcQ/D5bdW9zgyyYeQAESkHHgEuV9VPvfs09q1hu/neUETGAR+o6rpC61IAyoBhwG9U9WhgB77X0+3tfAM4Y6YTiD2QfBnoQfPXuh2CbJ5fM8hGPtgCfMWzPcBJa5eISCdixniBqj7qJL/vvrpy/n9QKP1ywL8D1SJSR2w4YiSxcdVezutMaL/nfDOwWVWfd7YfJmag2/P5BhgFvK2qH6rqHuBRYv2gI5xzCD6/rbrXmUE28sGLwMHODMzOxCZ/LCqwTjnBGTv9HbBRVWd6di0CJjq/JwIL861brlDV/6eqA1S1gti5Xa2q5wBPAd91srWrNruo6nvAP0XkECfpP4E3aMfn2+Fd4DgR6e70ebfd7f6cOwSd30XAec5s6+OATzyvtlvEInUZeUFExhIbZywF5qjqDYXVKDeIyLeAPwEb+GI8dSqxceSHgIHElqw8Q1X9E0XaPCISAa5S1XEi8lViHvP+wMvAuaq6q4Dq5QQRGUpsMltn4C3gfGLOTrs+3yJyPXAmsS8LXgYuJDZe2q7OuYjcD0SILbH4PnAd8EeSnF/n4eTXxF7ffwacr6prQ8syg2wYhmEYhcdeWRuGYRhGEWAG2TAMwzCKADPIhmEYhlEEmEE2DMMwjCLADLJhGIZhFAFmkA3DKDgiUuFdTccwOiJmkA3DaJd4IkYZRpvADLJhGMVCqYjMctbYfUJEuonIUBF5zllb9jHPurNrRGS487uPE7YTEZkkIotEZDXwpIj0E5FnROQVZ93eE5x8DSLyK0fWkyJygIgcKCLrnP1HiYiKyEBn+x8i0r0QB8XoOJhBNgyjWDgYuFNVjwA+Bk4D5gM/VdUhxKKfXReinmHAd1X1P4DvAY+r6lBiaxW/4uTpAax1ZD0NXKeqHwBdRWRf4ARgLXCCiAwitnjGZ1lppWEEYK90DMMoFt5W1Vec3+uAfwN6qerTTto84A8h6lnpCVP5IjDHWfDjj576m4AHnd/3EVscAeBZYosknAjcSCwEohALh2oYOcU8ZMMwigVvzONGoFeKvHv54v7V1bdvh/vDWVz+RGIr7swVkfMC6nNjCD9DzDseRGzBgKMANz65YeQUM8iGYRQrnwD/csd9ge8Te70MUAcc4/z+LgE4r5vfV9VZxBaAGObsKvGU+x7wZ+f3n4BzgU2q2gR8BIz17DeMnGGvrA3DKGYmAnc5E6rclZQAbgEeEpGLgaUpykeA/xaRPUAD4HrIO4BjRWQasbVszwRQ1TpnxZ5nnHx/Bgao6r+y1yTDSI6t9mQYRodDRBpUtbzQehiGF3tlbRiGYRhFgHnIhmEYhlEEmIdsGIZhGEWAGWTDMAzDKALMIBuGYRhGEWAG2TAMwzCKADPIhmEYhlEE/P8GsQUlK0mRngAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Marital Status, income, vs education level","metadata":{}},{"cell_type":"code","source":"ax = sns.boxplot(x=\"edu_num\", y=\"marital\", hue=\"income\", data=df)\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:58.083034Z","iopub.execute_input":"2021-08-08T15:08:58.083330Z","iopub.status.idle":"2021-08-08T15:08:58.797049Z","shell.execute_reply.started":"2021-08-08T15:08:58.083298Z","shell.execute_reply":"2021-08-08T15:08:58.796159Z"},"trusted":true},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Data Visualization: Age vs Education level","metadata":{}},{"cell_type":"code","source":"sns.lmplot(x=\"age\", y=\"edu_num\", data=df, x_estimator=np.mean);\n","metadata":{"execution":{"iopub.status.busy":"2021-08-08T15:08:58.798367Z","iopub.execute_input":"2021-08-08T15:08:58.798641Z","iopub.status.idle":"2021-08-08T15:09:03.510607Z","shell.execute_reply.started":"2021-08-08T15:08:58.798615Z","shell.execute_reply":"2021-08-08T15:09:03.509638Z"},"trusted":true},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 360x360 with 1 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\n"},"metadata":{"needs_background":"light"}}]}]}