taxobservatory-dataset-eda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data For Good - Tax Observatory dataset EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a few exploratory analysis to understand what data we are dealing with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset in a DataFrame\n",
    "df = pd.read_csv('data_final_dataviz.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>mnc</th>\n",
       "      <th>year</th>\n",
       "      <th>empty</th>\n",
       "      <th>sector</th>\n",
       "      <th>upe_code</th>\n",
       "      <th>upe_name</th>\n",
       "      <th>upe_region</th>\n",
       "      <th>upe_tax_haven</th>\n",
       "      <th>upe_lat</th>\n",
       "      <th>upe_lon</th>\n",
       "      <th>...</th>\n",
       "      <th>tax_accrued</th>\n",
       "      <th>tax_paid</th>\n",
       "      <th>employees</th>\n",
       "      <th>unrelated_revenues</th>\n",
       "      <th>related_revenues</th>\n",
       "      <th>stated_capital</th>\n",
       "      <th>accumulated_earnings</th>\n",
       "      <th>tangible_assets</th>\n",
       "      <th>total_revenues</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8597</th>\n",
       "      <td>YARA</td>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>Mining &amp; Extraction</td>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>ACS</td>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>Construction</td>\n",
       "      <td>ESP</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6887</th>\n",
       "      <td>SAIPEM</td>\n",
       "      <td>2021</td>\n",
       "      <td>False</td>\n",
       "      <td>Mining &amp; Extraction</td>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>42.8333</td>\n",
       "      <td>12.8333</td>\n",
       "      <td>...</td>\n",
       "      <td>20200000.0</td>\n",
       "      <td>24300000.0</td>\n",
       "      <td>9293.0</td>\n",
       "      <td>2.045000e+09</td>\n",
       "      <td>224800000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.269800e+09</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>ATLANTIA</td>\n",
       "      <td>2021</td>\n",
       "      <td>False</td>\n",
       "      <td>Transport, Freight &amp; Storage</td>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>42.8333</td>\n",
       "      <td>12.8333</td>\n",
       "      <td>...</td>\n",
       "      <td>923086.0</td>\n",
       "      <td>1060972.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>4.377649e+07</td>\n",
       "      <td>1483296.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2078565.0</td>\n",
       "      <td>4.525979e+07</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>EL CORTE INGLÉS</td>\n",
       "      <td>2022</td>\n",
       "      <td>True</td>\n",
       "      <td>Retail</td>\n",
       "      <td>ESP</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mnc  year  empty                        sector upe_code  \\\n",
       "8597             YARA  2019  False           Mining & Extraction      NOR   \n",
       "114               ACS  2019  False                  Construction      ESP   \n",
       "6887           SAIPEM  2021  False           Mining & Extraction      ITA   \n",
       "1185         ATLANTIA  2021  False  Transport, Freight & Storage      ITA   \n",
       "2349  EL CORTE INGLÉS  2022   True                        Retail      ESP   \n",
       "\n",
       "     upe_name upe_region  upe_tax_haven  upe_lat  upe_lon  ... tax_accrued  \\\n",
       "8597   Norway     Europe          False  62.0000  10.0000  ...         NaN   \n",
       "114     Spain     Europe          False  40.0000  -4.0000  ...         NaN   \n",
       "6887    Italy     Europe          False  42.8333  12.8333  ...  20200000.0   \n",
       "1185    Italy     Europe          False  42.8333  12.8333  ...    923086.0   \n",
       "2349    Spain     Europe          False  40.0000  -4.0000  ...         NaN   \n",
       "\n",
       "        tax_paid employees unrelated_revenues  related_revenues  \\\n",
       "8597         NaN       2.0                NaN               NaN   \n",
       "114    2024000.0       NaN                NaN               NaN   \n",
       "6887  24300000.0    9293.0       2.045000e+09       224800000.0   \n",
       "1185   1060972.0     311.0       4.377649e+07         1483296.0   \n",
       "2349         NaN       NaN                NaN               NaN   \n",
       "\n",
       "      stated_capital  accumulated_earnings  tangible_assets  total_revenues  \\\n",
       "8597             NaN                   NaN              NaN             NaN   \n",
       "114              NaN                   NaN              NaN             NaN   \n",
       "6887             NaN                   NaN              NaN    2.269800e+09   \n",
       "1185             NaN                   NaN        2078565.0    4.525979e+07   \n",
       "2349             NaN                   NaN              NaN             NaN   \n",
       "\n",
       "      currency  \n",
       "8597       EUR  \n",
       "114        EUR  \n",
       "6887       EUR  \n",
       "1185       EUR  \n",
       "2349       EUR  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display sample of data\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of the datset are :\n",
      "- 8846 rows ;\n",
      "- 28 columns.\n"
     ]
    }
   ],
   "source": [
    "# Display dimensions\n",
    "print(f'Dimensions of the datset are :\\n- {df.shape[0]} rows ;\\n- {df.shape[1]} columns.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mnc', 'year', 'empty', 'sector', 'upe_code', 'upe_name', 'upe_region',\n",
       "       'upe_tax_haven', 'upe_lat', 'upe_lon', 'upe_fj', 'jur_code', 'jur_name',\n",
       "       'jur_region', 'jur_tax_haven', 'jur_lat', 'jur_lon',\n",
       "       'profit_before_tax', 'tax_accrued', 'tax_paid', 'employees',\n",
       "       'unrelated_revenues', 'related_revenues', 'stated_capital',\n",
       "       'accumulated_earnings', 'tangible_assets', 'total_revenues',\n",
       "       'currency'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mnc                      object\n",
       "year                      int64\n",
       "empty                      bool\n",
       "sector                   object\n",
       "upe_code                 object\n",
       "upe_name                 object\n",
       "upe_region               object\n",
       "upe_tax_haven              bool\n",
       "upe_lat                 float64\n",
       "upe_lon                 float64\n",
       "upe_fj                   object\n",
       "jur_code                 object\n",
       "jur_name                 object\n",
       "jur_region               object\n",
       "jur_tax_haven              bool\n",
       "jur_lat                 float64\n",
       "jur_lon                 float64\n",
       "profit_before_tax       float64\n",
       "tax_accrued             float64\n",
       "tax_paid                float64\n",
       "employees               float64\n",
       "unrelated_revenues      float64\n",
       "related_revenues        float64\n",
       "stated_capital          float64\n",
       "accumulated_earnings    float64\n",
       "tangible_assets         float64\n",
       "total_revenues          float64\n",
       "currency                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display data types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mnc                      0.0 %\n",
       "year                     0.0 %\n",
       "empty                    0.0 %\n",
       "sector                   0.0 %\n",
       "upe_code                 0.0 %\n",
       "upe_name                 0.0 %\n",
       "upe_region               0.0 %\n",
       "upe_tax_haven            0.0 %\n",
       "upe_lat                  0.0 %\n",
       "upe_lon                  0.0 %\n",
       "upe_fj                   0.0 %\n",
       "jur_code                 0.0 %\n",
       "jur_name                 0.0 %\n",
       "jur_region               2.3 %\n",
       "jur_tax_haven            0.0 %\n",
       "jur_lat                  3.5 %\n",
       "jur_lon                  3.5 %\n",
       "profit_before_tax       13.0 %\n",
       "tax_accrued             37.1 %\n",
       "tax_paid                20.7 %\n",
       "employees               23.5 %\n",
       "unrelated_revenues      47.2 %\n",
       "related_revenues        49.2 %\n",
       "stated_capital          72.6 %\n",
       "accumulated_earnings    64.1 %\n",
       "tangible_assets         46.2 %\n",
       "total_revenues          27.0 %\n",
       "currency                 0.0 %\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate percentage of missing values per column\n",
    "(df.isna().sum() / len(df) * 100).apply(lambda x: '{:.1f} %'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>year</th>\n",
       "      <th>upe_lat</th>\n",
       "      <th>upe_lon</th>\n",
       "      <th>jur_lat</th>\n",
       "      <th>jur_lon</th>\n",
       "      <th>profit_before_tax</th>\n",
       "      <th>tax_accrued</th>\n",
       "      <th>tax_paid</th>\n",
       "      <th>employees</th>\n",
       "      <th>unrelated_revenues</th>\n",
       "      <th>related_revenues</th>\n",
       "      <th>stated_capital</th>\n",
       "      <th>accumulated_earnings</th>\n",
       "      <th>tangible_assets</th>\n",
       "      <th>total_revenues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8846.00</td>\n",
       "      <td>8846.00</td>\n",
       "      <td>8846.00</td>\n",
       "      <td>8535.00</td>\n",
       "      <td>8535.00</td>\n",
       "      <td>7.692000e+03</td>\n",
       "      <td>5.564000e+03</td>\n",
       "      <td>7.012000e+03</td>\n",
       "      <td>6764.00</td>\n",
       "      <td>4.672000e+03</td>\n",
       "      <td>4.494000e+03</td>\n",
       "      <td>2.425000e+03</td>\n",
       "      <td>3.179000e+03</td>\n",
       "      <td>4.763000e+03</td>\n",
       "      <td>6.462000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2020.21</td>\n",
       "      <td>43.39</td>\n",
       "      <td>13.02</td>\n",
       "      <td>27.87</td>\n",
       "      <td>14.34</td>\n",
       "      <td>1.511271e+08</td>\n",
       "      <td>4.276441e+07</td>\n",
       "      <td>4.576455e+07</td>\n",
       "      <td>1377.66</td>\n",
       "      <td>1.743339e+09</td>\n",
       "      <td>7.417463e+08</td>\n",
       "      <td>6.196322e+09</td>\n",
       "      <td>4.313461e+08</td>\n",
       "      <td>9.995024e+08</td>\n",
       "      <td>1.830316e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.31</td>\n",
       "      <td>20.74</td>\n",
       "      <td>41.29</td>\n",
       "      <td>26.98</td>\n",
       "      <td>64.23</td>\n",
       "      <td>2.830960e+09</td>\n",
       "      <td>3.485937e+08</td>\n",
       "      <td>3.604713e+08</td>\n",
       "      <td>4899.42</td>\n",
       "      <td>2.575547e+10</td>\n",
       "      <td>4.786330e+09</td>\n",
       "      <td>7.052096e+10</td>\n",
       "      <td>1.184383e+10</td>\n",
       "      <td>4.443516e+09</td>\n",
       "      <td>8.965564e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2014.00</td>\n",
       "      <td>-30.00</td>\n",
       "      <td>-102.00</td>\n",
       "      <td>-41.00</td>\n",
       "      <td>-159.77</td>\n",
       "      <td>-9.403671e+10</td>\n",
       "      <td>-8.728412e+09</td>\n",
       "      <td>-1.142904e+09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.689594e+08</td>\n",
       "      <td>-2.532630e+08</td>\n",
       "      <td>-6.202200e+07</td>\n",
       "      <td>-2.493958e+11</td>\n",
       "      <td>-1.755843e+06</td>\n",
       "      <td>-2.017610e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2019.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>-3.049762e+05</td>\n",
       "      <td>1.767500e+02</td>\n",
       "      <td>1.354070e+03</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1.544882e+06</td>\n",
       "      <td>4.207121e+04</td>\n",
       "      <td>2.801590e+05</td>\n",
       "      <td>-7.030014e+06</td>\n",
       "      <td>2.436093e+05</td>\n",
       "      <td>5.233032e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2020.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>5.75</td>\n",
       "      <td>38.00</td>\n",
       "      <td>12.83</td>\n",
       "      <td>1.122569e+06</td>\n",
       "      <td>8.039459e+05</td>\n",
       "      <td>8.933127e+05</td>\n",
       "      <td>116.50</td>\n",
       "      <td>3.800000e+07</td>\n",
       "      <td>2.795113e+06</td>\n",
       "      <td>1.378100e+07</td>\n",
       "      <td>8.455230e+05</td>\n",
       "      <td>8.000000e+06</td>\n",
       "      <td>7.542851e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2021.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>12.83</td>\n",
       "      <td>49.75</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2.906625e+07</td>\n",
       "      <td>8.751259e+06</td>\n",
       "      <td>8.944000e+06</td>\n",
       "      <td>793.50</td>\n",
       "      <td>3.741421e+08</td>\n",
       "      <td>5.496840e+07</td>\n",
       "      <td>1.924975e+08</td>\n",
       "      <td>3.860350e+07</td>\n",
       "      <td>1.878199e+08</td>\n",
       "      <td>5.779899e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>138.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>175.00</td>\n",
       "      <td>1.309563e+11</td>\n",
       "      <td>1.077550e+10</td>\n",
       "      <td>1.502801e+10</td>\n",
       "      <td>89539.00</td>\n",
       "      <td>1.642447e+12</td>\n",
       "      <td>8.800072e+10</td>\n",
       "      <td>1.875593e+12</td>\n",
       "      <td>2.276791e+11</td>\n",
       "      <td>1.167106e+11</td>\n",
       "      <td>1.803194e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  upe_lat  upe_lon  jur_lat  jur_lon  profit_before_tax  \\\n",
       "count  8846.00  8846.00  8846.00  8535.00  8535.00       7.692000e+03   \n",
       "mean   2020.21    43.39    13.02    27.87    14.34       1.511271e+08   \n",
       "std       1.31    20.74    41.29    26.98    64.23       2.830960e+09   \n",
       "min    2014.00   -30.00  -102.00   -41.00  -159.77      -9.403671e+10   \n",
       "25%    2019.00    40.00    -2.00    10.00    -8.00      -3.049762e+05   \n",
       "50%    2020.00    47.00     5.75    38.00    12.83       1.122569e+06   \n",
       "75%    2021.00    54.00    12.83    49.75    35.00       2.906625e+07   \n",
       "max    2023.00    64.00   138.00    72.00   175.00       1.309563e+11   \n",
       "\n",
       "        tax_accrued      tax_paid  employees  unrelated_revenues  \\\n",
       "count  5.564000e+03  7.012000e+03    6764.00        4.672000e+03   \n",
       "mean   4.276441e+07  4.576455e+07    1377.66        1.743339e+09   \n",
       "std    3.485937e+08  3.604713e+08    4899.42        2.575547e+10   \n",
       "min   -8.728412e+09 -1.142904e+09       0.00       -4.689594e+08   \n",
       "25%    1.767500e+02  1.354070e+03      14.00        1.544882e+06   \n",
       "50%    8.039459e+05  8.933127e+05     116.50        3.800000e+07   \n",
       "75%    8.751259e+06  8.944000e+06     793.50        3.741421e+08   \n",
       "max    1.077550e+10  1.502801e+10   89539.00        1.642447e+12   \n",
       "\n",
       "       related_revenues  stated_capital  accumulated_earnings  \\\n",
       "count      4.494000e+03    2.425000e+03          3.179000e+03   \n",
       "mean       7.417463e+08    6.196322e+09          4.313461e+08   \n",
       "std        4.786330e+09    7.052096e+10          1.184383e+10   \n",
       "min       -2.532630e+08   -6.202200e+07         -2.493958e+11   \n",
       "25%        4.207121e+04    2.801590e+05         -7.030014e+06   \n",
       "50%        2.795113e+06    1.378100e+07          8.455230e+05   \n",
       "75%        5.496840e+07    1.924975e+08          3.860350e+07   \n",
       "max        8.800072e+10    1.875593e+12          2.276791e+11   \n",
       "\n",
       "       tangible_assets  total_revenues  \n",
       "count     4.763000e+03    6.462000e+03  \n",
       "mean      9.995024e+08    1.830316e+09  \n",
       "std       4.443516e+09    8.965564e+09  \n",
       "min      -1.755843e+06   -2.017610e+09  \n",
       "25%       2.436093e+05    5.233032e+06  \n",
       "50%       8.000000e+06    7.542851e+07  \n",
       "75%       1.878199e+08    5.779899e+08  \n",
       "max       1.167106e+11    1.803194e+11  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate descriptive statistics\n",
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What does the `empty` column mean ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>mnc</th>\n",
       "      <th>year</th>\n",
       "      <th>empty</th>\n",
       "      <th>sector</th>\n",
       "      <th>upe_code</th>\n",
       "      <th>upe_name</th>\n",
       "      <th>upe_region</th>\n",
       "      <th>upe_tax_haven</th>\n",
       "      <th>upe_lat</th>\n",
       "      <th>upe_lon</th>\n",
       "      <th>...</th>\n",
       "      <th>tax_accrued</th>\n",
       "      <th>tax_paid</th>\n",
       "      <th>employees</th>\n",
       "      <th>unrelated_revenues</th>\n",
       "      <th>related_revenues</th>\n",
       "      <th>stated_capital</th>\n",
       "      <th>accumulated_earnings</th>\n",
       "      <th>tangible_assets</th>\n",
       "      <th>total_revenues</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4131</th>\n",
       "      <td>IBEROSTAR</td>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>Travel, Personal &amp; Leisure</td>\n",
       "      <td>ESP</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0000</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>BONAVA</td>\n",
       "      <td>2021</td>\n",
       "      <td>True</td>\n",
       "      <td>Construction</td>\n",
       "      <td>SWE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6836</th>\n",
       "      <td>RIO TINTO</td>\n",
       "      <td>2021</td>\n",
       "      <td>True</td>\n",
       "      <td>Mining &amp; Extraction</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Asia</td>\n",
       "      <td>False</td>\n",
       "      <td>-27.0000</td>\n",
       "      <td>133.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846</th>\n",
       "      <td>HYDRO</td>\n",
       "      <td>2018</td>\n",
       "      <td>True</td>\n",
       "      <td>Metals &amp; Metal Products</td>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7836</th>\n",
       "      <td>TIM</td>\n",
       "      <td>2021</td>\n",
       "      <td>True</td>\n",
       "      <td>Communications</td>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>False</td>\n",
       "      <td>42.8333</td>\n",
       "      <td>12.8333</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EUR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            mnc  year  empty                      sector upe_code   upe_name  \\\n",
       "4131  IBEROSTAR  2020   True  Travel, Personal & Leisure      ESP      Spain   \n",
       "1418     BONAVA  2021   True                Construction      SWE     Sweden   \n",
       "6836  RIO TINTO  2021   True         Mining & Extraction      AUS  Australia   \n",
       "3846      HYDRO  2018   True     Metals & Metal Products      NOR     Norway   \n",
       "7836        TIM  2021   True              Communications      ITA      Italy   \n",
       "\n",
       "     upe_region  upe_tax_haven  upe_lat   upe_lon  ... tax_accrued tax_paid  \\\n",
       "4131     Europe          False  40.0000   -4.0000  ...         NaN      NaN   \n",
       "1418     Europe          False  62.0000   15.0000  ...         NaN      NaN   \n",
       "6836       Asia          False -27.0000  133.0000  ...         NaN      NaN   \n",
       "3846     Europe          False  62.0000   10.0000  ...         NaN      NaN   \n",
       "7836     Europe          False  42.8333   12.8333  ...         NaN      NaN   \n",
       "\n",
       "     employees unrelated_revenues  related_revenues  stated_capital  \\\n",
       "4131       NaN                NaN               NaN             NaN   \n",
       "1418       NaN                NaN               NaN             NaN   \n",
       "6836       NaN                NaN               NaN             NaN   \n",
       "3846       NaN                NaN               NaN             NaN   \n",
       "7836       NaN                NaN               NaN             NaN   \n",
       "\n",
       "      accumulated_earnings  tangible_assets  total_revenues  currency  \n",
       "4131                   NaN              NaN             NaN       EUR  \n",
       "1418                   NaN              NaN             NaN       EUR  \n",
       "6836                   NaN              NaN             NaN       EUR  \n",
       "3846                   NaN              NaN             NaN       EUR  \n",
       "7836                   NaN              NaN             NaN       EUR  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display a sample of True 'empty' columns.\n",
    "df[df['empty'] == True].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems to be rows where all financial information are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with no financial information : 224.\n"
     ]
    }
   ],
   "source": [
    "# List 'financial' columns\n",
    "cols = ['profit_before_tax', 'tax_accrued', 'tax_paid', 'employees',\n",
    "       'unrelated_revenues', 'related_revenues', 'stated_capital',\n",
    "       'accumulated_earnings', 'tangible_assets', 'total_revenues']\n",
    "\n",
    "# Calculate the number of rows where all financial columns are NaN\n",
    "print(f'Number of rows with no financial information : {len(df.loc[df[cols].isna().all(axis='columns')])}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows where \"empty\" columns is True : 224.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of rows where 'empty' is True\n",
    "print(f'Number of rows where \"empty\" columns is True : {len(df.loc[df['empty'] == True])}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Companies, years and countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of uniques values :\n",
      "- companies : 166 ;\n",
      "- years : 10 ;\n",
      "- countries : 29.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of unique companies, years and countries.\n",
    "print('Number of uniques values :')\n",
    "print(f'- companies : {df['mnc'].nunique()} ;')\n",
    "print(f'- years : {df['year'].nunique()} ;')\n",
    "print(f'- countries : {df['upe_name'].nunique()}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List years where we have informations\n",
    "df['year'].sort_values().unique()"
   ]
  }
 ],
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