Chapter 5 Statistical Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf36663b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv('world-data-2023.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8570d43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b7cc80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Country</th>\n",
       "      <th>Density\\n(P/Km2)</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>Agricultural Land( %)</th>\n",
       "      <th>Land Area(Km2)</th>\n",
       "      <th>Armed Forces size</th>\n",
       "      <th>Birth Rate</th>\n",
       "      <th>Calling Code</th>\n",
       "      <th>Capital/Major City</th>\n",
       "      <th>Co2-Emissions</th>\n",
       "      <th>...</th>\n",
       "      <th>Out of pocket health expenditure</th>\n",
       "      <th>Physicians per thousand</th>\n",
       "      <th>Population</th>\n",
       "      <th>Population: Labor force participation (%)</th>\n",
       "      <th>Tax revenue (%)</th>\n",
       "      <th>Total tax rate</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>60</td>\n",
       "      <td>AF</td>\n",
       "      <td>58.10%</td>\n",
       "      <td>6,52,230</td>\n",
       "      <td>3,23,000</td>\n",
       "      <td>32.49</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>8,672</td>\n",
       "      <td>...</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>0.28</td>\n",
       "      <td>3,80,41,754</td>\n",
       "      <td>48.90%</td>\n",
       "      <td>9.30%</td>\n",
       "      <td>71.40%</td>\n",
       "      <td>11.12%</td>\n",
       "      <td>97,97,273</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>105</td>\n",
       "      <td>AL</td>\n",
       "      <td>43.10%</td>\n",
       "      <td>28,748</td>\n",
       "      <td>9,000</td>\n",
       "      <td>11.78</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>4,536</td>\n",
       "      <td>...</td>\n",
       "      <td>56.90%</td>\n",
       "      <td>1.20</td>\n",
       "      <td>28,54,191</td>\n",
       "      <td>55.70%</td>\n",
       "      <td>18.60%</td>\n",
       "      <td>36.60%</td>\n",
       "      <td>12.33%</td>\n",
       "      <td>17,47,593</td>\n",
       "      <td>41.153332</td>\n",
       "      <td>20.168331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>18</td>\n",
       "      <td>DZ</td>\n",
       "      <td>17.40%</td>\n",
       "      <td>23,81,741</td>\n",
       "      <td>3,17,000</td>\n",
       "      <td>24.28</td>\n",
       "      <td>213.0</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>1,50,006</td>\n",
       "      <td>...</td>\n",
       "      <td>28.10%</td>\n",
       "      <td>1.72</td>\n",
       "      <td>4,30,53,054</td>\n",
       "      <td>41.20%</td>\n",
       "      <td>37.20%</td>\n",
       "      <td>66.10%</td>\n",
       "      <td>11.70%</td>\n",
       "      <td>3,15,10,100</td>\n",
       "      <td>28.033886</td>\n",
       "      <td>1.659626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>164</td>\n",
       "      <td>AD</td>\n",
       "      <td>40.00%</td>\n",
       "      <td>468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>376.0</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>469</td>\n",
       "      <td>...</td>\n",
       "      <td>36.40%</td>\n",
       "      <td>3.33</td>\n",
       "      <td>77,142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67,873</td>\n",
       "      <td>42.506285</td>\n",
       "      <td>1.521801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>26</td>\n",
       "      <td>AO</td>\n",
       "      <td>47.50%</td>\n",
       "      <td>12,46,700</td>\n",
       "      <td>1,17,000</td>\n",
       "      <td>40.73</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>34,693</td>\n",
       "      <td>...</td>\n",
       "      <td>33.40%</td>\n",
       "      <td>0.21</td>\n",
       "      <td>3,18,25,295</td>\n",
       "      <td>77.50%</td>\n",
       "      <td>9.20%</td>\n",
       "      <td>49.10%</td>\n",
       "      <td>6.89%</td>\n",
       "      <td>2,10,61,025</td>\n",
       "      <td>-11.202692</td>\n",
       "      <td>17.873887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country Density\\n(P/Km2) Abbreviation Agricultural Land( %)  \\\n",
       "0  Afghanistan               60           AF                58.10%   \n",
       "1      Albania              105           AL                43.10%   \n",
       "2      Algeria               18           DZ                17.40%   \n",
       "3      Andorra              164           AD                40.00%   \n",
       "4       Angola               26           AO                47.50%   \n",
       "\n",
       "  Land Area(Km2) Armed Forces size  Birth Rate  Calling Code  \\\n",
       "0       6,52,230          3,23,000       32.49          93.0   \n",
       "1         28,748             9,000       11.78         355.0   \n",
       "2      23,81,741          3,17,000       24.28         213.0   \n",
       "3            468               NaN        7.20         376.0   \n",
       "4      12,46,700          1,17,000       40.73         244.0   \n",
       "\n",
       "  Capital/Major City Co2-Emissions  ... Out of pocket health expenditure  \\\n",
       "0              Kabul         8,672  ...                           78.40%   \n",
       "1             Tirana         4,536  ...                           56.90%   \n",
       "2            Algiers      1,50,006  ...                           28.10%   \n",
       "3   Andorra la Vella           469  ...                           36.40%   \n",
       "4             Luanda        34,693  ...                           33.40%   \n",
       "\n",
       "  Physicians per thousand   Population  \\\n",
       "0                    0.28  3,80,41,754   \n",
       "1                    1.20    28,54,191   \n",
       "2                    1.72  4,30,53,054   \n",
       "3                    3.33       77,142   \n",
       "4                    0.21  3,18,25,295   \n",
       "\n",
       "   Population: Labor force participation (%) Tax revenue (%) Total tax rate  \\\n",
       "0                                     48.90%           9.30%         71.40%   \n",
       "1                                     55.70%          18.60%         36.60%   \n",
       "2                                     41.20%          37.20%         66.10%   \n",
       "3                                        NaN             NaN            NaN   \n",
       "4                                     77.50%           9.20%         49.10%   \n",
       "\n",
       "  Unemployment rate Urban_population   Latitude  Longitude  \n",
       "0            11.12%        97,97,273  33.939110  67.709953  \n",
       "1            12.33%        17,47,593  41.153332  20.168331  \n",
       "2            11.70%      3,15,10,100  28.033886   1.659626  \n",
       "3               NaN           67,873  42.506285   1.521801  \n",
       "4             6.89%      2,10,61,025 -11.202692  17.873887  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48a8d8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 35)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0477b589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country                                       0\n",
      "Density\\n(P/Km2)                              0\n",
      "Abbreviation                                  7\n",
      "Agricultural Land( %)                         7\n",
      "Land Area(Km2)                                1\n",
      "Armed Forces size                            24\n",
      "Birth Rate                                    6\n",
      "Calling Code                                  1\n",
      "Capital/Major City                            3\n",
      "Co2-Emissions                                 7\n",
      "CPI                                          17\n",
      "CPI Change (%)                               16\n",
      "Currency-Code                                15\n",
      "Fertility Rate                                7\n",
      "Forested Area (%)                             7\n",
      "Gasoline Price                               20\n",
      "GDP                                           2\n",
      "Gross primary education enrollment (%)        7\n",
      "Gross tertiary education enrollment (%)      12\n",
      "Infant mortality                              6\n",
      "Largest city                                  6\n",
      "Life expectancy                               8\n",
      "Maternal mortality ratio                     14\n",
      "Minimum wage                                 45\n",
      "Official language                             5\n",
      "Out of pocket health expenditure              7\n",
      "Physicians per thousand                       7\n",
      "Population                                    1\n",
      "Population: Labor force participation (%)    19\n",
      "Tax revenue (%)                              26\n",
      "Total tax rate                               12\n",
      "Unemployment rate                            19\n",
      "Urban_population                              5\n",
      "Latitude                                      1\n",
      "Longitude                                     1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bb415e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['Land Area(Km2)','Density\\n(P/Km2)', 'Agricultural Land( %)','Armed Forces size', 'Population', 'Urban_population', 'Co2-Emissions', 'CPI Change (%)', 'Forested Area (%)', 'Gasoline Price','GDP', 'Gross primary education enrollment (%)','Gross tertiary education enrollment (%)','Minimum wage','Total tax rate', 'Unemployment rate', 'Out of pocket health expenditure','Population: Labor force participation (%)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a457a1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "461e1cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in columns:\n",
    "    df[col] = df[col].str.replace(',', '')\n",
    "    df[col] = df[col].str.replace('$', '')\n",
    "    df[col] = df[col].str.replace('%', '')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "846290a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in columns:\n",
    "    df[col] = pd.to_numeric(df[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee7d0753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                                       object\n",
       "Density\\n(P/Km2)                               int64\n",
       "Abbreviation                                  object\n",
       "Agricultural Land( %)                        float64\n",
       "Land Area(Km2)                               float64\n",
       "Armed Forces size                            float64\n",
       "Birth Rate                                   float64\n",
       "Calling Code                                 float64\n",
       "Capital/Major City                            object\n",
       "Co2-Emissions                                float64\n",
       "CPI                                           object\n",
       "CPI Change (%)                               float64\n",
       "Currency-Code                                 object\n",
       "Fertility Rate                               float64\n",
       "Forested Area (%)                            float64\n",
       "Gasoline Price                               float64\n",
       "GDP                                          float64\n",
       "Gross primary education enrollment (%)       float64\n",
       "Gross tertiary education enrollment (%)      float64\n",
       "Infant mortality                             float64\n",
       "Largest city                                  object\n",
       "Life expectancy                              float64\n",
       "Maternal mortality ratio                     float64\n",
       "Minimum wage                                 float64\n",
       "Official language                             object\n",
       "Out of pocket health expenditure             float64\n",
       "Physicians per thousand                      float64\n",
       "Population                                   float64\n",
       "Population: Labor force participation (%)    float64\n",
       "Tax revenue (%)                               object\n",
       "Total tax rate                               float64\n",
       "Unemployment rate                            float64\n",
       "Urban_population                             float64\n",
       "Latitude                                     float64\n",
       "Longitude                                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea56f063",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Latitude', 'Longitude', 'Calling Code'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a742c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\Temp\\ipykernel_9108\\964755826.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Population: Labor force participation (%)'].fillna(median, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "median = df['Population: Labor force participation (%)'].median()\n",
    "\n",
    "df['Population: Labor force participation (%)'].fillna(median, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3e80da6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df['Population: Labor force participation (%)'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9421bcb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\Temp\\ipykernel_9108\\34354699.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['Unemployment rate'].fillna(median, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "median = df['Unemployment rate'].median()\n",
    "\n",
    "df['Unemployment rate'].fillna(median, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec642976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df['Unemployment rate'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f160ad0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78c33a25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    }
   ],
   "source": [
    "# Histogram\n",
    "df.hist(bins=50, figsize=(20,15))\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot\n",
    "sns.pairplot(df)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c049f9c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, ax = plt.subplots(figsize = (12,10))\n",
    "sns.heatmap(df.loc[:, ['Land Area(Km2)','Agricultural Land( %)', 'Population', \n",
    "                       'Urban_population', 'Co2-Emissions', 'Forested Area (%)','GDP', \n",
    "                       \n",
    "                       'Unemployment rate', 'Out of pocket health expenditure','Population: Labor force participation (%)']].corr(),\n",
    "            annot = True,\n",
    "            linecolor = 'r',\n",
    "            linewidths = .5,\n",
    "            fmt = '.1f',\n",
    "            ax = ax);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef33755",
   "metadata": {},
   "source": [
    "# Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61272917",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descriptive statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9207287a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skewness and kurtosis\n",
    "df.loc[:, ['Land Area(Km2)','Agricultural Land( %)', 'Population', \n",
    "                       'Urban_population', 'Co2-Emissions', 'Forested Area (%)','GDP', \n",
    "                       \n",
    "                       'Unemployment rate', 'Out of pocket health expenditure','Population: Labor force participation (%)']].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebcc3d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(df['Urban_population'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da7e9da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Total tax rate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5587b5b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Unemployment rate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c12b01d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "192a4f9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Minimum wage'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8464668",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Population'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f96a2f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Density\\n(P/Km2)', 'Abbreviation', 'Agricultural Land( %)',\n",
       "       'Land Area(Km2)', 'Armed Forces size', 'Birth Rate',\n",
       "       'Capital/Major City', 'Co2-Emissions', 'CPI', 'CPI Change (%)',\n",
       "       'Currency-Code', 'Fertility Rate', 'Forested Area (%)',\n",
       "       'Gasoline Price', 'GDP', 'Gross primary education enrollment (%)',\n",
       "       'Gross tertiary education enrollment (%)', 'Infant mortality',\n",
       "       'Largest city', 'Life expectancy', 'Maternal mortality ratio',\n",
       "       'Minimum wage', 'Official language', 'Out of pocket health expenditure',\n",
       "       'Physicians per thousand', 'Population',\n",
       "       'Population: Labor force participation (%)', 'Tax revenue (%)',\n",
       "       'Total tax rate', 'Unemployment rate', 'Urban_population'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7c4a3398",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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cccUVGj9+vH755Rdr7ODBg3rwwQd15ZVXNlo4AACAU2lQmVm4cKGKi4sVHR2t888/X506dVJMTIyKi4v17LPPNnZGAACAGjXonJnIyEh9/fXXSk1N1e7du2WMUffu3TVw4MDGzgcAAFCreu2Z+fjjj9W9e3cVFRVJkgYNGqRx48bpgQceUL9+/dSjRw99+umnTRIUAACgOvUqMwsWLNBdd92loKCgKsuCg4N1zz33aN68eY0WDgAA4FTqdZjpm2++0ZNPPlnj8sGDB+upp5467VAAmk52drby8/PtjqHMzEy7IwA4S9SrzPz666/VfiTb2piPj/7zn/+cdigATSM7O1tdu3ZTSckRu6NYjpaW2R0BgMPVq8ycd955ysjIUKdOnapd/u233yo8PLxRggFofPn5+SopOaL425MUFB5ta5acjC+0c80SHTt2zNYcAJyvXmXm6quv1r/+9S8NGTJEfn5+HstKSkqUlJSka665plEDAmh8QeHRah3VxdYMRTn7bH1+AGePepWZRx99VKtXr1bnzp11//33q0uXLnK5XMrMzNSiRYtUXl6u6dOnN1VWAACAKupVZkJDQ7Vlyxbdd999mjZtmowxkiSXy6WrrrpKzz33nEJDQ5skKAAAQHXq/aV5HTp00AcffKCCggL98MMPMsYoNjZW5557blPkAwAAqFWDvgFYks4991z169evMbMAAADUW4OuzQQAAOAtKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRbC0zs2fPVr9+/RQYGKh27drphhtu0J49ezzWMcYoOTlZERER8vf3V2Jionbt2mVTYgAA4G1sLTObN2/W2LFjtXXrVqWmpurYsWMaPHiwfv/9d2udOXPmaN68eVq4cKHS0tIUFhamQYMGqbi42MbkAADAWzT4qtmNYe3atR73ly1bpnbt2mn79u267LLLZIzRggULNH36dA0bNkyStGLFCoWGhmrlypW655577IgNAAC8iFedM1NYWChJat26tSQpKytLubm5Gjx4sLWO2+3WgAEDtGXLlmq3UVpaqqKiIo8bAAA4e3lNmTHGaOLEibr00kvVs2dPSVJubq4kKTQ01GPd0NBQa9nJZs+ereDgYOsWGRnZtMEBAICtvKbM3H///fr222/16quvVlnmcrk87htjqoxVmjZtmgoLC63bgQMHmiQvAADwDraeM1Np3LhxWrNmjT755BO1b9/eGg8LC5N0fA9NeHi4NZ6Xl1dlb00lt9stt9vdtIEBAIDXsHXPjDFG999/v1avXq2PP/5YMTExHstjYmIUFham1NRUa6ysrEybN29WQkLCmY4LAAC8kK17ZsaOHauVK1fqnXfeUWBgoHUeTHBwsPz9/eVyuTRhwgSlpKQoNjZWsbGxSklJUUBAgIYPH25ndAAA4CVsLTOLFy+WJCUmJnqML1u2TKNHj5YkTZ48WSUlJRozZowKCgoUHx+vdevWKTAw8AynBQAA3sjWMmOMOeU6LpdLycnJSk5ObvpAAADAcbzm00wAAAANQZkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACO5mN3AODPIDs7W/n5+XbHUGZmpt0RAKDRUWaAJpadna2uXbuppOSI3VEsR0vL7I4AAI2GMgM0sfz8fJWUHFH87UkKCo+2NUtOxhfauWaJjh07ZmsOAGhMlBngDAkKj1brqC62ZijK2Wfr8wNAU+AEYAAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GiUGQAA4GhcmwkAvExmZqbdESRJISEhioqKsjsGcEqUGQDwEiWFhyS5NHLkSLujSJL8/QO0e3cmhQZejzIDAF7i6JFiSUZ9hk9R25iutmYpytmnL1+aofz8fMoMvB5lBgC8TMt2UWod1cXuGIBjcAIwAABwNMoMAABwNMoMAABwNMoMAABwNMoMAABwNMoMAABwNMoMAABwNMoMAABwNMoMAABwNMoMAABwNC5nAACokTdcwZurd+NUKDMAgCq86QreXL0bp0KZAQBU4S1X8Obq3agLygwAoEZcwRtOwAnAAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0SgzAADA0bg2EwDA62VmZtodQZIUEhLCBS+9EGUGAOC1SgoPSXJp5MiRdkeRJPn7B2j37kwKjZehzAAAvNbRI8WSjPoMn6K2MV1tzVKUs09fvjRD+fn5lBkvQ5kBAHi9lu2i1Dqqi90x4KVsPQH4k08+0bXXXquIiAi5XC69/fbbHsuNMUpOTlZERIT8/f2VmJioXbt22RMWAAB4JVvLzO+//67evXtr4cKF1S6fM2eO5s2bp4ULFyotLU1hYWEaNGiQiouLz3BSAADgrWw9zDRkyBANGTKk2mXGGC1YsEDTp0/XsGHDJEkrVqxQaGioVq5cqXvuuedMRgUAAF7Ka8+ZycrKUm5urgYPHmyNud1uDRgwQFu2bKmxzJSWlqq0tNS6X1RU1ORZ4Sk7O1v5+fl2x5B0/P3gdrttzeAtHykFgLOV15aZ3NxcSVJoaKjHeGhoqPbv31/j42bPnq0ZM2Y0aTbULDs7W127dlNJyRG7oxzncknG2J1CknS0tMzuCABwVvLaMlPJ5XJ53DfGVBk70bRp0zRx4kTrflFRkSIjI5ssHzzl5+erpOSI4m9PUlB4tK1ZcjK+0M41S2z/SGdljmPHjtmWAQDOZl5bZsLCwiQd30MTHh5ujefl5VXZW3Mit9tt+2EFSEHh0bZ/jLIoZ58k+z/SWZkDANA0vPbaTDExMQoLC1Nqaqo1VlZWps2bNyshIcHGZAAAwJvYumfm8OHD+uGHH6z7WVlZSk9PV+vWrRUVFaUJEyYoJSVFsbGxio2NVUpKigICAjR8+HAbUwMAAG9ia5nZtm2bLr/8cut+5bkuo0aN0vLlyzV58mSVlJRozJgxKigoUHx8vNatW6fAwEC7IgMAAC9ja5lJTEyUqeWTJi6XS8nJyUpOTj5zoQAAgKN47TkzAAAAdUGZAQAAjkaZAQAAjkaZAQAAjkaZAQAAjkaZAQAAjkaZAQAAjua112ZC/WRnZys/P9/uGMrMzLQ7AgDgT4YycxbIzs5W167dVFJyxO4olqOlZXZHAAD8SVBmzgL5+fkqKTmi+NuTFBQebWuWnIwvtHPNEh07dszWHACAPw/KzFkkKDxaraO62JqhKGefrc8PAPjz4QRgAADgaJQZAADgaJQZAADgaJwzAwCAA3nLV3KEhIQoKirK1gyUGQAAHMabvpLD3z9Au3dn2lpoKDMAADiMt3wlR1HOPn350gzl5+dTZgAAQP15w1dyeANOAAYAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI5GmQEAAI7mY3cAAACcJDMz0+4IXpHBm1BmAACog5LCQ5JcGjlypN1RLEdLy+yO4BUoMwAA1MHRI8WSjPoMn6K2MV1tzZKT8YV2rlmiY8eO2ZrDW1BmAACoh5btotQ6qoutGYpy9tn6/N6GE4ABAICjUWYAAICjUWYAAICjUWYAAICjcQLwacrOzlZ+fr6tGfi+AQDAnxll5jRkZ2era9duKik5YncUSXzfAADgz4kycxry8/NVUnJE8bcnKSg82rYcfN8AAODPjDLTCILCo239zgG+bwAA8GfGCcAAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRKDMAAMDRHFFmnnvuOcXExMjPz08XXXSRPv30U7sjAQAAL+H1Zea1117ThAkTNH36dO3YsUP/7//9Pw0ZMkTZ2dl2RwMAAF7A68vMvHnzdMcdd+jOO+9Ut27dtGDBAkVGRmrx4sV2RwMAAF7Ax+4AtSkrK9P27ds1depUj/HBgwdry5Yt1T6mtLRUpaWl1v3CwkJJUlFRUaPnO3z4sCTpt/17dKy0pNG3X1dFOfslSYUH98rXx2VbDrJ4dw6yeHcOsnh3DrLUkCP3+FGSw4cPN/rP2crtGWNOvbLxYgcPHjSSzOeff+4x/vjjj5vOnTtX+5ikpCQjiRs3bty4ceN2FtwOHDhwyr7g1XtmKrlcnq3TGFNlrNK0adM0ceJE635FRYV+++03tWnTpsbH2K2oqEiRkZE6cOCAgoKC7I5z1mO+zzzm/Mxivs885rzxGWNUXFysiIiIU67r1WUmJCRE55xzjnJzcz3G8/LyFBoaWu1j3G633G63x1irVq2aKmKjCgoK4n+CM4j5PvOY8zOL+T7zmPPGFRwcXKf1vPoE4ObNm+uiiy5Samqqx3hqaqoSEhJsSgUAALyJV++ZkaSJEyfq1ltvVVxcnPr3768lS5YoOztb9957r93RAACAF/D6MnPTTTfp0KFDmjlzpnJyctSzZ0998MEH6tChg93RGo3b7VZSUlKVw2NoGsz3mcecn1nM95nHnNvLZUxdPvMEAADgnbz6nBkAAIBTocwAAABHo8wAAABHo8wAAABHo8zYZPbs2XK5XJowYYI1ZoxRcnKyIiIi5O/vr8TERO3atcu+kA6XnJwsl8vlcQsLC7OWM9+N7+DBgxo5cqTatGmjgIAA9enTR9u3b7eWM+eNKzo6usp73OVyaezYsZKY78Z27NgxPfroo4qJiZG/v786duyomTNnqqKiwlqHObfJ6Vw7CQ3z1VdfmejoaNOrVy8zfvx4a/yJJ54wgYGB5s033zQZGRnmpptuMuHh4aaoqMi+sA6WlJRkevToYXJycqxbXl6etZz5bly//fab6dChgxk9erT58ssvTVZWllm/fr354YcfrHWY88aVl5fn8f5OTU01kszGjRuNMcx3Y5s1a5Zp06aNee+990xWVpZ54403TMuWLc2CBQusdZhze1BmzrDi4mITGxtrUlNTzYABA6wyU1FRYcLCwswTTzxhrfvHH3+Y4OBg8/zzz9uU1tmSkpJM7969q13GfDe+KVOmmEsvvbTG5cx50xs/frw5//zzTUVFBfPdBIYOHWpuv/12j7Fhw4aZkSNHGmN4j9uJw0xn2NixYzV06FANHDjQYzwrK0u5ubkaPHiwNeZ2uzVgwABt2bLlTMc8a+zdu1cRERGKiYnRzTffrJ9++kkS890U1qxZo7i4OP39739Xu3bt1LdvXy1dutRazpw3rbKyMr388su6/fbb5XK5mO8mcOmll2rDhg36/vvvJUnffPONPvvsM1199dWSeI/byeu/AfhssmrVKn399ddKS0ursqzyYponX0AzNDRU+/fvPyP5zjbx8fH697//rc6dO+vXX3/VrFmzlJCQoF27djHfTeCnn37S4sWLNXHiRD3yyCP66quv9MADD8jtduu2225jzpvY22+/rf/+978aPXq0JP5NaQpTpkxRYWGhunbtqnPOOUfl5eV6/PHHdcstt0hizu1EmTlDDhw4oPHjx2vdunXy8/OrcT2Xy+Vx3xhTZQx1M2TIEOvPF1xwgfr376/zzz9fK1as0F/+8hdJzHdjqqioUFxcnFJSUiRJffv21a5du7R48WLddttt1nrMedN48cUXNWTIEEVERHiMM9+N57XXXtPLL7+slStXqkePHkpPT9eECRMUERGhUaNGWesx52ceh5nOkO3btysvL08XXXSRfHx85OPjo82bN+uZZ56Rj4+P1eQrm32lvLy8Ki0fDdOiRQtdcMEF2rt3r/WpJua78YSHh6t79+4eY926dVN2drYkMedNaP/+/Vq/fr3uvPNOa4z5bnyTJk3S1KlTdfPNN+uCCy7QrbfeqgcffFCzZ8+WxJzbiTJzhlx55ZXKyMhQenq6dYuLi9OIESOUnp6ujh07KiwsTKmpqdZjysrKtHnzZiUkJNiY/OxRWlqqzMxMhYeHKyYmhvluZJdccon27NnjMfb9999bF4VlzpvOsmXL1K5dOw0dOtQaY74b35EjR9SsmeePzXPOOcf6aDZzbiNbTz/+kzvx00zGHP9IX3BwsFm9erXJyMgwt9xyCx/pOw0PPfSQ2bRpk/npp5/M1q1bzTXXXGMCAwPNvn37jDHMd2P76quvjI+Pj3n88cfN3r17zSuvvGICAgLMyy+/bK3DnDe+8vJyExUVZaZMmVJlGfPduEaNGmXOO+8866PZq1evNiEhIWby5MnWOsy5PSgzNjq5zFRUVJikpCQTFhZm3G63ueyyy0xGRoZ9AR2u8vsdfH19TUREhBk2bJjZtWuXtZz5bnzvvvuu6dmzp3G73aZr165myZIlHsuZ88b30UcfGUlmz549VZYx342rqKjIjB8/3kRFRRk/Pz/TsWNHM336dFNaWmqtw5zbw2WMMXbvHQIAAGgozpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBAACORpkBajB69GjdcMMNXrOdM23fvn1yuVxKT08/Y8+ZnJys0NBQuVwuvf3222fseb3R8uXL1apVqzqvHx0drQULFjRZnhOdyb+fyy67TCtXrjytbfTr10+rV69upETwRpQZeJ3Ro0fL5XLJ5XLJ19dXHTt21MMPP6zff//d7mi1qumH/9NPP63ly5ef8Tz1/WFot8zMTM2YMUMvvPCCcnJyPK56frarrojcdNNN+v777+u8jbS0NN19992Nmis5OVl9+vSpMn6m/n7ee+895ebm6uabb7bGJk6cqNatWysqKkqrVq3yWP/111/XtddeW2U7jz32mKZOnWpdQwlnH8oMvNJf//pX5eTk6KefftKsWbP03HPP6eGHH7Y7VoMEBwc7qlQ0tbKysmrHf/zxR0nS9ddfr7CwMLnd7gZt/+jRow3OdqbVNBeS5O/vr3bt2tV5W23btlVAQEBjxDql0/n7qY9nnnlG//znP62LO7777rtauXKl1q1bpyeffFL//Oc/dejQIUnSf//7X02fPl2LFi2qsp2hQ4eqsLBQH330UZNnhj0oM/BKbrdbYWFhioyM1PDhwzVixAhrt3ZpaakeeOABtWvXTn5+frr00kuVlpZmPXbTpk1yuVx6//331bt3b/n5+Sk+Pl4ZGRnWOtX9xrlgwQJFR0fXmGnt2rW69NJL1apVK7Vp00bXXHON9QNYOn7FXEnq27evXC6XEhMTJVU9zFTX/Bs2bFBcXJwCAgKUkJBQ5YrUp+tUr6fS7t27lZCQID8/P/Xo0UObNm3yWL5582ZdfPHFcrvdCg8P19SpU3Xs2DFreWJiou6//35NnDhRISEhGjRoUJXnSE5Otn6jbtasmVwulySpoqJCM2fOVPv27eV2u9WnTx+tXbvWelzl3rDXX39diYmJ8vPz08svvyxJeumll9SjRw8r1/333289rrCwUHfffbfatWunoKAgXXHFFfrmm29qnKvK51m1alWNc1FeXq477rhDMTEx8vf3V5cuXfT00097bKfyvTB79mxFRESoc+fOSkxM1P79+/Xggw9aeySl6vesrVmzRnFxcfLz81NISIiGDRtmLTt5747L5dLixYs1ZMgQ+fv7KyYmRm+88YbH9qZMmaLOnTsrICBAHTt21GOPPWaVweXLl2vGjBn65ptvrFyVexhPPsyUkZGhK664Qv7+/mrTpo3uvvtuHT58uMrrfuqppxQeHq42bdpo7NixtRbP/Px8rV+/Xtddd501lpmZqcTERMXFxemWW25RUFCQfvrpJ0nS5MmTNWbMGEVFRVXZ1jnnnKOrr75ar776ao3PB2ejzMAR/P39rX/4Jk+erDfffFMrVqzQ119/rU6dOumqq67Sb7/95vGYSZMm6amnnlJaWpratWun66677rR+a//99981ceJEpaWlacOGDWrWrJn+9re/Wbuuv/rqK0nS+vXrlZOTU+Mx+rrmnz59uubOnatt27bJx8dHt99+u7Ws8ofrycWiMV9PpUmTJumhhx7Sjh07lJCQoOuuu876bfjgwYO6+uqr1a9fP33zzTdavHixXnzxRc2aNctjGytWrJCPj48+//xzvfDCC1WyPPzww1q2bJmk44cwcnJyJB0/RDd37lw99dRT+vbbb3XVVVfpuuuu0969ez0eP2XKFD3wwAPKzMzUVVddpcWLF2vs2LG6++67lZGRoTVr1qhTp06SJGOMhg4dqtzcXH3wwQfavn27LrzwQl155ZVV/g5OVttcVFRUqH379nr99df13Xff6V//+pceeeQRvf766x7b2LBhgzIzM5Wamqr33ntPq1evVvv27TVz5kyP136y999/X8OGDdPQoUO1Y8cOq+zW5rHHHtONN96ob775RiNHjtQtt9yizMxMa3lgYKCWL1+u7777Tk8//bSWLl2q+fPnSzp+mOuhhx5Sjx49rFw33XRTlec4cuSI/vrXv+rcc89VWlqa3njjDa1fv96jPErSxo0b9eOPP2rjxo1asWKFli9fXuvh188++0wBAQHq1q2bNda7d29t27ZNBQUF2r59u0pKStSpUyd99tln+vrrr/XAAw/UuL2LL75Yn376aa3zBQez+UKXQBWjRo0y119/vXX/yy+/NG3atDH/+Mc/zOHDh42vr6955ZVXrOVlZWUmIiLCzJkzxxhjzMaNG40ks2rVKmudQ4cOGX9/f/Paa68ZY4xJSkoyvXv39nje+fPnmw4dOtSY42R5eXlGknVF3KysLCPJ7Nixo8bXU5/869evt9Z5//33jSRTUlJijDHm559/Nl26dDFffvlljfmWLVtmgoODa1xe19fzxBNPWOscPXrUtG/f3jz55JPGGGMeeeQR06VLF1NRUWGts2jRItOyZUtTXl5ujDl+dfg+ffqc8vnfeustc/I/SREREebxxx/3GOvXr58ZM2aMR8YFCxZUedz06dOrfZ4NGzaYoKAg88cff3iMn3/++eaFF16o9jF1mYvqjBkzxtx4443W/VGjRpnQ0FCPqywbY0yHDh3M/PnzPcZO/vvr37+/GTFiRI3PdfI2JJl7773XY534+Hhz33331biNOXPmmIsuusi6X93/J5Xbfuutt4wxxixZssSce+655vDhw9by999/3zRr1szk5uYaY46/7g4dOphjx45Z6/z97383N910U41Z5s+fbzp27FhlPCkpyZx//vmmZ8+eZvXq1aa0tNT07NnTbNu2zTz77LOmc+fOJiEhwezcudPjce+8845p1qyZ9b7E2YU9M/BK7733nlq2bCk/Pz/1799fl112mZ599ln9+OOPOnr0qC655BJrXV9fX1188cUev3FKUv/+/a0/t27dWl26dKmyTn38+OOPGj58uDp27KigoCDrsFJ2dna9tlHX/L169bL+HB4eLknKy8uTJJ133nnavXu3Lr744iZ/PSfOo4+Pj+Li4qysmZmZ6t+/v3VoRJIuueQSHT58WD///LM1dqo9CNUpKirSL7/84jFXlds/ea5O3H5eXp5++eUXXXnlldVud/v27Tp8+LDatGmjli1bWresrKxqD7OdqLa5kKTnn39ecXFxatu2rVq2bKmlS5dWmc8LLrhAzZs3r/3FVyM9Pb3G11SXvJX3T8z7f//3f7r00ksVFhamli1b6rHHHqvX+1k6/h7o3bu3WrRoYY1dcsklqqio8Dg02qNHD51zzjnW/fDwcOv9XJ2SkhL5+flVGU9OTtYPP/ygjIwM/e1vf1NKSooGDhwoX19fzZo1S5999pnuvPNO3XbbbR6P8/f3V0VFhUpLS+v1+uAMPnYHAKpz+eWXa/HixfL19VVERIR8fX0lydoFf+IPT+n4oYOTx6pTuU6zZs1kjPFYdqpDUNdee60iIyO1dOlSRUREqKKiQj179qz1JM6TVT5nXfJXvuYT12/MT2OczuupzFNd7upe44k/6OqrLnN14vb9/f1r3V5FRYXCw8OrPUTXkBO1K7O8/vrrevDBBzV37lz1799fgYGB+t///V99+eWXNWatj1O9rrqqzLt161bdfPPNmjFjhq666ioFBwdr1apVmjt3br22V9v/eyeOn/h+rlxW2/s5JCREBQUFtT737t279corr2jHjh166aWXdNlll6lt27b6xz/+odtvv11FRUUKCgqSJP32228KCAhotHmEd2HPDLxSixYt1KlTJ3Xo0MHjH8FOnTqpefPm+uyzz6yxo0ePatu2bR7H1qXj/1hXKigo0Pfff6+uXbtKOv7Jj9zcXI9CU9v3qRw6dEiZmZl69NFHdeWVV6pbt25V/qGt/G27vLy8xu3UJ39TqsvrqXTiPB47dkzbt2+35rF79+7asmWLxzxu2bJFgYGBOu+8804rY1BQkCIiIjzmqnL7tc1VYGCgoqOjtWHDhmqXX3jhhcrNzZWPj486derkcQsJCak1U21z8emnnyohIUFjxoxR37591alTp1Pu6anUvHnzWt830vE9dTW9prrkrbxfmffzzz9Xhw4dNH36dMXFxSk2Nlb79++vd67u3bsrPT3d46sTPv/8czVr1kydO3euV94T9e3bV7m5uTW+L40xuvvuuzV37ly1bNlS5eXl1i8klf89sSzt3LlTF154YYPzwLtRZuAoLVq00H333adJkyZp7dq1+u6773TXXXfpyJEjuuOOOzzWnTlzpjZs2KCdO3dq9OjRCgkJsT5VlJiYqP/85z+aM2eOfvzxRy1atEgffvhhjc977rnnqk2bNlqyZIl++OEHffzxx5o4caLHOu3atZO/v7/Wrl2rX3/9VYWFhaeVvzYHDx5U165drZOOa1JeXq709HSP23fffVen11Np0aJFeuutt7R7926NHTtWBQUF1snIY8aM0YEDBzRu3Djt3r1b77zzjpKSkjRx4kTr47SnY9KkSXryySf12muvac+ePZo6darS09M1fvz4Wh+XnJysuXPn6plnntHevXv19ddf69lnn5UkDRw4UP3799cNN9ygjz76SPv27dOWLVv06KOPatu2bbVut7a56NSpk7Zt26aPPvpI33//vR577DGPT6nVJjo6Wp988okOHjyo/Pz8atdJSkrSq6++qqSkJGVmZiojI0Nz5sypdbtvvPGGXnrpJX3//fdKSkrSV199ZZ2Y26lTJ2VnZ2vVqlX68ccf9cwzz+itt96qkisrK0vp6enKz8+v9hDNiBEj5Ofnp1GjRmnnzp3auHGjxo0bp1tvvVWhoaF1ev3V6du3r9q2bavPP/+82uVLly61TuyXjh/a+vjjj7V161bNnz9f3bt399jT9umnn2rw4MENzgMvZ9O5OkCNTnXibUlJiRk3bpwJCQkxbrfbXHLJJearr76ylleeQPvuu++aHj16mObNm5t+/fqZ9PR0j+0sXrzYREZGmhYtWpjbbrvNPP7447WeAJyammq6detm3G636dWrl9m0aZPHiZDGGLN06VITGRlpmjVrZgYMGFDtduqav6CgwBrbsWOHkWSysrKMMf//CakbN26scZ6WLVtmJFW5Vb7GU72eyudYuXKliY+PN82bNzfdunUzGzZs8HieTZs2mX79+pnmzZubsLAwM2XKFHP06FFr+YABA8z48eNrzFmpuhOAy8vLzYwZM8x5551nfH19Te/evc2HH35oLa/ppGtjjHn++edNly5djK+vrwkPDzfjxo2zlhUVFZlx48aZiIgI4+vrayIjI82IESNMdnZ2tdnqMhd//PGHGT16tAkODjatWrUy9913n5k6darHCbQ1vbe/+OIL06tXL+N2u605qO4E7jfffNP06dPHNG/e3ISEhJhhw4ZZy6o7AXjRokVm0KBBxu12mw4dOphXX33VY3uTJk0ybdq0MS1btjQ33XSTmT9/vsdz/vHHH+bGG280rVq1MpLMsmXLrG2f+L7/9ttvzeWXX278/PxM69atzV133WWKi4trfd3jx4+3/h+pydSpU83NN99cZTw3N9d06NDBHDx40GN8xowZpnXr1qZr164eJ8f//PPPxtfX1xw4cKDW54NzuYw56cQBwOE2bdqkyy+/XAUFBXxZHRrFvn37FBMTox07dlT7jbjeyOVy6a233nLkpTQq/frrr+rRo4e2b9+uDh06NHg7kyZNUmFhoZYsWdKI6eBNOMwEAPBKoaGhevHFF+v9CauTtWvXTv/zP//TSKngjfg0EwDAa11//fWnvY1JkyY1QhJ4Mw4zAQAAR+MwEwAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcDTKDAAAcLT/DwZp/MxH4qZlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Population: Labor force participation (%)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0ba3d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Life expectancy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02c99efa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df['Gross primary education enrollment (%)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9c1583f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Land Area(Km2)                                35.995016\n",
       "Agricultural Land( %)                         -0.947000\n",
       "Population                                    75.571246\n",
       "Urban_population                              81.149152\n",
       "Co2-Emissions                                102.748248\n",
       "Forested Area (%)                             -0.451377\n",
       "GDP                                           78.039315\n",
       "Unemployment rate                              2.490526\n",
       "Out of pocket health expenditure              -0.323330\n",
       "Population: Labor force participation (%)     -0.014539\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['Land Area(Km2)','Agricultural Land( %)', 'Population', \n",
    "                       'Urban_population', 'Co2-Emissions', 'Forested Area (%)','GDP','Unemployment rate', 'Out of pocket health expenditure','Population: Labor force participation (%)']].kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0300d60c",
   "metadata": {},
   "source": [
    "## Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2000bfe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normal distribution\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = np.random.normal(loc=0.0, scale=1.0, size=10000)\n",
    "plt.hist(data, bins=30)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8d123407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define different standard deviations\n",
    "stds = [0.5, 1, 2, 3]\n",
    "\n",
    "# Generate and plot normal distributions for each standard deviation\n",
    "for std in stds:\n",
    "    # Generate data and calculate PDF\n",
    "    data = np.random.normal(loc=0, scale=std, size=1000)\n",
    "    x = np.linspace(-3 * std, 3 * std, 100)\n",
    "    y = 1 / (std * np.sqrt(2 * np.pi)) * np.exp(-(x**2) / (2 * std**2))\n",
    "\n",
    "    # Plot the curve\n",
    "    plt.plot(x, y, label=f\"σ = {std}\")\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Probability Density\")\n",
    "plt.title(\"Normal Distributions with Varying Standard Deviations\")\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4293b15f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate normally distributed data\n",
    "data = np.random.normal(loc=0, scale=1, size=10000)\n",
    "\n",
    "# Calculate mean and standard deviation\n",
    "mean = np.mean(data)\n",
    "std = np.std(data)\n",
    "\n",
    "# Create ranges for each standard deviation\n",
    "x_min = mean - 3 * std\n",
    "x_max = mean + 3 * std\n",
    "x = np.linspace(x_min, x_max, 100)\n",
    "\n",
    "# Create the normal distribution curve\n",
    "y = 1 / (std * np.sqrt(2 * np.pi)) * np.exp(-(x - mean)**2 / (2 * std**2))\n",
    "\n",
    "# Plot the curve\n",
    "plt.plot(x, y, color='blue', label='Normal Distribution')\n",
    "\n",
    "# Fill areas representing 68%, 95%, and 99.7%\n",
    "plt.fill_between(x, y, where=(x >= mean - std) & (x <= mean + std), color='red', alpha=0.5, label='68%')\n",
    "plt.fill_between(x, y, where=(x >= mean - 2 * std) & (x <= mean + 2 * std), color='lightblue', alpha=0.3, label='95%')\n",
    "plt.fill_between(x, y, where=(x >= mean - 3 * std) & (x <= mean + 3 * std), color='lightgreen', alpha=0.2, label='99.7%')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Probability Density\")\n",
    "plt.title(\"68-95-99.7 Rule (Empirical Rule)\")\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "91c0a10b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uniform distribution\n",
    "\n",
    "\n",
    "# Importing required libraries\n",
    "from scipy.stats import uniform\n",
    "# Generating random variables from Uniform distribution\n",
    "data = uniform.rvs(size = 100000, loc = 5, scale=10)\n",
    "\n",
    "# Plotting the results\n",
    "sns.set_style('whitegrid')\n",
    "sns.histplot(data, bins = 30, color = 'k')\n",
    "plt.xlabel('interval')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aadd7049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Poisson distribution\n",
    "\n",
    "from scipy.stats import poisson\n",
    "\n",
    "data_poisson = poisson.rvs(mu=3, size=10000)\n",
    "plt.hist(data_poisson, bins=30)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d46ee56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1508\\AppData\\Local\\Temp\\ipykernel_12912\\3376377086.py:6: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  ax = sns.distplot(data_expon, kde=True, bins=100, color='skyblue', hist_kws={\"linewidth\": 15,'alpha':1})\n",
      "C:\\Users\\1508\\AppData\\Local\\anaconda30\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Exponential Distribution'), Text(0, 0.5, 'Frequency')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lambda distribution\n",
    "\n",
    "from scipy.stats import expon\n",
    "\n",
    "data_expon = expon.rvs(scale=1,loc=0,size=1000)\n",
    "ax = sns.distplot(data_expon, kde=True, bins=100, color='skyblue', hist_kws={\"linewidth\": 15,'alpha':1})\n",
    "ax.set(xlabel='Exponential Distribution', ylabel='Frequency')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "56fb0653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Binomial distribution\n",
    "\n",
    "from scipy.stats import binom\n",
    "\n",
    "data_binom = binom.rvs(n=10,p=0.8,size=10000)\n",
    "plt.hist(data_binom, bins=30)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "467b7289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing required libraries\n",
    "from scipy.stats import binom\n",
    "import numpy as np\n",
    "\n",
    "# Defining the parameters for the binomial distribution\n",
    "n = 20  # Number of trials\n",
    "p = 0.6  # Probability of success\n",
    "\n",
    "# Generating binomial distribution\n",
    "x = np.arange(1,21)\n",
    "pmf = binom.pmf(x, n, p)\n",
    "\n",
    "# Plotting the results\n",
    "sns.set_style('whitegrid')\n",
    "plt.vlines(x ,0, pmf, colors='k', linestyles='-', lw=5)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel('Intervals')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "15c79f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import f\n",
    "import numpy as np\n",
    "\n",
    "dfn, dfd = 5, 2  # degrees of freedom numerator and denominator\n",
    "rv = f(dfn, dfd)  # F-distribution\n",
    "\n",
    "x = np.linspace(f.ppf(0.01, dfn, dfd), f.ppf(0.99, dfn, dfd), 100)\n",
    "y = rv.pdf(x)  # probability density function\n",
    "\n",
    "# plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x, y)\n",
    "plt.title('F-distribution')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('pdf(x)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd73465",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e69a2e0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d62ea3d1",
   "metadata": {},
   "source": [
    "# Studying Labor Market Dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baa89d6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import shapiro, pearsonr, ttest_ind, mannwhitneyu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b96621f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(df['Population: Labor force participation (%)'], df['Unemployment rate'])\n",
    "plt.xlabel('Labor Force Participation')\n",
    "plt.ylabel('Unemployment Rate')\n",
    "plt.title('Unemployment Rate vs Labor Force Participation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c990bd74",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = df['Unemployment rate'].corr(df['Population: Labor force participation (%)'])\n",
    "print('Correlation:', correlation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e03fa533",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming your DataFrame 'df' is already loaded with the data\n",
    "#https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41858166",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normality Test for 'Unemployment rate'\n",
    "stat, p = shapiro(df['Unemployment rate'])\n",
    "\n",
    "print('Unemployment rate:', 'Statistics=%.2f, p=%.2f' % (stat, p))\n",
    "\n",
    "if p > 0.05:\n",
    "    print('Sample looks Gaussian')\n",
    "else:\n",
    "    print('Sample does not look Gaussian')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16a4dace",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normality Test for 'Labor force participation'\n",
    "stat, p = shapiro(df['Population: Labor force participation (%)'])\n",
    "\n",
    "print('Unemployment rate:', 'Statistics=%.2f, p=%.2f' % (stat, p))\n",
    "\n",
    "if p > 0.05:\n",
    "    print('Sample looks Gaussian')\n",
    "else:\n",
    "    print('Sample does not look Gaussian')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc7e119e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat, p = shapiro(df['Birth Rate'])\n",
    "\n",
    "print('Birth rate:', 'Statistics=%.2f, p=%.2f' % (stat, p))\n",
    "\n",
    "if p > 0.05:\n",
    "    print('Sample looks Gaussian')\n",
    "else:\n",
    "    print('Sample does not look Gaussian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa287378",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat, p = ttest_ind(df['Unemployment rate'], df['Population: Labor force participation (%)'])\n",
    "print('Statistics=%.2f, p=%.2f' % (stat, p))\n",
    "\n",
    "if p > 0.05:\n",
    "    print('Samples are likely drawn from the same distributions (fail to reject H0)')\n",
    "else:\n",
    "    print('Samples are likely drawn from different distributions (reject H0)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30fa0ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat, p = mannwhitneyu(df['Unemployment rate'], df['Population: Labor force participation (%)'])\n",
    "print('Statistics=%.2f, p=%.2f' % (stat, p))\n",
    "\n",
    "if p > 0.05:\n",
    "    print('Samples are likely drawn from the same distributions (fail to reject H0)')\n",
    "else:\n",
    "    print('Samples are likely drawn from different distributions (reject H0)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7355386",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from statsmodels.formula.api import ols\n",
    "#import statsmodels.api as sm\n",
    "\n",
    "# One-sample t-test\n",
    "t_statistic, p_value = stats.ttest_1samp(df['Unemployment rate'], 0.0)\n",
    "print('t-statistic:', t_statistic)\n",
    "print('p-value:', p_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73fd7dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45a67416",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Chi-Square test using created dummy data\n",
    "\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Sample DataFrame\n",
    "data = {'Rain': ['Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes'],\n",
    "        'Temperature': ['High', 'Low', 'High', 'High', 'Low', 'Low', 'High']}\n",
    "df1 = pd.DataFrame(data)\n",
    "#print(df1)\n",
    "\n",
    "contingency_table = pd.crosstab(df1['Rain'], df1['Temperature'])\n",
    "chi2, p, dof, expected = chi2_contingency(contingency_table)\n",
    "print('Chi-square statistic:', chi2)\n",
    "print('p-value:', p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d33954d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression analysis\n",
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(df['Population: Labor force participation (%)']) # adding a constant\n",
    "model = sm.OLS(df['Unemployment rate'], X).fit()\n",
    "print_model = model.summary()\n",
    "print(print_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d58d95f5",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'statsmodels' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m result \u001b[38;5;241m=\u001b[39m\u001b[43mstatsmodels\u001b[49m\u001b[38;5;241m.\u001b[39mtsa\u001b[38;5;241m.\u001b[39mstattools\u001b[38;5;241m.\u001b[39madfuller(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPopulation: Labor force participation (\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mADF Statistic: \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m result[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp-value: \u001b[39m\u001b[38;5;132;01m%f\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m result[\u001b[38;5;241m1\u001b[39m])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'statsmodels' is not defined"
     ]
    }
   ],
   "source": [
    "result =statsmodels.tsa.stattools.adfuller(df['Population: Labor force participation (%)'])\n",
    "\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "56939ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'group': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],\n",
    "    'score': [371, 542, 275, 389, 2145, 1459, 153, 874, 321, 525]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b11c6cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_A = df[df['group'] == 'A']['score']\n",
    "group_B = df[df['group'] == 'B']['score']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "15471a84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic: 0.18480230929708708\n",
      "p-value: 0.8579830736566254\n"
     ]
    }
   ],
   "source": [
    "t_statistic, p_value = ttest_ind(group_A, group_B)\n",
    "print('t-statistic:', t_statistic)\n",
    "print('p-value:', p_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b7ba4bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>group</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>2145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  score\n",
       "0     A    371\n",
       "1     A    542\n",
       "2     A    275\n",
       "3     A    389\n",
       "4     A   2145"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1f77935b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03415189352153622 0.8579830736566272\n"
     ]
    }
   ],
   "source": [
    "# ANOVA\n",
    "df = pd.DataFrame({\n",
    "    'group': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],\n",
    "    'score': [371, 542, 275, 389, 2145, 1459, 153, 874, 321, 525]\n",
    "})\n",
    "group_A = df[df['group'] == 'A']['score']\n",
    "group_B = df[df['group'] == 'B']['score']\n",
    "\n",
    "fvalue, pvalue = stats.f_oneway(group_A , group_B)\n",
    "print(fvalue, pvalue)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7adaa5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (0.13.5)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (0.5.3)\n",
      "Requirement already satisfied: numpy>=1.22.3 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (1.23.5)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (22.0)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (2.1.4)\n",
      "Requirement already satisfied: scipy>=1.3 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (1.11.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from pandas>=0.25->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from pandas>=0.25->statsmodels) (2022.7)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from pandas>=0.25->statsmodels) (2023.3)\n",
      "Requirement already satisfied: six in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!python -m pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "14f42634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (1.9.3)\n",
      "Collecting scipy\n",
      "  Downloading scipy-1.11.4-cp310-cp310-win_amd64.whl (44.1 MB)\n",
      "     ---------------------------------------- 44.1/44.1 MB 3.1 MB/s eta 0:00:00\n",
      "Requirement already satisfied: statsmodels in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (0.13.5)\n",
      "Collecting statsmodels\n",
      "  Downloading statsmodels-0.14.1-cp310-cp310-win_amd64.whl (9.8 MB)\n",
      "     ---------------------------------------- 9.8/9.8 MB 3.8 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from scipy) (1.23.5)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (22.0)\n",
      "Collecting patsy>=0.5.4\n",
      "  Downloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n",
      "     -------------------------------------- 233.9/233.9 kB 7.2 MB/s eta 0:00:00\n",
      "Requirement already satisfied: pandas!=2.1.0,>=1.0 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from statsmodels) (2.1.4)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2023.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2022.7)\n",
      "Requirement already satisfied: six in c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n",
      "Installing collected packages: scipy, patsy, statsmodels\n",
      "  Attempting uninstall: scipy\n",
      "    Found existing installation: scipy 1.9.3\n",
      "    Uninstalling scipy-1.9.3:\n",
      "      Successfully uninstalled scipy-1.9.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "    WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'C:\\\\Users\\\\1508\\\\AppData\\\\Local\\\\anaconda30\\\\Lib\\\\site-packages\\\\~~ipy\\\\signal\\\\_max_len_seq_inner.cp310-win_amd64.pyd'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -cipy (c:\\users\\1508\\appdata\\local\\anaconda30\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade scipy statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b7191cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc9d2393",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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