removing_unnecessary_columns_rows.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi = pd.read_csv('world_development_indicators.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 446 entries, 0 to 445\n",
      "Data columns (total 22 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   country_name                    446 non-null    object \n",
      " 1   access_to_electricity_pct       446 non-null    float64\n",
      " 2   atms_per_100000                 417 non-null    float64\n",
      " 3   compulsory_education_years      424 non-null    float64\n",
      " 4   health_expenditure_pct_of_gdp   439 non-null    float64\n",
      " 5   gdp_per_capita_usd              446 non-null    float64\n",
      " 6   gdp_per_capita_ppp              446 non-null    float64\n",
      " 7   life_expectancy_female          446 non-null    float64\n",
      " 8   life_expectancy_male            446 non-null    float64\n",
      " 9   life_expectancy                 446 non-null    float64\n",
      " 10  population_density              444 non-null    float64\n",
      " 11  population                      446 non-null    float64\n",
      " 12  alcohol_consumption_per_capita  220 non-null    float64\n",
      " 13  unemployment_rate_female        446 non-null    float64\n",
      " 14  unemployment_rate_male          446 non-null    float64\n",
      " 15  unemployment_rate               446 non-null    float64\n",
      " 16  year                            446 non-null    int64  \n",
      " 17  Country Name                    446 non-null    object \n",
      " 18  Country Code                    446 non-null    object \n",
      " 19  country_category                289 non-null    object \n",
      " 20  is_region                       446 non-null    int64  \n",
      " 21  planet                          446 non-null    object \n",
      "dtypes: float64(15), int64(2), object(5)\n",
      "memory usage: 76.8+ KB\n"
     ]
    }
   ],
   "source": [
    "wdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>planet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Earth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Earth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Earth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Earth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Earth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_name Country Name Country Code planet\n",
       "0  Afghanistan  Afghanistan          AFG  Earth\n",
       "1      Albania      Albania          ALB  Earth\n",
       "2      Algeria      Algeria          DZA  Earth\n",
       "3       Angola       Angola          AGO  Earth\n",
       "4   Arab World   Arab World          ARB  Earth"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[['country_name', 'Country Name', 'Country Code', 'planet']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name                      217\n",
       "access_to_electricity_pct         240\n",
       "atms_per_100000                   359\n",
       "compulsory_education_years         16\n",
       "health_expenditure_pct_of_gdp     421\n",
       "gdp_per_capita_usd                430\n",
       "gdp_per_capita_ppp                430\n",
       "life_expectancy_female            390\n",
       "life_expectancy_male              402\n",
       "life_expectancy                   425\n",
       "population_density                428\n",
       "population                        430\n",
       "alcohol_consumption_per_capita    204\n",
       "unemployment_rate_female          422\n",
       "unemployment_rate_male            430\n",
       "unemployment_rate                 428\n",
       "year                                2\n",
       "Country Name                      217\n",
       "Country Code                      217\n",
       "country_category                    2\n",
       "is_region                           2\n",
       "planet                              1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Earth    446\n",
       "Name: planet, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['planet'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Earth    1.0\n",
       "Name: planet, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['planet'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>...</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 Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>country_category</th>\n",
       "      <th>is_region</th>\n",
       "      <th>planet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country_name, access_to_electricity_pct, atms_per_100000, compulsory_education_years, health_expenditure_pct_of_gdp, gdp_per_capita_usd, gdp_per_capita_ppp, life_expectancy_female, life_expectancy_male, life_expectancy, population_density, population, alcohol_consumption_per_capita, unemployment_rate_female, unemployment_rate_male, unemployment_rate, year, Country Name, Country Code, country_category, is_region, planet]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[wdi['country_name'] != wdi['Country Name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>self</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [self, other]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['country_name'].compare(wdi['Country Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>self</th>\n",
       "      <th>other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>UKR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>PHL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>IDN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>COM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>446 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     self other\n",
       "0             Afghanistan   AFG\n",
       "1                 Albania   ALB\n",
       "2                 Algeria   DZA\n",
       "3                  Angola   AGO\n",
       "4              Arab World   ARB\n",
       "..                    ...   ...\n",
       "441               Ukraine   UKR\n",
       "442           Philippines   PHL\n",
       "443             Indonesia   IDN\n",
       "444  United Arab Emirates   ARE\n",
       "445               Comoros   COM\n",
       "\n",
       "[446 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi['country_name'].compare(wdi['Country Code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country_name   Country Code\n",
       "Euro area      EMU             3\n",
       "Nepal          NPL             3\n",
       "Timor-Leste    TLS             3\n",
       "Belarus        BLR             3\n",
       "Ukraine        UKR             3\n",
       "                              ..\n",
       "Guinea         GIN             2\n",
       "Guinea-Bissau  GNB             2\n",
       "Guyana         GUY             2\n",
       "Haiti          HTI             2\n",
       "Zimbabwe       ZWE             2\n",
       "Length: 217, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[['country_name', 'Country Code']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi = wdi.drop(columns=['Country Name', 'Country Code', 'planet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 446 entries, 0 to 445\n",
      "Data columns (total 19 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   country_name                    446 non-null    object \n",
      " 1   access_to_electricity_pct       446 non-null    float64\n",
      " 2   atms_per_100000                 417 non-null    float64\n",
      " 3   compulsory_education_years      424 non-null    float64\n",
      " 4   health_expenditure_pct_of_gdp   439 non-null    float64\n",
      " 5   gdp_per_capita_usd              446 non-null    float64\n",
      " 6   gdp_per_capita_ppp              446 non-null    float64\n",
      " 7   life_expectancy_female          446 non-null    float64\n",
      " 8   life_expectancy_male            446 non-null    float64\n",
      " 9   life_expectancy                 446 non-null    float64\n",
      " 10  population_density              444 non-null    float64\n",
      " 11  population                      446 non-null    float64\n",
      " 12  alcohol_consumption_per_capita  220 non-null    float64\n",
      " 13  unemployment_rate_female        446 non-null    float64\n",
      " 14  unemployment_rate_male          446 non-null    float64\n",
      " 15  unemployment_rate               446 non-null    float64\n",
      " 16  year                            446 non-null    int64  \n",
      " 17  country_category                289 non-null    object \n",
      " 18  is_region                       446 non-null    int64  \n",
      "dtypes: float64(15), int64(2), object(2)\n",
      "memory usage: 66.3+ KB\n"
     ]
    }
   ],
   "source": [
    "wdi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "441     True\n",
       "442     True\n",
       "443     True\n",
       "444     True\n",
       "445     True\n",
       "Length: 446, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<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>434</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>55.667175</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.759748</td>\n",
       "      <td>5761.747120</td>\n",
       "      <td>18280.198919</td>\n",
       "      <td>79.200000</td>\n",
       "      <td>69.300000</td>\n",
       "      <td>74.129268</td>\n",
       "      <td>46.792242</td>\n",
       "      <td>9.498264e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.039000</td>\n",
       "      <td>7.228000</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>South Asia (IDA &amp; IBRD)</td>\n",
       "      <td>89.824336</td>\n",
       "      <td>9.996436</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.459347</td>\n",
       "      <td>1866.159019</td>\n",
       "      <td>5769.175349</td>\n",
       "      <td>70.505723</td>\n",
       "      <td>67.949390</td>\n",
       "      <td>69.171312</td>\n",
       "      <td>375.730176</td>\n",
       "      <td>1.792836e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.448446</td>\n",
       "      <td>5.031694</td>\n",
       "      <td>5.125435</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>80.003647</td>\n",
       "      <td>11.993800</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.336554</td>\n",
       "      <td>1286.405751</td>\n",
       "      <td>3145.480591</td>\n",
       "      <td>71.092000</td>\n",
       "      <td>67.053000</td>\n",
       "      <td>69.007000</td>\n",
       "      <td>83.608675</td>\n",
       "      <td>1.243261e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.017000</td>\n",
       "      <td>3.352000</td>\n",
       "      <td>4.566000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>99.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.239480</td>\n",
       "      <td>826.621531</td>\n",
       "      <td>3313.519458</td>\n",
       "      <td>73.177000</td>\n",
       "      <td>68.727000</td>\n",
       "      <td>70.879000</td>\n",
       "      <td>65.572714</td>\n",
       "      <td>9.100837e+06</td>\n",
       "      <td>3.280000</td>\n",
       "      <td>10.083000</td>\n",
       "      <td>11.759000</td>\n",
       "      <td>11.133000</td>\n",
       "      <td>2018</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Euro area</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>85.639518</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.158286</td>\n",
       "      <td>37140.669853</td>\n",
       "      <td>45788.687122</td>\n",
       "      <td>84.564658</td>\n",
       "      <td>79.433986</td>\n",
       "      <td>81.932215</td>\n",
       "      <td>127.351000</td>\n",
       "      <td>3.411636e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.445836</td>\n",
       "      <td>8.730447</td>\n",
       "      <td>9.060117</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>IDA total</td>\n",
       "      <td>58.911049</td>\n",
       "      <td>7.226120</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.080011</td>\n",
       "      <td>1365.854651</td>\n",
       "      <td>3861.811514</td>\n",
       "      <td>65.931954</td>\n",
       "      <td>62.597892</td>\n",
       "      <td>64.247800</td>\n",
       "      <td>73.040032</td>\n",
       "      <td>1.633482e+09</td>\n",
       "      <td>3.684085</td>\n",
       "      <td>5.188501</td>\n",
       "      <td>4.321692</td>\n",
       "      <td>4.671868</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>92.413193</td>\n",
       "      <td>10.970099</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.488255</td>\n",
       "      <td>911.444267</td>\n",
       "      <td>3099.306082</td>\n",
       "      <td>71.586000</td>\n",
       "      <td>68.710000</td>\n",
       "      <td>70.169000</td>\n",
       "      <td>192.724967</td>\n",
       "      <td>2.762712e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.233000</td>\n",
       "      <td>1.604000</td>\n",
       "      <td>1.396000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>97.386986</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.723917</td>\n",
       "      <td>3096.817402</td>\n",
       "      <td>12629.141661</td>\n",
       "      <td>76.720000</td>\n",
       "      <td>66.690000</td>\n",
       "      <td>71.582683</td>\n",
       "      <td>77.029671</td>\n",
       "      <td>4.462252e+07</td>\n",
       "      <td>8.320000</td>\n",
       "      <td>7.424000</td>\n",
       "      <td>10.029000</td>\n",
       "      <td>8.799000</td>\n",
       "      <td>2018</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>28.135059</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.446060</td>\n",
       "      <td>3123.234229</td>\n",
       "      <td>8120.842309</td>\n",
       "      <td>75.268000</td>\n",
       "      <td>66.971000</td>\n",
       "      <td>70.952000</td>\n",
       "      <td>352.729195</td>\n",
       "      <td>1.051733e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.702000</td>\n",
       "      <td>2.459000</td>\n",
       "      <td>2.552000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>98.510000</td>\n",
       "      <td>54.376093</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.870530</td>\n",
       "      <td>3893.846425</td>\n",
       "      <td>11648.542673</td>\n",
       "      <td>73.748000</td>\n",
       "      <td>69.375000</td>\n",
       "      <td>71.509000</td>\n",
       "      <td>147.752190</td>\n",
       "      <td>2.676634e+08</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>4.251000</td>\n",
       "      <td>4.679000</td>\n",
       "      <td>4.511000</td>\n",
       "      <td>2018</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>64.478076</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.225904</td>\n",
       "      <td>43839.356349</td>\n",
       "      <td>68599.618178</td>\n",
       "      <td>79.164000</td>\n",
       "      <td>77.133000</td>\n",
       "      <td>77.814000</td>\n",
       "      <td>135.609110</td>\n",
       "      <td>9.630959e+06</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>6.187000</td>\n",
       "      <td>1.391000</td>\n",
       "      <td>2.229000</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>79.419693</td>\n",
       "      <td>5.100094</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.648014</td>\n",
       "      <td>1323.811590</td>\n",
       "      <td>3032.254682</td>\n",
       "      <td>65.675000</td>\n",
       "      <td>62.210000</td>\n",
       "      <td>63.912000</td>\n",
       "      <td>437.341200</td>\n",
       "      <td>8.138920e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.748000</td>\n",
       "      <td>4.106000</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                country_name  access_to_electricity_pct  atms_per_100000  \\\n",
       "434                  Belarus                 100.000000        55.667175   \n",
       "435  South Asia (IDA & IBRD)                  89.824336         9.996436   \n",
       "436              Timor-Leste                  80.003647        11.993800   \n",
       "437               Tajikistan                  99.300000              NaN   \n",
       "438                Euro area                 100.000000        85.639518   \n",
       "439                IDA total                  58.911049         7.226120   \n",
       "440                    Nepal                  92.413193        10.970099   \n",
       "441                  Ukraine                 100.000000        97.386986   \n",
       "442              Philippines                  93.000000        28.135059   \n",
       "443                Indonesia                  98.510000        54.376093   \n",
       "444     United Arab Emirates                 100.000000        64.478076   \n",
       "445                  Comoros                  79.419693         5.100094   \n",
       "\n",
       "     compulsory_education_years  health_expenditure_pct_of_gdp  \\\n",
       "434                         9.0                       5.759748   \n",
       "435                         9.5                       3.459347   \n",
       "436                         9.0                       4.336554   \n",
       "437                         9.0                       7.239480   \n",
       "438                        10.0                      10.158286   \n",
       "439                         9.0                       4.080011   \n",
       "440                        10.0                       5.488255   \n",
       "441                        11.0                       7.723917   \n",
       "442                        11.0                       4.446060   \n",
       "443                         9.0                       2.870530   \n",
       "444                         6.0                       4.225904   \n",
       "445                         6.0                       4.648014   \n",
       "\n",
       "     gdp_per_capita_usd  gdp_per_capita_ppp  life_expectancy_female  \\\n",
       "434         5761.747120        18280.198919               79.200000   \n",
       "435         1866.159019         5769.175349               70.505723   \n",
       "436         1286.405751         3145.480591               71.092000   \n",
       "437          826.621531         3313.519458               73.177000   \n",
       "438        37140.669853        45788.687122               84.564658   \n",
       "439         1365.854651         3861.811514               65.931954   \n",
       "440          911.444267         3099.306082               71.586000   \n",
       "441         3096.817402        12629.141661               76.720000   \n",
       "442         3123.234229         8120.842309               75.268000   \n",
       "443         3893.846425        11648.542673               73.748000   \n",
       "444        43839.356349        68599.618178               79.164000   \n",
       "445         1323.811590         3032.254682               65.675000   \n",
       "\n",
       "     life_expectancy_male  life_expectancy  population_density    population  \\\n",
       "434             69.300000        74.129268           46.792242  9.498264e+06   \n",
       "435             67.949390        69.171312          375.730176  1.792836e+09   \n",
       "436             67.053000        69.007000           83.608675  1.243261e+06   \n",
       "437             68.727000        70.879000           65.572714  9.100837e+06   \n",
       "438             79.433986        81.932215          127.351000  3.411636e+08   \n",
       "439             62.597892        64.247800           73.040032  1.633482e+09   \n",
       "440             68.710000        70.169000          192.724967  2.762712e+07   \n",
       "441             66.690000        71.582683           77.029671  4.462252e+07   \n",
       "442             66.971000        70.952000          352.729195  1.051733e+08   \n",
       "443             69.375000        71.509000          147.752190  2.676634e+08   \n",
       "444             77.133000        77.814000          135.609110  9.630959e+06   \n",
       "445             62.210000        63.912000          437.341200  8.138920e+05   \n",
       "\n",
       "     alcohol_consumption_per_capita  unemployment_rate_female  \\\n",
       "434                             NaN                  4.039000   \n",
       "435                             NaN                  5.448446   \n",
       "436                             NaN                  6.017000   \n",
       "437                        3.280000                 10.083000   \n",
       "438                             NaN                  9.445836   \n",
       "439                        3.684085                  5.188501   \n",
       "440                             NaN                  1.233000   \n",
       "441                        8.320000                  7.424000   \n",
       "442                             NaN                  2.702000   \n",
       "443                        0.570000                  4.251000   \n",
       "444                        3.900000                  6.187000   \n",
       "445                             NaN                  4.748000   \n",
       "\n",
       "     unemployment_rate_male  unemployment_rate  year country_category  \\\n",
       "434                7.228000           5.650000  2017              NaN   \n",
       "435                5.031694           5.125435  2017              NaN   \n",
       "436                3.352000           4.566000  2017       DEVELOPING   \n",
       "437               11.759000          11.133000  2018       DEVELOPING   \n",
       "438                8.730447           9.060117  2017              NaN   \n",
       "439                4.321692           4.671868  2018              NaN   \n",
       "440                1.604000           1.396000  2017       DEVELOPING   \n",
       "441               10.029000           8.799000  2018       DEVELOPING   \n",
       "442                2.459000           2.552000  2017       DEVELOPING   \n",
       "443                4.679000           4.511000  2018       DEVELOPING   \n",
       "444                1.391000           2.229000  2018              NaN   \n",
       "445                4.106000           4.375000  2017       DEVELOPING   \n",
       "\n",
       "     is_region  \n",
       "434          0  \n",
       "435          1  \n",
       "436          0  \n",
       "437          0  \n",
       "438          1  \n",
       "439          1  \n",
       "440          0  \n",
       "441          0  \n",
       "442          0  \n",
       "443          0  \n",
       "444          0  \n",
       "445          0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[wdi.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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>NaN</td>\n",
       "      <td>14.090000</td>\n",
       "      <td>10.416000</td>\n",
       "      <td>11.184000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>12.563000</td>\n",
       "      <td>14.590000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>21.114000</td>\n",
       "      <td>10.021000</td>\n",
       "      <td>11.996000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>7.467000</td>\n",
       "      <td>6.769000</td>\n",
       "      <td>7.119000</td>\n",
       "      <td>2017</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>20.361396</td>\n",
       "      <td>8.097088</td>\n",
       "      <td>10.641313</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</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",
       "      <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</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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>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>DEVELOPING</td>\n",
       "      <td>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</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</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</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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</td>\n",
       "      <td>DEVELOPING</td>\n",
       "      <td>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                            NaN   \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                               NaN                 14.090000   \n",
       "1                               NaN                 12.563000   \n",
       "2                               NaN                 21.114000   \n",
       "3                               NaN                  7.467000   \n",
       "4                               NaN                 20.361396   \n",
       "..                              ...                       ...   \n",
       "429                         8.66000                  1.831000   \n",
       "430                             NaN                 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       DEVELOPING   \n",
       "1                 14.590000          13.750000  2017       DEVELOPING   \n",
       "2                 10.021000          11.996000  2017       DEVELOPING   \n",
       "3                  6.769000           7.119000  2017       DEVELOPING   \n",
       "4                  8.097088          10.641313  2017              NaN   \n",
       "..                      ...                ...   ...              ...   \n",
       "429                2.142000           1.993000  2018       DEVELOPING   \n",
       "430               22.393999          26.256001  2018       DEVELOPING   \n",
       "431                5.271615           5.392213  2018              NaN   \n",
       "432               10.614000          11.500000  2018       DEVELOPING   \n",
       "433                4.555000           5.087000  2018       DEVELOPING   \n",
       "\n",
       "     is_region  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            1  \n",
       "..         ...  \n",
       "429          0  \n",
       "430          0  \n",
       "431          1  \n",
       "432          0  \n",
       "433          0  \n",
       "\n",
       "[434 rows x 19 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.drop_duplicates(ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi = wdi.drop_duplicates(ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(434, 19)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [country_name, access_to_electricity_pct, atms_per_100000, compulsory_education_years, health_expenditure_pct_of_gdp, gdp_per_capita_usd, gdp_per_capita_ppp, life_expectancy_female, life_expectancy_male, life_expectancy, population_density, population, alcohol_consumption_per_capita, unemployment_rate_female, unemployment_rate_male, unemployment_rate, year, country_category, is_region]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi[wdi.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi.to_pickle('wdi.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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