1a.Data_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<summary>Table of Contents</summary>\n",
    "\n",
    "- [1. First look at dataset](#1-first-look-at-dataset)\n",
    "- [2. Analysis of countries and zones in dataset](#2-analysis-of-countries-and-zones-in-dataset)\n",
    "- [3. Missing values analysis](#3-missing-values-analysis)\n",
    "- [4. Top 5 countries](#4-top-5-countries)\n",
    "- [5. Missing values in top 5 countries](#5-missing-values-in-top-5-countries)\n",
    "- [6. Outliers](#6-outliers)\n",
    "\n",
    "\n",
    "This is the first notebook. It provides an overview to the full dataset. It contains many countries and many columns. Generally, it shows why we selected our 5 countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. First look at dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>AT_load_actual_entsoe_transparency</th>\n",
       "      <th>AT_solar_generation_actual</th>\n",
       "      <th>AT_wind_onshore_generation_actual</th>\n",
       "      <th>BE_load_actual_entsoe_transparency</th>\n",
       "      <th>BE_solar_generation_actual</th>\n",
       "      <th>BE_wind_generation_actual</th>\n",
       "      <th>BE_wind_offshore_generation_actual</th>\n",
       "      <th>BE_wind_onshore_generation_actual</th>\n",
       "      <th>BG_load_actual_entsoe_transparency</th>\n",
       "      <th>...</th>\n",
       "      <th>SE_3_wind_onshore_generation_actual</th>\n",
       "      <th>SE_4_load_actual_entsoe_transparency</th>\n",
       "      <th>SE_4_wind_onshore_generation_actual</th>\n",
       "      <th>SI_load_actual_entsoe_transparency</th>\n",
       "      <th>SI_solar_generation_actual</th>\n",
       "      <th>SI_wind_onshore_generation_actual</th>\n",
       "      <th>SK_load_actual_entsoe_transparency</th>\n",
       "      <th>SK_solar_generation_actual</th>\n",
       "      <th>SK_wind_onshore_generation_actual</th>\n",
       "      <th>UA_load_actual_entsoe_transparency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31 23:00:00+00:00</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 00:00:00+00:00</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9484.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 01:00:00+00:00</td>\n",
       "      <td>5726.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9152.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>734.81</td>\n",
       "      <td>518.66</td>\n",
       "      <td>216.15</td>\n",
       "      <td>4585.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2281.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2728.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 02:00:00+00:00</td>\n",
       "      <td>5347.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8799.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>766.64</td>\n",
       "      <td>529.46</td>\n",
       "      <td>237.18</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2626.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 03:00:00+00:00</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8567.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>733.13</td>\n",
       "      <td>406.94</td>\n",
       "      <td>326.19</td>\n",
       "      <td>4418.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2372.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>983.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2618.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 179 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date  AT_load_actual_entsoe_transparency  \\\n",
       "0 2014-12-31 23:00:00+00:00                                 NaN   \n",
       "1 2015-01-01 00:00:00+00:00                              5946.0   \n",
       "2 2015-01-01 01:00:00+00:00                              5726.0   \n",
       "3 2015-01-01 02:00:00+00:00                              5347.0   \n",
       "4 2015-01-01 03:00:00+00:00                              5249.0   \n",
       "\n",
       "   AT_solar_generation_actual  AT_wind_onshore_generation_actual  \\\n",
       "0                         NaN                                NaN   \n",
       "1                         NaN                               69.0   \n",
       "2                         NaN                               64.0   \n",
       "3                         NaN                               65.0   \n",
       "4                         NaN                               64.0   \n",
       "\n",
       "   BE_load_actual_entsoe_transparency  BE_solar_generation_actual  \\\n",
       "0                                 NaN                         NaN   \n",
       "1                              9484.0                         NaN   \n",
       "2                              9152.0                         NaN   \n",
       "3                              8799.0                         NaN   \n",
       "4                              8567.0                         NaN   \n",
       "\n",
       "   BE_wind_generation_actual  BE_wind_offshore_generation_actual  \\\n",
       "0                        NaN                                 NaN   \n",
       "1                        NaN                                 NaN   \n",
       "2                     734.81                              518.66   \n",
       "3                     766.64                              529.46   \n",
       "4                     733.13                              406.94   \n",
       "\n",
       "   BE_wind_onshore_generation_actual  BG_load_actual_entsoe_transparency  ...  \\\n",
       "0                                NaN                                 NaN  ...   \n",
       "1                                NaN                                 NaN  ...   \n",
       "2                             216.15                              4585.0  ...   \n",
       "3                             237.18                              4440.0  ...   \n",
       "4                             326.19                              4418.0  ...   \n",
       "\n",
       "   SE_3_wind_onshore_generation_actual  SE_4_load_actual_entsoe_transparency  \\\n",
       "0                                  NaN                                   NaN   \n",
       "1                                  NaN                                   NaN   \n",
       "2                                  NaN                                2281.0   \n",
       "3                                  NaN                                2313.0   \n",
       "4                                  NaN                                2372.0   \n",
       "\n",
       "   SE_4_wind_onshore_generation_actual  SI_load_actual_entsoe_transparency  \\\n",
       "0                                  NaN                                 NaN   \n",
       "1                                  NaN                                 NaN   \n",
       "2                                  NaN                             1045.47   \n",
       "3                                  NaN                             1004.79   \n",
       "4                                  NaN                              983.79   \n",
       "\n",
       "   SI_solar_generation_actual  SI_wind_onshore_generation_actual  \\\n",
       "0                         NaN                                NaN   \n",
       "1                         NaN                                NaN   \n",
       "2                         NaN                               1.17   \n",
       "3                         NaN                               1.04   \n",
       "4                         NaN                               1.61   \n",
       "\n",
       "   SK_load_actual_entsoe_transparency  SK_solar_generation_actual  \\\n",
       "0                                 NaN                         NaN   \n",
       "1                                 NaN                         NaN   \n",
       "2                              2728.0                         3.8   \n",
       "3                              2626.0                         3.8   \n",
       "4                              2618.0                         3.8   \n",
       "\n",
       "   SK_wind_onshore_generation_actual  UA_load_actual_entsoe_transparency  \n",
       "0                                NaN                                 NaN  \n",
       "1                                NaN                                 NaN  \n",
       "2                                NaN                                 NaN  \n",
       "3                                NaN                                 NaN  \n",
       "4                                NaN                                 NaN  \n",
       "\n",
       "[5 rows x 179 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import date\n",
    "\n",
    "from utils.descriptive_analysis import plot_missings,count_outliers\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# TODO 1: Only plot for training data\n",
    "# TODO 2: Perform tests only on train data\n",
    "\n",
    "data = pd.read_csv('./datasets/time_series_60min_singleindex.csv')\n",
    "\n",
    "# Prepare date column\n",
    "data = data.rename(columns={'utc_timestamp': 'date'}\n",
    "                   ).drop(['cet_cest_timestamp'], axis=1)\n",
    "\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count columns that contain word load, solar and wind.\n",
    "Wind has the biggest number of columns, because it is divided into offshore and onshore.\n",
    "However, not all countries have offshore wind generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of load columns: 57\n",
      "Number of solar columns: 39\n",
      "Number of wind columns: 82\n"
     ]
    }
   ],
   "source": [
    "cols_load = [col for col in data.columns if 'load' in col]\n",
    "cols_solar = [col for col in data.columns if 'solar' in col]\n",
    "cols_wind = [col for col in data.columns if 'wind' in col]\n",
    "print(f'Number of load columns: {len(cols_load)}\\nNumber of solar columns: {len(cols_solar)}\\nNumber of wind columns: {len(cols_wind)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of countries that contain onshore and offshore generation.\n",
    "They are then summed up to the \"total wind generation\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BE_wind_generation_actual',\n",
       " 'DE_wind_generation_actual',\n",
       " 'DE_50hertz_wind_generation_actual',\n",
       " 'DE_LU_wind_generation_actual',\n",
       " 'DE_tennet_wind_generation_actual',\n",
       " 'DK_wind_generation_actual',\n",
       " 'DK_1_wind_generation_actual',\n",
       " 'DK_2_wind_generation_actual',\n",
       " 'GB_GBN_wind_generation_actual',\n",
       " 'GB_UKM_wind_generation_actual',\n",
       " 'NL_wind_generation_actual',\n",
       " 'PT_wind_generation_actual']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_wind_generation = [col for col in cols_wind if 'wind_generation' in col]\n",
    "cols_wind_generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BE_wind_offshore_generation_actual',\n",
       " 'DE_wind_offshore_generation_actual',\n",
       " 'DE_50hertz_wind_offshore_generation_actual',\n",
       " 'DE_LU_wind_offshore_generation_actual',\n",
       " 'DE_tennet_wind_offshore_generation_actual',\n",
       " 'DK_wind_offshore_generation_actual',\n",
       " 'DK_1_wind_offshore_generation_actual',\n",
       " 'DK_2_wind_offshore_generation_actual',\n",
       " 'GB_GBN_wind_offshore_generation_actual',\n",
       " 'GB_UKM_wind_offshore_generation_actual',\n",
       " 'NL_wind_offshore_generation_actual',\n",
       " 'PT_wind_offshore_generation_actual']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So, they reported as total generation only columns that have onshore and offshore generation\n",
    "cols_wind_offshore_generation = [col for col in cols_wind if 'wind_offshore_generation' in col]\n",
    "cols_wind_offshore_generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate how many mismatches between sum of offshore and onshore, and their respective \"total\" wind generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mismatches for BE: 303\n",
      "Number of mismatches for DE: 12591\n",
      "Number of mismatches for DE_50hertz: 11832\n",
      "Number of mismatches for DE_LU: 4341\n",
      "Number of mismatches for DE_tennet: 12822\n",
      "Number of mismatches for DK_1: 6\n",
      "Number of mismatches for DK_2: 10\n",
      "Number of mismatches for GB_GBN: 13890\n",
      "Number of mismatches for GB_UKM: 12509\n",
      "Number of mismatches for NL: 11766\n",
      "Number of mismatches for PT: 0\n",
      "Total number of rows where the sum is not equal: 80070\n"
     ]
    }
   ],
   "source": [
    "check_sums = ['BE', 'DE', 'DE_50hertz', 'DE_LU', 'DE_tennet', 'DK_1', 'DK_2', 'GB_GBN', 'GB_UKM', 'NL', 'PT']\n",
    "\n",
    "num_mismatch_sum = 0\n",
    "\n",
    "for area in check_sums:\n",
    "    offshore_col = f\"{area}_wind_offshore_generation_actual\"\n",
    "    onshore_col = f\"{area}_wind_onshore_generation_actual\"\n",
    "    generation_col = f\"{area}_wind_generation_actual\"\n",
    "\n",
    "    # Check for missing values in offshore_col and onshore_col\n",
    "    non_missing_values = (~data[offshore_col].isnull() & ~data[onshore_col].isnull())\n",
    "    \n",
    "    # Check whether the sum of offshore_col and onshore_col equals generation_col only when both are non-missing\n",
    "    mismatch_condition = (((data[offshore_col] + data[onshore_col]).round(2) != data[generation_col]) & non_missing_values)\n",
    "    \n",
    "    # Count the number of mismatches\n",
    "    num_mismatch = mismatch_condition.sum()\n",
    "    \n",
    "    print(f\"Number of mismatches for {area}: {num_mismatch}\")\n",
    "    num_mismatch_sum += num_mismatch\n",
    "\n",
    "print(f\"Total number of rows where the sum is not equal: {num_mismatch_sum}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Max difference between sum of onshore and offshore, and their respective \"total\" wind generation column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max difference for BE: 3.000195647473447e-09\n",
      "Min difference for BE: -2.9999682737980038e-09\n",
      "Max difference for DE: 1.0\n",
      "Min difference for DE: -69.0\n",
      "Max difference for DE_50hertz: 1.0\n",
      "Min difference for DE_50hertz: -1.0\n",
      "Max difference for DE_LU: 1.0\n",
      "Min difference for DE_LU: -1.0\n",
      "Max difference for DE_tennet: 1.0\n",
      "Min difference for DE_tennet: -1.0\n",
      "Max difference for DK_1: 2.9999682737980038e-09\n",
      "Min difference for DK_1: -2.9999682737980038e-09\n",
      "Max difference for DK_2: 1.0001031114370562e-09\n",
      "Min difference for DK_2: -1.0001031114370562e-09\n",
      "Max difference for GB_GBN: 1.0\n",
      "Min difference for GB_GBN: -1.0\n",
      "Max difference for GB_UKM: 10.0\n",
      "Min difference for GB_UKM: -1.0\n",
      "Max difference for NL: 1.0\n",
      "Min difference for NL: -1.0\n",
      "Max difference for PT: 4.547473508864641e-13\n",
      "Min difference for PT: -4.547473508864641e-13\n"
     ]
    }
   ],
   "source": [
    "# Iterate over each area\n",
    "for area in check_sums:\n",
    "    offshore_col = f\"{area}_wind_offshore_generation_actual\"\n",
    "    onshore_col = f\"{area}_wind_onshore_generation_actual\"\n",
    "    generation_col = f\"{area}_wind_generation_actual\"\n",
    "    \n",
    "    # Filter the DataFrame based on non-missing values in both offshore and onshore columns\n",
    "    non_missing_values = ~data[[offshore_col, onshore_col]].isnull().any(axis=1)\n",
    "    filtered_data = data[non_missing_values]\n",
    "    \n",
    "    # Calculate the sum of offshore and onshore columns\n",
    "    sum_offshore_onshore = filtered_data[offshore_col] + filtered_data[onshore_col]\n",
    "    \n",
    "    # Calculate the difference between the sum of offshore and onshore and generation columns\n",
    "    diff = sum_offshore_onshore - filtered_data[generation_col]\n",
    "\n",
    "    print(f'Max difference for {area}:', diff.max())\n",
    "    print(f'Min difference for {area}:', diff.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Check, maximum missing values (in offshore or onshore) with \"total\" wind generation missing. \n",
    " They are the same (except GB_UKM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BE max missing in offshore or onshore: 3935, total col: 3935\n",
      "DE max missing in offshore or onshore: 75, total col: 75\n",
      "DE_50hertz max missing in offshore or onshore: 1, total col: 1\n",
      "DE_LU max missing in offshore or onshore: 32855, total col: 32855\n",
      "DE_tennet max missing in offshore or onshore: 1, total col: 1\n",
      "DK_1 max missing in offshore or onshore: 3, total col: 3\n",
      "DK_2 max missing in offshore or onshore: 3, total col: 3\n",
      "GB_GBN max missing in offshore or onshore: 41, total col: 41\n",
      "GB_UKM max missing in offshore or onshore: 128, total col: 129\n",
      "NL max missing in offshore or onshore: 1967, total col: 1967\n",
      "PT max missing in offshore or onshore: 47509, total col: 47509\n"
     ]
    }
   ],
   "source": [
    "for area in check_sums:\n",
    "    max_missing = max(data[f\"{area}_wind_offshore_generation_actual\"].isna().sum(), data[f\"{area}_wind_onshore_generation_actual\"].isna().sum())\n",
    "    ready_col = data[f\"{area}_wind_generation_actual\"].isnull().sum()\n",
    "    print(f\"{area} max missing in offshore or onshore: {max_missing}, total col: {ready_col}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Analysis of countries and zones in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>area ID</th>\n",
       "      <th>ISO 3166-1 alpha-2</th>\n",
       "      <th>MapCode ENTSO-E</th>\n",
       "      <th>primary AreaName ENTSO-E</th>\n",
       "      <th>primary concept</th>\n",
       "      <th>EU memberstate</th>\n",
       "      <th>EU inside</th>\n",
       "      <th>15min</th>\n",
       "      <th>30min</th>\n",
       "      <th>60min</th>\n",
       "      <th>...</th>\n",
       "      <th>control area</th>\n",
       "      <th>market balance area</th>\n",
       "      <th>load from ENTSO-E TP</th>\n",
       "      <th>generation from ENTSOE-TP</th>\n",
       "      <th>price from ENTSO-E TP</th>\n",
       "      <th>generation from TSO</th>\n",
       "      <th>price from TSO</th>\n",
       "      <th>load from ENTSO-E data portal</th>\n",
       "      <th>load from TSO</th>\n",
       "      <th>Capacity from OPSD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT</td>\n",
       "      <td>AT</td>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>country</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>APG</td>\n",
       "      <td>APG</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BA</td>\n",
       "      <td>BA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>country</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>NOS BiH</td>\n",
       "      <td>NOS BiH</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>BE</td>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>country</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>Elia</td>\n",
       "      <td>Elia</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>BG</td>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>country</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>ESO</td>\n",
       "      <td>ESO</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CH</td>\n",
       "      <td>CH</td>\n",
       "      <td>CH</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>country</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>swissgrid</td>\n",
       "      <td>swissgrid</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  area ID ISO 3166-1 alpha-2 MapCode ENTSO-E primary AreaName ENTSO-E  \\\n",
       "0      AT                 AT              AT                  Austria   \n",
       "1      BA                 BA             NaN                      NaN   \n",
       "2      BE                 BE              BE                  Belgium   \n",
       "3      BG                 BG              BG                 Bulgaria   \n",
       "4      CH                 CH              CH              Switzerland   \n",
       "\n",
       "  primary concept  EU memberstate  EU inside  15min  30min 60min  ...  \\\n",
       "0         country            True       True   True  False  True  ...   \n",
       "1         country           False      False  False  False  True  ...   \n",
       "2         country            True       True  False  False  True  ...   \n",
       "3         country            True       True  False  False  True  ...   \n",
       "4         country           False      False  False  False  True  ...   \n",
       "\n",
       "  control area market balance area load from ENTSO-E TP  \\\n",
       "0          APG                 APG                 True   \n",
       "1      NOS BiH             NOS BiH                False   \n",
       "2         Elia                Elia                 True   \n",
       "3          ESO                 ESO                 True   \n",
       "4    swissgrid           swissgrid                 True   \n",
       "\n",
       "  generation from ENTSOE-TP price from ENTSO-E TP generation from TSO  \\\n",
       "0                      True                  True                True   \n",
       "1                     False                 False               False   \n",
       "2                      True                 False               False   \n",
       "3                      True                 False               False   \n",
       "4                      True                 False               False   \n",
       "\n",
       "  price from TSO  load from ENTSO-E data portal  load from TSO  \\\n",
       "0          False                           True          False   \n",
       "1          False                           True          False   \n",
       "2          False                           True          False   \n",
       "3          False                           True          False   \n",
       "4          False                           True          False   \n",
       "\n",
       "   Capacity from OPSD  \n",
       "0               False  \n",
       "1               False  \n",
       "2               False  \n",
       "3               False  \n",
       "4               False  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_areas = pd.read_csv('./datasets/areas.csv')\n",
    "data_areas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains the data on countries level, as well as other levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['country', 'control area', 'bidding zone', 'region',\n",
       "       'market balance area', 'bidding zone aggregation'], dtype=object)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_areas['primary concept'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "82 unique area IDs in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_areas['area ID'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the ENTSO-E areas and their IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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",
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       "    }\n",
       "\n",
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       "        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>area ID</th>\n",
       "      <th>primary AreaName ENTSO-E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CH</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CY</td>\n",
       "      <td>Cyprus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CZ</td>\n",
       "      <td>Czech Republic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DK</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EE</td>\n",
       "      <td>Estonia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ES</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FI</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GB_UKM</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GB_GBN</td>\n",
       "      <td>National Grid BZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GB_NIR</td>\n",
       "      <td>SONI CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GR</td>\n",
       "      <td>Greece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HR</td>\n",
       "      <td>Croatia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HU</td>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>IE</td>\n",
       "      <td>Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>IT</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LT</td>\n",
       "      <td>Lithuania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LU</td>\n",
       "      <td>Luxembourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LV</td>\n",
       "      <td>Latvia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ME</td>\n",
       "      <td>Montenegro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MK</td>\n",
       "      <td>Former Yugoslav Republic of Macedonia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NO</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PL</td>\n",
       "      <td>Poland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PT</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RO</td>\n",
       "      <td>Romania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>RS</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SE</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SI</td>\n",
       "      <td>Slovenia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MT</td>\n",
       "      <td>Malta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>UA</td>\n",
       "      <td>Ukraine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area ID               primary AreaName ENTSO-E\n",
       "0       AT                                Austria\n",
       "1       BE                                Belgium\n",
       "2       BG                               Bulgaria\n",
       "3       CH                            Switzerland\n",
       "4       CY                                 Cyprus\n",
       "5       CZ                         Czech Republic\n",
       "6       DE                                Germany\n",
       "7       DK                                Denmark\n",
       "8       EE                                Estonia\n",
       "9       ES                                  Spain\n",
       "10      FI                                Finland\n",
       "11      FR                                 France\n",
       "12  GB_UKM                         United Kingdom\n",
       "13  GB_GBN                       National Grid BZ\n",
       "14  GB_NIR                                SONI CA\n",
       "15      GR                                 Greece\n",
       "16      HR                                Croatia\n",
       "17      HU                                Hungary\n",
       "18      IE                                Ireland\n",
       "19      IT                                  Italy\n",
       "20      LT                              Lithuania\n",
       "21      LU                             Luxembourg\n",
       "22      LV                                 Latvia\n",
       "23      ME                             Montenegro\n",
       "24      MK  Former Yugoslav Republic of Macedonia\n",
       "25      NL                            Netherlands\n",
       "26      NO                                 Norway\n",
       "27      PL                                 Poland\n",
       "28      PT                               Portugal\n",
       "29      RO                                Romania\n",
       "30      RS                                 Serbia\n",
       "31      SE                                 Sweden\n",
       "32      SI                               Slovenia\n",
       "33      SK                               Slovakia\n",
       "34      MT                                  Malta\n",
       "35      UA                                Ukraine"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_areas_filtered = data_areas.query('`primary concept`==\"country\" and `primary AreaName ENTSO-E`.notnull()')[['area ID', 'primary AreaName ENTSO-E']].reset_index(drop=True)\n",
    "data_areas_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BA', 'CS', 'DE_50hertz', 'DE_amprion', 'DE_tennet',\n",
       "       'DE_transnetbw', 'DE_AT_LU', 'DK_1', 'DK_2', 'DK_energinet',\n",
       "       'GB_EAW', 'GB_SCT', 'IE_sem', 'IS', 'IT_BRNN', 'IT_CNOR',\n",
       "       'IT_CSUD', 'IT_FOGN', 'IT_GR', 'IT_NORD', 'IT_NORD_AT',\n",
       "       'IT_NORD_CH', 'IT_NORD_FR', 'IT_NORD_SI', 'IT_PRGP', 'IT_ROSN',\n",
       "       'IT_SACO_AC', 'IT_SACO_DC', 'IT_SARD', 'IT_SICI', 'IT_SUD', 'NO_1',\n",
       "       'NO_2', 'NO_3', 'NO_4', 'NO_5', 'SE_1', 'SE_2', 'SE_3', 'SE_4',\n",
       "       'AL', 'DE_LU', 'TR', 'UA_west', 'UA_east'], dtype=object)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query that does not exclude Luxembourg, UA and MT\n",
    "data_filter = data_areas.query(\n",
    "    '(`primary concept`!=\"country\" \\\n",
    "      or `primary AreaName ENTSO-E`.isnull()) \\\n",
    "      and `area ID`.notnull()')['area ID'].unique()\n",
    "data_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exclude non-countries\n",
    "v_to_delete = ['GB_NIR', 'GB_GBN']\n",
    "\n",
    "# to keep ttrack of countries in dataset\n",
    "data_areas_filtered = data_areas_filtered[~data_areas_filtered['area ID'].isin(v_to_delete)].reset_index(drop=True)\n",
    "data_filter = np.append(data_filter, v_to_delete) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnVals = data.columns.map(lambda x: x.startswith(tuple(data_filter)))\n",
    "filtered_data = data.loc[:, ~columnVals].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>date</th>\n",
       "      <th>AT_load_actual_entsoe_transparency</th>\n",
       "      <th>AT_solar_generation_actual</th>\n",
       "      <th>AT_wind_onshore_generation_actual</th>\n",
       "      <th>BE_load_actual_entsoe_transparency</th>\n",
       "      <th>BE_solar_generation_actual</th>\n",
       "      <th>BE_wind_generation_actual</th>\n",
       "      <th>BE_wind_offshore_generation_actual</th>\n",
       "      <th>BE_wind_onshore_generation_actual</th>\n",
       "      <th>BG_load_actual_entsoe_transparency</th>\n",
       "      <th>...</th>\n",
       "      <th>RS_load_actual_entsoe_transparency</th>\n",
       "      <th>SE_load_actual_entsoe_transparency</th>\n",
       "      <th>SE_wind_onshore_generation_actual</th>\n",
       "      <th>SI_load_actual_entsoe_transparency</th>\n",
       "      <th>SI_solar_generation_actual</th>\n",
       "      <th>SI_wind_onshore_generation_actual</th>\n",
       "      <th>SK_load_actual_entsoe_transparency</th>\n",
       "      <th>SK_solar_generation_actual</th>\n",
       "      <th>SK_wind_onshore_generation_actual</th>\n",
       "      <th>UA_load_actual_entsoe_transparency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31 23:00:00+00:00</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 00:00:00+00:00</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9484.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 01:00:00+00:00</td>\n",
       "      <td>5726.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9152.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>734.81</td>\n",
       "      <td>518.66</td>\n",
       "      <td>216.15</td>\n",
       "      <td>4585.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14485.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2728.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 02:00:00+00:00</td>\n",
       "      <td>5347.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8799.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>766.64</td>\n",
       "      <td>529.46</td>\n",
       "      <td>237.18</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14298.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2626.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 03:00:00+00:00</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8567.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>733.13</td>\n",
       "      <td>406.94</td>\n",
       "      <td>326.19</td>\n",
       "      <td>4418.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>983.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2618.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date  AT_load_actual_entsoe_transparency  \\\n",
       "0 2014-12-31 23:00:00+00:00                                 NaN   \n",
       "1 2015-01-01 00:00:00+00:00                              5946.0   \n",
       "2 2015-01-01 01:00:00+00:00                              5726.0   \n",
       "3 2015-01-01 02:00:00+00:00                              5347.0   \n",
       "4 2015-01-01 03:00:00+00:00                              5249.0   \n",
       "\n",
       "   AT_solar_generation_actual  AT_wind_onshore_generation_actual  \\\n",
       "0                         NaN                                NaN   \n",
       "1                         NaN                               69.0   \n",
       "2                         NaN                               64.0   \n",
       "3                         NaN                               65.0   \n",
       "4                         NaN                               64.0   \n",
       "\n",
       "   BE_load_actual_entsoe_transparency  BE_solar_generation_actual  \\\n",
       "0                                 NaN                         NaN   \n",
       "1                              9484.0                         NaN   \n",
       "2                              9152.0                         NaN   \n",
       "3                              8799.0                         NaN   \n",
       "4                              8567.0                         NaN   \n",
       "\n",
       "   BE_wind_generation_actual  BE_wind_offshore_generation_actual  \\\n",
       "0                        NaN                                 NaN   \n",
       "1                        NaN                                 NaN   \n",
       "2                     734.81                              518.66   \n",
       "3                     766.64                              529.46   \n",
       "4                     733.13                              406.94   \n",
       "\n",
       "   BE_wind_onshore_generation_actual  BG_load_actual_entsoe_transparency  ...  \\\n",
       "0                                NaN                                 NaN  ...   \n",
       "1                                NaN                                 NaN  ...   \n",
       "2                             216.15                              4585.0  ...   \n",
       "3                             237.18                              4440.0  ...   \n",
       "4                             326.19                              4418.0  ...   \n",
       "\n",
       "   RS_load_actual_entsoe_transparency  SE_load_actual_entsoe_transparency  \\\n",
       "0                                 NaN                                 NaN   \n",
       "1                                 NaN                                 NaN   \n",
       "2                                 NaN                             14485.0   \n",
       "3                                 NaN                             14298.0   \n",
       "4                                 NaN                             14249.0   \n",
       "\n",
       "   SE_wind_onshore_generation_actual  SI_load_actual_entsoe_transparency  \\\n",
       "0                                NaN                                 NaN   \n",
       "1                                NaN                                 NaN   \n",
       "2                                NaN                             1045.47   \n",
       "3                                NaN                             1004.79   \n",
       "4                                NaN                              983.79   \n",
       "\n",
       "   SI_solar_generation_actual  SI_wind_onshore_generation_actual  \\\n",
       "0                         NaN                                NaN   \n",
       "1                         NaN                                NaN   \n",
       "2                         NaN                               1.17   \n",
       "3                         NaN                               1.04   \n",
       "4                         NaN                               1.61   \n",
       "\n",
       "   SK_load_actual_entsoe_transparency  SK_solar_generation_actual  \\\n",
       "0                                 NaN                         NaN   \n",
       "1                                 NaN                         NaN   \n",
       "2                              2728.0                         3.8   \n",
       "3                              2626.0                         3.8   \n",
       "4                              2618.0                         3.8   \n",
       "\n",
       "   SK_wind_onshore_generation_actual  UA_load_actual_entsoe_transparency  \n",
       "0                                NaN                                 NaN  \n",
       "1                                NaN                                 NaN  \n",
       "2                                NaN                                 NaN  \n",
       "3                                NaN                                 NaN  \n",
       "4                                NaN                                 NaN  \n",
       "\n",
       "[5 rows x 96 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many countries have wind_generation and offshore generation columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BE_wind_generation_actual',\n",
       " 'DE_wind_generation_actual',\n",
       " 'DK_wind_generation_actual',\n",
       " 'GB_UKM_wind_generation_actual',\n",
       " 'NL_wind_generation_actual',\n",
       " 'PT_wind_generation_actual']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col for col in filtered_data.columns if 'wind_generation' in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BE_wind_offshore_generation_actual',\n",
       " 'DE_wind_offshore_generation_actual',\n",
       " 'DK_wind_offshore_generation_actual',\n",
       " 'GB_UKM_wind_offshore_generation_actual',\n",
       " 'NL_wind_offshore_generation_actual',\n",
       " 'PT_wind_offshore_generation_actual']"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[col for col in filtered_data.columns if 'wind_offshore_generation' in col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Missing values analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We saw earlier that not all the countries have offshore generation, but just few of them.\n",
    "\n",
    "From the dataset we can see that Portugal has 94% of missings in those columns, leaving us with small period of observations.\n",
    "Therefore, we can exclude analysis of these columns in Portugal.\n",
    "\n",
    "2. As we can see, HR(Croatia) has almost 100% missing values in solar and wind_onshore columns. -> Exclude this country from analysis.\n",
    "\n",
    "We can also exclude Poland, Hungary, Slovakia, Montenegro, Ukraine and Cyprus, because they have more than 30% of missings.\n",
    "\n",
    "3. We want to have \"consistent\" countries, in other words, with the same columns and same time period.\n",
    "\n",
    "In general, this part just shows the logic we followed to select the countries for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HR_solar_generation_actual            99.980159\n",
      "HR_wind_onshore_generation_actual     99.956350\n",
      "PT_wind_offshore_generation_actual    94.262019\n",
      "PT_wind_generation_actual             94.262019\n",
      "PL_solar_generation_actual            91.738259\n",
      "HU_solar_generation_actual            82.212655\n",
      "SK_wind_onshore_generation_actual     80.796016\n",
      "ME_wind_onshore_generation_actual     59.405567\n",
      "UA_load_actual_entsoe_transparency    49.137914\n",
      "CY_load_actual_entsoe_transparency    34.662011\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Missing values\n",
    "missing_values = filtered_data.isnull()\n",
    "\n",
    "# Calculate percentage of missing values in each column\n",
    "percentage_missing = (missing_values.sum() / len(filtered_data)) * 100\n",
    "\n",
    "# Print or inspect the percentage of missing values\n",
    "print(percentage_missing[percentage_missing >= 30].sort_values(ascending=False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_missing[percentage_missing >= 30].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_to_delete = ['HR', 'PL', 'HU', 'SK', 'ME', 'UA', 'CY']\n",
    "\n",
    "# to keep ttrack of countries in dataset\n",
    "data_areas_filtered = data_areas_filtered[~data_areas_filtered['area ID'].isin(v_to_delete)].reset_index(drop=True)\n",
    "columnVals = filtered_data.columns.map(lambda x: x.startswith(tuple(v_to_delete)))\n",
    "filtered_data = filtered_data.loc[:, ~columnVals].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>date</th>\n",
       "      <th>AT_load_actual_entsoe_transparency</th>\n",
       "      <th>AT_solar_generation_actual</th>\n",
       "      <th>AT_wind_onshore_generation_actual</th>\n",
       "      <th>BE_load_actual_entsoe_transparency</th>\n",
       "      <th>BE_solar_generation_actual</th>\n",
       "      <th>BE_wind_generation_actual</th>\n",
       "      <th>BE_wind_offshore_generation_actual</th>\n",
       "      <th>BE_wind_onshore_generation_actual</th>\n",
       "      <th>BG_load_actual_entsoe_transparency</th>\n",
       "      <th>...</th>\n",
       "      <th>PT_wind_onshore_generation_actual</th>\n",
       "      <th>RO_load_actual_entsoe_transparency</th>\n",
       "      <th>RO_solar_generation_actual</th>\n",
       "      <th>RO_wind_onshore_generation_actual</th>\n",
       "      <th>RS_load_actual_entsoe_transparency</th>\n",
       "      <th>SE_load_actual_entsoe_transparency</th>\n",
       "      <th>SE_wind_onshore_generation_actual</th>\n",
       "      <th>SI_load_actual_entsoe_transparency</th>\n",
       "      <th>SI_solar_generation_actual</th>\n",
       "      <th>SI_wind_onshore_generation_actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31 23:00:00+00:00</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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 00:00:00+00:00</td>\n",
       "      <td>5946.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>9484.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 01:00:00+00:00</td>\n",
       "      <td>5726.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9152.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>734.81</td>\n",
       "      <td>518.66</td>\n",
       "      <td>216.15</td>\n",
       "      <td>4585.0</td>\n",
       "      <td>...</td>\n",
       "      <td>551.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14485.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 02:00:00+00:00</td>\n",
       "      <td>5347.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8799.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>766.64</td>\n",
       "      <td>529.46</td>\n",
       "      <td>237.18</td>\n",
       "      <td>4440.0</td>\n",
       "      <td>...</td>\n",
       "      <td>596.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14298.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 03:00:00+00:00</td>\n",
       "      <td>5249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8567.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>733.13</td>\n",
       "      <td>406.94</td>\n",
       "      <td>326.19</td>\n",
       "      <td>4418.0</td>\n",
       "      <td>...</td>\n",
       "      <td>706.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>983.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date  AT_load_actual_entsoe_transparency  \\\n",
       "0 2014-12-31 23:00:00+00:00                                 NaN   \n",
       "1 2015-01-01 00:00:00+00:00                              5946.0   \n",
       "2 2015-01-01 01:00:00+00:00                              5726.0   \n",
       "3 2015-01-01 02:00:00+00:00                              5347.0   \n",
       "4 2015-01-01 03:00:00+00:00                              5249.0   \n",
       "\n",
       "   AT_solar_generation_actual  AT_wind_onshore_generation_actual  \\\n",
       "0                         NaN                                NaN   \n",
       "1                         NaN                               69.0   \n",
       "2                         NaN                               64.0   \n",
       "3                         NaN                               65.0   \n",
       "4                         NaN                               64.0   \n",
       "\n",
       "   BE_load_actual_entsoe_transparency  BE_solar_generation_actual  \\\n",
       "0                                 NaN                         NaN   \n",
       "1                              9484.0                         NaN   \n",
       "2                              9152.0                         NaN   \n",
       "3                              8799.0                         NaN   \n",
       "4                              8567.0                         NaN   \n",
       "\n",
       "   BE_wind_generation_actual  BE_wind_offshore_generation_actual  \\\n",
       "0                        NaN                                 NaN   \n",
       "1                        NaN                                 NaN   \n",
       "2                     734.81                              518.66   \n",
       "3                     766.64                              529.46   \n",
       "4                     733.13                              406.94   \n",
       "\n",
       "   BE_wind_onshore_generation_actual  BG_load_actual_entsoe_transparency  ...  \\\n",
       "0                                NaN                                 NaN  ...   \n",
       "1                                NaN                                 NaN  ...   \n",
       "2                             216.15                              4585.0  ...   \n",
       "3                             237.18                              4440.0  ...   \n",
       "4                             326.19                              4418.0  ...   \n",
       "\n",
       "   PT_wind_onshore_generation_actual  RO_load_actual_entsoe_transparency  \\\n",
       "0                                NaN                                 NaN   \n",
       "1                                NaN                                 NaN   \n",
       "2                              551.0                                 NaN   \n",
       "3                              596.5                                 NaN   \n",
       "4                              706.3                                 NaN   \n",
       "\n",
       "   RO_solar_generation_actual  RO_wind_onshore_generation_actual  \\\n",
       "0                         NaN                                NaN   \n",
       "1                         NaN                                NaN   \n",
       "2                         NaN                                NaN   \n",
       "3                         NaN                                NaN   \n",
       "4                         NaN                                NaN   \n",
       "\n",
       "   RS_load_actual_entsoe_transparency  SE_load_actual_entsoe_transparency  \\\n",
       "0                                 NaN                                 NaN   \n",
       "1                                 NaN                                 NaN   \n",
       "2                                 NaN                             14485.0   \n",
       "3                                 NaN                             14298.0   \n",
       "4                                 NaN                             14249.0   \n",
       "\n",
       "   SE_wind_onshore_generation_actual  SI_load_actual_entsoe_transparency  \\\n",
       "0                                NaN                                 NaN   \n",
       "1                                NaN                                 NaN   \n",
       "2                                NaN                             1045.47   \n",
       "3                                NaN                             1004.79   \n",
       "4                                NaN                              983.79   \n",
       "\n",
       "   SI_solar_generation_actual  SI_wind_onshore_generation_actual  \n",
       "0                         NaN                                NaN  \n",
       "1                         NaN                                NaN  \n",
       "2                         NaN                               1.17  \n",
       "3                         NaN                               1.04  \n",
       "4                         NaN                               1.61  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove PT wind_generation, PT wind_offshore_generation columns \n",
    "filtered_data = filtered_data.drop(columns=['PT_wind_generation_actual', 'PT_wind_offshore_generation_actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "27 countries left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>area ID</th>\n",
       "      <th>primary AreaName ENTSO-E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CH</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ</td>\n",
       "      <td>Czech Republic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DK</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EE</td>\n",
       "      <td>Estonia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ES</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FI</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GB_UKM</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GR</td>\n",
       "      <td>Greece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IE</td>\n",
       "      <td>Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IT</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LT</td>\n",
       "      <td>Lithuania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LU</td>\n",
       "      <td>Luxembourg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LV</td>\n",
       "      <td>Latvia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MK</td>\n",
       "      <td>Former Yugoslav Republic of Macedonia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NO</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PT</td>\n",
       "      <td>Portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RO</td>\n",
       "      <td>Romania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RS</td>\n",
       "      <td>Serbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SE</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SI</td>\n",
       "      <td>Slovenia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MT</td>\n",
       "      <td>Malta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area ID               primary AreaName ENTSO-E\n",
       "0       AT                                Austria\n",
       "1       BE                                Belgium\n",
       "2       BG                               Bulgaria\n",
       "3       CH                            Switzerland\n",
       "4       CZ                         Czech Republic\n",
       "5       DE                                Germany\n",
       "6       DK                                Denmark\n",
       "7       EE                                Estonia\n",
       "8       ES                                  Spain\n",
       "9       FI                                Finland\n",
       "10      FR                                 France\n",
       "11  GB_UKM                         United Kingdom\n",
       "12      GR                                 Greece\n",
       "13      IE                                Ireland\n",
       "14      IT                                  Italy\n",
       "15      LT                              Lithuania\n",
       "16      LU                             Luxembourg\n",
       "17      LV                                 Latvia\n",
       "18      MK  Former Yugoslav Republic of Macedonia\n",
       "19      NL                            Netherlands\n",
       "20      NO                                 Norway\n",
       "21      PT                               Portugal\n",
       "22      RO                                Romania\n",
       "23      RS                                 Serbia\n",
       "24      SE                                 Sweden\n",
       "25      SI                               Slovenia\n",
       "26      MT                                  Malta"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_areas_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of load columns: 25\n",
      "Number of solar generation columns: 18\n",
      "Number of wind generation columns: 33\n"
     ]
    }
   ],
   "source": [
    "cols_load = [col for col in filtered_data.columns if 'load' in col]\n",
    "cols_solar_generation = [col for col in filtered_data.columns if 'solar' in col]\n",
    "cols_wind_generation = [col for col in filtered_data.columns if 'wind' in col]\n",
    "print(f'Number of load columns: {len(cols_load)}\\nNumber of solar generation columns: {len(cols_solar_generation)}\\nNumber of wind generation columns: {len(cols_wind_generation)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Top 5 countries\n",
    "\n",
    "Wind onshore: Germany, Spain, France, Britain, Italy\n",
    "\n",
    "Solar: Germany, Italy, Spain, France, Britain\n",
    "\n",
    "Load: Germany, France, Britain, Italy, Spain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE_wind_onshore_generation_actual        99621663.0\n",
       "ES_wind_onshore_generation_actual        52348168.0\n",
       "FR_wind_onshore_generation_actual        32697605.0\n",
       "GB_UKM_wind_onshore_generation_actual    27241553.0\n",
       "IT_wind_onshore_generation_actual        19937169.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only 2019\n",
    "# Because it is the most recent full year with observations\n",
    "data_2019 = filtered_data[filtered_data['date'].dt.year == 2019]\n",
    "wind_onshore = data_2019[[col for col in data_2019.columns if 'wind_onshore_generation' in col]].copy()\n",
    "wind_onshore.sum(axis=0).sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE_solar_generation_actual        41914747.0\n",
       "IT_solar_generation_actual        19084518.0\n",
       "ES_solar_generation_actual        14421347.0\n",
       "FR_solar_generation_actual        11420469.0\n",
       "GB_UKM_solar_generation_actual    11012711.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar = data_2019[[col for col in data_2019.columns if 'solar' in col]].copy()\n",
    "solar.sum(axis=0).sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE_load_actual_entsoe_transparency        490474666.0\n",
       "FR_load_actual_entsoe_transparency        467369654.0\n",
       "GB_UKM_load_actual_entsoe_transparency    303731763.0\n",
       "IT_load_actual_entsoe_transparency        294149910.0\n",
       "ES_load_actual_entsoe_transparency        249988215.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar = data_2019[[col for col in data_2019.columns if 'load' in col]].copy()\n",
    "solar.sum(axis=0).sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_countries = ['DE', 'GB', 'ES', 'FR', 'IT']\n",
    "prefixes_to_keep = [col for col in filtered_data.columns if col.startswith(tuple(top_5_countries))]\n",
    "prefixes_to_keep.insert(0, 'date')\n",
    "df_top_5 = filtered_data[prefixes_to_keep].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>date</th>\n",
       "      <th>DE_load_actual_entsoe_transparency</th>\n",
       "      <th>DE_solar_generation_actual</th>\n",
       "      <th>DE_wind_generation_actual</th>\n",
       "      <th>DE_wind_offshore_generation_actual</th>\n",
       "      <th>DE_wind_onshore_generation_actual</th>\n",
       "      <th>ES_load_actual_entsoe_transparency</th>\n",
       "      <th>ES_solar_generation_actual</th>\n",
       "      <th>ES_wind_onshore_generation_actual</th>\n",
       "      <th>FR_load_actual_entsoe_transparency</th>\n",
       "      <th>FR_solar_generation_actual</th>\n",
       "      <th>FR_wind_onshore_generation_actual</th>\n",
       "      <th>GB_UKM_load_actual_entsoe_transparency</th>\n",
       "      <th>GB_UKM_solar_generation_actual</th>\n",
       "      <th>GB_UKM_wind_generation_actual</th>\n",
       "      <th>GB_UKM_wind_offshore_generation_actual</th>\n",
       "      <th>GB_UKM_wind_onshore_generation_actual</th>\n",
       "      <th>IT_load_actual_entsoe_transparency</th>\n",
       "      <th>IT_solar_generation_actual</th>\n",
       "      <th>IT_wind_onshore_generation_actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-31 23:00:00+00:00</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>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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 00:00:00+00:00</td>\n",
       "      <td>41151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8852.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>8336.0</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>27607.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 01:00:00+00:00</td>\n",
       "      <td>40135.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9054.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>8540.0</td>\n",
       "      <td>22734.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5461.0</td>\n",
       "      <td>69773.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>27948.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>21534.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 02:00:00+00:00</td>\n",
       "      <td>39106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9070.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>8552.0</td>\n",
       "      <td>21286.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5238.0</td>\n",
       "      <td>66417.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>25210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1139.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>20219.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 03:00:00+00:00</td>\n",
       "      <td>38765.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9163.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>8643.0</td>\n",
       "      <td>20264.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4935.0</td>\n",
       "      <td>64182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>23693.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>19470.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50396</th>\n",
       "      <td>2020-09-30 19:00:00+00:00</td>\n",
       "      <td>57559.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10654.0</td>\n",
       "      <td>4754.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>30860.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6047.0</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3632.0</td>\n",
       "      <td>38640.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7083.0</td>\n",
       "      <td>4740.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>35217.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50397</th>\n",
       "      <td>2020-09-30 20:00:00+00:00</td>\n",
       "      <td>54108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11836.0</td>\n",
       "      <td>5194.0</td>\n",
       "      <td>6642.0</td>\n",
       "      <td>27994.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6617.0</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3965.0</td>\n",
       "      <td>34913.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7431.0</td>\n",
       "      <td>5307.0</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>31537.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50398</th>\n",
       "      <td>2020-09-30 21:00:00+00:00</td>\n",
       "      <td>49845.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12168.0</td>\n",
       "      <td>5339.0</td>\n",
       "      <td>6829.0</td>\n",
       "      <td>25706.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6636.0</td>\n",
       "      <td>48058.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>30637.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7788.0</td>\n",
       "      <td>5674.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>28730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50399</th>\n",
       "      <td>2020-09-30 22:00:00+00:00</td>\n",
       "      <td>46886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12533.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>7134.0</td>\n",
       "      <td>24926.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6783.0</td>\n",
       "      <td>44869.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>25362.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4627.0</td>\n",
       "      <td>2998.0</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>26269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50400</th>\n",
       "      <td>2020-09-30 23:00:00+00:00</td>\n",
       "      <td>45461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12798.0</td>\n",
       "      <td>5381.0</td>\n",
       "      <td>7417.0</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>22208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7382.0</td>\n",
       "      <td>5572.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50401 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           date  DE_load_actual_entsoe_transparency  \\\n",
       "0     2014-12-31 23:00:00+00:00                                 NaN   \n",
       "1     2015-01-01 00:00:00+00:00                             41151.0   \n",
       "2     2015-01-01 01:00:00+00:00                             40135.0   \n",
       "3     2015-01-01 02:00:00+00:00                             39106.0   \n",
       "4     2015-01-01 03:00:00+00:00                             38765.0   \n",
       "...                         ...                                 ...   \n",
       "50396 2020-09-30 19:00:00+00:00                             57559.0   \n",
       "50397 2020-09-30 20:00:00+00:00                             54108.0   \n",
       "50398 2020-09-30 21:00:00+00:00                             49845.0   \n",
       "50399 2020-09-30 22:00:00+00:00                             46886.0   \n",
       "50400 2020-09-30 23:00:00+00:00                             45461.0   \n",
       "\n",
       "       DE_solar_generation_actual  DE_wind_generation_actual  \\\n",
       "0                             NaN                        NaN   \n",
       "1                             NaN                     8852.0   \n",
       "2                             NaN                     9054.0   \n",
       "3                             NaN                     9070.0   \n",
       "4                             NaN                     9163.0   \n",
       "...                           ...                        ...   \n",
       "50396                         0.0                    10654.0   \n",
       "50397                         0.0                    11836.0   \n",
       "50398                         0.0                    12168.0   \n",
       "50399                         0.0                    12533.0   \n",
       "50400                         0.0                    12798.0   \n",
       "\n",
       "       DE_wind_offshore_generation_actual  DE_wind_onshore_generation_actual  \\\n",
       "0                                     NaN                                NaN   \n",
       "1                                   517.0                             8336.0   \n",
       "2                                   514.0                             8540.0   \n",
       "3                                   518.0                             8552.0   \n",
       "4                                   520.0                             8643.0   \n",
       "...                                   ...                                ...   \n",
       "50396                              4754.0                             5900.0   \n",
       "50397                              5194.0                             6642.0   \n",
       "50398                              5339.0                             6829.0   \n",
       "50399                              5399.0                             7134.0   \n",
       "50400                              5381.0                             7417.0   \n",
       "\n",
       "       ES_load_actual_entsoe_transparency  ES_solar_generation_actual  \\\n",
       "0                                     NaN                         NaN   \n",
       "1                                     NaN                         NaN   \n",
       "2                                 22734.0                        50.0   \n",
       "3                                 21286.0                        50.0   \n",
       "4                                 20264.0                        42.0   \n",
       "...                                   ...                         ...   \n",
       "50396                             30860.0                        29.0   \n",
       "50397                             27994.0                        21.0   \n",
       "50398                             25706.0                        21.0   \n",
       "50399                             24926.0                        22.0   \n",
       "50400                                 NaN                         NaN   \n",
       "\n",
       "       ES_wind_onshore_generation_actual  FR_load_actual_entsoe_transparency  \\\n",
       "0                                    NaN                                 NaN   \n",
       "1                                    NaN                                 NaN   \n",
       "2                                 5461.0                             69773.0   \n",
       "3                                 5238.0                             66417.0   \n",
       "4                                 4935.0                             64182.0   \n",
       "...                                  ...                                 ...   \n",
       "50396                             6047.0                             48210.0   \n",
       "50397                             6617.0                             48210.0   \n",
       "50398                             6636.0                             48058.0   \n",
       "50399                             6783.0                             44869.0   \n",
       "50400                                NaN                                 NaN   \n",
       "\n",
       "       FR_solar_generation_actual  FR_wind_onshore_generation_actual  \\\n",
       "0                             NaN                                NaN   \n",
       "1                             NaN                                NaN   \n",
       "2                             NaN                             1464.0   \n",
       "3                             NaN                             1543.0   \n",
       "4                             NaN                             1579.0   \n",
       "...                           ...                                ...   \n",
       "50396                       142.0                             3632.0   \n",
       "50397                       142.0                             3965.0   \n",
       "50398                       143.0                             4201.0   \n",
       "50399                       142.0                             4428.0   \n",
       "50400                         NaN                                NaN   \n",
       "\n",
       "       GB_UKM_load_actual_entsoe_transparency  GB_UKM_solar_generation_actual  \\\n",
       "0                                         NaN                             NaN   \n",
       "1                                     27607.0                             NaN   \n",
       "2                                     27948.0                             NaN   \n",
       "3                                     25210.0                             NaN   \n",
       "4                                     23693.0                             NaN   \n",
       "...                                       ...                             ...   \n",
       "50396                                 38640.0                             NaN   \n",
       "50397                                 34913.0                             NaN   \n",
       "50398                                 30637.0                             NaN   \n",
       "50399                                 25362.0                             NaN   \n",
       "50400                                 22208.0                             NaN   \n",
       "\n",
       "       GB_UKM_wind_generation_actual  GB_UKM_wind_offshore_generation_actual  \\\n",
       "0                                NaN                                     NaN   \n",
       "1                                NaN                                     NaN   \n",
       "2                             1148.0                                   117.0   \n",
       "3                             1139.0                                   120.0   \n",
       "4                             1053.0                                   115.0   \n",
       "...                              ...                                     ...   \n",
       "50396                         7083.0                                  4740.0   \n",
       "50397                         7431.0                                  5307.0   \n",
       "50398                         7788.0                                  5674.0   \n",
       "50399                         4627.0                                  2998.0   \n",
       "50400                         7382.0                                  5572.0   \n",
       "\n",
       "       GB_UKM_wind_onshore_generation_actual  \\\n",
       "0                                        NaN   \n",
       "1                                      385.0   \n",
       "2                                     1041.0   \n",
       "3                                     1020.0   \n",
       "4                                      938.0   \n",
       "...                                      ...   \n",
       "50396                                 2343.0   \n",
       "50397                                 2124.0   \n",
       "50398                                 2114.0   \n",
       "50399                                 1629.0   \n",
       "50400                                 1810.0   \n",
       "\n",
       "       IT_load_actual_entsoe_transparency  IT_solar_generation_actual  \\\n",
       "0                                     NaN                         NaN   \n",
       "1                                     NaN                         NaN   \n",
       "2                                 21534.0                         NaN   \n",
       "3                                 20219.0                         NaN   \n",
       "4                                 19470.0                         NaN   \n",
       "...                                   ...                         ...   \n",
       "50396                             35217.0                         NaN   \n",
       "50397                             31537.0                         NaN   \n",
       "50398                             28730.0                         NaN   \n",
       "50399                             26269.0                         NaN   \n",
       "50400                                 NaN                         NaN   \n",
       "\n",
       "       IT_wind_onshore_generation_actual  \n",
       "0                                    NaN  \n",
       "1                                    NaN  \n",
       "2                                    NaN  \n",
       "3                                    NaN  \n",
       "4                                    NaN  \n",
       "...                                  ...  \n",
       "50396                              515.0  \n",
       "50397                              546.0  \n",
       "50398                              519.0  \n",
       "50399                              489.0  \n",
       "50400                                NaN  \n",
       "\n",
       "[50401 rows x 20 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Missing values in top 5 countries\n",
    "\n",
    "Truncate and fill missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_missings(df_top_5.loc[:, df_top_5.columns != 'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>date</th>\n",
       "      <th>DE_load_actual_entsoe_transparency</th>\n",
       "      <th>DE_solar_generation_actual</th>\n",
       "      <th>DE_wind_generation_actual</th>\n",
       "      <th>DE_wind_offshore_generation_actual</th>\n",
       "      <th>DE_wind_onshore_generation_actual</th>\n",
       "      <th>ES_load_actual_entsoe_transparency</th>\n",
       "      <th>ES_solar_generation_actual</th>\n",
       "      <th>ES_wind_onshore_generation_actual</th>\n",
       "      <th>FR_load_actual_entsoe_transparency</th>\n",
       "      <th>FR_solar_generation_actual</th>\n",
       "      <th>FR_wind_onshore_generation_actual</th>\n",
       "      <th>GB_UKM_load_actual_entsoe_transparency</th>\n",
       "      <th>GB_UKM_solar_generation_actual</th>\n",
       "      <th>GB_UKM_wind_generation_actual</th>\n",
       "      <th>GB_UKM_wind_offshore_generation_actual</th>\n",
       "      <th>GB_UKM_wind_onshore_generation_actual</th>\n",
       "      <th>IT_load_actual_entsoe_transparency</th>\n",
       "      <th>IT_solar_generation_actual</th>\n",
       "      <th>IT_wind_onshore_generation_actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50391</th>\n",
       "      <td>2020-09-30 14:00:00+00:00</td>\n",
       "      <td>61396.0</td>\n",
       "      <td>10270.0</td>\n",
       "      <td>3377.0</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>29115.0</td>\n",
       "      <td>5028.0</td>\n",
       "      <td>3814.0</td>\n",
       "      <td>49154.0</td>\n",
       "      <td>3983.0</td>\n",
       "      <td>2778.0</td>\n",
       "      <td>38546.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>8224.0</td>\n",
       "      <td>4637.0</td>\n",
       "      <td>3587.0</td>\n",
       "      <td>37263.0</td>\n",
       "      <td>4943.0</td>\n",
       "      <td>696.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50392</th>\n",
       "      <td>2020-09-30 15:00:00+00:00</td>\n",
       "      <td>61436.0</td>\n",
       "      <td>4943.0</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>2696.0</td>\n",
       "      <td>1144.0</td>\n",
       "      <td>29012.0</td>\n",
       "      <td>4160.0</td>\n",
       "      <td>4368.0</td>\n",
       "      <td>48388.0</td>\n",
       "      <td>2546.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>7489.0</td>\n",
       "      <td>4594.0</td>\n",
       "      <td>2894.0</td>\n",
       "      <td>36850.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>831.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50393</th>\n",
       "      <td>2020-09-30 16:00:00+00:00</td>\n",
       "      <td>61432.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>1633.0</td>\n",
       "      <td>28772.0</td>\n",
       "      <td>2489.0</td>\n",
       "      <td>4760.0</td>\n",
       "      <td>50111.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>2731.0</td>\n",
       "      <td>42353.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>7292.0</td>\n",
       "      <td>4499.0</td>\n",
       "      <td>2793.0</td>\n",
       "      <td>37177.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50394</th>\n",
       "      <td>2020-09-30 17:00:00+00:00</td>\n",
       "      <td>63459.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6444.0</td>\n",
       "      <td>3877.0</td>\n",
       "      <td>2568.0</td>\n",
       "      <td>29096.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>4876.0</td>\n",
       "      <td>53304.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>42344.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>7347.0</td>\n",
       "      <td>4733.0</td>\n",
       "      <td>2614.0</td>\n",
       "      <td>40102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50395</th>\n",
       "      <td>2020-09-30 18:00:00+00:00</td>\n",
       "      <td>61177.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8526.0</td>\n",
       "      <td>4416.0</td>\n",
       "      <td>4111.0</td>\n",
       "      <td>31492.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>5395.0</td>\n",
       "      <td>51975.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3185.0</td>\n",
       "      <td>41750.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7070.0</td>\n",
       "      <td>4531.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>38208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50396</th>\n",
       "      <td>2020-09-30 19:00:00+00:00</td>\n",
       "      <td>57559.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10654.0</td>\n",
       "      <td>4754.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>30860.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6047.0</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3632.0</td>\n",
       "      <td>38640.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7083.0</td>\n",
       "      <td>4740.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>35217.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50397</th>\n",
       "      <td>2020-09-30 20:00:00+00:00</td>\n",
       "      <td>54108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11836.0</td>\n",
       "      <td>5194.0</td>\n",
       "      <td>6642.0</td>\n",
       "      <td>27994.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6617.0</td>\n",
       "      <td>48210.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3965.0</td>\n",
       "      <td>34913.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7431.0</td>\n",
       "      <td>5307.0</td>\n",
       "      <td>2124.0</td>\n",
       "      <td>31537.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50398</th>\n",
       "      <td>2020-09-30 21:00:00+00:00</td>\n",
       "      <td>49845.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12168.0</td>\n",
       "      <td>5339.0</td>\n",
       "      <td>6829.0</td>\n",
       "      <td>25706.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6636.0</td>\n",
       "      <td>48058.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>30637.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7788.0</td>\n",
       "      <td>5674.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>28730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50399</th>\n",
       "      <td>2020-09-30 22:00:00+00:00</td>\n",
       "      <td>46886.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12533.0</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>7134.0</td>\n",
       "      <td>24926.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6783.0</td>\n",
       "      <td>44869.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4428.0</td>\n",
       "      <td>25362.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4627.0</td>\n",
       "      <td>2998.0</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>26269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50400</th>\n",
       "      <td>2020-09-30 23:00:00+00:00</td>\n",
       "      <td>45461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12798.0</td>\n",
       "      <td>5381.0</td>\n",
       "      <td>7417.0</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>22208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7382.0</td>\n",
       "      <td>5572.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           date  DE_load_actual_entsoe_transparency  \\\n",
       "50391 2020-09-30 14:00:00+00:00                             61396.0   \n",
       "50392 2020-09-30 15:00:00+00:00                             61436.0   \n",
       "50393 2020-09-30 16:00:00+00:00                             61432.0   \n",
       "50394 2020-09-30 17:00:00+00:00                             63459.0   \n",
       "50395 2020-09-30 18:00:00+00:00                             61177.0   \n",
       "50396 2020-09-30 19:00:00+00:00                             57559.0   \n",
       "50397 2020-09-30 20:00:00+00:00                             54108.0   \n",
       "50398 2020-09-30 21:00:00+00:00                             49845.0   \n",
       "50399 2020-09-30 22:00:00+00:00                             46886.0   \n",
       "50400 2020-09-30 23:00:00+00:00                             45461.0   \n",
       "\n",
       "       DE_solar_generation_actual  DE_wind_generation_actual  \\\n",
       "50391                     10270.0                     3377.0   \n",
       "50392                      4943.0                     3840.0   \n",
       "50393                       807.0                     4538.0   \n",
       "50394                         1.0                     6444.0   \n",
       "50395                         0.0                     8526.0   \n",
       "50396                         0.0                    10654.0   \n",
       "50397                         0.0                    11836.0   \n",
       "50398                         0.0                    12168.0   \n",
       "50399                         0.0                    12533.0   \n",
       "50400                         0.0                    12798.0   \n",
       "\n",
       "       DE_wind_offshore_generation_actual  DE_wind_onshore_generation_actual  \\\n",
       "50391                              2369.0                             1008.0   \n",
       "50392                              2696.0                             1144.0   \n",
       "50393                              2905.0                             1633.0   \n",
       "50394                              3877.0                             2568.0   \n",
       "50395                              4416.0                             4111.0   \n",
       "50396                              4754.0                             5900.0   \n",
       "50397                              5194.0                             6642.0   \n",
       "50398                              5339.0                             6829.0   \n",
       "50399                              5399.0                             7134.0   \n",
       "50400                              5381.0                             7417.0   \n",
       "\n",
       "       ES_load_actual_entsoe_transparency  ES_solar_generation_actual  \\\n",
       "50391                             29115.0                      5028.0   \n",
       "50392                             29012.0                      4160.0   \n",
       "50393                             28772.0                      2489.0   \n",
       "50394                             29096.0                       630.0   \n",
       "50395                             31492.0                       108.0   \n",
       "50396                             30860.0                        29.0   \n",
       "50397                             27994.0                        21.0   \n",
       "50398                             25706.0                        21.0   \n",
       "50399                             24926.0                        22.0   \n",
       "50400                                 NaN                         NaN   \n",
       "\n",
       "       ES_wind_onshore_generation_actual  FR_load_actual_entsoe_transparency  \\\n",
       "50391                             3814.0                             49154.0   \n",
       "50392                             4368.0                             48388.0   \n",
       "50393                             4760.0                             50111.0   \n",
       "50394                             4876.0                             53304.0   \n",
       "50395                             5395.0                             51975.0   \n",
       "50396                             6047.0                             48210.0   \n",
       "50397                             6617.0                             48210.0   \n",
       "50398                             6636.0                             48058.0   \n",
       "50399                             6783.0                             44869.0   \n",
       "50400                                NaN                                 NaN   \n",
       "\n",
       "       FR_solar_generation_actual  FR_wind_onshore_generation_actual  \\\n",
       "50391                      3983.0                             2778.0   \n",
       "50392                      2546.0                             2711.0   \n",
       "50393                      1002.0                             2731.0   \n",
       "50394                       204.0                             2827.0   \n",
       "50395                       140.0                             3185.0   \n",
       "50396                       142.0                             3632.0   \n",
       "50397                       142.0                             3965.0   \n",
       "50398                       143.0                             4201.0   \n",
       "50399                       142.0                             4428.0   \n",
       "50400                         NaN                                NaN   \n",
       "\n",
       "       GB_UKM_load_actual_entsoe_transparency  GB_UKM_solar_generation_actual  \\\n",
       "50391                                 38546.0                           646.0   \n",
       "50392                                 41124.0                           484.0   \n",
       "50393                                 42353.0                           296.0   \n",
       "50394                                 42344.0                           142.0   \n",
       "50395                                 41750.0                             NaN   \n",
       "50396                                 38640.0                             NaN   \n",
       "50397                                 34913.0                             NaN   \n",
       "50398                                 30637.0                             NaN   \n",
       "50399                                 25362.0                             NaN   \n",
       "50400                                 22208.0                             NaN   \n",
       "\n",
       "       GB_UKM_wind_generation_actual  GB_UKM_wind_offshore_generation_actual  \\\n",
       "50391                         8224.0                                  4637.0   \n",
       "50392                         7489.0                                  4594.0   \n",
       "50393                         7292.0                                  4499.0   \n",
       "50394                         7347.0                                  4733.0   \n",
       "50395                         7070.0                                  4531.0   \n",
       "50396                         7083.0                                  4740.0   \n",
       "50397                         7431.0                                  5307.0   \n",
       "50398                         7788.0                                  5674.0   \n",
       "50399                         4627.0                                  2998.0   \n",
       "50400                         7382.0                                  5572.0   \n",
       "\n",
       "       GB_UKM_wind_onshore_generation_actual  \\\n",
       "50391                                 3587.0   \n",
       "50392                                 2894.0   \n",
       "50393                                 2793.0   \n",
       "50394                                 2614.0   \n",
       "50395                                 2540.0   \n",
       "50396                                 2343.0   \n",
       "50397                                 2124.0   \n",
       "50398                                 2114.0   \n",
       "50399                                 1629.0   \n",
       "50400                                 1810.0   \n",
       "\n",
       "       IT_load_actual_entsoe_transparency  IT_solar_generation_actual  \\\n",
       "50391                             37263.0                      4943.0   \n",
       "50392                             36850.0                      2345.0   \n",
       "50393                             37177.0                         NaN   \n",
       "50394                             40102.0                         NaN   \n",
       "50395                             38208.0                         NaN   \n",
       "50396                             35217.0                         NaN   \n",
       "50397                             31537.0                         NaN   \n",
       "50398                             28730.0                         NaN   \n",
       "50399                             26269.0                         NaN   \n",
       "50400                                 NaN                         NaN   \n",
       "\n",
       "       IT_wind_onshore_generation_actual  \n",
       "50391                              696.0  \n",
       "50392                              831.0  \n",
       "50393                              721.0  \n",
       "50394                              562.0  \n",
       "50395                              518.0  \n",
       "50396                              515.0  \n",
       "50397                              546.0  \n",
       "50398                              519.0  \n",
       "50399                              489.0  \n",
       "50400                                NaN  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep in mind we have to drop last day, because it is not full\n",
    "df_top_5.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-10-24 23:00:00+0000', tz='UTC')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The start of notna data\n",
    "min_date = df_top_5.loc[df_top_5['IT_wind_onshore_generation_actual'].notna()]['date'].min()\n",
    "df_top_5 = df_top_5[df_top_5['date'] >= min_date]\n",
    "min_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Italy has missing values at the start of the dataframe. Since we want to have unified periods, we should drop them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have missing values, let's treat them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                         0\n",
       "DE_load_actual_entsoe_transparency           0\n",
       "DE_solar_generation_actual                  72\n",
       "DE_wind_generation_actual                   74\n",
       "DE_wind_offshore_generation_actual          74\n",
       "DE_wind_onshore_generation_actual           72\n",
       "ES_load_actual_entsoe_transparency           4\n",
       "ES_solar_generation_actual                   9\n",
       "ES_wind_onshore_generation_actual            9\n",
       "FR_load_actual_entsoe_transparency          42\n",
       "FR_solar_generation_actual                   6\n",
       "FR_wind_onshore_generation_actual            6\n",
       "GB_UKM_load_actual_entsoe_transparency     163\n",
       "GB_UKM_solar_generation_actual              38\n",
       "GB_UKM_wind_generation_actual              120\n",
       "GB_UKM_wind_offshore_generation_actual      32\n",
       "GB_UKM_wind_onshore_generation_actual      120\n",
       "IT_load_actual_entsoe_transparency           1\n",
       "IT_solar_generation_actual                  17\n",
       "IT_wind_onshore_generation_actual         1618\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missings again\n",
    "df_top_5.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2015-10-26',\n",
       " '2015-10-27',\n",
       " '2015-10-28',\n",
       " '2015-10-29',\n",
       " '2015-10-30',\n",
       " '2015-10-31',\n",
       " '2015-11-01',\n",
       " '2015-11-02',\n",
       " '2015-11-03',\n",
       " '2015-11-04',\n",
       " '2015-11-05',\n",
       " '2015-11-06',\n",
       " '2015-11-07',\n",
       " '2015-11-08',\n",
       " '2015-11-09',\n",
       " '2015-11-10',\n",
       " '2015-11-11',\n",
       " '2015-11-12',\n",
       " '2015-11-13',\n",
       " '2015-11-14',\n",
       " '2015-11-15',\n",
       " '2015-11-16',\n",
       " '2015-11-17',\n",
       " '2015-11-18',\n",
       " '2015-11-19',\n",
       " '2015-11-20',\n",
       " '2015-11-21',\n",
       " '2015-11-22',\n",
       " '2015-11-23',\n",
       " '2015-11-24',\n",
       " '2015-11-25',\n",
       " '2015-11-26',\n",
       " '2015-11-27',\n",
       " '2015-11-28',\n",
       " '2015-11-29',\n",
       " '2015-11-30',\n",
       " '2015-12-01',\n",
       " '2015-12-02',\n",
       " '2015-12-03',\n",
       " '2015-12-04',\n",
       " '2015-12-05',\n",
       " '2015-12-06',\n",
       " '2015-12-07',\n",
       " '2015-12-08',\n",
       " '2015-12-09',\n",
       " '2015-12-10',\n",
       " '2015-12-11',\n",
       " '2015-12-12',\n",
       " '2015-12-13',\n",
       " '2015-12-14',\n",
       " '2015-12-15',\n",
       " '2015-12-16',\n",
       " '2015-12-17',\n",
       " '2015-12-18',\n",
       " '2015-12-19',\n",
       " '2015-12-20',\n",
       " '2015-12-21',\n",
       " '2015-12-22',\n",
       " '2015-12-23',\n",
       " '2015-12-24',\n",
       " '2015-12-25',\n",
       " '2015-12-26',\n",
       " '2015-12-27',\n",
       " '2015-12-28',\n",
       " '2015-12-29',\n",
       " '2015-12-30']"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IT_wind_onshore_generation_actual still has many missings\n",
    "# Everyday till the end of the 2015\n",
    "\n",
    "df_top_5.set_index('date', inplace=True)\n",
    "missing_days = df_top_5['IT_wind_onshore_generation_actual'].resample('D').apply(lambda x: x.isna().all())\n",
    "\n",
    "# Filter to get only the days with missing data\n",
    "missing_days[missing_days].index.strftime('%Y-%m-%d').tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2015-11-30']"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Full November, if resample by month\n",
    "missing_days = df_top_5['IT_wind_onshore_generation_actual'].resample('M').apply(lambda x: x.isna().all())\n",
    "\n",
    "# Filter to get only the days with missing data\n",
    "missing_days[missing_days].index.strftime('%Y-%m-%d').tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE_load_actual_entsoe_transparency          0\n",
       "DE_solar_generation_actual                 72\n",
       "DE_wind_generation_actual                  74\n",
       "DE_wind_offshore_generation_actual         74\n",
       "DE_wind_onshore_generation_actual          72\n",
       "ES_load_actual_entsoe_transparency          4\n",
       "ES_solar_generation_actual                  9\n",
       "ES_wind_onshore_generation_actual           9\n",
       "FR_load_actual_entsoe_transparency         42\n",
       "FR_solar_generation_actual                  6\n",
       "FR_wind_onshore_generation_actual           6\n",
       "GB_UKM_load_actual_entsoe_transparency    149\n",
       "GB_UKM_solar_generation_actual             38\n",
       "GB_UKM_wind_generation_actual             117\n",
       "GB_UKM_wind_offshore_generation_actual     32\n",
       "GB_UKM_wind_onshore_generation_actual     117\n",
       "IT_load_actual_entsoe_transparency          1\n",
       "IT_solar_generation_actual                 17\n",
       "IT_wind_onshore_generation_actual          33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop until that date\n",
    "df_top_5 = df_top_5[df_top_5.index.date > date(2015, 12, 30)]\n",
    "\n",
    "# Check again\n",
    "df_top_5.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select the columns with daily seasonality to impute missing values there with the previous day's values at exactly same hour. \n",
    "We suppose that such imputation is a better choice here than simple linear extrapolation. For example, for solar generation values at night might be zeroes every day, therefore we should impute such missings with zeroes rather that linearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_columns = [col for col in df_top_5.columns if 'load' in col or 'solar' in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute missing values for Load and Solar with previous day observations at the same time\n",
    "columns_shift = df_top_5[selected_columns].shift(freq='24H')\n",
    "\n",
    "# Fill missing values with values from the previous day\n",
    "df_top_5 = df_top_5.fillna(columns_shift)\n",
    "\n",
    "# Fill wind columns with linear/time interpolation\n",
    "# In our case it is linear interpolation (because distance between time points is the same)\n",
    "df_top_5.interpolate(method='time', axis=0, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DE_load_actual_entsoe_transparency         0\n",
       "DE_solar_generation_actual                 0\n",
       "DE_wind_generation_actual                  0\n",
       "DE_wind_offshore_generation_actual         0\n",
       "DE_wind_onshore_generation_actual          0\n",
       "ES_load_actual_entsoe_transparency         0\n",
       "ES_solar_generation_actual                 0\n",
       "ES_wind_onshore_generation_actual          0\n",
       "FR_load_actual_entsoe_transparency         0\n",
       "FR_solar_generation_actual                 0\n",
       "FR_wind_onshore_generation_actual          0\n",
       "GB_UKM_load_actual_entsoe_transparency     0\n",
       "GB_UKM_solar_generation_actual             0\n",
       "GB_UKM_wind_generation_actual              0\n",
       "GB_UKM_wind_offshore_generation_actual     0\n",
       "GB_UKM_wind_onshore_generation_actual      0\n",
       "IT_load_actual_entsoe_transparency         0\n",
       "IT_solar_generation_actual                 0\n",
       "IT_wind_onshore_generation_actual         23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_5.isna().sum()\n",
    "# 2015-12-31 has 23 hours missing, but we keep it because later we will change time to local time\n",
    "# In Italy, there will be +1 hour to UTC in winter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column name                              Number of outliers Percentage in dataset\n",
      "DE_load_actual_entsoe_transparency                     0                  0.0%\n",
      "DE_solar_generation_actual                          2935                  7.0%\n",
      "DE_wind_generation_actual                           1225                  2.9%\n",
      "DE_wind_offshore_generation_actual                     0                  0.0%\n",
      "DE_wind_onshore_generation_actual                   1831                  4.4%\n",
      "ES_load_actual_entsoe_transparency                     0                  0.0%\n",
      "ES_solar_generation_actual                           803                  1.9%\n",
      "ES_wind_onshore_generation_actual                    497                  1.2%\n",
      "FR_load_actual_entsoe_transparency                   106                  0.3%\n",
      "FR_solar_generation_actual                          1142                  2.7%\n",
      "FR_wind_onshore_generation_actual                   2203                  5.3%\n",
      "GB_UKM_load_actual_entsoe_transparency                27                  0.1%\n",
      "GB_UKM_solar_generation_actual                      2675                  6.4%\n",
      "GB_UKM_wind_generation_actual                        107                  0.3%\n",
      "GB_UKM_wind_offshore_generation_actual               214                  0.5%\n",
      "GB_UKM_wind_onshore_generation_actual                 45                  0.1%\n",
      "IT_load_actual_entsoe_transparency                     0                  0.0%\n",
      "IT_solar_generation_actual                            29                  0.1%\n",
      "IT_wind_onshore_generation_actual                    188                  0.5%\n"
     ]
    }
   ],
   "source": [
    "# 1.5 IQR - regular\n",
    "print(f\"{'Column name':<40} {'Number of outliers':>15} {'Percentage in dataset':>20}\")\n",
    "for col_name in df_top_5.select_dtypes(include='float').columns:\n",
    "    count_outliers(df_top_5[col_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column name: 'DE_solar_generation_actual'\n",
      "lower bound:      -11353.875,   upper bound:       18923.125\n",
      "min value:               0.0,    max:                 32947.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'DE_wind_generation_actual'\n",
      "lower bound:       -13201.75,   upper bound:        34984.25\n",
      "min value:             135.0,    max:                 46064.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'DE_wind_onshore_generation_actual'\n",
      "lower bound:        -11252.0,   upper bound:         28692.0\n",
      "min value:             119.0,    max:                 40752.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'ES_solar_generation_actual'\n",
      "lower bound:       -4235.375,   upper bound:        7237.625\n",
      "min value:               0.0,    max:                  9338.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'ES_wind_onshore_generation_actual'\n",
      "lower bound:         -3764.5,   upper bound:         14327.5\n",
      "min value:               0.0,    max:                 17343.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'FR_load_actual_entsoe_transparency'\n",
      "lower bound:        18511.75,   upper bound:        86917.75\n",
      "min value:           29398.0,    max:                158000.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'FR_solar_generation_actual'\n",
      "lower bound:         -3160.5,   upper bound:          5267.5\n",
      "min value:               0.0,    max:                  7417.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'FR_wind_onshore_generation_actual'\n",
      "lower bound:         -2662.0,   upper bound:          8106.0\n",
      "min value:             330.0,    max:                 12976.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'GB_UKM_load_actual_entsoe_transparency'\n",
      "lower bound:         11401.0,   upper bound:         59009.0\n",
      "min value:            1183.0,    max:                 57901.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'GB_UKM_solar_generation_actual'\n",
      "lower bound:         -3060.0,   upper bound:          5100.0\n",
      "min value:               0.0,    max:                  9544.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'GB_UKM_wind_generation_actual'\n",
      "lower bound:         -4194.0,   upper bound:         13686.0\n",
      "min value:             311.0,    max:                 14477.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'GB_UKM_wind_offshore_generation_actual'\n",
      "lower bound:         -2391.5,   upper bound:          6068.5\n",
      "min value:               0.0,    max:                  6562.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'GB_UKM_wind_onshore_generation_actual'\n",
      "lower bound:         -2704.0,   upper bound:          8440.0\n",
      "min value:             143.0,    max:                  9048.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'IT_solar_generation_actual'\n",
      "lower bound:         -6399.0,   upper bound:         10665.0\n",
      "min value:               0.0,    max:                 11389.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'IT_wind_onshore_generation_actual'\n",
      "lower bound:         -2445.5,   upper bound:          6278.5\n",
      "min value:              20.0,    max:                  7448.0\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col_name in df_top_5.select_dtypes(include='float').columns:\n",
    "    count_outliers(df_top_5[col_name], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column name                              Number of outliers Percentage in dataset\n",
      "DE_load_actual_entsoe_transparency                     0                  0.0%\n",
      "DE_solar_generation_actual                            43                  0.1%\n",
      "DE_wind_generation_actual                              0                  0.0%\n",
      "DE_wind_offshore_generation_actual                     0                  0.0%\n",
      "DE_wind_onshore_generation_actual                      0                  0.0%\n",
      "ES_load_actual_entsoe_transparency                     0                  0.0%\n",
      "ES_solar_generation_actual                             0                  0.0%\n",
      "ES_wind_onshore_generation_actual                      0                  0.0%\n",
      "FR_load_actual_entsoe_transparency                     1                  0.0%\n",
      "FR_solar_generation_actual                             0                  0.0%\n",
      "FR_wind_onshore_generation_actual                     61                  0.1%\n",
      "GB_UKM_load_actual_entsoe_transparency                 0                  0.0%\n",
      "GB_UKM_solar_generation_actual                       162                  0.4%\n",
      "GB_UKM_wind_generation_actual                          0                  0.0%\n",
      "GB_UKM_wind_offshore_generation_actual                 0                  0.0%\n",
      "GB_UKM_wind_onshore_generation_actual                  0                  0.0%\n",
      "IT_load_actual_entsoe_transparency                     0                  0.0%\n",
      "IT_solar_generation_actual                             0                  0.0%\n",
      "IT_wind_onshore_generation_actual                      0                  0.0%\n"
     ]
    }
   ],
   "source": [
    "# 3 IQR - extreme\n",
    "print(f\"{'Column name':<40} {'Number of outliers':>15} {'Percentage in dataset':>20}\")\n",
    "for col_name in df_top_5.select_dtypes(include='float').columns:\n",
    "    count_outliers(df_top_5[col_name], extreme=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column name                              Number of outliers Percentage in dataset\n",
      "Column name: 'DE_solar_generation_actual'\n",
      "lower bound:       -22707.75,   upper bound:         30277.0\n",
      "min value:               0.0,    max:                 32947.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'FR_load_actual_entsoe_transparency'\n",
      "lower bound:         -7140.5,   upper bound:        112570.0\n",
      "min value:           29398.0,    max:                158000.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'FR_wind_onshore_generation_actual'\n",
      "lower bound:         -6700.0,   upper bound:         12144.0\n",
      "min value:             330.0,    max:                 12976.0\n",
      "--------------------------------------------------------------------------------\n",
      "Column name: 'GB_UKM_solar_generation_actual'\n",
      "lower bound:         -6120.0,   upper bound:          8160.0\n",
      "min value:               0.0,    max:                  9544.0\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 3 IQR - extreme\n",
    "print(f\"{'Column name':<40} {'Number of outliers':>15} {'Percentage in dataset':>20}\")\n",
    "for col_name in df_top_5.select_dtypes(include='float').columns:\n",
    "    count_outliers(df_top_5[col_name], extreme=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2017-01-18 07:00:00+00:00     92199.0\n",
       "2017-01-18 08:00:00+00:00     92419.0\n",
       "2017-01-18 09:00:00+00:00     91669.0\n",
       "2017-01-18 10:00:00+00:00     90613.0\n",
       "2017-01-18 17:00:00+00:00     91463.0\n",
       "2017-01-18 18:00:00+00:00     91891.0\n",
       "2017-01-19 06:00:00+00:00     91654.0\n",
       "2017-01-19 07:00:00+00:00     93256.0\n",
       "2017-01-19 08:00:00+00:00     93123.0\n",
       "2017-01-19 09:00:00+00:00     91987.0\n",
       "2017-01-19 10:00:00+00:00     90849.0\n",
       "2017-01-19 17:00:00+00:00     91437.0\n",
       "2017-01-19 18:00:00+00:00     91967.0\n",
       "2017-01-20 06:00:00+00:00     91644.0\n",
       "2017-01-20 07:00:00+00:00     93696.0\n",
       "2017-01-20 08:00:00+00:00     93473.0\n",
       "2017-01-20 09:00:00+00:00     92073.0\n",
       "2017-01-20 10:00:00+00:00     90676.0\n",
       "2017-01-24 07:00:00+00:00     90816.0\n",
       "2017-01-24 08:00:00+00:00     90860.0\n",
       "2017-01-25 08:00:00+00:00     90520.0\n",
       "2017-01-25 09:00:00+00:00     90809.0\n",
       "2017-01-25 10:00:00+00:00     90478.0\n",
       "2017-01-25 11:00:00+00:00     90003.0\n",
       "2017-01-25 17:00:00+00:00     91006.0\n",
       "2017-01-25 18:00:00+00:00     91403.0\n",
       "2017-01-26 06:00:00+00:00     90576.0\n",
       "2017-01-26 07:00:00+00:00     92433.0\n",
       "2017-01-26 08:00:00+00:00     92674.0\n",
       "2017-01-26 09:00:00+00:00     91726.0\n",
       "2017-01-26 10:00:00+00:00     90888.0\n",
       "2018-02-26 18:00:00+00:00     90209.0\n",
       "2018-02-27 07:00:00+00:00     91316.0\n",
       "2018-02-27 08:00:00+00:00     91741.0\n",
       "2018-02-27 09:00:00+00:00     91037.0\n",
       "2018-02-27 10:00:00+00:00     90912.0\n",
       "2018-02-27 11:00:00+00:00     90393.0\n",
       "2018-02-27 18:00:00+00:00     92688.0\n",
       "2018-02-28 06:00:00+00:00     90221.0\n",
       "2018-02-28 07:00:00+00:00     93095.0\n",
       "2018-02-28 08:00:00+00:00     94492.0\n",
       "2018-02-28 09:00:00+00:00     94354.0\n",
       "2018-02-28 10:00:00+00:00     94320.0\n",
       "2018-02-28 11:00:00+00:00     93956.0\n",
       "2018-02-28 12:00:00+00:00     92180.0\n",
       "2018-02-28 13:00:00+00:00     90567.0\n",
       "2018-02-28 17:00:00+00:00     91637.0\n",
       "2018-02-28 18:00:00+00:00     94306.0\n",
       "2020-07-07 17:00:00+00:00    158000.0\n",
       "Name: FR_load_actual_entsoe_transparency, dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is only one such outlier and will be in the test set\n",
    "df_top_5[df_top_5['FR_load_actual_entsoe_transparency'] > 90000]['FR_load_actual_entsoe_transparency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-07-07 17:00:00+00:00    158000.0\n",
       "Name: FR_load_actual_entsoe_transparency, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is only one such outlier and will be in the test set\n",
    "df_top_5[df_top_5['FR_load_actual_entsoe_transparency'] > 100000]['FR_load_actual_entsoe_transparency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-07-06 16:00:00+00:00     47379.0\n",
       "2020-07-06 17:00:00+00:00     47983.0\n",
       "2020-07-06 18:00:00+00:00     45767.0\n",
       "2020-07-06 19:00:00+00:00     43882.0\n",
       "2020-07-06 20:00:00+00:00     46020.0\n",
       "2020-07-06 21:00:00+00:00     46690.0\n",
       "2020-07-06 22:00:00+00:00     42824.0\n",
       "2020-07-06 23:00:00+00:00     40020.0\n",
       "2020-07-07 00:00:00+00:00     38440.0\n",
       "2020-07-07 01:00:00+00:00     36403.0\n",
       "2020-07-07 02:00:00+00:00     35912.0\n",
       "2020-07-07 03:00:00+00:00     37375.0\n",
       "2020-07-07 04:00:00+00:00     39979.0\n",
       "2020-07-07 05:00:00+00:00     43814.0\n",
       "2020-07-07 06:00:00+00:00     47227.0\n",
       "2020-07-07 07:00:00+00:00     49414.0\n",
       "2020-07-07 08:00:00+00:00     50484.0\n",
       "2020-07-07 09:00:00+00:00     51590.0\n",
       "2020-07-07 10:00:00+00:00     52780.0\n",
       "2020-07-07 11:00:00+00:00     51871.0\n",
       "2020-07-07 12:00:00+00:00     51250.0\n",
       "2020-07-07 13:00:00+00:00     49880.0\n",
       "2020-07-07 14:00:00+00:00     48735.0\n",
       "2020-07-07 15:00:00+00:00     47869.0\n",
       "2020-07-07 16:00:00+00:00     49768.0\n",
       "2020-07-07 17:00:00+00:00    158000.0\n",
       "2020-07-07 18:00:00+00:00     46325.0\n",
       "2020-07-07 19:00:00+00:00     44683.0\n",
       "2020-07-07 20:00:00+00:00     46852.0\n",
       "2020-07-07 21:00:00+00:00     47640.0\n",
       "2020-07-07 22:00:00+00:00     43906.0\n",
       "2020-07-07 23:00:00+00:00     40910.0\n",
       "2020-07-08 00:00:00+00:00     39225.0\n",
       "2020-07-08 01:00:00+00:00     36945.0\n",
       "2020-07-08 02:00:00+00:00     36332.0\n",
       "2020-07-08 03:00:00+00:00     37553.0\n",
       "2020-07-08 04:00:00+00:00     39930.0\n",
       "2020-07-08 05:00:00+00:00     43736.0\n",
       "2020-07-08 06:00:00+00:00     47150.0\n",
       "2020-07-08 07:00:00+00:00     49436.0\n",
       "2020-07-08 08:00:00+00:00     50701.0\n",
       "2020-07-08 09:00:00+00:00     51903.0\n",
       "2020-07-08 10:00:00+00:00     53373.0\n",
       "2020-07-08 11:00:00+00:00     52130.0\n",
       "2020-07-08 12:00:00+00:00     51173.0\n",
       "2020-07-08 13:00:00+00:00     50272.0\n",
       "2020-07-08 14:00:00+00:00     49691.0\n",
       "2020-07-08 15:00:00+00:00     48895.0\n",
       "2020-07-08 16:00:00+00:00     49716.0\n",
       "2020-07-08 17:00:00+00:00     49981.0\n",
       "2020-07-08 18:00:00+00:00     47514.0\n",
       "Name: FR_load_actual_entsoe_transparency, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Must likely a data collection error, because \n",
    "# in the previous day and next day to this time consumption was around the sorrounding values.\n",
    "\n",
    "target_date = pd.Timestamp('2020-07-07 17:00:00+00:00')\n",
    "time_delta = pd.Timedelta(hours=25)  \n",
    "df_top_5[(df_top_5.index >= target_date - time_delta) & (df_top_5.index <= target_date + time_delta)]['FR_load_actual_entsoe_transparency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We reset it with 50000 so this error will not impact our evaluation too much.\n",
    "# The other values seem to be plausible values.\n",
    "df_top_5.at['2020-07-07 17:00:00+00:00', 'FR_load_actual_entsoe_transparency'] = 50000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a folder named \"datasets\" if it doesn't exist\n",
    "folder_name = \"datasets\"\n",
    "if not os.path.exists(folder_name):\n",
    "    os.makedirs(folder_name)\n",
    "    \n",
    "# Save to csv\n",
    "df_top_5.to_csv('./datasets/top_5_countries.csv')"
   ]
  }
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