Eurostat_nrg_inf_lbpc.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import eurostat\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import pycountry\n",
    "import pickle\n",
    "import networkx as nx\n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "%load_ext jupyter_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_code = \"NRG_INF_LBPC\"\n",
    "dict_siec = eurostat.get_dic(dataset_code, \"siec\", frmt=\"dict\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>code</th>\n",
       "      <th>type</th>\n",
       "      <th>last update of data</th>\n",
       "      <th>last table structure change</th>\n",
       "      <th>data start</th>\n",
       "      <th>data end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gross weight of goods transported to/from main...</td>\n",
       "      <td>MAR_GO_AM_TR</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2011-04-11T23:00:00+0200</td>\n",
       "      <td>2023-07-27T23:00:00+0200</td>\n",
       "      <td>2008</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gross weight of goods transported to/from main...</td>\n",
       "      <td>MAR_GO_AM_UK</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2010-04-22T11:00:00+0200</td>\n",
       "      <td>2023-11-22T11:00:00+0100</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gross weight of goods transported to/from main...</td>\n",
       "      <td>MAR_GO_QM</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-05-03T23:00:00+0200</td>\n",
       "      <td>2024-03-26T23:00:00+0100</td>\n",
       "      <td>1997-Q1</td>\n",
       "      <td>2023-Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gross weight of goods handled in main ports by...</td>\n",
       "      <td>MAR_GO_QMC</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-05-03T23:00:00+0200</td>\n",
       "      <td>2024-03-26T23:00:00+0100</td>\n",
       "      <td>1997-Q1</td>\n",
       "      <td>2023-Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gross weight of goods transported to/from main...</td>\n",
       "      <td>MAR_GO_QM_BE</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-03-26T23:00:00+0100</td>\n",
       "      <td>2024-03-05T23:00:00+0100</td>\n",
       "      <td>1997-Q1</td>\n",
       "      <td>2023-Q3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title          code     type  \\\n",
       "0  Gross weight of goods transported to/from main...  MAR_GO_AM_TR  dataset   \n",
       "1  Gross weight of goods transported to/from main...  MAR_GO_AM_UK  dataset   \n",
       "2  Gross weight of goods transported to/from main...     MAR_GO_QM  dataset   \n",
       "3  Gross weight of goods handled in main ports by...    MAR_GO_QMC  dataset   \n",
       "4  Gross weight of goods transported to/from main...  MAR_GO_QM_BE  dataset   \n",
       "\n",
       "        last update of data last table structure change data start data end  \n",
       "0  2011-04-11T23:00:00+0200    2023-07-27T23:00:00+0200       2008     2022  \n",
       "1  2010-04-22T11:00:00+0200    2023-11-22T11:00:00+0100       2000     2019  \n",
       "2  2024-05-03T23:00:00+0200    2024-03-26T23:00:00+0100    1997-Q1  2023-Q4  \n",
       "3  2024-05-03T23:00:00+0200    2024-03-26T23:00:00+0100    1997-Q1  2023-Q4  \n",
       "4  2024-03-26T23:00:00+0100    2024-03-05T23:00:00+0100    1997-Q1  2023-Q3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset = eurostat.get_toc_df()\n",
    "df_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>code</th>\n",
       "      <th>type</th>\n",
       "      <th>last update of data</th>\n",
       "      <th>last table structure change</th>\n",
       "      <th>data start</th>\n",
       "      <th>data end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Liquid biofuels production capacities</td>\n",
       "      <td>NRG_INF_LBPC</td>\n",
       "      <td>dataset</td>\n",
       "      <td>2024-04-16T23:00:00+0200</td>\n",
       "      <td>2024-04-16T23:00:00+0200</td>\n",
       "      <td>1990</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                     title          code     type  \\\n",
       "937  Liquid biofuels production capacities  NRG_INF_LBPC  dataset   \n",
       "\n",
       "          last update of data last table structure change data start data end  \n",
       "937  2024-04-16T23:00:00+0200    2024-04-16T23:00:00+0200       1990     2022  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dataset[df_dataset.code == \"NRG_INF_LBPC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['freq', 'siec', 'plant_tec', 'unit', 'geo']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = eurostat.get_pars(dataset_code)\n",
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "freq\n",
      "['A']\n",
      "siec\n",
      "['R5210P', 'R5220P', 'R5230P', 'R5290']\n",
      "plant_tec\n",
      "['CAP_PRD']\n",
      "unit\n",
      "['THS_TY']\n",
      "geo\n",
      "['EU27_2020', 'EA20', 'BE', 'BG', 'CZ', 'DK', 'DE', 'EE', 'IE', 'EL', 'ES', 'FR', 'HR', 'IT', 'CY', 'LV', 'LT', 'LU', 'HU', 'MT', 'NL', 'AT', 'PL', 'PT', 'RO', 'SI', 'SK', 'FI', 'SE', 'IS', 'LI', 'NO', 'UK', 'BA', 'ME', 'MD', 'MK', 'GE', 'AL', 'RS', 'TR', 'UA', 'XK']\n"
     ]
    }
   ],
   "source": [
    "for parameter in parameters:\n",
    "    print(parameter)\n",
    "    values = eurostat.get_par_values(dataset_code, parameter)\n",
    "    print(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('freq',\n",
       "  'Time frequency',\n",
       "  'This code list contains the periodicity that refers to the frequency.'),\n",
       " ('siec',\n",
       "  'Standard international energy product classification (SIEC)',\n",
       "  'This code list contains the energy products according to the Standard International Energy Product Classification (SIEC) which has been developped as part of the International Recommendations for Energy Statistics (IRES) adopted by the UNSD.'),\n",
       " ('plant_tec',\n",
       "  'Technical characteristics of plants',\n",
       "  'This code list refers to the technical characteristics of a plant, such as capacity or production specificities.'),\n",
       " ('unit', 'Unit of measure', None),\n",
       " ('geo',\n",
       "  'Geopolitical entity (reporting)',\n",
       "  'This code list defines the reporting geopolitical entities.')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = eurostat.get_dic(dataset_code)\n",
    "dic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq</th>\n",
       "      <th>siec</th>\n",
       "      <th>plant_tec</th>\n",
       "      <th>unit</th>\n",
       "      <th>geo\\TIME_PERIOD</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>...</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>BA</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>BE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>369.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>BG</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  freq    siec plant_tec    unit geo\\TIME_PERIOD  1990  1991  1992  1993  \\\n",
       "0    A  R5210P   CAP_PRD  THS_TY              AL   0.0   0.0   0.0   0.0   \n",
       "1    A  R5210P   CAP_PRD  THS_TY              AT   0.0   0.0   0.0   0.0   \n",
       "2    A  R5210P   CAP_PRD  THS_TY              BA   NaN   NaN   NaN   NaN   \n",
       "3    A  R5210P   CAP_PRD  THS_TY              BE   0.0   0.0   0.0   0.0   \n",
       "4    A  R5210P   CAP_PRD  THS_TY              BG   0.0   0.0   0.0   0.0   \n",
       "\n",
       "   1994  ...   2013   2014   2015   2016   2017   2018   2019   2020   2021  \\\n",
       "0   0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "1   0.0  ...  191.0  210.0  210.0  220.5  220.5  220.5  220.5  220.5  220.5   \n",
       "2   NaN  ...    NaN    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3   0.0  ...  369.0  369.0  408.0  408.0  408.0  408.0  408.0  408.0  408.0   \n",
       "4   0.0  ...   21.9   24.0   26.0   27.0   27.0   27.0   27.0   27.0   27.0   \n",
       "\n",
       "    2022  \n",
       "0    0.0  \n",
       "1  220.5  \n",
       "2    0.0  \n",
       "3  408.0  \n",
       "4   30.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = eurostat.get_data_df(dataset_code)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freq</th>\n",
       "      <th>siec</th>\n",
       "      <th>siec_name</th>\n",
       "      <th>plant_tec</th>\n",
       "      <th>unit</th>\n",
       "      <th>geo</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>...</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>Pure biogasoline</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>Pure biogasoline</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>AT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>Pure biogasoline</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>BA</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>Pure biogasoline</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>BE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>369.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>R5210P</td>\n",
       "      <td>Pure biogasoline</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>BG</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>A</td>\n",
       "      <td>R5290</td>\n",
       "      <td>Other liquid biofuels</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>SK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>A</td>\n",
       "      <td>R5290</td>\n",
       "      <td>Other liquid biofuels</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>TR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>A</td>\n",
       "      <td>R5290</td>\n",
       "      <td>Other liquid biofuels</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>UA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>A</td>\n",
       "      <td>R5290</td>\n",
       "      <td>Other liquid biofuels</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>UK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>A</td>\n",
       "      <td>R5290</td>\n",
       "      <td>Other liquid biofuels</td>\n",
       "      <td>CAP_PRD</td>\n",
       "      <td>THS_TY</td>\n",
       "      <td>XK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    freq    siec              siec_name plant_tec    unit geo  1990  1991  \\\n",
       "0      A  R5210P       Pure biogasoline   CAP_PRD  THS_TY  AL   0.0   0.0   \n",
       "1      A  R5210P       Pure biogasoline   CAP_PRD  THS_TY  AT   0.0   0.0   \n",
       "2      A  R5210P       Pure biogasoline   CAP_PRD  THS_TY  BA   NaN   NaN   \n",
       "3      A  R5210P       Pure biogasoline   CAP_PRD  THS_TY  BE   0.0   0.0   \n",
       "4      A  R5210P       Pure biogasoline   CAP_PRD  THS_TY  BG   0.0   0.0   \n",
       "..   ...     ...                    ...       ...     ...  ..   ...   ...   \n",
       "167    A   R5290  Other liquid biofuels   CAP_PRD  THS_TY  SK   0.0   0.0   \n",
       "168    A   R5290  Other liquid biofuels   CAP_PRD  THS_TY  TR   0.0   0.0   \n",
       "169    A   R5290  Other liquid biofuels   CAP_PRD  THS_TY  UA   0.0   0.0   \n",
       "170    A   R5290  Other liquid biofuels   CAP_PRD  THS_TY  UK   0.0   0.0   \n",
       "171    A   R5290  Other liquid biofuels   CAP_PRD  THS_TY  XK   NaN   NaN   \n",
       "\n",
       "     1992  1993  ...   2013   2014   2015   2016   2017   2018   2019   2020  \\\n",
       "0     0.0   0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "1     0.0   0.0  ...  191.0  210.0  210.0  220.5  220.5  220.5  220.5  220.5   \n",
       "2     NaN   NaN  ...    NaN    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3     0.0   0.0  ...  369.0  369.0  408.0  408.0  408.0  408.0  408.0  408.0   \n",
       "4     0.0   0.0  ...   21.9   24.0   26.0   27.0   27.0   27.0   27.0   27.0   \n",
       "..    ...   ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "167   0.0   0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "168   0.0   0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "169   0.0   0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "170   0.0   0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    NaN   \n",
       "171   NaN   NaN  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "      2021   2022  \n",
       "0      0.0    0.0  \n",
       "1    220.5  220.5  \n",
       "2      0.0    0.0  \n",
       "3    408.0  408.0  \n",
       "4     27.0   30.0  \n",
       "..     ...    ...  \n",
       "167    0.0    0.0  \n",
       "168    0.0    0.0  \n",
       "169    NaN    NaN  \n",
       "170    NaN    NaN  \n",
       "171    0.0    0.0  \n",
       "\n",
       "[172 rows x 39 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"siec_name\"] = df.siec.replace(dict_siec)\n",
    "df = df.rename(columns={\"geo\\TIME_PERIOD\": \"geo\"})\n",
    "df = df[\n",
    "    [\n",
    "        \"freq\",\n",
    "        \"siec\",\n",
    "        \"siec_name\",\n",
    "        \"plant_tec\",\n",
    "        \"unit\",\n",
    "        \"geo\",\n",
    "        \"1990\",\n",
    "        \"1991\",\n",
    "        \"1992\",\n",
    "        \"1993\",\n",
    "        \"1994\",\n",
    "        \"1995\",\n",
    "        \"1996\",\n",
    "        \"1997\",\n",
    "        \"1998\",\n",
    "        \"1999\",\n",
    "        \"2000\",\n",
    "        \"2001\",\n",
    "        \"2002\",\n",
    "        \"2003\",\n",
    "        \"2004\",\n",
    "        \"2005\",\n",
    "        \"2006\",\n",
    "        \"2007\",\n",
    "        \"2008\",\n",
    "        \"2009\",\n",
    "        \"2010\",\n",
    "        \"2011\",\n",
    "        \"2012\",\n",
    "        \"2013\",\n",
    "        \"2014\",\n",
    "        \"2015\",\n",
    "        \"2016\",\n",
    "        \"2017\",\n",
    "        \"2018\",\n",
    "        \"2019\",\n",
    "        \"2020\",\n",
    "        \"2021\",\n",
    "        \"2022\",\n",
    "    ]\n",
    "]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(f\"data/{dataset_code}.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Year</th>\n",
       "      <th>1990-01-01</th>\n",
       "      <th>1991-01-01</th>\n",
       "      <th>1992-01-01</th>\n",
       "      <th>1993-01-01</th>\n",
       "      <th>1994-01-01</th>\n",
       "      <th>1995-01-01</th>\n",
       "      <th>1996-01-01</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1999-01-01</th>\n",
       "      <th>...</th>\n",
       "      <th>2013-01-01</th>\n",
       "      <th>2014-01-01</th>\n",
       "      <th>2015-01-01</th>\n",
       "      <th>2016-01-01</th>\n",
       "      <th>2017-01-01</th>\n",
       "      <th>2018-01-01</th>\n",
       "      <th>2019-01-01</th>\n",
       "      <th>2020-01-01</th>\n",
       "      <th>2021-01-01</th>\n",
       "      <th>2022-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>siec_name</th>\n",
       "      <th>geo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Pure biogasoline</th>\n",
       "      <th>AL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>191.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>220.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>369.0</td>\n",
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       "      <td>408.0</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Year                  1990-01-01  1991-01-01  1992-01-01  1993-01-01  \\\n",
       "siec_name        geo                                                   \n",
       "Pure biogasoline AL          0.0         0.0         0.0         0.0   \n",
       "                 AT          0.0         0.0         0.0         0.0   \n",
       "                 BA          NaN         NaN         NaN         NaN   \n",
       "                 BE          0.0         0.0         0.0         0.0   \n",
       "                 BG          0.0         0.0         0.0         0.0   \n",
       "\n",
       "Year                  1994-01-01  1995-01-01  1996-01-01  1997-01-01  \\\n",
       "siec_name        geo                                                   \n",
       "Pure biogasoline AL          0.0         0.0         0.0         0.0   \n",
       "                 AT          0.0         0.0         0.0         0.0   \n",
       "                 BA          NaN         NaN         NaN         NaN   \n",
       "                 BE          0.0         0.0         0.0         0.0   \n",
       "                 BG          0.0         0.0         0.0         0.0   \n",
       "\n",
       "Year                  1998-01-01  1999-01-01  ...  2013-01-01  2014-01-01  \\\n",
       "siec_name        geo                          ...                           \n",
       "Pure biogasoline AL          0.0         0.0  ...         0.0         0.0   \n",
       "                 AT          0.0         0.0  ...       191.0       210.0   \n",
       "                 BA          NaN         NaN  ...         NaN         0.0   \n",
       "                 BE          0.0         0.0  ...       369.0       369.0   \n",
       "                 BG          0.0         0.0  ...        21.9        24.0   \n",
       "\n",
       "Year                  2015-01-01  2016-01-01  2017-01-01  2018-01-01  \\\n",
       "siec_name        geo                                                   \n",
       "Pure biogasoline AL          0.0         0.0         0.0         0.0   \n",
       "                 AT        210.0       220.5       220.5       220.5   \n",
       "                 BA          0.0         0.0         0.0         0.0   \n",
       "                 BE        408.0       408.0       408.0       408.0   \n",
       "                 BG         26.0        27.0        27.0        27.0   \n",
       "\n",
       "Year                  2019-01-01  2020-01-01  2021-01-01  2022-01-01  \n",
       "siec_name        geo                                                  \n",
       "Pure biogasoline AL          0.0         0.0         0.0         0.0  \n",
       "                 AT        220.5       220.5       220.5       220.5  \n",
       "                 BA          0.0         0.0         0.0         0.0  \n",
       "                 BE        408.0       408.0       408.0       408.0  \n",
       "                 BG         27.0        27.0        27.0        30.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(f\"data/{dataset_code}.csv\", index_col=0)\n",
    "df = df[df.unit == \"THS_TY\"]\n",
    "df = df.set_index([\"siec_name\", \"geo\"])[\n",
    "    [\n",
    "        \"1990\",\n",
    "        \"1991\",\n",
    "        \"1992\",\n",
    "        \"1993\",\n",
    "        \"1994\",\n",
    "        \"1995\",\n",
    "        \"1996\",\n",
    "        \"1997\",\n",
    "        \"1998\",\n",
    "        \"1999\",\n",
    "        \"2000\",\n",
    "        \"2001\",\n",
    "        \"2002\",\n",
    "        \"2003\",\n",
    "        \"2004\",\n",
    "        \"2005\",\n",
    "        \"2006\",\n",
    "        \"2007\",\n",
    "        \"2008\",\n",
    "        \"2009\",\n",
    "        \"2010\",\n",
    "        \"2011\",\n",
    "        \"2012\",\n",
    "        \"2013\",\n",
    "        \"2014\",\n",
    "        \"2015\",\n",
    "        \"2016\",\n",
    "        \"2017\",\n",
    "        \"2018\",\n",
    "        \"2019\",\n",
    "        \"2020\",\n",
    "        \"2021\",\n",
    "        \"2022\",\n",
    "    ]\n",
    "]\n",
    "df.columns = pd.to_datetime(df.columns)\n",
    "df.columns.name = \"Year\"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ..., 220.5, 220.5, 220.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]]])\n",
       "Coordinates:\n",
       "  * Energy   (Energy) object 32B &#x27;Other liquid biofuels&#x27; ... &#x27;Pure biogasoline&#x27;\n",
       "  * Country  (Country) object 344B &#x27;AL&#x27; &#x27;AT&#x27; &#x27;BA&#x27; &#x27;BE&#x27; ... &#x27;TR&#x27; &#x27;UA&#x27; &#x27;UK&#x27; &#x27;XK&#x27;\n",
       "  * Year     (Year) datetime64[ns] 264B 1990-01-01 1991-01-01 ... 2022-01-01</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Energy</span>: 4</li><li><span class='xr-has-index'>Country</span>: 43</li><li><span class='xr-has-index'>Year</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2890224d-8a6e-4266-a677-55388f9b34fd' class='xr-array-in' type='checkbox' checked><label for='section-2890224d-8a6e-4266-a677-55388f9b34fd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. , 200. , 200. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ..., 220.5, 220.5, 220.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-084e3e87-e493-4f0f-a890-4a7390803f52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-084e3e87-e493-4f0f-a890-4a7390803f52' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Energy</span></div><div class='xr-var-dims'>(Energy)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Other liquid biofuels&#x27; ... &#x27;Pur...</div><input id='attrs-d6871deb-92b1-418f-bd01-2848ca7b896b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6871deb-92b1-418f-bd01-2848ca7b896b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7236d4f-d631-4b22-8d7e-f82877b50a77' class='xr-var-data-in' type='checkbox'><label for='data-f7236d4f-d631-4b22-8d7e-f82877b50a77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Other liquid biofuels&#x27;, &#x27;Pure bio jet kerosene&#x27;, &#x27;Pure biodiesels&#x27;,\n",
       "       &#x27;Pure biogasoline&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Country</span></div><div class='xr-var-dims'>(Country)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;AL&#x27; &#x27;AT&#x27; &#x27;BA&#x27; ... &#x27;UA&#x27; &#x27;UK&#x27; &#x27;XK&#x27;</div><input id='attrs-693650ed-333c-4548-b8e2-491834bf517f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-693650ed-333c-4548-b8e2-491834bf517f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94e4f04b-ad57-4181-8057-d1b5fe8d8113' class='xr-var-data-in' type='checkbox'><label for='data-94e4f04b-ad57-4181-8057-d1b5fe8d8113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BA&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;, &#x27;DK&#x27;, &#x27;EA20&#x27;, &#x27;EE&#x27;,\n",
       "       &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;EU27_2020&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;GE&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;NL&#x27;, &#x27;NO&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;,\n",
       "       &#x27;RO&#x27;, &#x27;RS&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UA&#x27;, &#x27;UK&#x27;, &#x27;XK&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Year</span></div><div class='xr-var-dims'>(Year)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-01-01 ... 2022-01-01</div><input id='attrs-cfac4dde-9ef0-4e6e-8c30-d81c67cf4dde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfac4dde-9ef0-4e6e-8c30-d81c67cf4dde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70e9b922-4cab-42f6-84fe-1d678fe720d5' class='xr-var-data-in' type='checkbox'><label for='data-70e9b922-4cab-42f6-84fe-1d678fe720d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1993-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-01-01T00:00:00.000000000&#x27;, &#x27;1995-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-01-01T00:00:00.000000000&#x27;, &#x27;1997-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2001-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2003-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0457758e-fb10-40e4-ba65-a14ce4c7fd65' class='xr-section-summary-in' type='checkbox'  ><label for='section-0457758e-fb10-40e4-ba65-a14ce4c7fd65' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Energy</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37d083da-324f-469e-a83b-b02ca46ee1a9' class='xr-index-data-in' type='checkbox'/><label for='index-37d083da-324f-469e-a83b-b02ca46ee1a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Other liquid biofuels&#x27;, &#x27;Pure bio jet kerosene&#x27;, &#x27;Pure biodiesels&#x27;,\n",
       "       &#x27;Pure biogasoline&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;Energy&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Country</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8be6dac3-31e3-4d85-8934-c275e7470eca' class='xr-index-data-in' type='checkbox'/><label for='index-8be6dac3-31e3-4d85-8934-c275e7470eca' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BA&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;, &#x27;DK&#x27;, &#x27;EA20&#x27;, &#x27;EE&#x27;,\n",
       "       &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;EU27_2020&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;GE&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;NL&#x27;, &#x27;NO&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;,\n",
       "       &#x27;RO&#x27;, &#x27;RS&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UA&#x27;, &#x27;UK&#x27;, &#x27;XK&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;Country&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c52ca00-9298-456a-bc33-3fa493e7ed31' class='xr-index-data-in' type='checkbox'/><label for='index-2c52ca00-9298-456a-bc33-3fa493e7ed31' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1990-01-01&#x27;, &#x27;1991-01-01&#x27;, &#x27;1992-01-01&#x27;, &#x27;1993-01-01&#x27;,\n",
       "               &#x27;1994-01-01&#x27;, &#x27;1995-01-01&#x27;, &#x27;1996-01-01&#x27;, &#x27;1997-01-01&#x27;,\n",
       "               &#x27;1998-01-01&#x27;, &#x27;1999-01-01&#x27;, &#x27;2000-01-01&#x27;, &#x27;2001-01-01&#x27;,\n",
       "               &#x27;2002-01-01&#x27;, &#x27;2003-01-01&#x27;, &#x27;2004-01-01&#x27;, &#x27;2005-01-01&#x27;,\n",
       "               &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;, &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;,\n",
       "               &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;, &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;,\n",
       "               &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;, &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;,\n",
       "               &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;,\n",
       "               &#x27;2022-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Year&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-494524fc-bf21-4021-b5e8-74781d008fa0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-494524fc-bf21-4021-b5e8-74781d008fa0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (Energy: 4, Country: 43, Year: 33)> Size: 45kB\n",
       "array([[[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. , 200. , 200. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ..., 220.5, 220.5, 220.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]]])\n",
       "Coordinates:\n",
       "  * Energy   (Energy) object 32B 'Other liquid biofuels' ... 'Pure biogasoline'\n",
       "  * Country  (Country) object 344B 'AL' 'AT' 'BA' 'BE' ... 'TR' 'UA' 'UK' 'XK'\n",
       "  * Year     (Year) datetime64[ns] 264B 1990-01-01 1991-01-01 ... 2022-01-01"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = df.stack().to_xarray()\n",
    "ds = ds.rename({\"siec_name\": \"Energy\", \"geo\": \"Country\"})\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_netcdf(f\"data/{dataset_code}.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (Energy: 4, Country: 43, Year: 33)&gt; Size: 45kB\n",
       "array([[[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. , 200. , 200. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ..., 220.5, 220.5, 220.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]]])\n",
       "Coordinates:\n",
       "  * Country  (Country) object 344B &#x27;AL&#x27; &#x27;AT&#x27; &#x27;BA&#x27; &#x27;BE&#x27; ... &#x27;TR&#x27; &#x27;UA&#x27; &#x27;UK&#x27; &#x27;XK&#x27;\n",
       "  * Year     (Year) datetime64[ns] 264B 1990-01-01 1991-01-01 ... 2022-01-01\n",
       "  * Energy   (Energy) object 32B &#x27;Other liquid biofuels&#x27; ... &#x27;Pure biogasoline&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>Energy</span>: 4</li><li><span class='xr-has-index'>Country</span>: 43</li><li><span class='xr-has-index'>Year</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ab185cfb-663f-4f15-93ee-72da42a5a7aa' class='xr-array-in' type='checkbox' checked><label for='section-ab185cfb-663f-4f15-93ee-72da42a5a7aa' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. , 200. , 200. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ..., 220.5, 220.5, 220.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-c2975bd4-358d-4fbc-8056-1444bd7122d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2975bd4-358d-4fbc-8056-1444bd7122d0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Country</span></div><div class='xr-var-dims'>(Country)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;AL&#x27; &#x27;AT&#x27; &#x27;BA&#x27; ... &#x27;UA&#x27; &#x27;UK&#x27; &#x27;XK&#x27;</div><input id='attrs-42adb36d-ec6f-4246-9cd0-84494177fb77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42adb36d-ec6f-4246-9cd0-84494177fb77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8451520b-f644-4b22-81ea-a2f86eb09866' class='xr-var-data-in' type='checkbox'><label for='data-8451520b-f644-4b22-81ea-a2f86eb09866' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BA&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;, &#x27;DK&#x27;, &#x27;EA20&#x27;, &#x27;EE&#x27;,\n",
       "       &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;EU27_2020&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;GE&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;NL&#x27;, &#x27;NO&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;,\n",
       "       &#x27;RO&#x27;, &#x27;RS&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UA&#x27;, &#x27;UK&#x27;, &#x27;XK&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Year</span></div><div class='xr-var-dims'>(Year)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-01-01 ... 2022-01-01</div><input id='attrs-bc7ec0a8-a59a-4ca6-b222-5e9cbaa0daef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc7ec0a8-a59a-4ca6-b222-5e9cbaa0daef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbf87416-426c-4a30-94bf-9169057faadb' class='xr-var-data-in' type='checkbox'><label for='data-fbf87416-426c-4a30-94bf-9169057faadb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1993-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-01-01T00:00:00.000000000&#x27;, &#x27;1995-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-01-01T00:00:00.000000000&#x27;, &#x27;1997-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2001-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2003-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Energy</span></div><div class='xr-var-dims'>(Energy)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Other liquid biofuels&#x27; ... &#x27;Pur...</div><input id='attrs-308c0322-08f1-42cc-9b88-6b7a92b8f08c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-308c0322-08f1-42cc-9b88-6b7a92b8f08c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b5b144a-6076-4b66-84c6-a3c4eb9e9179' class='xr-var-data-in' type='checkbox'><label for='data-5b5b144a-6076-4b66-84c6-a3c4eb9e9179' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Other liquid biofuels&#x27;, &#x27;Pure bio jet kerosene&#x27;, &#x27;Pure biodiesels&#x27;,\n",
       "       &#x27;Pure biogasoline&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0637e66-0828-45cc-830b-59ea52a683c1' class='xr-section-summary-in' type='checkbox'  ><label for='section-c0637e66-0828-45cc-830b-59ea52a683c1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Country</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d7503fac-f829-4c35-8560-71b0e81033cc' class='xr-index-data-in' type='checkbox'/><label for='index-d7503fac-f829-4c35-8560-71b0e81033cc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AL&#x27;, &#x27;AT&#x27;, &#x27;BA&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;, &#x27;DK&#x27;, &#x27;EA20&#x27;, &#x27;EE&#x27;,\n",
       "       &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;EU27_2020&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;GE&#x27;, &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;,\n",
       "       &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;, &#x27;MT&#x27;, &#x27;NL&#x27;, &#x27;NO&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;,\n",
       "       &#x27;RO&#x27;, &#x27;RS&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;TR&#x27;, &#x27;UA&#x27;, &#x27;UK&#x27;, &#x27;XK&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;Country&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8bd7a9b0-88d2-48bd-a5e5-b81c24ea0e40' class='xr-index-data-in' type='checkbox'/><label for='index-8bd7a9b0-88d2-48bd-a5e5-b81c24ea0e40' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1990-01-01&#x27;, &#x27;1991-01-01&#x27;, &#x27;1992-01-01&#x27;, &#x27;1993-01-01&#x27;,\n",
       "               &#x27;1994-01-01&#x27;, &#x27;1995-01-01&#x27;, &#x27;1996-01-01&#x27;, &#x27;1997-01-01&#x27;,\n",
       "               &#x27;1998-01-01&#x27;, &#x27;1999-01-01&#x27;, &#x27;2000-01-01&#x27;, &#x27;2001-01-01&#x27;,\n",
       "               &#x27;2002-01-01&#x27;, &#x27;2003-01-01&#x27;, &#x27;2004-01-01&#x27;, &#x27;2005-01-01&#x27;,\n",
       "               &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;, &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;,\n",
       "               &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;, &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;,\n",
       "               &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;, &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;,\n",
       "               &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;,\n",
       "               &#x27;2022-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Year&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Energy</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8692163d-79dc-40ca-9962-d6bde981c97c' class='xr-index-data-in' type='checkbox'/><label for='index-8692163d-79dc-40ca-9962-d6bde981c97c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Other liquid biofuels&#x27;, &#x27;Pure bio jet kerosene&#x27;, &#x27;Pure biodiesels&#x27;,\n",
       "       &#x27;Pure biogasoline&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;Energy&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13700186-a277-40c2-8b57-3c48368ad3a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13700186-a277-40c2-8b57-3c48368ad3a0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (Energy: 4, Country: 43, Year: 33)> Size: 45kB\n",
       "array([[[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. , 200. , 200. ],\n",
       "        [ 28. ,  28. ,  28. , ..., 645.5, 645.5, 645.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]],\n",
       "\n",
       "       [[  0. ,   0. ,   0. , ...,   0. ,   0. ,   0. ],\n",
       "        [  0. ,   0. ,   0. , ..., 220.5, 220.5, 220.5],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ],\n",
       "        ...,\n",
       "        [  0. ,   0. ,   0. , ...,   0. ,   nan,   nan],\n",
       "        [  0. ,   0. ,   0. , ...,   nan,   nan,   nan],\n",
       "        [  nan,   nan,   nan, ...,   0. ,   0. ,   0. ]]])\n",
       "Coordinates:\n",
       "  * Country  (Country) object 344B 'AL' 'AT' 'BA' 'BE' ... 'TR' 'UA' 'UK' 'XK'\n",
       "  * Year     (Year) datetime64[ns] 264B 1990-01-01 1991-01-01 ... 2022-01-01\n",
       "  * Energy   (Energy) object 32B 'Other liquid biofuels' ... 'Pure biogasoline'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.load_dataarray(f\"data/{dataset_code}.nc\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfp = ds.sel(Country=\"EU27_2020\").to_dataset(\"Energy\").to_dataframe()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "dfp[\n",
    "    [\n",
    "        \"Pure biodiesels\",\n",
    "        \"Pure biogasoline\",\n",
    "        \"Pure bio jet kerosene\",\n",
    "        \"Other liquid biofuels\",\n",
    "    ]\n",
    "].plot(ax=ax, kind=\"area\", alpha=0.6)\n",
    "ax.set_ylabel(\"Production capacity (kton)\")\n",
    "plt.savefig(\"plot/figure18.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}