tackling_missing_data_imputing_constant.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fillna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "wdi = pd.read_pickle('wdi.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 434 entries, 0 to 433\n",
      "Data columns (total 19 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   country_name                    434 non-null    object \n",
      " 1   access_to_electricity_pct       434 non-null    float64\n",
      " 2   atms_per_100000                 406 non-null    float64\n",
      " 3   compulsory_education_years      412 non-null    float64\n",
      " 4   health_expenditure_pct_of_gdp   427 non-null    float64\n",
      " 5   gdp_per_capita_usd              434 non-null    float64\n",
      " 6   gdp_per_capita_ppp              434 non-null    float64\n",
      " 7   life_expectancy_female          434 non-null    float64\n",
      " 8   life_expectancy_male            434 non-null    float64\n",
      " 9   life_expectancy                 434 non-null    float64\n",
      " 10  population_density              432 non-null    float64\n",
      " 11  population                      434 non-null    float64\n",
      " 12  alcohol_consumption_per_capita  215 non-null    float64\n",
      " 13  unemployment_rate_female        434 non-null    float64\n",
      " 14  unemployment_rate_male          434 non-null    float64\n",
      " 15  unemployment_rate               434 non-null    float64\n",
      " 16  year                            434 non-null    int64  \n",
      " 17  country_category                282 non-null    object \n",
      " 18  is_region                       434 non-null    int64  \n",
      "dtypes: float64(15), int64(2), object(2)\n",
      "memory usage: 64.5+ KB\n"
     ]
    }
   ],
   "source": [
    "wdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>access_to_electricity_pct</th>\n",
       "      <th>atms_per_100000</th>\n",
       "      <th>compulsory_education_years</th>\n",
       "      <th>health_expenditure_pct_of_gdp</th>\n",
       "      <th>gdp_per_capita_usd</th>\n",
       "      <th>gdp_per_capita_ppp</th>\n",
       "      <th>life_expectancy_female</th>\n",
       "      <th>life_expectancy_male</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>population_density</th>\n",
       "      <th>population</th>\n",
       "      <th>alcohol_consumption_per_capita</th>\n",
       "      <th>unemployment_rate_female</th>\n",
       "      <th>unemployment_rate_male</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>is_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97.700000</td>\n",
       "      <td>1.213235</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.777236</td>\n",
       "      <td>519.884773</td>\n",
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       "      <td>65.656000</td>\n",
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       "      <td>64.130000</td>\n",
       "      <td>55.595993</td>\n",
       "      <td>36296400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.090000</td>\n",
       "      <td>10.416000</td>\n",
       "      <td>11.184000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>31.714076</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.010597</td>\n",
       "      <td>4531.020806</td>\n",
       "      <td>13037.010016</td>\n",
       "      <td>80.148000</td>\n",
       "      <td>76.60100</td>\n",
       "      <td>78.333000</td>\n",
       "      <td>104.870693</td>\n",
       "      <td>2873457.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.563000</td>\n",
       "      <td>14.590000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.130677</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.380329</td>\n",
       "      <td>4111.294110</td>\n",
       "      <td>11737.409353</td>\n",
       "      <td>77.735000</td>\n",
       "      <td>75.30700</td>\n",
       "      <td>76.499000</td>\n",
       "      <td>17.377715</td>\n",
       "      <td>41389198.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.114000</td>\n",
       "      <td>10.021000</td>\n",
       "      <td>11.996000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.962894</td>\n",
       "      <td>19.079250</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.791503</td>\n",
       "      <td>4095.812942</td>\n",
       "      <td>7310.901738</td>\n",
       "      <td>63.252000</td>\n",
       "      <td>57.67700</td>\n",
       "      <td>60.379000</td>\n",
       "      <td>23.916538</td>\n",
       "      <td>29816748.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.467000</td>\n",
       "      <td>6.769000</td>\n",
       "      <td>7.119000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.283638</td>\n",
       "      <td>27.360620</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.561266</td>\n",
       "      <td>6108.588220</td>\n",
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       "      <td>69.92125</td>\n",
       "      <td>71.622526</td>\n",
       "      <td>36.669804</td>\n",
       "      <td>411898967.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.361396</td>\n",
       "      <td>8.097088</td>\n",
       "      <td>10.641313</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   access_to_electricity_pct  atms_per_100000  compulsory_education_years  \\\n",
       "0                  97.700000         1.213235                         9.0   \n",
       "1                 100.000000        31.714076                         9.0   \n",
       "2                 100.000000         9.130677                        10.0   \n",
       "3                  41.962894        19.079250                         6.0   \n",
       "4                  90.283638        27.360620                         9.0   \n",
       "\n",
       "   health_expenditure_pct_of_gdp  gdp_per_capita_usd  gdp_per_capita_ppp  \\\n",
       "0                      11.777236          519.884773         2058.383832   \n",
       "1                       5.010597         4531.020806        13037.010016   \n",
       "2                       6.380329         4111.294110        11737.409353   \n",
       "3                       2.791503         4095.812942         7310.901738   \n",
       "4                       5.561266         6108.588220        14562.367966   \n",
       "\n",
       "   life_expectancy_female  life_expectancy_male  life_expectancy  \\\n",
       "0               65.656000              62.70100        64.130000   \n",
       "1               80.148000              76.60100        78.333000   \n",
       "2               77.735000              75.30700        76.499000   \n",
       "3               63.252000              57.67700        60.379000   \n",
       "4               73.493366              69.92125        71.622526   \n",
       "\n",
       "   population_density   population  alcohol_consumption_per_capita  \\\n",
       "0           55.595993   36296400.0                             NaN   \n",
       "1          104.870693    2873457.0                             NaN   \n",
       "2           17.377715   41389198.0                             NaN   \n",
       "3           23.916538   29816748.0                             NaN   \n",
       "4           36.669804  411898967.0                             NaN   \n",
       "\n",
       "   unemployment_rate_female  unemployment_rate_male  unemployment_rate  year  \\\n",
       "0                 14.090000               10.416000          11.184000  2017   \n",
       "1                 12.563000               14.590000          13.750000  2017   \n",
       "2                 21.114000               10.021000          11.996000  2017   \n",
       "3                  7.467000                6.769000           7.119000  2017   \n",
       "4                 20.361396                8.097088          10.641313  2017   \n",
       "\n",
       "   is_region  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi_numeric = wdi.select_dtypes(include='number')\n",
    "wdi_numeric.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['access_to_electricity_pct', 'atms_per_100000',\n",
       "       'compulsory_education_years', 'health_expenditure_pct_of_gdp',\n",
       "       'gdp_per_capita_usd', 'gdp_per_capita_ppp', 'life_expectancy_female',\n",
       "       'life_expectancy_male', 'life_expectancy', 'population_density',\n",
       "       'population', 'alcohol_consumption_per_capita',\n",
       "       'unemployment_rate_female', 'unemployment_rate_male',\n",
       "       'unemployment_rate', 'year', 'is_region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols = wdi_numeric.columns\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country_name', 'country_category'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = wdi.select_dtypes(exclude='number').columns\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>access_to_electricity_pct</th>\n",
       "      <th>atms_per_100000</th>\n",
       "      <th>compulsory_education_years</th>\n",
       "      <th>health_expenditure_pct_of_gdp</th>\n",
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       "      <td>97.700000</td>\n",
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       "      <td>14.090000</td>\n",
       "      <td>10.416000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>31.714076</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.010597</td>\n",
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       "      <td>80.148000</td>\n",
       "      <td>76.601000</td>\n",
       "      <td>78.333000</td>\n",
       "      <td>104.870693</td>\n",
       "      <td>2.873457e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.563000</td>\n",
       "      <td>14.590000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.000000</td>\n",
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       "      <td>21.114000</td>\n",
       "      <td>10.021000</td>\n",
       "      <td>11.996000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.962894</td>\n",
       "      <td>19.079250</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.791503</td>\n",
       "      <td>4095.812942</td>\n",
       "      <td>7310.901738</td>\n",
       "      <td>63.252000</td>\n",
       "      <td>57.677000</td>\n",
       "      <td>60.379000</td>\n",
       "      <td>23.916538</td>\n",
       "      <td>2.981675e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.467000</td>\n",
       "      <td>6.769000</td>\n",
       "      <td>7.119000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.283638</td>\n",
       "      <td>27.360620</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.561266</td>\n",
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       "      <td>69.921250</td>\n",
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       "      <td>4.118990e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.361396</td>\n",
       "      <td>8.097088</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>5.917897</td>\n",
       "      <td>2566.596950</td>\n",
       "      <td>7771.189420</td>\n",
       "      <td>79.442000</td>\n",
       "      <td>71.207000</td>\n",
       "      <td>75.317000</td>\n",
       "      <td>308.125246</td>\n",
       "      <td>9.554040e+07</td>\n",
       "      <td>8.66000</td>\n",
       "      <td>1.831000</td>\n",
       "      <td>2.142000</td>\n",
       "      <td>1.993000</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>24.735190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3562.330943</td>\n",
       "      <td>6472.121785</td>\n",
       "      <td>75.595000</td>\n",
       "      <td>72.263000</td>\n",
       "      <td>73.895000</td>\n",
       "      <td>758.984551</td>\n",
       "      <td>4.569087e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.849998</td>\n",
       "      <td>22.393999</td>\n",
       "      <td>26.256001</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>89.588551</td>\n",
       "      <td>40.949620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.848779</td>\n",
       "      <td>11385.675227</td>\n",
       "      <td>17025.827360</td>\n",
       "      <td>74.871815</td>\n",
       "      <td>70.398743</td>\n",
       "      <td>72.563282</td>\n",
       "      <td>59.617978</td>\n",
       "      <td>7.591945e+09</td>\n",
       "      <td>6.18124</td>\n",
       "      <td>5.580954</td>\n",
       "      <td>5.271615</td>\n",
       "      <td>5.392213</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>39.812622</td>\n",
       "      <td>11.554664</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.934843</td>\n",
       "      <td>1516.390661</td>\n",
       "      <td>3607.304442</td>\n",
       "      <td>66.447000</td>\n",
       "      <td>60.533000</td>\n",
       "      <td>63.510000</td>\n",
       "      <td>23.341479</td>\n",
       "      <td>1.735182e+07</td>\n",
       "      <td>6.54000</td>\n",
       "      <td>12.448000</td>\n",
       "      <td>10.614000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>41.041584</td>\n",
       "      <td>6.625208</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.734331</td>\n",
       "      <td>1683.740577</td>\n",
       "      <td>3206.277079</td>\n",
       "      <td>62.598000</td>\n",
       "      <td>59.501000</td>\n",
       "      <td>61.195000</td>\n",
       "      <td>37.324591</td>\n",
       "      <td>1.443902e+07</td>\n",
       "      <td>4.67000</td>\n",
       "      <td>5.598000</td>\n",
       "      <td>4.555000</td>\n",
       "      <td>5.087000</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>434 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     access_to_electricity_pct  atms_per_100000  compulsory_education_years  \\\n",
       "0                    97.700000         1.213235                         9.0   \n",
       "1                   100.000000        31.714076                         9.0   \n",
       "2                   100.000000         9.130677                        10.0   \n",
       "3                    41.962894        19.079250                         6.0   \n",
       "4                    90.283638        27.360620                         9.0   \n",
       "..                         ...              ...                         ...   \n",
       "429                 100.000000        25.322499                        10.0   \n",
       "430                 100.000000        24.735190                        10.0   \n",
       "431                  89.588551        40.949620                        10.0   \n",
       "432                  39.812622        11.554664                         7.0   \n",
       "433                  41.041584         6.625208                         7.0   \n",
       "\n",
       "     health_expenditure_pct_of_gdp  gdp_per_capita_usd  gdp_per_capita_ppp  \\\n",
       "0                        11.777236          519.884773         2058.383832   \n",
       "1                         5.010597         4531.020806        13037.010016   \n",
       "2                         6.380329         4111.294110        11737.409353   \n",
       "3                         2.791503         4095.812942         7310.901738   \n",
       "4                         5.561266         6108.588220        14562.367966   \n",
       "..                             ...                 ...                 ...   \n",
       "429                       5.917897         2566.596950         7771.189420   \n",
       "430                            NaN         3562.330943         6472.121785   \n",
       "431                       9.848779        11385.675227        17025.827360   \n",
       "432                       4.934843         1516.390661         3607.304442   \n",
       "433                       4.734331         1683.740577         3206.277079   \n",
       "\n",
       "     life_expectancy_female  life_expectancy_male  life_expectancy  \\\n",
       "0                 65.656000             62.701000        64.130000   \n",
       "1                 80.148000             76.601000        78.333000   \n",
       "2                 77.735000             75.307000        76.499000   \n",
       "3                 63.252000             57.677000        60.379000   \n",
       "4                 73.493366             69.921250        71.622526   \n",
       "..                      ...                   ...              ...   \n",
       "429               79.442000             71.207000        75.317000   \n",
       "430               75.595000             72.263000        73.895000   \n",
       "431               74.871815             70.398743        72.563282   \n",
       "432               66.447000             60.533000        63.510000   \n",
       "433               62.598000             59.501000        61.195000   \n",
       "\n",
       "     population_density    population  alcohol_consumption_per_capita  \\\n",
       "0             55.595993  3.629640e+07                             NaN   \n",
       "1            104.870693  2.873457e+06                             NaN   \n",
       "2             17.377715  4.138920e+07                             NaN   \n",
       "3             23.916538  2.981675e+07                             NaN   \n",
       "4             36.669804  4.118990e+08                             NaN   \n",
       "..                  ...           ...                             ...   \n",
       "429          308.125246  9.554040e+07                         8.66000   \n",
       "430          758.984551  4.569087e+06                             NaN   \n",
       "431           59.617978  7.591945e+09                         6.18124   \n",
       "432           23.341479  1.735182e+07                         6.54000   \n",
       "433           37.324591  1.443902e+07                         4.67000   \n",
       "\n",
       "     unemployment_rate_female  unemployment_rate_male  unemployment_rate  \\\n",
       "0                   14.090000               10.416000          11.184000   \n",
       "1                   12.563000               14.590000          13.750000   \n",
       "2                   21.114000               10.021000          11.996000   \n",
       "3                    7.467000                6.769000           7.119000   \n",
       "4                   20.361396                8.097088          10.641313   \n",
       "..                        ...                     ...                ...   \n",
       "429                  1.831000                2.142000           1.993000   \n",
       "430                 41.849998               22.393999          26.256001   \n",
       "431                  5.580954                5.271615           5.392213   \n",
       "432                 12.448000               10.614000          11.500000   \n",
       "433                  5.598000                4.555000           5.087000   \n",
       "\n",
       "     year  is_region  \n",
       "0    2017          0  \n",
       "1    2017          0  \n",
       "2    2017          0  \n",
       "3    2017          0  \n",
       "4    2017          1  \n",
       "..    ...        ...  \n",
       "429  2018          0  \n",
       "430  2018          0  \n",
       "431  2018          1  \n",
       "432  2018          0  \n",
       "433  2018          0  \n",
       "\n",
       "[434 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[numeric_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>access_to_electricity_pct</th>\n",
       "      <th>atms_per_100000</th>\n",
       "      <th>compulsory_education_years</th>\n",
       "      <th>health_expenditure_pct_of_gdp</th>\n",
       "      <th>gdp_per_capita_usd</th>\n",
       "      <th>gdp_per_capita_ppp</th>\n",
       "      <th>life_expectancy_female</th>\n",
       "      <th>life_expectancy_male</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>population_density</th>\n",
       "      <th>population</th>\n",
       "      <th>alcohol_consumption_per_capita</th>\n",
       "      <th>unemployment_rate_female</th>\n",
       "      <th>unemployment_rate_male</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>is_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97.700000</td>\n",
       "      <td>1.213235</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.777236</td>\n",
       "      <td>519.884773</td>\n",
       "      <td>2058.383832</td>\n",
       "      <td>65.656000</td>\n",
       "      <td>62.701000</td>\n",
       "      <td>64.130000</td>\n",
       "      <td>55.595993</td>\n",
       "      <td>3.629640e+07</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>14.090000</td>\n",
       "      <td>10.416000</td>\n",
       "      <td>11.184000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>31.714076</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.010597</td>\n",
       "      <td>4531.020806</td>\n",
       "      <td>13037.010016</td>\n",
       "      <td>80.148000</td>\n",
       "      <td>76.601000</td>\n",
       "      <td>78.333000</td>\n",
       "      <td>104.870693</td>\n",
       "      <td>2.873457e+06</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>12.563000</td>\n",
       "      <td>14.590000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.130677</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.380329</td>\n",
       "      <td>4111.294110</td>\n",
       "      <td>11737.409353</td>\n",
       "      <td>77.735000</td>\n",
       "      <td>75.307000</td>\n",
       "      <td>76.499000</td>\n",
       "      <td>17.377715</td>\n",
       "      <td>4.138920e+07</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>21.114000</td>\n",
       "      <td>10.021000</td>\n",
       "      <td>11.996000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41.962894</td>\n",
       "      <td>19.079250</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.791503</td>\n",
       "      <td>4095.812942</td>\n",
       "      <td>7310.901738</td>\n",
       "      <td>63.252000</td>\n",
       "      <td>57.677000</td>\n",
       "      <td>60.379000</td>\n",
       "      <td>23.916538</td>\n",
       "      <td>2.981675e+07</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>7.467000</td>\n",
       "      <td>6.769000</td>\n",
       "      <td>7.119000</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.283638</td>\n",
       "      <td>27.360620</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.561266</td>\n",
       "      <td>6108.588220</td>\n",
       "      <td>14562.367966</td>\n",
       "      <td>73.493366</td>\n",
       "      <td>69.921250</td>\n",
       "      <td>71.622526</td>\n",
       "      <td>36.669804</td>\n",
       "      <td>4.118990e+08</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>20.361396</td>\n",
       "      <td>8.097088</td>\n",
       "      <td>10.641313</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>25.322499</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.917897</td>\n",
       "      <td>2566.596950</td>\n",
       "      <td>7771.189420</td>\n",
       "      <td>79.442000</td>\n",
       "      <td>71.207000</td>\n",
       "      <td>75.317000</td>\n",
       "      <td>308.125246</td>\n",
       "      <td>9.554040e+07</td>\n",
       "      <td>8.66000</td>\n",
       "      <td>1.831000</td>\n",
       "      <td>2.142000</td>\n",
       "      <td>1.993000</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>24.735190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>3562.330943</td>\n",
       "      <td>6472.121785</td>\n",
       "      <td>75.595000</td>\n",
       "      <td>72.263000</td>\n",
       "      <td>73.895000</td>\n",
       "      <td>758.984551</td>\n",
       "      <td>4.569087e+06</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>41.849998</td>\n",
       "      <td>22.393999</td>\n",
       "      <td>26.256001</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>89.588551</td>\n",
       "      <td>40.949620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.848779</td>\n",
       "      <td>11385.675227</td>\n",
       "      <td>17025.827360</td>\n",
       "      <td>74.871815</td>\n",
       "      <td>70.398743</td>\n",
       "      <td>72.563282</td>\n",
       "      <td>59.617978</td>\n",
       "      <td>7.591945e+09</td>\n",
       "      <td>6.18124</td>\n",
       "      <td>5.580954</td>\n",
       "      <td>5.271615</td>\n",
       "      <td>5.392213</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>39.812622</td>\n",
       "      <td>11.554664</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.934843</td>\n",
       "      <td>1516.390661</td>\n",
       "      <td>3607.304442</td>\n",
       "      <td>66.447000</td>\n",
       "      <td>60.533000</td>\n",
       "      <td>63.510000</td>\n",
       "      <td>23.341479</td>\n",
       "      <td>1.735182e+07</td>\n",
       "      <td>6.54000</td>\n",
       "      <td>12.448000</td>\n",
       "      <td>10.614000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>41.041584</td>\n",
       "      <td>6.625208</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.734331</td>\n",
       "      <td>1683.740577</td>\n",
       "      <td>3206.277079</td>\n",
       "      <td>62.598000</td>\n",
       "      <td>59.501000</td>\n",
       "      <td>61.195000</td>\n",
       "      <td>37.324591</td>\n",
       "      <td>1.443902e+07</td>\n",
       "      <td>4.67000</td>\n",
       "      <td>5.598000</td>\n",
       "      <td>4.555000</td>\n",
       "      <td>5.087000</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>434 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     access_to_electricity_pct  atms_per_100000  compulsory_education_years  \\\n",
       "0                    97.700000         1.213235                         9.0   \n",
       "1                   100.000000        31.714076                         9.0   \n",
       "2                   100.000000         9.130677                        10.0   \n",
       "3                    41.962894        19.079250                         6.0   \n",
       "4                    90.283638        27.360620                         9.0   \n",
       "..                         ...              ...                         ...   \n",
       "429                 100.000000        25.322499                        10.0   \n",
       "430                 100.000000        24.735190                        10.0   \n",
       "431                  89.588551        40.949620                        10.0   \n",
       "432                  39.812622        11.554664                         7.0   \n",
       "433                  41.041584         6.625208                         7.0   \n",
       "\n",
       "     health_expenditure_pct_of_gdp  gdp_per_capita_usd  gdp_per_capita_ppp  \\\n",
       "0                        11.777236          519.884773         2058.383832   \n",
       "1                         5.010597         4531.020806        13037.010016   \n",
       "2                         6.380329         4111.294110        11737.409353   \n",
       "3                         2.791503         4095.812942         7310.901738   \n",
       "4                         5.561266         6108.588220        14562.367966   \n",
       "..                             ...                 ...                 ...   \n",
       "429                       5.917897         2566.596950         7771.189420   \n",
       "430                    -999.000000         3562.330943         6472.121785   \n",
       "431                       9.848779        11385.675227        17025.827360   \n",
       "432                       4.934843         1516.390661         3607.304442   \n",
       "433                       4.734331         1683.740577         3206.277079   \n",
       "\n",
       "     life_expectancy_female  life_expectancy_male  life_expectancy  \\\n",
       "0                 65.656000             62.701000        64.130000   \n",
       "1                 80.148000             76.601000        78.333000   \n",
       "2                 77.735000             75.307000        76.499000   \n",
       "3                 63.252000             57.677000        60.379000   \n",
       "4                 73.493366             69.921250        71.622526   \n",
       "..                      ...                   ...              ...   \n",
       "429               79.442000             71.207000        75.317000   \n",
       "430               75.595000             72.263000        73.895000   \n",
       "431               74.871815             70.398743        72.563282   \n",
       "432               66.447000             60.533000        63.510000   \n",
       "433               62.598000             59.501000        61.195000   \n",
       "\n",
       "     population_density    population  alcohol_consumption_per_capita  \\\n",
       "0             55.595993  3.629640e+07                      -999.00000   \n",
       "1            104.870693  2.873457e+06                      -999.00000   \n",
       "2             17.377715  4.138920e+07                      -999.00000   \n",
       "3             23.916538  2.981675e+07                      -999.00000   \n",
       "4             36.669804  4.118990e+08                      -999.00000   \n",
       "..                  ...           ...                             ...   \n",
       "429          308.125246  9.554040e+07                         8.66000   \n",
       "430          758.984551  4.569087e+06                      -999.00000   \n",
       "431           59.617978  7.591945e+09                         6.18124   \n",
       "432           23.341479  1.735182e+07                         6.54000   \n",
       "433           37.324591  1.443902e+07                         4.67000   \n",
       "\n",
       "     unemployment_rate_female  unemployment_rate_male  unemployment_rate  \\\n",
       "0                   14.090000               10.416000          11.184000   \n",
       "1                   12.563000               14.590000          13.750000   \n",
       "2                   21.114000               10.021000          11.996000   \n",
       "3                    7.467000                6.769000           7.119000   \n",
       "4                   20.361396                8.097088          10.641313   \n",
       "..                        ...                     ...                ...   \n",
       "429                  1.831000                2.142000           1.993000   \n",
       "430                 41.849998               22.393999          26.256001   \n",
       "431                  5.580954                5.271615           5.392213   \n",
       "432                 12.448000               10.614000          11.500000   \n",
       "433                  5.598000                4.555000           5.087000   \n",
       "\n",
       "     year  is_region  \n",
       "0    2017          0  \n",
       "1    2017          0  \n",
       "2    2017          0  \n",
       "3    2017          0  \n",
       "4    2017          1  \n",
       "..    ...        ...  \n",
       "429  2018          0  \n",
       "430  2018          0  \n",
       "431  2018          1  \n",
       "432  2018          0  \n",
       "433  2018          0  \n",
       "\n",
       "[434 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[numeric_cols].fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi[numeric_cols] = wdi[numeric_cols].fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi[cat_cols] = wdi[cat_cols].fillna('MISSING')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 434 entries, 0 to 433\n",
      "Data columns (total 19 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   country_name                    434 non-null    object \n",
      " 1   access_to_electricity_pct       434 non-null    float64\n",
      " 2   atms_per_100000                 434 non-null    float64\n",
      " 3   compulsory_education_years      434 non-null    float64\n",
      " 4   health_expenditure_pct_of_gdp   434 non-null    float64\n",
      " 5   gdp_per_capita_usd              434 non-null    float64\n",
      " 6   gdp_per_capita_ppp              434 non-null    float64\n",
      " 7   life_expectancy_female          434 non-null    float64\n",
      " 8   life_expectancy_male            434 non-null    float64\n",
      " 9   life_expectancy                 434 non-null    float64\n",
      " 10  population_density              434 non-null    float64\n",
      " 11  population                      434 non-null    float64\n",
      " 12  alcohol_consumption_per_capita  434 non-null    float64\n",
      " 13  unemployment_rate_female        434 non-null    float64\n",
      " 14  unemployment_rate_male          434 non-null    float64\n",
      " 15  unemployment_rate               434 non-null    float64\n",
      " 16  year                            434 non-null    int64  \n",
      " 17  country_category                434 non-null    object \n",
      " 18  is_region                       434 non-null    int64  \n",
      "dtypes: float64(15), int64(2), object(2)\n",
      "memory usage: 64.5+ KB\n"
     ]
    }
   ],
   "source": [
    "wdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-999.000000    219\n",
       " 0.690000        3\n",
       " 12.030000       2\n",
       " 2.050000        2\n",
       " 0.019000        2\n",
       "              ... \n",
       " 2.300000        1\n",
       " 8.940000        1\n",
       " 4.065725        1\n",
       " 11.350000       1\n",
       " 2.710000        1\n",
       "Name: alcohol_consumption_per_capita, Length: 205, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['alcohol_consumption_per_capita'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEVELOPING    224\n",
       "MISSING       152\n",
       "DEVELOPED      58\n",
       "Name: country_category, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['country_category'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi = pd.read_pickle('wdi.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = wdi.select_dtypes(include='number').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = wdi.select_dtypes(exclude='number').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(fill_value=-999, strategy='constant')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SimpleImputer(strategy='constant', fill_value=-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.77000000e+01, 1.21323506e+00, 9.00000000e+00, ...,\n",
       "        1.11840000e+01, 2.01700000e+03, 0.00000000e+00],\n",
       "       [1.00000000e+02, 3.17140757e+01, 9.00000000e+00, ...,\n",
       "        1.37500000e+01, 2.01700000e+03, 0.00000000e+00],\n",
       "       [1.00000000e+02, 9.13067681e+00, 1.00000000e+01, ...,\n",
       "        1.19960003e+01, 2.01700000e+03, 0.00000000e+00],\n",
       "       ...,\n",
       "       [8.95885512e+01, 4.09496204e+01, 1.00000000e+01, ...,\n",
       "        5.39221306e+00, 2.01800000e+03, 1.00000000e+00],\n",
       "       [3.98126221e+01, 1.15546644e+01, 7.00000000e+00, ...,\n",
       "        1.15000000e+01, 2.01800000e+03, 0.00000000e+00],\n",
       "       [4.10415840e+01, 6.62520801e+00, 7.00000000e+00, ...,\n",
       "        5.08699989e+00, 2.01800000e+03, 0.00000000e+00]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_imp_num = SimpleImputer(strategy='constant', fill_value=-999)\n",
    "simple_imp_num.fit_transform(wdi[numeric_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(simple_imp_num.fit_transform(wdi[numeric_cols]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>access_to_electricity_pct</th>\n",
       "      <th>atms_per_100000</th>\n",
       "      <th>compulsory_education_years</th>\n",
       "      <th>health_expenditure_pct_of_gdp</th>\n",
       "      <th>gdp_per_capita_usd</th>\n",
       "      <th>gdp_per_capita_ppp</th>\n",
       "      <th>life_expectancy_female</th>\n",
       "      <th>life_expectancy_male</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>population_density</th>\n",
       "      <th>population</th>\n",
       "      <th>alcohol_consumption_per_capita</th>\n",
       "      <th>unemployment_rate_female</th>\n",
       "      <th>unemployment_rate_male</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>is_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97.7</td>\n",
       "      <td>1.213235</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.777236</td>\n",
       "      <td>519.884773</td>\n",
       "      <td>2058.383832</td>\n",
       "      <td>65.656</td>\n",
       "      <td>62.701</td>\n",
       "      <td>64.130</td>\n",
       "      <td>55.595993</td>\n",
       "      <td>36296400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.090</td>\n",
       "      <td>10.416</td>\n",
       "      <td>11.184</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>31.714076</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.010597</td>\n",
       "      <td>4531.020806</td>\n",
       "      <td>13037.010016</td>\n",
       "      <td>80.148</td>\n",
       "      <td>76.601</td>\n",
       "      <td>78.333</td>\n",
       "      <td>104.870693</td>\n",
       "      <td>2873457.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.563</td>\n",
       "      <td>14.590</td>\n",
       "      <td>13.750</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   access_to_electricity_pct  atms_per_100000  compulsory_education_years  \\\n",
       "0                       97.7         1.213235                         9.0   \n",
       "1                      100.0        31.714076                         9.0   \n",
       "\n",
       "   health_expenditure_pct_of_gdp  gdp_per_capita_usd  gdp_per_capita_ppp  \\\n",
       "0                      11.777236          519.884773         2058.383832   \n",
       "1                       5.010597         4531.020806        13037.010016   \n",
       "\n",
       "   life_expectancy_female  life_expectancy_male  life_expectancy  \\\n",
       "0                  65.656                62.701           64.130   \n",
       "1                  80.148                76.601           78.333   \n",
       "\n",
       "   population_density  population  alcohol_consumption_per_capita  \\\n",
       "0           55.595993  36296400.0                             NaN   \n",
       "1          104.870693   2873457.0                             NaN   \n",
       "\n",
       "   unemployment_rate_female  unemployment_rate_male  unemployment_rate  year  \\\n",
       "0                    14.090                  10.416             11.184  2017   \n",
       "1                    12.563                  14.590             13.750  2017   \n",
       "\n",
       "   is_region  \n",
       "0          0  \n",
       "1          0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[numeric_cols].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(434, 17)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_num = simple_imp_num.fit_transform(wdi[numeric_cols])\n",
    "imputed_num.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi[numeric_cols] = imputed_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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_name</th>\n",
       "      <th>access_to_electricity_pct</th>\n",
       "      <th>atms_per_100000</th>\n",
       "      <th>compulsory_education_years</th>\n",
       "      <th>health_expenditure_pct_of_gdp</th>\n",
       "      <th>gdp_per_capita_usd</th>\n",
       "      <th>gdp_per_capita_ppp</th>\n",
       "      <th>life_expectancy_female</th>\n",
       "      <th>life_expectancy_male</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>population_density</th>\n",
       "      <th>population</th>\n",
       "      <th>alcohol_consumption_per_capita</th>\n",
       "      <th>unemployment_rate_female</th>\n",
       "      <th>unemployment_rate_male</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>country_category</th>\n",
       "      <th>is_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>97.700000</td>\n",
       "      <td>1.213235</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.777236</td>\n",
       "      <td>519.884773</td>\n",
       "      <td>2058.383832</td>\n",
       "      <td>65.656000</td>\n",
       "      <td>62.701000</td>\n",
       "      <td>64.130000</td>\n",
       "      <td>55.595993</td>\n",
       "      <td>3.629640e+07</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>14.090000</td>\n",
       "      <td>10.416000</td>\n",
       "      <td>11.184000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>31.714076</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.010597</td>\n",
       "      <td>4531.020806</td>\n",
       "      <td>13037.010016</td>\n",
       "      <td>80.148000</td>\n",
       "      <td>76.601000</td>\n",
       "      <td>78.333000</td>\n",
       "      <td>104.870693</td>\n",
       "      <td>2.873457e+06</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>12.563000</td>\n",
       "      <td>14.590000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.130677</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.380329</td>\n",
       "      <td>4111.294110</td>\n",
       "      <td>11737.409353</td>\n",
       "      <td>77.735000</td>\n",
       "      <td>75.307000</td>\n",
       "      <td>76.499000</td>\n",
       "      <td>17.377715</td>\n",
       "      <td>4.138920e+07</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>21.114000</td>\n",
       "      <td>10.021000</td>\n",
       "      <td>11.996000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>41.962894</td>\n",
       "      <td>19.079250</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.791503</td>\n",
       "      <td>4095.812942</td>\n",
       "      <td>7310.901738</td>\n",
       "      <td>63.252000</td>\n",
       "      <td>57.677000</td>\n",
       "      <td>60.379000</td>\n",
       "      <td>23.916538</td>\n",
       "      <td>2.981675e+07</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>7.467000</td>\n",
       "      <td>6.769000</td>\n",
       "      <td>7.119000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>90.283638</td>\n",
       "      <td>27.360620</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.561266</td>\n",
       "      <td>6108.588220</td>\n",
       "      <td>14562.367966</td>\n",
       "      <td>73.493366</td>\n",
       "      <td>69.921250</td>\n",
       "      <td>71.622526</td>\n",
       "      <td>36.669804</td>\n",
       "      <td>4.118990e+08</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>20.361396</td>\n",
       "      <td>8.097088</td>\n",
       "      <td>10.641313</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>429</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>25.322499</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.917897</td>\n",
       "      <td>2566.596950</td>\n",
       "      <td>7771.189420</td>\n",
       "      <td>79.442000</td>\n",
       "      <td>71.207000</td>\n",
       "      <td>75.317000</td>\n",
       "      <td>308.125246</td>\n",
       "      <td>9.554040e+07</td>\n",
       "      <td>8.66000</td>\n",
       "      <td>1.831000</td>\n",
       "      <td>2.142000</td>\n",
       "      <td>1.993000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>24.735190</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>3562.330943</td>\n",
       "      <td>6472.121785</td>\n",
       "      <td>75.595000</td>\n",
       "      <td>72.263000</td>\n",
       "      <td>73.895000</td>\n",
       "      <td>758.984551</td>\n",
       "      <td>4.569087e+06</td>\n",
       "      <td>-999.00000</td>\n",
       "      <td>41.849998</td>\n",
       "      <td>22.393999</td>\n",
       "      <td>26.256001</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>World</td>\n",
       "      <td>89.588551</td>\n",
       "      <td>40.949620</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.848779</td>\n",
       "      <td>11385.675227</td>\n",
       "      <td>17025.827360</td>\n",
       "      <td>74.871815</td>\n",
       "      <td>70.398743</td>\n",
       "      <td>72.563282</td>\n",
       "      <td>59.617978</td>\n",
       "      <td>7.591945e+09</td>\n",
       "      <td>6.18124</td>\n",
       "      <td>5.580954</td>\n",
       "      <td>5.271615</td>\n",
       "      <td>5.392213</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>39.812622</td>\n",
       "      <td>11.554664</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.934843</td>\n",
       "      <td>1516.390661</td>\n",
       "      <td>3607.304442</td>\n",
       "      <td>66.447000</td>\n",
       "      <td>60.533000</td>\n",
       "      <td>63.510000</td>\n",
       "      <td>23.341479</td>\n",
       "      <td>1.735182e+07</td>\n",
       "      <td>6.54000</td>\n",
       "      <td>12.448000</td>\n",
       "      <td>10.614000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>41.041584</td>\n",
       "      <td>6.625208</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.734331</td>\n",
       "      <td>1683.740577</td>\n",
       "      <td>3206.277079</td>\n",
       "      <td>62.598000</td>\n",
       "      <td>59.501000</td>\n",
       "      <td>61.195000</td>\n",
       "      <td>37.324591</td>\n",
       "      <td>1.443902e+07</td>\n",
       "      <td>4.67000</td>\n",
       "      <td>5.598000</td>\n",
       "      <td>4.555000</td>\n",
       "      <td>5.087000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>434 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           country_name  access_to_electricity_pct  atms_per_100000  \\\n",
       "0           Afghanistan                  97.700000         1.213235   \n",
       "1               Albania                 100.000000        31.714076   \n",
       "2               Algeria                 100.000000         9.130677   \n",
       "3                Angola                  41.962894        19.079250   \n",
       "4            Arab World                  90.283638        27.360620   \n",
       "..                  ...                        ...              ...   \n",
       "429             Vietnam                 100.000000        25.322499   \n",
       "430  West Bank and Gaza                 100.000000        24.735190   \n",
       "431               World                  89.588551        40.949620   \n",
       "432              Zambia                  39.812622        11.554664   \n",
       "433            Zimbabwe                  41.041584         6.625208   \n",
       "\n",
       "     compulsory_education_years  health_expenditure_pct_of_gdp  \\\n",
       "0                           9.0                      11.777236   \n",
       "1                           9.0                       5.010597   \n",
       "2                          10.0                       6.380329   \n",
       "3                           6.0                       2.791503   \n",
       "4                           9.0                       5.561266   \n",
       "..                          ...                            ...   \n",
       "429                        10.0                       5.917897   \n",
       "430                        10.0                    -999.000000   \n",
       "431                        10.0                       9.848779   \n",
       "432                         7.0                       4.934843   \n",
       "433                         7.0                       4.734331   \n",
       "\n",
       "     gdp_per_capita_usd  gdp_per_capita_ppp  life_expectancy_female  \\\n",
       "0            519.884773         2058.383832               65.656000   \n",
       "1           4531.020806        13037.010016               80.148000   \n",
       "2           4111.294110        11737.409353               77.735000   \n",
       "3           4095.812942         7310.901738               63.252000   \n",
       "4           6108.588220        14562.367966               73.493366   \n",
       "..                  ...                 ...                     ...   \n",
       "429         2566.596950         7771.189420               79.442000   \n",
       "430         3562.330943         6472.121785               75.595000   \n",
       "431        11385.675227        17025.827360               74.871815   \n",
       "432         1516.390661         3607.304442               66.447000   \n",
       "433         1683.740577         3206.277079               62.598000   \n",
       "\n",
       "     life_expectancy_male  life_expectancy  population_density    population  \\\n",
       "0               62.701000        64.130000           55.595993  3.629640e+07   \n",
       "1               76.601000        78.333000          104.870693  2.873457e+06   \n",
       "2               75.307000        76.499000           17.377715  4.138920e+07   \n",
       "3               57.677000        60.379000           23.916538  2.981675e+07   \n",
       "4               69.921250        71.622526           36.669804  4.118990e+08   \n",
       "..                    ...              ...                 ...           ...   \n",
       "429             71.207000        75.317000          308.125246  9.554040e+07   \n",
       "430             72.263000        73.895000          758.984551  4.569087e+06   \n",
       "431             70.398743        72.563282           59.617978  7.591945e+09   \n",
       "432             60.533000        63.510000           23.341479  1.735182e+07   \n",
       "433             59.501000        61.195000           37.324591  1.443902e+07   \n",
       "\n",
       "     alcohol_consumption_per_capita  unemployment_rate_female  \\\n",
       "0                        -999.00000                 14.090000   \n",
       "1                        -999.00000                 12.563000   \n",
       "2                        -999.00000                 21.114000   \n",
       "3                        -999.00000                  7.467000   \n",
       "4                        -999.00000                 20.361396   \n",
       "..                              ...                       ...   \n",
       "429                         8.66000                  1.831000   \n",
       "430                      -999.00000                 41.849998   \n",
       "431                         6.18124                  5.580954   \n",
       "432                         6.54000                 12.448000   \n",
       "433                         4.67000                  5.598000   \n",
       "\n",
       "     unemployment_rate_male  unemployment_rate    year country_category  \\\n",
       "0                 10.416000          11.184000  2017.0       DEVELOPING   \n",
       "1                 14.590000          13.750000  2017.0       DEVELOPING   \n",
       "2                 10.021000          11.996000  2017.0       DEVELOPING   \n",
       "3                  6.769000           7.119000  2017.0       DEVELOPING   \n",
       "4                  8.097088          10.641313  2017.0              NaN   \n",
       "..                      ...                ...     ...              ...   \n",
       "429                2.142000           1.993000  2018.0       DEVELOPING   \n",
       "430               22.393999          26.256001  2018.0       DEVELOPING   \n",
       "431                5.271615           5.392213  2018.0              NaN   \n",
       "432               10.614000          11.500000  2018.0       DEVELOPING   \n",
       "433                4.555000           5.087000  2018.0       DEVELOPING   \n",
       "\n",
       "     is_region  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          1.0  \n",
       "..         ...  \n",
       "429        0.0  \n",
       "430        0.0  \n",
       "431        1.0  \n",
       "432        0.0  \n",
       "433        0.0  \n",
       "\n",
       "[434 rows x 19 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 434 entries, 0 to 433\n",
      "Data columns (total 19 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   country_name                    434 non-null    object \n",
      " 1   access_to_electricity_pct       434 non-null    float64\n",
      " 2   atms_per_100000                 434 non-null    float64\n",
      " 3   compulsory_education_years      434 non-null    float64\n",
      " 4   health_expenditure_pct_of_gdp   434 non-null    float64\n",
      " 5   gdp_per_capita_usd              434 non-null    float64\n",
      " 6   gdp_per_capita_ppp              434 non-null    float64\n",
      " 7   life_expectancy_female          434 non-null    float64\n",
      " 8   life_expectancy_male            434 non-null    float64\n",
      " 9   life_expectancy                 434 non-null    float64\n",
      " 10  population_density              434 non-null    float64\n",
      " 11  population                      434 non-null    float64\n",
      " 12  alcohol_consumption_per_capita  434 non-null    float64\n",
      " 13  unemployment_rate_female        434 non-null    float64\n",
      " 14  unemployment_rate_male          434 non-null    float64\n",
      " 15  unemployment_rate               434 non-null    float64\n",
      " 16  year                            434 non-null    float64\n",
      " 17  country_category                282 non-null    object \n",
      " 18  is_region                       434 non-null    float64\n",
      "dtypes: float64(17), object(2)\n",
      "memory usage: 64.5+ KB\n"
     ]
    }
   ],
   "source": [
    "wdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_imp_cat = SimpleImputer(strategy='constant', fill_value='MISSING')\n",
    "imputed_cat = simple_imp_cat.fit_transform(wdi[cat_cols])\n",
    "wdi[cat_cols] = imputed_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 434 entries, 0 to 433\n",
      "Data columns (total 19 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   country_name                    434 non-null    object \n",
      " 1   access_to_electricity_pct       434 non-null    float64\n",
      " 2   atms_per_100000                 434 non-null    float64\n",
      " 3   compulsory_education_years      434 non-null    float64\n",
      " 4   health_expenditure_pct_of_gdp   434 non-null    float64\n",
      " 5   gdp_per_capita_usd              434 non-null    float64\n",
      " 6   gdp_per_capita_ppp              434 non-null    float64\n",
      " 7   life_expectancy_female          434 non-null    float64\n",
      " 8   life_expectancy_male            434 non-null    float64\n",
      " 9   life_expectancy                 434 non-null    float64\n",
      " 10  population_density              434 non-null    float64\n",
      " 11  population                      434 non-null    float64\n",
      " 12  alcohol_consumption_per_capita  434 non-null    float64\n",
      " 13  unemployment_rate_female        434 non-null    float64\n",
      " 14  unemployment_rate_male          434 non-null    float64\n",
      " 15  unemployment_rate               434 non-null    float64\n",
      " 16  year                            434 non-null    float64\n",
      " 17  country_category                434 non-null    object \n",
      " 18  is_region                       434 non-null    float64\n",
      "dtypes: float64(17), object(2)\n",
      "memory usage: 64.5+ KB\n"
     ]
    }
   ],
   "source": [
    "wdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEVELOPING    224\n",
       "MISSING       152\n",
       "DEVELOPED      58\n",
       "Name: country_category, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['country_category'].value_counts(dropna=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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