01 Working with excel.ipynb

{
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   "cell_type": "code",
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   "source": [
    "import pandas as pd   # for data analysis\n",
    "import openpyxl       # read, write Excel xlsx/xlsm files\n",
    "\n",
    "import matplotlib.pyplot as plt      # data visualization\n",
    "import plotly.express as px          # data visualization\n",
    "import plotly.graph_objects as go    # data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "# Question: How to work with excel files?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Python package xlrd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "https://xlrd.readthedocs.io/en/latest/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\tools\\miniconda3\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "file = \"data/env_wasgen_new.xlsx\"\n",
    "book = openpyxl.load_workbook(file, data_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of worksheets is 62\n",
      "Worksheet name(s):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(62,\n",
       " ['Summary',\n",
       "  'Structure',\n",
       "  'Sheet 1',\n",
       "  'Sheet 2',\n",
       "  'Sheet 3',\n",
       "  'Sheet 4',\n",
       "  'Sheet 5',\n",
       "  'Sheet 6',\n",
       "  'Sheet 7',\n",
       "  'Sheet 8'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"The number of worksheets is {len(book.worksheets)}\")\n",
    "\n",
    "print(\"Worksheet name(s):\")\n",
    "len(book.sheetnames), book.sheetnames[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sheet name: Summary, dimensions: A1:O75\n",
      "Cell A1: ('General', None, 'n')\n"
     ]
    }
   ],
   "source": [
    "sh = book[book.sheetnames[0]]\n",
    "\n",
    "print(f\"Sheet name: {sh.title}, dimensions: {sh.dimensions}\")\n",
    "\n",
    "cell_A1 = sh.cell(row=1, column=1)\n",
    "print(f\"Cell A1: {cell_A1.number_format, cell_A1.value, cell_A1.data_type}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Generation of waste by waste category, hazardousness and NACE Rev. 2 activity [ENV_WASGEN__custom_3738744]', None, None, None, None, None, None, None, None, None, None, None, None, None, None)\n",
      "('Open product page', 'Open in Data Browser', None, None, None, None, None, None, None, None, None, None, None, None, None)\n",
      "('Description: ', '-', None, None, None, None, None, None, None, None, None, None, None, None, None)\n",
      "('Last update of data: ', None, None, '13/09/2022 11:00', None, None, None, None, None, None, None, None, None, None, None)\n",
      "('Last change of data structure: ', None, None, '13/09/2022 11:00', None, None, None, None, None, None, None, None, None, None, None)\n",
      "(None, 'Institutional source(s)', None, None, None, None, None, None, None, None, None, None, None, None, None)\n",
      "(None, None, 'Eurostat', None, None, None, None, None, None, None, None, None, None, None, None)\n",
      "(None, 'Contents', 'Time frequency [FREQ]', 'Unit of measure [UNIT]', 'Hazard class [HAZARD]', 'Statistical classification of economic activities in the European Community (NACE Rev. 2) [NACE_R2]', 'Waste categories [WASTE]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 1', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Agriculture, forestry and fishing [A]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 2', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Mining and quarrying [B]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 3', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Manufacturing [C]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 4', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Electricity, gas, steam and air conditioning supply [D]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 5', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Water supply; sewerage, waste management and remediation activities [E]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 6', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Construction [F]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 7', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Services (except wholesale of waste and scrap) [G-U_X_G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 8', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Wholesale of waste and scrap [G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 9', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Households [EP_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 10', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'All NACE activities plus households [TOTAL_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 11', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Agriculture, forestry and fishing [A]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 12', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Mining and quarrying [B]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 13', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Manufacturing [C]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 14', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Electricity, gas, steam and air conditioning supply [D]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 15', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Water supply; sewerage, waste management and remediation activities [E]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 16', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Construction [F]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 17', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Services (except wholesale of waste and scrap) [G-U_X_G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 18', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Wholesale of waste and scrap [G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 19', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'Households [EP_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 20', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Hazardous [HAZ]', 'All NACE activities plus households [TOTAL_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 21', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Agriculture, forestry and fishing [A]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 22', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Mining and quarrying [B]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 23', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Manufacturing [C]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 24', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Electricity, gas, steam and air conditioning supply [D]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 25', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Water supply; sewerage, waste management and remediation activities [E]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 26', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Construction [F]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Sheet 27', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Services (except wholesale of waste and scrap) [G-U_X_G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 28', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Wholesale of waste and scrap [G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 29', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'Households [EP_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 30', 'Annual [A]', 'Kilograms per capita [KG_HAB]', 'Non-hazardous [NHAZ]', 'All NACE activities plus households [TOTAL_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 31', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Agriculture, forestry and fishing [A]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 32', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Mining and quarrying [B]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 33', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Manufacturing [C]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 34', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Electricity, gas, steam and air conditioning supply [D]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 35', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Water supply; sewerage, waste management and remediation activities [E]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 36', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Construction [F]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 37', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Services (except wholesale of waste and scrap) [G-U_X_G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 38', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Wholesale of waste and scrap [G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 39', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'Households [EP_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 40', 'Annual [A]', 'Tonne [T]', 'Hazardous and non-hazardous - Total [HAZ_NHAZ]', 'All NACE activities plus households [TOTAL_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 41', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Agriculture, forestry and fishing [A]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 42', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Mining and quarrying [B]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 43', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Manufacturing [C]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 44', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Electricity, gas, steam and air conditioning supply [D]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 45', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Water supply; sewerage, waste management and remediation activities [E]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 46', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Construction [F]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 47', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Services (except wholesale of waste and scrap) [G-U_X_G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 48', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Wholesale of waste and scrap [G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 49', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'Households [EP_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 50', 'Annual [A]', 'Tonne [T]', 'Hazardous [HAZ]', 'All NACE activities plus households [TOTAL_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 51', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Agriculture, forestry and fishing [A]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 52', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Mining and quarrying [B]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 53', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Manufacturing [C]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 54', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Electricity, gas, steam and air conditioning supply [D]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 55', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Water supply; sewerage, waste management and remediation activities [E]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 56', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Construction [F]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 57', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Services (except wholesale of waste and scrap) [G-U_X_G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 58', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Wholesale of waste and scrap [G4677]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 59', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'Households [EP_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n",
      "(None, 'Feuille 60', 'Annual [A]', 'Tonne [T]', 'Non-hazardous [NHAZ]', 'All NACE activities plus households [TOTAL_HH]', 'Total waste [TOTAL]', None, None, None, None, None, None, None, None)\n"
     ]
    }
   ],
   "source": [
    "sh = book[book.sheetnames[0]]\n",
    "\n",
    "# get rows of the sheet\n",
    "for row in sh.iter_rows(values_only=True):\n",
    "    if any(v is not None for v in row):\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Finding the right sheet\n",
    "\n",
    "We have the following information in each sheet \n",
    "\n",
    "- UNIT\n",
    "- HAZARD\n",
    "- WASTE\n",
    "- NACE_R2\n",
    "\n",
    "Now lets try to find a way to get all available information\n",
    "\n",
    "What is [NACE_R2](https://ec.europa.eu/eurostat/web/nace-rev2)?\n",
    "\n",
    "> NACE ist das Akronym zur Bezeichnung der verschiedenen statistischen Systematiken der Wirtschaftszweige, die seit\n",
    "1970 in der Europäischen Union entwickelt worden sind. Die NACE bildet den Rahmen für die Sammlung und Darstellung\n",
    "einer breiten Palette statistischer, nach Wirtschaftszweigen untergliederter Daten aus dem Bereich Wirtschaft\n",
    "(z. B. Produktion, Beschäftigung, Volkswirtschaftliche Gesamtrechnungen) und aus anderen Bereichen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict # why defaultdict? Cause if key is not found in the dictionary, then instead of KeyError, a new entry is created\n",
    "                                    # by declaration: list, set or int\n",
    "\n",
    "header_rows = range(6,10)\n",
    "\n",
    "header = defaultdict(set)\n",
    "for sheet in book.sheet_names():\n",
    "    sh = book.sheet_by_name(sheet)\n",
    "\n",
    "    for row in header_rows:\n",
    "        label, value = sh.cell_value(row, 0), sh.cell_value(row, 1)\n",
    "        header[label].add(value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(set,\n",
       "            {'UNIT': {'KG_HAB - Kilograms per capita', 'T - Tonne'},\n",
       "             'HAZARD': {'HAZ - Hazardous',\n",
       "              'HAZ_NHAZ - Hazardous and non-hazardous - Total',\n",
       "              'NHAZ - Non-hazardous'},\n",
       "             'WASTE': {'PRIM - Primary waste (TOTAL minus SEC)',\n",
       "              'SEC - Secondary waste (W033+W103+W128_13)',\n",
       "              'TOTAL - Total waste',\n",
       "              'TOT_X_MIN - Waste excluding major mineral wastes',\n",
       "              'W01-05 - Chemical and medical wastes (subtotal)',\n",
       "              'W06_07A - Recyclable wastes (subtotal, W06+W07 except W077)',\n",
       "              'W077_08 - Equipment (subtotal, W077+W08A+W081+W0841)',\n",
       "              'W09 - Animal and vegetal wastes (subtotal, W091+W092+W093)',\n",
       "              'W10 - Mixed ordinary wastes (subtotal, W101+W102+W103)',\n",
       "              'W11 - Common sludges',\n",
       "              'W12-13 - Mineral and solidified wastes (subtotal)'},\n",
       "             'NACE_R2': {'A - Agriculture, forestry and fishing',\n",
       "              'B - Mining and quarrying',\n",
       "              'C - Manufacturing',\n",
       "              'D - Electricity, gas, steam and air conditioning supply',\n",
       "              'E - Water supply; sewerage, waste management and remediation activities',\n",
       "              'EP_HH - Households',\n",
       "              'F - Construction',\n",
       "              'G-U_X_G4677 - Services (except wholesale of waste and scrap)',\n",
       "              'G4677 - Wholesale of waste and scrap',\n",
       "              'TOTAL_HH - All NACE activities plus households'}})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "codes = {k: {s.split(\" - \")[0]: s.split(\" - \")[1] for s in v} for k,v in header.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'UNIT': {'T': 'Tonne', 'KG_HAB': 'Kilograms per capita'},\n",
       " 'HAZARD': {'HAZ_NHAZ': 'Hazardous and non-hazardous',\n",
       "  'NHAZ': 'Non-hazardous',\n",
       "  'HAZ': 'Hazardous'},\n",
       " 'WASTE': {'W10': 'Mixed ordinary wastes (subtotal, W101+W102+W103)',\n",
       "  'TOT_X_MIN': 'Waste excluding major mineral wastes',\n",
       "  'SEC': 'Secondary waste (W033+W103+W128_13)',\n",
       "  'TOTAL': 'Total waste',\n",
       "  'W077_08': 'Equipment (subtotal, W077+W08A+W081+W0841)',\n",
       "  'W06_07A': 'Recyclable wastes (subtotal, W06+W07 except W077)',\n",
       "  'W11': 'Common sludges',\n",
       "  'W01-05': 'Chemical and medical wastes (subtotal)',\n",
       "  'PRIM': 'Primary waste (TOTAL minus SEC)',\n",
       "  'W12-13': 'Mineral and solidified wastes (subtotal)',\n",
       "  'W09': 'Animal and vegetal wastes (subtotal, W091+W092+W093)'},\n",
       " 'NACE_R2': {'EP_HH': 'Households',\n",
       "  'G4677': 'Wholesale of waste and scrap',\n",
       "  'B': 'Mining and quarrying',\n",
       "  'E': 'Water supply; sewerage, waste management and remediation activities',\n",
       "  'TOTAL_HH': 'All NACE activities plus households',\n",
       "  'G-U_X_G4677': 'Services (except wholesale of waste and scrap)',\n",
       "  'F': 'Construction',\n",
       "  'D': 'Electricity, gas, steam and air conditioning supply',\n",
       "  'C': 'Manufacturing',\n",
       "  'A': 'Agriculture, forestry and fishing'}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def print_codes():\n",
    "    for k,v in codes.items():\n",
    "        print(\"Category: \", k)\n",
    "        print(\"---------\")\n",
    "        for k,v in v.items():\n",
    "            print(f\"{k}: {v}\")\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  UNIT\n",
      "---------\n",
      "T: Tonne\n",
      "KG_HAB: Kilograms per capita\n",
      "\n",
      "Category:  HAZARD\n",
      "---------\n",
      "HAZ_NHAZ: Hazardous and non-hazardous\n",
      "NHAZ: Non-hazardous\n",
      "HAZ: Hazardous\n",
      "\n",
      "Category:  WASTE\n",
      "---------\n",
      "W10: Mixed ordinary wastes (subtotal, W101+W102+W103)\n",
      "TOT_X_MIN: Waste excluding major mineral wastes\n",
      "SEC: Secondary waste (W033+W103+W128_13)\n",
      "TOTAL: Total waste\n",
      "W077_08: Equipment (subtotal, W077+W08A+W081+W0841)\n",
      "W06_07A: Recyclable wastes (subtotal, W06+W07 except W077)\n",
      "W11: Common sludges\n",
      "W01-05: Chemical and medical wastes (subtotal)\n",
      "PRIM: Primary waste (TOTAL minus SEC)\n",
      "W12-13: Mineral and solidified wastes (subtotal)\n",
      "W09: Animal and vegetal wastes (subtotal, W091+W092+W093)\n",
      "\n",
      "Category:  NACE_R2\n",
      "---------\n",
      "EP_HH: Households\n",
      "G4677: Wholesale of waste and scrap\n",
      "B: Mining and quarrying\n",
      "E: Water supply; sewerage, waste management and remediation activities\n",
      "TOTAL_HH: All NACE activities plus households\n",
      "G-U_X_G4677: Services (except wholesale of waste and scrap)\n",
      "F: Construction\n",
      "D: Electricity, gas, steam and air conditioning supply\n",
      "C: Manufacturing\n",
      "A: Agriculture, forestry and fishing\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_codes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Reading xls with pandas\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "file = \"data/env_wasgen.xls\"\n",
    "df = pd.read_excel(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "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>Generation of waste by waste category, hazardousness and NACE Rev. 2 activity  [env_wasgen]</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Last update</td>\n",
       "      <td>2021-04-30 12:10:39</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>Extracted on</td>\n",
       "      <td>2021-05-31 21:43:06.784000</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>3</th>\n",
       "      <td>Source of data</td>\n",
       "      <td>Eurostat</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>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UNIT</td>\n",
       "      <td>KG_HAB - Kilograms per capita</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>6</th>\n",
       "      <td>HAZARD</td>\n",
       "      <td>HAZ_NHAZ - Hazardous and non-hazardous - Total</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>7</th>\n",
       "      <td>WASTE</td>\n",
       "      <td>TOTAL - Total waste</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>8</th>\n",
       "      <td>NACE_R2</td>\n",
       "      <td>A - Agriculture, forestry and fishing</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>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GEO</td>\n",
       "      <td>GEO(L)/TIME</td>\n",
       "      <td>2004</td>\n",
       "      <td>2006</td>\n",
       "      <td>2008</td>\n",
       "      <td>2010</td>\n",
       "      <td>2012</td>\n",
       "      <td>2014</td>\n",
       "      <td>2016</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EU27_2020</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>146</td>\n",
       "      <td>131</td>\n",
       "      <td>104</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>EU28</td>\n",
       "      <td>European Union - 28 countries (2013-2020)</td>\n",
       "      <td>130</td>\n",
       "      <td>116</td>\n",
       "      <td>93</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>114</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>94</td>\n",
       "      <td>83</td>\n",
       "      <td>101</td>\n",
       "      <td>84</td>\n",
       "      <td>124</td>\n",
       "      <td>116</td>\n",
       "      <td>87</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CZ</td>\n",
       "      <td>Czechia</td>\n",
       "      <td>122</td>\n",
       "      <td>31</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DK</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany (until 1990 former territory of the FRG)</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>EE</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>135</td>\n",
       "      <td>88</td>\n",
       "      <td>179</td>\n",
       "      <td>83</td>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>IE</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>EL</td>\n",
       "      <td>Greece</td>\n",
       "      <td>430</td>\n",
       "      <td>428</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ES</td>\n",
       "      <td>Spain</td>\n",
       "      <td>419</td>\n",
       "      <td>341</td>\n",
       "      <td>247</td>\n",
       "      <td>125</td>\n",
       "      <td>118</td>\n",
       "      <td>125</td>\n",
       "      <td>135</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HR</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>64</td>\n",
       "      <td>119</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>IT</td>\n",
       "      <td>Italy</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CY</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>520</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>156</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>LV</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>92</td>\n",
       "      <td>43</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>74</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LT</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>259</td>\n",
       "      <td>267</td>\n",
       "      <td>246</td>\n",
       "      <td>147</td>\n",
       "      <td>161</td>\n",
       "      <td>144</td>\n",
       "      <td>98</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LU</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HU</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>396</td>\n",
       "      <td>198</td>\n",
       "      <td>34</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>MT</td>\n",
       "      <td>Malta</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>147</td>\n",
       "      <td>163</td>\n",
       "      <td>211</td>\n",
       "      <td>282</td>\n",
       "      <td>276</td>\n",
       "      <td>283</td>\n",
       "      <td>299</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>66</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PL</td>\n",
       "      <td>Poland</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PT</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>66</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RO</td>\n",
       "      <td>Romania</td>\n",
       "      <td>980</td>\n",
       "      <td>992</td>\n",
       "      <td>935</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SI</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>38</td>\n",
       "      <td>107</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>113</td>\n",
       "      <td>138</td>\n",
       "      <td>147</td>\n",
       "      <td>97</td>\n",
       "      <td>102</td>\n",
       "      <td>106</td>\n",
       "      <td>145</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>FI</td>\n",
       "      <td>Finland</td>\n",
       "      <td>294</td>\n",
       "      <td>387</td>\n",
       "      <td>515</td>\n",
       "      <td>517</td>\n",
       "      <td>583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>68</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>IS</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>269</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>227</td>\n",
       "      <td>256</td>\n",
       "      <td>88</td>\n",
       "      <td>114</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>LI</td>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>NO</td>\n",
       "      <td>Norway</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>UK</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ME</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>MK</td>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>AL</td>\n",
       "      <td>Albania</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>47</th>\n",
       "      <td>RS</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>TR</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>BA</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>XK</td>\n",
       "      <td>Kosovo (under United Nations Security Council ...</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Special value:</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>53</th>\n",
       "      <td>:</td>\n",
       "      <td>not available</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Generation of waste by waste category, hazardousness and NACE Rev. 2 activity  [env_wasgen]  \\\n",
       "0                                                 NaN                                            \n",
       "1                                         Last update                                            \n",
       "2                                        Extracted on                                            \n",
       "3                                      Source of data                                            \n",
       "4                                                 NaN                                            \n",
       "5                                                UNIT                                            \n",
       "6                                              HAZARD                                            \n",
       "7                                               WASTE                                            \n",
       "8                                             NACE_R2                                            \n",
       "9                                                 NaN                                            \n",
       "10                                                GEO                                            \n",
       "11                                          EU27_2020                                            \n",
       "12                                               EU28                                            \n",
       "13                                                 BE                                            \n",
       "14                                                 BG                                            \n",
       "15                                                 CZ                                            \n",
       "16                                                 DK                                            \n",
       "17                                                 DE                                            \n",
       "18                                                 EE                                            \n",
       "19                                                 IE                                            \n",
       "20                                                 EL                                            \n",
       "21                                                 ES                                            \n",
       "22                                                 FR                                            \n",
       "23                                                 HR                                            \n",
       "24                                                 IT                                            \n",
       "25                                                 CY                                            \n",
       "26                                                 LV                                            \n",
       "27                                                 LT                                            \n",
       "28                                                 LU                                            \n",
       "29                                                 HU                                            \n",
       "30                                                 MT                                            \n",
       "31                                                 NL                                            \n",
       "32                                                 AT                                            \n",
       "33                                                 PL                                            \n",
       "34                                                 PT                                            \n",
       "35                                                 RO                                            \n",
       "36                                                 SI                                            \n",
       "37                                                 SK                                            \n",
       "38                                                 FI                                            \n",
       "39                                                 SE                                            \n",
       "40                                                 IS                                            \n",
       "41                                                 LI                                            \n",
       "42                                                 NO                                            \n",
       "43                                                 UK                                            \n",
       "44                                                 ME                                            \n",
       "45                                                 MK                                            \n",
       "46                                                 AL                                            \n",
       "47                                                 RS                                            \n",
       "48                                                 TR                                            \n",
       "49                                                 BA                                            \n",
       "50                                                 XK                                            \n",
       "51                                                NaN                                            \n",
       "52                                     Special value:                                            \n",
       "53                                                  :                                            \n",
       "\n",
       "                                           Unnamed: 1 Unnamed: 2 Unnamed: 3  \\\n",
       "0                                                 NaN        NaN        NaN   \n",
       "1                                 2021-04-30 12:10:39        NaN        NaN   \n",
       "2                          2021-05-31 21:43:06.784000        NaN        NaN   \n",
       "3                                            Eurostat        NaN        NaN   \n",
       "4                                                 NaN        NaN        NaN   \n",
       "5                       KG_HAB - Kilograms per capita        NaN        NaN   \n",
       "6      HAZ_NHAZ - Hazardous and non-hazardous - Total        NaN        NaN   \n",
       "7                                 TOTAL - Total waste        NaN        NaN   \n",
       "8               A - Agriculture, forestry and fishing        NaN        NaN   \n",
       "9                                                 NaN        NaN        NaN   \n",
       "10                                        GEO(L)/TIME       2004       2006   \n",
       "11          European Union - 27 countries (from 2020)        146        131   \n",
       "12          European Union - 28 countries (2013-2020)        130        116   \n",
       "13                                            Belgium        114         34   \n",
       "14                                           Bulgaria         94         83   \n",
       "15                                            Czechia        122         31   \n",
       "16                                            Denmark          4          5   \n",
       "17   Germany (until 1990 former territory of the FRG)         15         18   \n",
       "18                                            Estonia        135         88   \n",
       "19                                            Ireland          2          0   \n",
       "20                                             Greece        430        428   \n",
       "21                                              Spain        419        341   \n",
       "22                                             France         20         19   \n",
       "23                                            Croatia         28         17   \n",
       "24                                              Italy         11         10   \n",
       "25                                             Cyprus        520        175   \n",
       "26                                             Latvia         92         43   \n",
       "27                                          Lithuania        259        267   \n",
       "28                                         Luxembourg         25         24   \n",
       "29                                            Hungary        396        198   \n",
       "30                                              Malta          0          0   \n",
       "31                                        Netherlands        147        163   \n",
       "32                                            Austria         57         62   \n",
       "33                                             Poland         40         40   \n",
       "34                                           Portugal         66         20   \n",
       "35                                            Romania        980        992   \n",
       "36                                           Slovenia         38        107   \n",
       "37                                           Slovakia        113        138   \n",
       "38                                            Finland        294        387   \n",
       "39                                             Sweden         35         35   \n",
       "40                                            Iceland        269          :   \n",
       "41                                      Liechtenstein          :          :   \n",
       "42                                             Norway         32         37   \n",
       "43                                     United Kingdom         12         11   \n",
       "44                                         Montenegro          :          :   \n",
       "45                                    North Macedonia          :          :   \n",
       "46                                            Albania          :          :   \n",
       "47                                             Serbia          :          :   \n",
       "48                                             Turkey          0          0   \n",
       "49                             Bosnia and Herzegovina          :          :   \n",
       "50  Kosovo (under United Nations Security Council ...          :          :   \n",
       "51                                                NaN        NaN        NaN   \n",
       "52                                                NaN        NaN        NaN   \n",
       "53                                      not available        NaN        NaN   \n",
       "\n",
       "   Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  \n",
       "0         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "1         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "2         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "3         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "4         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "5         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "6         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "7         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "8         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "9         NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "10       2008       2010       2012       2014       2016       2018  \n",
       "11        104         47         47         41         45         45  \n",
       "12         93         42         42         37         41         41  \n",
       "13         27         21         15         28         24         39  \n",
       "14        101         84        124        116         87         44  \n",
       "15         25         11         19         13         11         39  \n",
       "16          7         34         13         21         35         65  \n",
       "17         16          3          8          5         14         12  \n",
       "18        179         83         59         93         87        105  \n",
       "19          4         22         21         21         22         61  \n",
       "20          0          0          1          1         24         45  \n",
       "21        247        125        118        125        135        134  \n",
       "22         20         21         20         19         20         20  \n",
       "23          4          3         18         64        119        135  \n",
       "24          6          5          7          5          5          6  \n",
       "25        161        156         21         23         23         23  \n",
       "26         34         32          1         18         74         69  \n",
       "27        246        147        161        144         98        105  \n",
       "28          5          5          3          6         53         14  \n",
       "29         34         49         46         53         49         46  \n",
       "30          7          7          6          7         23         25  \n",
       "31        211        282        276        283        299        272  \n",
       "32         55         66         21         15         15         16  \n",
       "33         35         16         23         13         14         11  \n",
       "34          7          7          6          5          5          6  \n",
       "35        935         30         23         28         26         30  \n",
       "36         65         69         80         50         31         30  \n",
       "37        147         97        102        106        145         97  \n",
       "38        515        517        583          1          0          0  \n",
       "39         34         33         29         68         81         95  \n",
       "40          :        227        256         88        114        134  \n",
       "41         17          8          0          0          1          0  \n",
       "42         39         40         30         37         31         33  \n",
       "43         11          7         10          9          9          9  \n",
       "44          :          :          0         17         53         24  \n",
       "45          0          0          0         41          0          0  \n",
       "46          :          :          :          :          :          :  \n",
       "47          :          0          0          0         14         12  \n",
       "48          0          :          :          :          0          0  \n",
       "49          :          :          0          0          0          0  \n",
       "50          :          :          0          0          0          :  \n",
       "51        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "52        NaN        NaN        NaN        NaN        NaN        NaN  \n",
       "53        NaN        NaN        NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(file, header=11, nrows=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>GEO(L)/TIME</th>\n",
       "      <th>2004</th>\n",
       "      <th>2006</th>\n",
       "      <th>2008</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU27_2020</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>146</td>\n",
       "      <td>131</td>\n",
       "      <td>104</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EU28</td>\n",
       "      <td>European Union - 28 countries (2013-2020)</td>\n",
       "      <td>130</td>\n",
       "      <td>116</td>\n",
       "      <td>93</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>114</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>94</td>\n",
       "      <td>83</td>\n",
       "      <td>101</td>\n",
       "      <td>84</td>\n",
       "      <td>124</td>\n",
       "      <td>116</td>\n",
       "      <td>87</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ</td>\n",
       "      <td>Czechia</td>\n",
       "      <td>122</td>\n",
       "      <td>31</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DK</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany (until 1990 former territory of the FRG)</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EE</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>135</td>\n",
       "      <td>88</td>\n",
       "      <td>179</td>\n",
       "      <td>83</td>\n",
       "      <td>59</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IE</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EL</td>\n",
       "      <td>Greece</td>\n",
       "      <td>430</td>\n",
       "      <td>428</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ES</td>\n",
       "      <td>Spain</td>\n",
       "      <td>419</td>\n",
       "      <td>341</td>\n",
       "      <td>247</td>\n",
       "      <td>125</td>\n",
       "      <td>118</td>\n",
       "      <td>125</td>\n",
       "      <td>135</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HR</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>28</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>64</td>\n",
       "      <td>119</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IT</td>\n",
       "      <td>Italy</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CY</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>520</td>\n",
       "      <td>175</td>\n",
       "      <td>161</td>\n",
       "      <td>156</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LV</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>92</td>\n",
       "      <td>43</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>74</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LT</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>259</td>\n",
       "      <td>267</td>\n",
       "      <td>246</td>\n",
       "      <td>147</td>\n",
       "      <td>161</td>\n",
       "      <td>144</td>\n",
       "      <td>98</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LU</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HU</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>396</td>\n",
       "      <td>198</td>\n",
       "      <td>34</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MT</td>\n",
       "      <td>Malta</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>147</td>\n",
       "      <td>163</td>\n",
       "      <td>211</td>\n",
       "      <td>282</td>\n",
       "      <td>276</td>\n",
       "      <td>283</td>\n",
       "      <td>299</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>66</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PL</td>\n",
       "      <td>Poland</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PT</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>66</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RO</td>\n",
       "      <td>Romania</td>\n",
       "      <td>980</td>\n",
       "      <td>992</td>\n",
       "      <td>935</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SI</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>38</td>\n",
       "      <td>107</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>113</td>\n",
       "      <td>138</td>\n",
       "      <td>147</td>\n",
       "      <td>97</td>\n",
       "      <td>102</td>\n",
       "      <td>106</td>\n",
       "      <td>145</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>FI</td>\n",
       "      <td>Finland</td>\n",
       "      <td>294</td>\n",
       "      <td>387</td>\n",
       "      <td>515</td>\n",
       "      <td>517</td>\n",
       "      <td>583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>68</td>\n",
       "      <td>81</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>IS</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>269</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>227</td>\n",
       "      <td>256</td>\n",
       "      <td>88</td>\n",
       "      <td>114</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>LI</td>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NO</td>\n",
       "      <td>Norway</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>UK</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ME</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>53</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MK</td>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>AL</td>\n",
       "      <td>Albania</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>36</th>\n",
       "      <td>RS</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TR</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>BA</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XK</td>\n",
       "      <td>Kosovo (under United Nations Security Council ...</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>:</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>:</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GEO                                        GEO(L)/TIME 2004 2006  \\\n",
       "0   EU27_2020          European Union - 27 countries (from 2020)  146  131   \n",
       "1        EU28          European Union - 28 countries (2013-2020)  130  116   \n",
       "2          BE                                            Belgium  114   34   \n",
       "3          BG                                           Bulgaria   94   83   \n",
       "4          CZ                                            Czechia  122   31   \n",
       "5          DK                                            Denmark    4    5   \n",
       "6          DE   Germany (until 1990 former territory of the FRG)   15   18   \n",
       "7          EE                                            Estonia  135   88   \n",
       "8          IE                                            Ireland    2    0   \n",
       "9          EL                                             Greece  430  428   \n",
       "10         ES                                              Spain  419  341   \n",
       "11         FR                                             France   20   19   \n",
       "12         HR                                            Croatia   28   17   \n",
       "13         IT                                              Italy   11   10   \n",
       "14         CY                                             Cyprus  520  175   \n",
       "15         LV                                             Latvia   92   43   \n",
       "16         LT                                          Lithuania  259  267   \n",
       "17         LU                                         Luxembourg   25   24   \n",
       "18         HU                                            Hungary  396  198   \n",
       "19         MT                                              Malta    0    0   \n",
       "20         NL                                        Netherlands  147  163   \n",
       "21         AT                                            Austria   57   62   \n",
       "22         PL                                             Poland   40   40   \n",
       "23         PT                                           Portugal   66   20   \n",
       "24         RO                                            Romania  980  992   \n",
       "25         SI                                           Slovenia   38  107   \n",
       "26         SK                                           Slovakia  113  138   \n",
       "27         FI                                            Finland  294  387   \n",
       "28         SE                                             Sweden   35   35   \n",
       "29         IS                                            Iceland  269    :   \n",
       "30         LI                                      Liechtenstein    :    :   \n",
       "31         NO                                             Norway   32   37   \n",
       "32         UK                                     United Kingdom   12   11   \n",
       "33         ME                                         Montenegro    :    :   \n",
       "34         MK                                    North Macedonia    :    :   \n",
       "35         AL                                            Albania    :    :   \n",
       "36         RS                                             Serbia    :    :   \n",
       "37         TR                                             Turkey    0    0   \n",
       "38         BA                             Bosnia and Herzegovina    :    :   \n",
       "39         XK  Kosovo (under United Nations Security Council ...    :    :   \n",
       "\n",
       "   2008 2010 2012 2014 2016 2018  \n",
       "0   104   47   47   41   45   45  \n",
       "1    93   42   42   37   41   41  \n",
       "2    27   21   15   28   24   39  \n",
       "3   101   84  124  116   87   44  \n",
       "4    25   11   19   13   11   39  \n",
       "5     7   34   13   21   35   65  \n",
       "6    16    3    8    5   14   12  \n",
       "7   179   83   59   93   87  105  \n",
       "8     4   22   21   21   22   61  \n",
       "9     0    0    1    1   24   45  \n",
       "10  247  125  118  125  135  134  \n",
       "11   20   21   20   19   20   20  \n",
       "12    4    3   18   64  119  135  \n",
       "13    6    5    7    5    5    6  \n",
       "14  161  156   21   23   23   23  \n",
       "15   34   32    1   18   74   69  \n",
       "16  246  147  161  144   98  105  \n",
       "17    5    5    3    6   53   14  \n",
       "18   34   49   46   53   49   46  \n",
       "19    7    7    6    7   23   25  \n",
       "20  211  282  276  283  299  272  \n",
       "21   55   66   21   15   15   16  \n",
       "22   35   16   23   13   14   11  \n",
       "23    7    7    6    5    5    6  \n",
       "24  935   30   23   28   26   30  \n",
       "25   65   69   80   50   31   30  \n",
       "26  147   97  102  106  145   97  \n",
       "27  515  517  583    1    0    0  \n",
       "28   34   33   29   68   81   95  \n",
       "29    :  227  256   88  114  134  \n",
       "30   17    8    0    0    1    0  \n",
       "31   39   40   30   37   31   33  \n",
       "32   11    7   10    9    9    9  \n",
       "33    :    :    0   17   53   24  \n",
       "34    0    0    0   41    0    0  \n",
       "35    :    :    :    :    :    :  \n",
       "36    :    0    0    0   14   12  \n",
       "37    0    :    :    :    0    0  \n",
       "38    :    :    0    0    0    0  \n",
       "39    :    :    0    0    0    :  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>GEO(L)/TIME</th>\n",
       "      <th>2004</th>\n",
       "      <th>2006</th>\n",
       "      <th>2008</th>\n",
       "      <th>2010</th>\n",
       "      <th>2012</th>\n",
       "      <th>2014</th>\n",
       "      <th>2016</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany (until 1990 former territory of the FRG)</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GEO                                       GEO(L)/TIME 2004 2006 2008 2010  \\\n",
       "6  DE  Germany (until 1990 former territory of the FRG)   15   18   16    3   \n",
       "\n",
       "  2012 2014 2016 2018  \n",
       "6    8    5   14   12  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"GEO\"] == \"DE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   GEO          40 non-null     object\n",
      " 1   GEO(L)/TIME  40 non-null     object\n",
      " 2   2004         40 non-null     object\n",
      " 3   2006         40 non-null     object\n",
      " 4   2008         40 non-null     object\n",
      " 5   2010         40 non-null     object\n",
      " 6   2012         40 non-null     object\n",
      " 7   2014         40 non-null     object\n",
      " 8   2016         40 non-null     object\n",
      " 9   2018         40 non-null     object\n",
      "dtypes: object(10)\n",
      "memory usage: 3.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()  # everything seems to be a string -> \":\" causes the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>GEO(L)/TIME</th>\n",
       "      <th>2004</th>\n",
       "      <th>2006</th>\n",
       "      <th>2008</th>\n",
       "      <th>2010</th>\n",
       "      <th>2012</th>\n",
       "      <th>2014</th>\n",
       "      <th>2016</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU27_2020</td>\n",
       "      <td>European Union - 27 countries (from 2020)</td>\n",
       "      <td>146.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EU28</td>\n",
       "      <td>European Union - 28 countries (2013-2020)</td>\n",
       "      <td>130.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>114.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>94.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ</td>\n",
       "      <td>Czechia</td>\n",
       "      <td>122.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DK</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>Germany (until 1990 former territory of the FRG)</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EE</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>135.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IE</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EL</td>\n",
       "      <td>Greece</td>\n",
       "      <td>430.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ES</td>\n",
       "      <td>Spain</td>\n",
       "      <td>419.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HR</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IT</td>\n",
       "      <td>Italy</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CY</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>520.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LV</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>92.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LT</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>259.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LU</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HU</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>396.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MT</td>\n",
       "      <td>Malta</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NL</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>147.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>57.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PL</td>\n",
       "      <td>Poland</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PT</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>66.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RO</td>\n",
       "      <td>Romania</td>\n",
       "      <td>980.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SI</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>38.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>113.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>FI</td>\n",
       "      <td>Finland</td>\n",
       "      <td>294.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>IS</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>LI</td>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NO</td>\n",
       "      <td>Norway</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>UK</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ME</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MK</td>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>AL</td>\n",
       "      <td>Albania</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>36</th>\n",
       "      <td>RS</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TR</td>\n",
       "      <td>Turkey</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>BA</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XK</td>\n",
       "      <td>Kosovo (under United Nations Security Council ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GEO                                        GEO(L)/TIME   2004  \\\n",
       "0   EU27_2020          European Union - 27 countries (from 2020)  146.0   \n",
       "1        EU28          European Union - 28 countries (2013-2020)  130.0   \n",
       "2          BE                                            Belgium  114.0   \n",
       "3          BG                                           Bulgaria   94.0   \n",
       "4          CZ                                            Czechia  122.0   \n",
       "5          DK                                            Denmark    4.0   \n",
       "6          DE   Germany (until 1990 former territory of the FRG)   15.0   \n",
       "7          EE                                            Estonia  135.0   \n",
       "8          IE                                            Ireland    2.0   \n",
       "9          EL                                             Greece  430.0   \n",
       "10         ES                                              Spain  419.0   \n",
       "11         FR                                             France   20.0   \n",
       "12         HR                                            Croatia   28.0   \n",
       "13         IT                                              Italy   11.0   \n",
       "14         CY                                             Cyprus  520.0   \n",
       "15         LV                                             Latvia   92.0   \n",
       "16         LT                                          Lithuania  259.0   \n",
       "17         LU                                         Luxembourg   25.0   \n",
       "18         HU                                            Hungary  396.0   \n",
       "19         MT                                              Malta    0.0   \n",
       "20         NL                                        Netherlands  147.0   \n",
       "21         AT                                            Austria   57.0   \n",
       "22         PL                                             Poland   40.0   \n",
       "23         PT                                           Portugal   66.0   \n",
       "24         RO                                            Romania  980.0   \n",
       "25         SI                                           Slovenia   38.0   \n",
       "26         SK                                           Slovakia  113.0   \n",
       "27         FI                                            Finland  294.0   \n",
       "28         SE                                             Sweden   35.0   \n",
       "29         IS                                            Iceland  269.0   \n",
       "30         LI                                      Liechtenstein    NaN   \n",
       "31         NO                                             Norway   32.0   \n",
       "32         UK                                     United Kingdom   12.0   \n",
       "33         ME                                         Montenegro    NaN   \n",
       "34         MK                                    North Macedonia    NaN   \n",
       "35         AL                                            Albania    NaN   \n",
       "36         RS                                             Serbia    NaN   \n",
       "37         TR                                             Turkey    0.0   \n",
       "38         BA                             Bosnia and Herzegovina    NaN   \n",
       "39         XK  Kosovo (under United Nations Security Council ...    NaN   \n",
       "\n",
       "     2006   2008   2010   2012   2014   2016   2018  \n",
       "0   131.0  104.0   47.0   47.0   41.0   45.0   45.0  \n",
       "1   116.0   93.0   42.0   42.0   37.0   41.0   41.0  \n",
       "2    34.0   27.0   21.0   15.0   28.0   24.0   39.0  \n",
       "3    83.0  101.0   84.0  124.0  116.0   87.0   44.0  \n",
       "4    31.0   25.0   11.0   19.0   13.0   11.0   39.0  \n",
       "5     5.0    7.0   34.0   13.0   21.0   35.0   65.0  \n",
       "6    18.0   16.0    3.0    8.0    5.0   14.0   12.0  \n",
       "7    88.0  179.0   83.0   59.0   93.0   87.0  105.0  \n",
       "8     0.0    4.0   22.0   21.0   21.0   22.0   61.0  \n",
       "9   428.0    0.0    0.0    1.0    1.0   24.0   45.0  \n",
       "10  341.0  247.0  125.0  118.0  125.0  135.0  134.0  \n",
       "11   19.0   20.0   21.0   20.0   19.0   20.0   20.0  \n",
       "12   17.0    4.0    3.0   18.0   64.0  119.0  135.0  \n",
       "13   10.0    6.0    5.0    7.0    5.0    5.0    6.0  \n",
       "14  175.0  161.0  156.0   21.0   23.0   23.0   23.0  \n",
       "15   43.0   34.0   32.0    1.0   18.0   74.0   69.0  \n",
       "16  267.0  246.0  147.0  161.0  144.0   98.0  105.0  \n",
       "17   24.0    5.0    5.0    3.0    6.0   53.0   14.0  \n",
       "18  198.0   34.0   49.0   46.0   53.0   49.0   46.0  \n",
       "19    0.0    7.0    7.0    6.0    7.0   23.0   25.0  \n",
       "20  163.0  211.0  282.0  276.0  283.0  299.0  272.0  \n",
       "21   62.0   55.0   66.0   21.0   15.0   15.0   16.0  \n",
       "22   40.0   35.0   16.0   23.0   13.0   14.0   11.0  \n",
       "23   20.0    7.0    7.0    6.0    5.0    5.0    6.0  \n",
       "24  992.0  935.0   30.0   23.0   28.0   26.0   30.0  \n",
       "25  107.0   65.0   69.0   80.0   50.0   31.0   30.0  \n",
       "26  138.0  147.0   97.0  102.0  106.0  145.0   97.0  \n",
       "27  387.0  515.0  517.0  583.0    1.0    0.0    0.0  \n",
       "28   35.0   34.0   33.0   29.0   68.0   81.0   95.0  \n",
       "29    NaN    NaN  227.0  256.0   88.0  114.0  134.0  \n",
       "30    NaN   17.0    8.0    0.0    0.0    1.0    0.0  \n",
       "31   37.0   39.0   40.0   30.0   37.0   31.0   33.0  \n",
       "32   11.0   11.0    7.0   10.0    9.0    9.0    9.0  \n",
       "33    NaN    NaN    NaN    0.0   17.0   53.0   24.0  \n",
       "34    NaN    0.0    0.0    0.0   41.0    0.0    0.0  \n",
       "35    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "36    NaN    NaN    0.0    0.0    0.0   14.0   12.0  \n",
       "37    0.0    0.0    NaN    NaN    NaN    0.0    0.0  \n",
       "38    NaN    NaN    NaN    0.0    0.0    0.0    0.0  \n",
       "39    NaN    NaN    NaN    0.0    0.0    0.0    NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(file, header=11, nrows=40, na_values=\":\")   # the last parameter fixes the problem with \":\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   GEO          40 non-null     object \n",
      " 1   GEO(L)/TIME  40 non-null     object \n",
      " 2   2004         33 non-null     float64\n",
      " 3   2006         32 non-null     float64\n",
      " 4   2008         34 non-null     float64\n",
      " 5   2010         35 non-null     float64\n",
      " 6   2012         38 non-null     float64\n",
      " 7   2014         38 non-null     float64\n",
      " 8   2016         39 non-null     float64\n",
      " 9   2018         38 non-null     float64\n",
      "dtypes: float64(8), object(2)\n",
      "memory usage: 3.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()  # now all number columns are correctly recognized by pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "def get_data_from_sheet(excel_file: str, header: tuple) -> pd.DataFrame:\n",
    "    book = xlrd.open_workbook(file, on_demand=True)\n",
    "\n",
    "    sheet_name = \"\"\n",
    "    for sheet in book.sheet_names():\n",
    "        sh = book.sheet_by_name(sheet)\n",
    "\n",
    "        value = tuple(x.split(\" - \")[0] for x in sh.col_values(1, 6, 10))\n",
    "\n",
    "        if value == header:\n",
    "            sheet_name = sheet\n",
    "            break\n",
    "\n",
    "    if sheet_name:\n",
    "        return pd.read_excel(file, sheet_name=sheet_name, header=11, nrows=40, na_values=\":\").drop(columns=\"GEO(L)/TIME\").set_index(\"GEO\")\n",
    "    else:\n",
    "        return pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  UNIT\n",
      "---------\n",
      "T: Tonne\n",
      "KG_HAB: Kilograms per capita\n",
      "\n",
      "Category:  HAZARD\n",
      "---------\n",
      "HAZ: Hazardous\n",
      "HAZ_NHAZ: Hazardous and non-hazardous\n",
      "NHAZ: Non-hazardous\n",
      "\n",
      "Category:  WASTE\n",
      "---------\n",
      "W09: Animal and vegetal wastes (subtotal, W091+W092+W093)\n",
      "SEC: Secondary waste (W033+W103+W128_13)\n",
      "TOT_X_MIN: Waste excluding major mineral wastes\n",
      "W06_07A: Recyclable wastes (subtotal, W06+W07 except W077)\n",
      "PRIM: Primary waste (TOTAL minus SEC)\n",
      "W11: Common sludges\n",
      "W10: Mixed ordinary wastes (subtotal, W101+W102+W103)\n",
      "TOTAL: Total waste\n",
      "W01-05: Chemical and medical wastes (subtotal)\n",
      "W12-13: Mineral and solidified wastes (subtotal)\n",
      "W077_08: Equipment (subtotal, W077+W08A+W081+W0841)\n",
      "\n",
      "Category:  NACE_R2\n",
      "---------\n",
      "EP_HH: Households\n",
      "B: Mining and quarrying\n",
      "A: Agriculture, forestry and fishing\n",
      "C: Manufacturing\n",
      "G4677: Wholesale of waste and scrap\n",
      "F: Construction\n",
      "TOTAL_HH: All NACE activities plus households\n",
      "D: Electricity, gas, steam and air conditioning supply\n",
      "G-U_X_G4677: Services (except wholesale of waste and scrap)\n",
      "E: Water supply; sewerage, waste management and remediation activities\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_codes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df = get_data_from_sheet(file, (\"KG_HAB\", \"HAZ_NHAZ\", \"TOTAL\", \"A\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2004</th>\n",
       "      <th>2006</th>\n",
       "      <th>2008</th>\n",
       "      <th>2010</th>\n",
       "      <th>2012</th>\n",
       "      <th>2014</th>\n",
       "      <th>2016</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EU27_2020</th>\n",
       "      <td>146.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>130.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>114.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>94.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>122.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>135.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>430.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>419.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>28.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>520.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>92.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>259.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>396.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>147.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>57.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>66.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>980.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>38.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>113.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>294.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\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",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2004   2006   2008   2010   2012   2014   2016   2018\n",
       "GEO                                                              \n",
       "EU27_2020  146.0  131.0  104.0   47.0   47.0   41.0   45.0   45.0\n",
       "EU28       130.0  116.0   93.0   42.0   42.0   37.0   41.0   41.0\n",
       "BE         114.0   34.0   27.0   21.0   15.0   28.0   24.0   39.0\n",
       "BG          94.0   83.0  101.0   84.0  124.0  116.0   87.0   44.0\n",
       "CZ         122.0   31.0   25.0   11.0   19.0   13.0   11.0   39.0\n",
       "DK           4.0    5.0    7.0   34.0   13.0   21.0   35.0   65.0\n",
       "DE          15.0   18.0   16.0    3.0    8.0    5.0   14.0   12.0\n",
       "EE         135.0   88.0  179.0   83.0   59.0   93.0   87.0  105.0\n",
       "IE           2.0    0.0    4.0   22.0   21.0   21.0   22.0   61.0\n",
       "EL         430.0  428.0    0.0    0.0    1.0    1.0   24.0   45.0\n",
       "ES         419.0  341.0  247.0  125.0  118.0  125.0  135.0  134.0\n",
       "FR          20.0   19.0   20.0   21.0   20.0   19.0   20.0   20.0\n",
       "HR          28.0   17.0    4.0    3.0   18.0   64.0  119.0  135.0\n",
       "IT          11.0   10.0    6.0    5.0    7.0    5.0    5.0    6.0\n",
       "CY         520.0  175.0  161.0  156.0   21.0   23.0   23.0   23.0\n",
       "LV          92.0   43.0   34.0   32.0    1.0   18.0   74.0   69.0\n",
       "LT         259.0  267.0  246.0  147.0  161.0  144.0   98.0  105.0\n",
       "LU          25.0   24.0    5.0    5.0    3.0    6.0   53.0   14.0\n",
       "HU         396.0  198.0   34.0   49.0   46.0   53.0   49.0   46.0\n",
       "MT           0.0    0.0    7.0    7.0    6.0    7.0   23.0   25.0\n",
       "NL         147.0  163.0  211.0  282.0  276.0  283.0  299.0  272.0\n",
       "AT          57.0   62.0   55.0   66.0   21.0   15.0   15.0   16.0\n",
       "PL          40.0   40.0   35.0   16.0   23.0   13.0   14.0   11.0\n",
       "PT          66.0   20.0    7.0    7.0    6.0    5.0    5.0    6.0\n",
       "RO         980.0  992.0  935.0   30.0   23.0   28.0   26.0   30.0\n",
       "SI          38.0  107.0   65.0   69.0   80.0   50.0   31.0   30.0\n",
       "SK         113.0  138.0  147.0   97.0  102.0  106.0  145.0   97.0\n",
       "FI         294.0  387.0  515.0  517.0  583.0    1.0    0.0    0.0\n",
       "SE          35.0   35.0   34.0   33.0   29.0   68.0   81.0   95.0\n",
       "IS         269.0    NaN    NaN  227.0  256.0   88.0  114.0  134.0\n",
       "LI           NaN    NaN   17.0    8.0    0.0    0.0    1.0    0.0\n",
       "NO          32.0   37.0   39.0   40.0   30.0   37.0   31.0   33.0\n",
       "UK          12.0   11.0   11.0    7.0   10.0    9.0    9.0    9.0\n",
       "ME           NaN    NaN    NaN    NaN    0.0   17.0   53.0   24.0\n",
       "MK           NaN    NaN    0.0    0.0    0.0   41.0    0.0    0.0\n",
       "AL           NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN\n",
       "RS           NaN    NaN    NaN    0.0    0.0    0.0   14.0   12.0\n",
       "TR           0.0    0.0    0.0    NaN    NaN    NaN    0.0    0.0\n",
       "BA           NaN    NaN    NaN    NaN    0.0    0.0    0.0    0.0\n",
       "XK           NaN    NaN    NaN    NaN    0.0    0.0    0.0    NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## Plotting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 703
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"DE\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total waste generation for Germany from 2004 to 2018\\n'}, ylabel='Kilograms per capita'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "image/png": {
       "height": 456,
       "width": 726
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"DE\"].plot.bar(title=\"Total waste generation for Germany from 2004 to 2018\\n\", ylabel=\"Kilograms per capita\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='GEO'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "image/png": {
       "height": 411,
       "width": 761
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdata_frame\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_row\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_col_wrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_row_spacing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfacet_col_spacing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mhover_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mhover_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcustom_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0merror_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0merror_x_minus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0merror_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
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       "\u001b[0;34m\u001b[0m    \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_discrete_sequence\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_discrete_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_continuous_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrange_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_continuous_midpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mopacity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbarmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relative'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlog_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlog_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrange_x\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrange_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "    In a bar plot, each row of `data_frame` is represented as a rectangular\n",
       "    mark.\n",
       "    \n",
       "Parameters\n",
       "----------\n",
       "data_frame: DataFrame or array-like or dict\n",
       "    This argument needs to be passed for column names (and not keyword\n",
       "    names) to be used. Array-like and dict are tranformed internally to a\n",
       "    pandas DataFrame. Optional: if missing, a DataFrame gets constructed\n",
       "    under the hood using the other arguments.\n",
       "x: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    position marks along the x axis in cartesian coordinates. Either `x` or\n",
       "    `y` can optionally be a list of column references or array_likes,  in\n",
       "    which case the data will be treated as if it were 'wide' rather than\n",
       "    'long'.\n",
       "y: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    position marks along the y axis in cartesian coordinates. Either `x` or\n",
       "    `y` can optionally be a list of column references or array_likes,  in\n",
       "    which case the data will be treated as if it were 'wide' rather than\n",
       "    'long'.\n",
       "color: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    assign color to marks.\n",
       "facet_row: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    assign marks to facetted subplots in the vertical direction.\n",
       "facet_col: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    assign marks to facetted subplots in the horizontal direction.\n",
       "facet_col_wrap: int\n",
       "    Maximum number of facet columns. Wraps the column variable at this\n",
       "    width, so that the column facets span multiple rows. Ignored if 0, and\n",
       "    forced to 0 if `facet_row` or a `marginal` is set.\n",
       "facet_row_spacing: float between 0 and 1\n",
       "    Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7\n",
       "    when facet_col_wrap is used.\n",
       "facet_col_spacing: float between 0 and 1\n",
       "    Spacing between facet columns, in paper units Default is 0.02.\n",
       "hover_name: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like appear in bold\n",
       "    in the hover tooltip.\n",
       "hover_data: list of str or int, or Series or array-like, or dict\n",
       "    Either a list of names of columns in `data_frame`, or pandas Series, or\n",
       "    array_like objects or a dict with column names as keys, with values\n",
       "    True (for default formatting) False (in order to remove this column\n",
       "    from hover information), or a formatting string, for example ':.3f' or\n",
       "    '|%a' or list-like data to appear in the hover tooltip or tuples with a\n",
       "    bool or formatting string as first element, and list-like data to\n",
       "    appear in hover as second element Values from these columns appear as\n",
       "    extra data in the hover tooltip.\n",
       "custom_data: list of str or int, or Series or array-like\n",
       "    Either names of columns in `data_frame`, or pandas Series, or\n",
       "    array_like objects Values from these columns are extra data, to be used\n",
       "    in widgets or Dash callbacks for example. This data is not user-visible\n",
       "    but is included in events emitted by the figure (lasso selection etc.)\n",
       "text: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like appear in the\n",
       "    figure as text labels.\n",
       "base: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    position the base of the bar.\n",
       "error_x: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    size x-axis error bars. If `error_x_minus` is `None`, error bars will\n",
       "    be symmetrical, otherwise `error_x` is used for the positive direction\n",
       "    only.\n",
       "error_x_minus: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    size x-axis error bars in the negative direction. Ignored if `error_x`\n",
       "    is `None`.\n",
       "error_y: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    size y-axis error bars. If `error_y_minus` is `None`, error bars will\n",
       "    be symmetrical, otherwise `error_y` is used for the positive direction\n",
       "    only.\n",
       "error_y_minus: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    size y-axis error bars in the negative direction. Ignored if `error_y`\n",
       "    is `None`.\n",
       "animation_frame: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    assign marks to animation frames.\n",
       "animation_group: str or int or Series or array-like\n",
       "    Either a name of a column in `data_frame`, or a pandas Series or\n",
       "    array_like object. Values from this column or array_like are used to\n",
       "    provide object-constancy across animation frames: rows with matching\n",
       "    `animation_group`s will be treated as if they describe the same object\n",
       "    in each frame.\n",
       "category_orders: dict with str keys and list of str values (default `{}`)\n",
       "    By default, in Python 3.6+, the order of categorical values in axes,\n",
       "    legends and facets depends on the order in which these values are first\n",
       "    encountered in `data_frame` (and no order is guaranteed by default in\n",
       "    Python below 3.6). This parameter is used to force a specific ordering\n",
       "    of values per column. The keys of this dict should correspond to column\n",
       "    names, and the values should be lists of strings corresponding to the\n",
       "    specific display order desired.\n",
       "labels: dict with str keys and str values (default `{}`)\n",
       "    By default, column names are used in the figure for axis titles, legend\n",
       "    entries and hovers. This parameter allows this to be overridden. The\n",
       "    keys of this dict should correspond to column names, and the values\n",
       "    should correspond to the desired label to be displayed.\n",
       "color_discrete_sequence: list of str\n",
       "    Strings should define valid CSS-colors. When `color` is set and the\n",
       "    values in the corresponding column are not numeric, values in that\n",
       "    column are assigned colors by cycling through `color_discrete_sequence`\n",
       "    in the order described in `category_orders`, unless the value of\n",
       "    `color` is a key in `color_discrete_map`. Various useful color\n",
       "    sequences are available in the `plotly.express.colors` submodules,\n",
       "    specifically `plotly.express.colors.qualitative`.\n",
       "color_discrete_map: dict with str keys and str values (default `{}`)\n",
       "    String values should define valid CSS-colors Used to override\n",
       "    `color_discrete_sequence` to assign a specific colors to marks\n",
       "    corresponding with specific values. Keys in `color_discrete_map` should\n",
       "    be values in the column denoted by `color`. Alternatively, if the\n",
       "    values of `color` are valid colors, the string `'identity'` may be\n",
       "    passed to cause them to be used directly.\n",
       "color_continuous_scale: list of str\n",
       "    Strings should define valid CSS-colors This list is used to build a\n",
       "    continuous color scale when the column denoted by `color` contains\n",
       "    numeric data. Various useful color scales are available in the\n",
       "    `plotly.express.colors` submodules, specifically\n",
       "    `plotly.express.colors.sequential`, `plotly.express.colors.diverging`\n",
       "    and `plotly.express.colors.cyclical`.\n",
       "range_color: list of two numbers\n",
       "    If provided, overrides auto-scaling on the continuous color scale.\n",
       "color_continuous_midpoint: number (default `None`)\n",
       "    If set, computes the bounds of the continuous color scale to have the\n",
       "    desired midpoint. Setting this value is recommended when using\n",
       "    `plotly.express.colors.diverging` color scales as the inputs to\n",
       "    `color_continuous_scale`.\n",
       "opacity: float\n",
       "    Value between 0 and 1. Sets the opacity for markers.\n",
       "orientation: str, one of `'h'` for horizontal or `'v'` for vertical. \n",
       "    (default `'v'` if `x` and `y` are provided and both continous or both\n",
       "    categorical,  otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and\n",
       "    `y`(`x`) is continuous,  otherwise `'v'`(`'h'`) if only `x`(`y`) is\n",
       "    provided)\n",
       "barmode: str (default `'relative'`)\n",
       "    One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,\n",
       "    bars are stacked above zero for positive values and below zero for\n",
       "    negative values. In `'overlay'` mode, bars are drawn on top of one\n",
       "    another. In `'group'` mode, bars are placed beside each other.\n",
       "log_x: boolean (default `False`)\n",
       "    If `True`, the x-axis is log-scaled in cartesian coordinates.\n",
       "log_y: boolean (default `False`)\n",
       "    If `True`, the y-axis is log-scaled in cartesian coordinates.\n",
       "range_x: list of two numbers\n",
       "    If provided, overrides auto-scaling on the x-axis in cartesian\n",
       "    coordinates.\n",
       "range_y: list of two numbers\n",
       "    If provided, overrides auto-scaling on the y-axis in cartesian\n",
       "    coordinates.\n",
       "title: str\n",
       "    The figure title.\n",
       "template: str or dict or plotly.graph_objects.layout.Template instance\n",
       "    The figure template name (must be a key in plotly.io.templates) or\n",
       "    definition.\n",
       "width: int (default `None`)\n",
       "    The figure width in pixels.\n",
       "height: int (default `None`)\n",
       "    The figure height in pixels.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "    plotly.graph_objects.Figure\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.8/dist-packages/plotly/express/_chart_types.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?px.bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
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      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.bar(df, x=df.columns, y=df.index, barmode=\"relative\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## wide vs long\n",
    "\n",
    "How can we combine different sheets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
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       "        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>2004</th>\n",
       "      <th>2006</th>\n",
       "      <th>2008</th>\n",
       "      <th>2010</th>\n",
       "      <th>2012</th>\n",
       "      <th>2014</th>\n",
       "      <th>2016</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EU27_2020</th>\n",
       "      <td>63350000.0</td>\n",
       "      <td>57030000.0</td>\n",
       "      <td>45850000.0</td>\n",
       "      <td>20540000.0</td>\n",
       "      <td>20770000.0</td>\n",
       "      <td>18090000.0</td>\n",
       "      <td>20140000.0</td>\n",
       "      <td>20260000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>64070000.0</td>\n",
       "      <td>57700000.0</td>\n",
       "      <td>46530000.0</td>\n",
       "      <td>21000000.0</td>\n",
       "      <td>21380000.0</td>\n",
       "      <td>18710000.0</td>\n",
       "      <td>20710000.0</td>\n",
       "      <td>20870000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>1186648.0</td>\n",
       "      <td>361827.0</td>\n",
       "      <td>287656.0</td>\n",
       "      <td>225622.0</td>\n",
       "      <td>165463.0</td>\n",
       "      <td>311835.0</td>\n",
       "      <td>269190.0</td>\n",
       "      <td>441047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>722228.0</td>\n",
       "      <td>632966.0</td>\n",
       "      <td>754286.0</td>\n",
       "      <td>618107.0</td>\n",
       "      <td>903119.0</td>\n",
       "      <td>835401.0</td>\n",
       "      <td>617689.0</td>\n",
       "      <td>308760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>1244149.0</td>\n",
       "      <td>315585.0</td>\n",
       "      <td>254546.0</td>\n",
       "      <td>113683.0</td>\n",
       "      <td>196063.0</td>\n",
       "      <td>135313.0</td>\n",
       "      <td>114575.0</td>\n",
       "      <td>411533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>24038.0</td>\n",
       "      <td>27550.0</td>\n",
       "      <td>40731.0</td>\n",
       "      <td>189101.0</td>\n",
       "      <td>75225.0</td>\n",
       "      <td>117682.0</td>\n",
       "      <td>201648.0</td>\n",
       "      <td>373978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>1226989.0</td>\n",
       "      <td>1515272.0</td>\n",
       "      <td>1350758.0</td>\n",
       "      <td>256272.0</td>\n",
       "      <td>648869.0</td>\n",
       "      <td>438829.0</td>\n",
       "      <td>1126134.0</td>\n",
       "      <td>982106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>183860.0</td>\n",
       "      <td>118829.0</td>\n",
       "      <td>240005.0</td>\n",
       "      <td>109873.0</td>\n",
       "      <td>77404.0</td>\n",
       "      <td>121677.0</td>\n",
       "      <td>113946.0</td>\n",
       "      <td>138195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>8706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19376.0</td>\n",
       "      <td>101143.0</td>\n",
       "      <td>96251.0</td>\n",
       "      <td>96251.0</td>\n",
       "      <td>105033.0</td>\n",
       "      <td>296865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>4711294.0</td>\n",
       "      <td>4711294.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5073.0</td>\n",
       "      <td>9979.0</td>\n",
       "      <td>10976.0</td>\n",
       "      <td>255169.0</td>\n",
       "      <td>485143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>17986083.0</td>\n",
       "      <td>15152405.0</td>\n",
       "      <td>11355660.0</td>\n",
       "      <td>5816630.0</td>\n",
       "      <td>5496458.0</td>\n",
       "      <td>5817439.0</td>\n",
       "      <td>6271464.0</td>\n",
       "      <td>6258251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>1221940.0</td>\n",
       "      <td>1221940.0</td>\n",
       "      <td>1312890.0</td>\n",
       "      <td>1371625.0</td>\n",
       "      <td>1290136.0</td>\n",
       "      <td>1274216.0</td>\n",
       "      <td>1303642.0</td>\n",
       "      <td>1309912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>121133.0</td>\n",
       "      <td>72167.0</td>\n",
       "      <td>18679.0</td>\n",
       "      <td>14223.0</td>\n",
       "      <td>75096.0</td>\n",
       "      <td>269111.0</td>\n",
       "      <td>496153.0</td>\n",
       "      <td>550471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>615288.0</td>\n",
       "      <td>558481.0</td>\n",
       "      <td>349096.0</td>\n",
       "      <td>310912.0</td>\n",
       "      <td>407979.0</td>\n",
       "      <td>323518.0</td>\n",
       "      <td>320928.0</td>\n",
       "      <td>332554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>378416.0</td>\n",
       "      <td>131541.0</td>\n",
       "      <td>126651.0</td>\n",
       "      <td>129184.0</td>\n",
       "      <td>18203.0</td>\n",
       "      <td>19856.0</td>\n",
       "      <td>19453.0</td>\n",
       "      <td>19783.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>208465.0</td>\n",
       "      <td>96126.0</td>\n",
       "      <td>74660.0</td>\n",
       "      <td>68024.0</td>\n",
       "      <td>2767.0</td>\n",
       "      <td>36209.0</td>\n",
       "      <td>144075.0</td>\n",
       "      <td>132746.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>874218.0</td>\n",
       "      <td>874219.0</td>\n",
       "      <td>786069.0</td>\n",
       "      <td>455856.0</td>\n",
       "      <td>481708.0</td>\n",
       "      <td>421071.0</td>\n",
       "      <td>280625.0</td>\n",
       "      <td>293676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>11324.0</td>\n",
       "      <td>11324.0</td>\n",
       "      <td>2486.0</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>3094.0</td>\n",
       "      <td>30986.0</td>\n",
       "      <td>8330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>3999629.0</td>\n",
       "      <td>1997533.0</td>\n",
       "      <td>336865.0</td>\n",
       "      <td>487655.0</td>\n",
       "      <td>460047.0</td>\n",
       "      <td>520961.0</td>\n",
       "      <td>484320.0</td>\n",
       "      <td>449607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>36.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>2709.0</td>\n",
       "      <td>3018.0</td>\n",
       "      <td>10565.0</td>\n",
       "      <td>12292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>2392596.0</td>\n",
       "      <td>2659520.0</td>\n",
       "      <td>3468475.0</td>\n",
       "      <td>4682517.0</td>\n",
       "      <td>4618778.0</td>\n",
       "      <td>4769683.0</td>\n",
       "      <td>5085249.0</td>\n",
       "      <td>4678699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>468629.0</td>\n",
       "      <td>512872.0</td>\n",
       "      <td>459096.0</td>\n",
       "      <td>550439.0</td>\n",
       "      <td>179044.0</td>\n",
       "      <td>128124.0</td>\n",
       "      <td>129834.0</td>\n",
       "      <td>139870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>1522976.0</td>\n",
       "      <td>1522976.0</td>\n",
       "      <td>1350163.0</td>\n",
       "      <td>603472.0</td>\n",
       "      <td>884425.0</td>\n",
       "      <td>492106.0</td>\n",
       "      <td>534931.0</td>\n",
       "      <td>431500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>694144.0</td>\n",
       "      <td>213722.0</td>\n",
       "      <td>74760.0</td>\n",
       "      <td>68829.0</td>\n",
       "      <td>63215.0</td>\n",
       "      <td>50294.0</td>\n",
       "      <td>55520.0</td>\n",
       "      <td>62073.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>21019098.0</td>\n",
       "      <td>21019098.0</td>\n",
       "      <td>19204236.0</td>\n",
       "      <td>608062.0</td>\n",
       "      <td>470756.0</td>\n",
       "      <td>559573.0</td>\n",
       "      <td>507712.0</td>\n",
       "      <td>584619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>76497.0</td>\n",
       "      <td>214335.0</td>\n",
       "      <td>132159.0</td>\n",
       "      <td>141285.0</td>\n",
       "      <td>164761.0</td>\n",
       "      <td>102716.0</td>\n",
       "      <td>63248.0</td>\n",
       "      <td>61665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>605600.0</td>\n",
       "      <td>741965.0</td>\n",
       "      <td>788788.0</td>\n",
       "      <td>525604.0</td>\n",
       "      <td>549391.0</td>\n",
       "      <td>574396.0</td>\n",
       "      <td>788559.0</td>\n",
       "      <td>529786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>1537025.0</td>\n",
       "      <td>2038408.0</td>\n",
       "      <td>2738973.0</td>\n",
       "      <td>2772031.0</td>\n",
       "      <td>3157813.0</td>\n",
       "      <td>4471.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>314317.0</td>\n",
       "      <td>314317.0</td>\n",
       "      <td>314314.0</td>\n",
       "      <td>308946.0</td>\n",
       "      <td>273071.0</td>\n",
       "      <td>655374.0</td>\n",
       "      <td>804382.0</td>\n",
       "      <td>962141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>78559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72071.0</td>\n",
       "      <td>82097.0</td>\n",
       "      <td>28885.0</td>\n",
       "      <td>38357.0</td>\n",
       "      <td>47418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>615.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>148558.0</td>\n",
       "      <td>172417.0</td>\n",
       "      <td>184332.0</td>\n",
       "      <td>195252.0</td>\n",
       "      <td>150002.0</td>\n",
       "      <td>192189.0</td>\n",
       "      <td>161946.0</td>\n",
       "      <td>174257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>719198.0</td>\n",
       "      <td>666485.0</td>\n",
       "      <td>681314.0</td>\n",
       "      <td>464297.0</td>\n",
       "      <td>610133.0</td>\n",
       "      <td>612429.0</td>\n",
       "      <td>576195.0</td>\n",
       "      <td>616865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10661.0</td>\n",
       "      <td>33290.0</td>\n",
       "      <td>14748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>84366.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101675.0</td>\n",
       "      <td>82473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\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",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 2004        2006        2008        2010        2012  \\\n",
       "GEO                                                                     \n",
       "EU27_2020  63350000.0  57030000.0  45850000.0  20540000.0  20770000.0   \n",
       "EU28       64070000.0  57700000.0  46530000.0  21000000.0  21380000.0   \n",
       "BE          1186648.0    361827.0    287656.0    225622.0    165463.0   \n",
       "BG           722228.0    632966.0    754286.0    618107.0    903119.0   \n",
       "CZ          1244149.0    315585.0    254546.0    113683.0    196063.0   \n",
       "DK            24038.0     27550.0     40731.0    189101.0     75225.0   \n",
       "DE          1226989.0   1515272.0   1350758.0    256272.0    648869.0   \n",
       "EE           183860.0    118829.0    240005.0    109873.0     77404.0   \n",
       "IE             8706.0         0.0     19376.0    101143.0     96251.0   \n",
       "EL          4711294.0   4711294.0         0.0      5073.0      9979.0   \n",
       "ES         17986083.0  15152405.0  11355660.0   5816630.0   5496458.0   \n",
       "FR          1221940.0   1221940.0   1312890.0   1371625.0   1290136.0   \n",
       "HR           121133.0     72167.0     18679.0     14223.0     75096.0   \n",
       "IT           615288.0    558481.0    349096.0    310912.0    407979.0   \n",
       "CY           378416.0    131541.0    126651.0    129184.0     18203.0   \n",
       "LV           208465.0     96126.0     74660.0     68024.0      2767.0   \n",
       "LT           874218.0    874219.0    786069.0    455856.0    481708.0   \n",
       "LU            11324.0     11324.0      2486.0      2547.0      1381.0   \n",
       "HU          3999629.0   1997533.0    336865.0    487655.0    460047.0   \n",
       "MT               36.0         7.0      2667.0      2888.0      2709.0   \n",
       "NL          2392596.0   2659520.0   3468475.0   4682517.0   4618778.0   \n",
       "AT           468629.0    512872.0    459096.0    550439.0    179044.0   \n",
       "PL          1522976.0   1522976.0   1350163.0    603472.0    884425.0   \n",
       "PT           694144.0    213722.0     74760.0     68829.0     63215.0   \n",
       "RO         21019098.0  21019098.0  19204236.0    608062.0    470756.0   \n",
       "SI            76497.0    214335.0    132159.0    141285.0    164761.0   \n",
       "SK           605600.0    741965.0    788788.0    525604.0    549391.0   \n",
       "FI          1537025.0   2038408.0   2738973.0   2772031.0   3157813.0   \n",
       "SE           314317.0    314317.0    314314.0    308946.0    273071.0   \n",
       "IS            78559.0         NaN         NaN     72071.0     82097.0   \n",
       "LI                NaN         NaN       615.0       306.0         0.0   \n",
       "NO           148558.0    172417.0    184332.0    195252.0    150002.0   \n",
       "UK           719198.0    666485.0    681314.0    464297.0    610133.0   \n",
       "ME                NaN         NaN         NaN         NaN         0.0   \n",
       "MK                NaN         NaN         0.0         0.0        23.0   \n",
       "AL                NaN         NaN         NaN         NaN         NaN   \n",
       "RS                NaN         NaN         NaN         0.0         0.0   \n",
       "TR                0.0         0.0         0.0         NaN         NaN   \n",
       "BA                NaN         NaN         NaN         NaN         0.0   \n",
       "XK                NaN         NaN         NaN         NaN         0.0   \n",
       "\n",
       "                 2014        2016        2018  \n",
       "GEO                                            \n",
       "EU27_2020  18090000.0  20140000.0  20260000.0  \n",
       "EU28       18710000.0  20710000.0  20870000.0  \n",
       "BE           311835.0    269190.0    441047.0  \n",
       "BG           835401.0    617689.0    308760.0  \n",
       "CZ           135313.0    114575.0    411533.0  \n",
       "DK           117682.0    201648.0    373978.0  \n",
       "DE           438829.0   1126134.0    982106.0  \n",
       "EE           121677.0    113946.0    138195.0  \n",
       "IE            96251.0    105033.0    296865.0  \n",
       "EL            10976.0    255169.0    485143.0  \n",
       "ES          5817439.0   6271464.0   6258251.0  \n",
       "FR          1274216.0   1303642.0   1309912.0  \n",
       "HR           269111.0    496153.0    550471.0  \n",
       "IT           323518.0    320928.0    332554.0  \n",
       "CY            19856.0     19453.0     19783.0  \n",
       "LV            36209.0    144075.0    132746.0  \n",
       "LT           421071.0    280625.0    293676.0  \n",
       "LU             3094.0     30986.0      8330.0  \n",
       "HU           520961.0    484320.0    449607.0  \n",
       "MT             3018.0     10565.0     12292.0  \n",
       "NL          4769683.0   5085249.0   4678699.0  \n",
       "AT           128124.0    129834.0    139870.0  \n",
       "PL           492106.0    534931.0    431500.0  \n",
       "PT            50294.0     55520.0     62073.0  \n",
       "RO           559573.0    507712.0    584619.0  \n",
       "SI           102716.0     63248.0     61665.0  \n",
       "SK           574396.0    788559.0    529786.0  \n",
       "FI             4471.0         0.0         0.0  \n",
       "SE           655374.0    804382.0    962141.0  \n",
       "IS            28885.0     38357.0     47418.0  \n",
       "LI                0.0        22.0         0.0  \n",
       "NO           192189.0    161946.0    174257.0  \n",
       "UK           612429.0    576195.0    616865.0  \n",
       "ME            10661.0     33290.0     14748.0  \n",
       "MK            84366.0        40.0         0.0  \n",
       "AL                NaN         NaN         NaN  \n",
       "RS                0.0    101675.0     82473.0  \n",
       "TR                NaN         0.0         0.0  \n",
       "BA                0.0         0.0         0.0  \n",
       "XK                0.0         0.0         NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mid_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvalue_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvar_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvalue_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcol_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'DataFrame'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.\n",
       "\n",
       "This function is useful to massage a DataFrame into a format where one\n",
       "or more columns are identifier variables (`id_vars`), while all other\n",
       "columns, considered measured variables (`value_vars`), are \"unpivoted\" to\n",
       "the row axis, leaving just two non-identifier columns, 'variable' and\n",
       "'value'.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "id_vars : tuple, list, or ndarray, optional\n",
       "    Column(s) to use as identifier variables.\n",
       "value_vars : tuple, list, or ndarray, optional\n",
       "    Column(s) to unpivot. If not specified, uses all columns that\n",
       "    are not set as `id_vars`.\n",
       "var_name : scalar\n",
       "    Name to use for the 'variable' column. If None it uses\n",
       "    ``frame.columns.name`` or 'variable'.\n",
       "value_name : scalar, default 'value'\n",
       "    Name to use for the 'value' column.\n",
       "col_level : int or str, optional\n",
       "    If columns are a MultiIndex then use this level to melt.\n",
       "ignore_index : bool, default True\n",
       "    If True, original index is ignored. If False, the original index is retained.\n",
       "    Index labels will be repeated as necessary.\n",
       "\n",
       "    .. versionadded:: 1.1.0\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame\n",
       "    Unpivoted DataFrame.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "melt : Identical method.\n",
       "pivot_table : Create a spreadsheet-style pivot table as a DataFrame.\n",
       "DataFrame.pivot : Return reshaped DataFrame organized\n",
       "    by given index / column values.\n",
       "DataFrame.explode : Explode a DataFrame from list-like\n",
       "        columns to long format.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},\n",
       "...                    'B': {0: 1, 1: 3, 2: 5},\n",
       "...                    'C': {0: 2, 1: 4, 2: 6}})\n",
       ">>> df\n",
       "   A  B  C\n",
       "0  a  1  2\n",
       "1  b  3  4\n",
       "2  c  5  6\n",
       "\n",
       ">>> df.melt(id_vars=['A'], value_vars=['B'])\n",
       "   A variable  value\n",
       "0  a        B      1\n",
       "1  b        B      3\n",
       "2  c        B      5\n",
       "\n",
       ">>> df.melt(id_vars=['A'], value_vars=['B', 'C'])\n",
       "   A variable  value\n",
       "0  a        B      1\n",
       "1  b        B      3\n",
       "2  c        B      5\n",
       "3  a        C      2\n",
       "4  b        C      4\n",
       "5  c        C      6\n",
       "\n",
       "The names of 'variable' and 'value' columns can be customized:\n",
       "\n",
       ">>> df.melt(id_vars=['A'], value_vars=['B'],\n",
       "...         var_name='myVarname', value_name='myValname')\n",
       "   A myVarname  myValname\n",
       "0  a         B          1\n",
       "1  b         B          3\n",
       "2  c         B          5\n",
       "\n",
       "Original index values can be kept around:\n",
       "\n",
       ">>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)\n",
       "   A variable  value\n",
       "0  a        B      1\n",
       "1  b        B      3\n",
       "2  c        B      5\n",
       "0  a        C      2\n",
       "1  b        C      4\n",
       "2  c        C      6\n",
       "\n",
       "If you have multi-index columns:\n",
       "\n",
       ">>> df.columns = [list('ABC'), list('DEF')]\n",
       ">>> df\n",
       "   A  B  C\n",
       "   D  E  F\n",
       "0  a  1  2\n",
       "1  b  3  4\n",
       "2  c  5  6\n",
       "\n",
       ">>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])\n",
       "   A variable  value\n",
       "0  a        B      1\n",
       "1  b        B      3\n",
       "2  c        B      5\n",
       "\n",
       ">>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])\n",
       "  (A, D) variable_0 variable_1  value\n",
       "0      a          B          E      1\n",
       "1      b          B          E      3\n",
       "2      c          B          E      5\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?df.melt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "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>KG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EU27_2020</th>\n",
       "      <td>2004</td>\n",
       "      <td>63350000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2004</td>\n",
       "      <td>64070000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2004</td>\n",
       "      <td>1186648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2004</td>\n",
       "      <td>722228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2004</td>\n",
       "      <td>1244149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2018</td>\n",
       "      <td>82473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year          KG\n",
       "GEO                        \n",
       "EU27_2020  2004  63350000.0\n",
       "EU28       2004  64070000.0\n",
       "BE         2004   1186648.0\n",
       "BG         2004    722228.0\n",
       "CZ         2004   1244149.0\n",
       "...         ...         ...\n",
       "AL         2018         NaN\n",
       "RS         2018     82473.0\n",
       "TR         2018         0.0\n",
       "BA         2018         0.0\n",
       "XK         2018         NaN\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.melt(var_name=\"year\", value_name=\"KG\", ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df = get_data_from_sheet(file, (\"KG_HAB\", \"HAZ_NHAZ\", \"TOTAL\", \"A\"))\n",
    "df = df.reset_index().melt(id_vars=\"GEO\", var_name=\"year\", value_name=\"value\")\n",
    "df = df.assign(unit=\"KG_HAB\", hazard=\"HAZ_NHAZ\", waste=\"TOTAL\", nace_r2=\"A\")\n",
    "df.year = df.year.astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>GEO</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>waste</th>\n",
       "      <th>nace_r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU27_2020</td>\n",
       "      <td>2004</td>\n",
       "      <td>146.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2004</td>\n",
       "      <td>130.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2004</td>\n",
       "      <td>114.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>2004</td>\n",
       "      <td>94.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ</td>\n",
       "      <td>2004</td>\n",
       "      <td>122.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>AL</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>RS</td>\n",
       "      <td>2018</td>\n",
       "      <td>12.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>TR</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>BA</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>XK</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           GEO  year  value    unit    hazard  waste nace_r2\n",
       "0    EU27_2020  2004  146.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "1         EU28  2004  130.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "2           BE  2004  114.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "3           BG  2004   94.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "4           CZ  2004  122.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "..         ...   ...    ...     ...       ...    ...     ...\n",
       "315         AL  2018    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "316         RS  2018   12.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "317         TR  2018    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "318         BA  2018    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "319         XK  2018    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "\n",
       "[320 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 320 entries, 0 to 319\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype   \n",
      "---  ------   --------------  -----   \n",
      " 0   GEO      320 non-null    object  \n",
      " 1   year     320 non-null    category\n",
      " 2   value    287 non-null    float64 \n",
      " 3   unit     320 non-null    object  \n",
      " 4   hazard   320 non-null    object  \n",
      " 5   waste    320 non-null    object  \n",
      " 6   nace_r2  320 non-null    object  \n",
      "dtypes: category(1), float64(1), object(5)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
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    "hideCode": false,
    "hidePrompt": false
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    {
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     "execution_count": 36,
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     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "px.bar(df, x=\"GEO\", y=\"value\", color=\"year\", barmode=\"relative\", labels={\"value\": \"Kilograms per capita\", \"GEO\": \"Country\"}, category_orders={\"GEO\": df.groupby(\"GEO\").value.agg(\"sum\").sort_values(ascending=True).index.tolist()})"
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEO</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>waste</th>\n",
       "      <th>nace_r2</th>\n",
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       "  <tbody>\n",
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       "      <th>48</th>\n",
       "      <td>IE</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>MT</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
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       "      <th>77</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>DK</td>\n",
       "      <td>2006</td>\n",
       "      <td>5.0</td>\n",
       "      <td>KG_HAB</td>\n",
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       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>UK</td>\n",
       "      <td>2006</td>\n",
       "      <td>11.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>HR</td>\n",
       "      <td>2006</td>\n",
       "      <td>17.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>DE</td>\n",
       "      <td>2006</td>\n",
       "      <td>18.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>FR</td>\n",
       "      <td>2006</td>\n",
       "      <td>19.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>PT</td>\n",
       "      <td>2006</td>\n",
       "      <td>20.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>LU</td>\n",
       "      <td>2006</td>\n",
       "      <td>24.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>CZ</td>\n",
       "      <td>2006</td>\n",
       "      <td>31.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>BE</td>\n",
       "      <td>2006</td>\n",
       "      <td>34.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>SE</td>\n",
       "      <td>2006</td>\n",
       "      <td>35.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>NO</td>\n",
       "      <td>2006</td>\n",
       "      <td>37.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>PL</td>\n",
       "      <td>2006</td>\n",
       "      <td>40.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>LV</td>\n",
       "      <td>2006</td>\n",
       "      <td>43.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>AT</td>\n",
       "      <td>2006</td>\n",
       "      <td>62.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>BG</td>\n",
       "      <td>2006</td>\n",
       "      <td>83.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>EE</td>\n",
       "      <td>2006</td>\n",
       "      <td>88.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>SI</td>\n",
       "      <td>2006</td>\n",
       "      <td>107.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2006</td>\n",
       "      <td>116.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>EU27_2020</td>\n",
       "      <td>2006</td>\n",
       "      <td>131.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SK</td>\n",
       "      <td>2006</td>\n",
       "      <td>138.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>NL</td>\n",
       "      <td>2006</td>\n",
       "      <td>163.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>CY</td>\n",
       "      <td>2006</td>\n",
       "      <td>175.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>HU</td>\n",
       "      <td>2006</td>\n",
       "      <td>198.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>LT</td>\n",
       "      <td>2006</td>\n",
       "      <td>267.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ES</td>\n",
       "      <td>2006</td>\n",
       "      <td>341.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>FI</td>\n",
       "      <td>2006</td>\n",
       "      <td>387.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>EL</td>\n",
       "      <td>2006</td>\n",
       "      <td>428.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>RO</td>\n",
       "      <td>2006</td>\n",
       "      <td>992.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>IS</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>LI</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ME</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>MK</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>AL</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>RS</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>BA</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>XK</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GEO  year  value    unit    hazard  waste nace_r2\n",
       "48         IE  2006    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "59         MT  2006    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "77         TR  2006    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "45         DK  2006    5.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "53         IT  2006   10.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "72         UK  2006   11.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "52         HR  2006   17.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "46         DE  2006   18.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "51         FR  2006   19.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "63         PT  2006   20.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "57         LU  2006   24.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "44         CZ  2006   31.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "42         BE  2006   34.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "68         SE  2006   35.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "71         NO  2006   37.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "62         PL  2006   40.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "55         LV  2006   43.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "61         AT  2006   62.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "43         BG  2006   83.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "47         EE  2006   88.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "65         SI  2006  107.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "41       EU28  2006  116.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "40  EU27_2020  2006  131.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "66         SK  2006  138.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "60         NL  2006  163.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "54         CY  2006  175.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "58         HU  2006  198.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "56         LT  2006  267.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "50         ES  2006  341.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "67         FI  2006  387.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "49         EL  2006  428.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "64         RO  2006  992.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "69         IS  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "70         LI  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "73         ME  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "74         MK  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "75         AL  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "76         RS  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "78         BA  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "79         XK  2006    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"year\",\"value\"]).query(\"year == '2006'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "image/png": {
       "height": 424,
       "width": 716
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"year\").sum().plot(ylim=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "source": [
    "## join datasets\n",
    "\n",
    "With the long format it is now much easier to combine two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "hideCode": false,
    "hidePrompt": false
   },
   "outputs": [],
   "source": [
    "df1 = get_data_from_sheet(file, (\"KG_HAB\", \"HAZ_NHAZ\", \"TOTAL\", \"A\"))\n",
    "df1 = df1.reset_index().melt(id_vars=\"GEO\", var_name=\"year\", value_name=\"value\")\n",
    "df1 = df1.assign(unit=\"KG_HAB\", hazard=\"HAZ_NHAZ\", waste=\"TOTAL\", nace_r2=\"A\")\n",
    "df1.year = df1.year.astype(\"category\")\n",
    "\n",
    "df2 = get_data_from_sheet(file, (\"KG_HAB\", \"HAZ_NHAZ\", \"TOTAL\", \"B\"))\n",
    "df2 = df2.reset_index().melt(id_vars=\"GEO\", var_name=\"year\", value_name=\"value\")\n",
    "df2 = df2.assign(unit=\"KG_HAB\", hazard=\"HAZ_NHAZ\", waste=\"TOTAL\", nace_r2=\"B\")\n",
    "df2.year = df2.year.astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
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    {
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       "      <th>GEO</th>\n",
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       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>317</th>\n",
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       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
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       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>BA</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>XK</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           GEO  year  value    unit    hazard  waste nace_r2\n",
       "0    EU27_2020  2004  146.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "1         EU28  2004  130.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "2           BE  2004  114.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "3           BG  2004   94.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "4           CZ  2004  122.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "..         ...   ...    ...     ...       ...    ...     ...\n",
       "315         AL  2018    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "316         RS  2018   12.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "317         TR  2018    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "318         BA  2018    0.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "319         XK  2018    NaN  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "\n",
       "[320 rows x 7 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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    "df1"
   ]
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   "cell_type": "code",
   "execution_count": 61,
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    {
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       "      <td>214.0</td>\n",
       "      <td>KG_HAB</td>\n",
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       "      <td>TOTAL</td>\n",
       "      <td>B</td>\n",
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      ],
      "text/plain": [
       "           GEO  year    value    unit    hazard  waste nace_r2\n",
       "0    EU27_2020  2004   1655.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "1         EU28  2004   1525.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "2           BE  2004     37.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "3           BG  2004  22184.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "4           CZ  2004     69.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "..         ...   ...      ...     ...       ...    ...     ...\n",
       "315         AL  2018      NaN  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "316         RS  2018   5532.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "317         TR  2018    214.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "318         BA  2018    158.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "319         XK  2018      NaN  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "\n",
       "[320 rows x 7 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
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    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "df = df1.append(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
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      "text/plain": [
       "           GEO  year   value    unit    hazard  waste nace_r2\n",
       "0    EU27_2020  2004   146.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "1         EU28  2004   130.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "2           BE  2004   114.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "3           BG  2004    94.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "4           CZ  2004   122.0  KG_HAB  HAZ_NHAZ  TOTAL       A\n",
       "..         ...   ...     ...     ...       ...    ...     ...\n",
       "315         AL  2018     NaN  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "316         RS  2018  5532.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "317         TR  2018   214.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "318         BA  2018   158.0  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "319         XK  2018     NaN  KG_HAB  HAZ_NHAZ  TOTAL       B\n",
       "\n",
       "[640 rows x 7 columns]"
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     "execution_count": 63,
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     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.bar(df[(df.GEO == \"DE\")], x=\"year\", y=\"value\", color=\"nace_r2\")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Summary\n",
    "\n",
    "We have written two very convenient helper functions: \n",
    "1. `print_codes` to get a nice report about all codes in the dataset\n",
    "1. `get_data_from_excel` to get either a single, multiple or the complete data from the excel as data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "file = \"data/env_wasgen_new.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  freq\n",
      "---------\n",
      "A: Annual\n",
      "\n",
      "Category:  unit\n",
      "---------\n",
      "T: Tonne\n",
      "\n",
      "Category:  hazard\n",
      "---------\n",
      "HAZ_NHAZ: Hazardous and non-hazardous - Total\n",
      "\n",
      "Category:  nace_r2\n",
      "---------\n",
      "TOTAL_HH: All NACE activities plus households\n",
      "\n",
      "Category:  waste\n",
      "---------\n",
      "TOTAL: Total waste\n",
      "\n",
      "Category:  geo\n",
      "---------\n",
      "EU27_2020: European Union - 27 countries (from 2020)\n",
      "EU28: European Union - 28 countries (2013-2020)\n",
      "BE: Belgium\n",
      "BG: Bulgaria\n",
      "CZ: Czechia\n",
      "DK: Denmark\n",
      "DE: Germany (until 1990 former territory of the FRG)\n",
      "EE: Estonia\n",
      "IE: Ireland\n",
      "EL: Greece\n",
      "ES: Spain\n",
      "FR: France\n",
      "HR: Croatia\n",
      "IT: Italy\n",
      "CY: Cyprus\n",
      "LV: Latvia\n",
      "LT: Lithuania\n",
      "LU: Luxembourg\n",
      "HU: Hungary\n",
      "MT: Malta\n",
      "NL: Netherlands\n",
      "AT: Austria\n",
      "PL: Poland\n",
      "PT: Portugal\n",
      "RO: Romania\n",
      "SI: Slovenia\n",
      "SK: Slovakia\n",
      "FI: Finland\n",
      "SE: Sweden\n",
      "IS: Iceland\n",
      "LI: Liechtenstein\n",
      "NO: Norway\n",
      "UK: United Kingdom\n",
      "ME: Montenegro\n",
      "MK: North Macedonia\n",
      "AL: Albania\n",
      "RS: Serbia\n",
      "TR: Türkiye\n",
      "BA: Bosnia and Herzegovina\n",
      "XK: Kosovo (under United Nations Security Council Resolution 1244/99)\n",
      "\n",
      "Category:  time\n",
      "---------\n",
      "2004: 2004\n",
      "2006: 2006\n",
      "2008: 2008\n",
      "2010: 2010\n",
      "2012: 2012\n",
      "2014: 2014\n",
      "2016: 2016\n",
      "2018: 2018\n",
      "2020: 2020\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_codes(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = get_data_from_excel(file, [(\"KG_HAB\", \"HAZ_NHAZ\", \"A\", \"TOTAL\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>HAZ_NHAZ</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>BA</th>\n",
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       "<p>360 rows × 6 columns</p>\n",
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       "           year  value    unit    hazard nace_r2  waste\n",
       "geo                                                    \n",
       "EU27_2020  2004  146.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "EU28       2004  130.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "BE         2004  114.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "BG         2004   94.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "CZ         2004  122.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "...         ...    ...     ...       ...     ...    ...\n",
       "AL         2020    NaN  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "RS         2020   13.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "TR         2020    0.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "BA         2020    NaN  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "XK         2020    NaN  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "\n",
       "[360 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "CategoricalIndex: 360 entries, EU27_2020 to XK\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype   \n",
      "---  ------   --------------  -----   \n",
      " 0   year     360 non-null    int32   \n",
      " 1   value    321 non-null    float64 \n",
      " 2   unit     360 non-null    category\n",
      " 3   hazard   360 non-null    category\n",
      " 4   nace_r2  360 non-null    category\n",
      " 5   waste    360 non-null    category\n",
      "dtypes: category(4), float64(1), int32(1)\n",
      "memory usage: 7.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\tools\\miniconda3\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    },
    {
     "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>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>nace_r2</th>\n",
       "      <th>waste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>EU27_2020</th>\n",
       "      <td>2004</td>\n",
       "      <td>146.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2004</td>\n",
       "      <td>130.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2004</td>\n",
       "      <td>114.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2004</td>\n",
       "      <td>94.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2004</td>\n",
       "      <td>122.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>B</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2020</td>\n",
       "      <td>6626.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>B</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2020</td>\n",
       "      <td>331.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>B</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>B</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>B</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year   value    unit    hazard nace_r2  waste\n",
       "geo                                                     \n",
       "EU27_2020  2004   146.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "EU28       2004   130.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "BE         2004   114.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "BG         2004    94.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "CZ         2004   122.0  KG_HAB  HAZ_NHAZ       A  TOTAL\n",
       "...         ...     ...     ...       ...     ...    ...\n",
       "AL         2020     NaN  KG_HAB  HAZ_NHAZ       B  TOTAL\n",
       "RS         2020  6626.0  KG_HAB  HAZ_NHAZ       B  TOTAL\n",
       "TR         2020   331.0  KG_HAB  HAZ_NHAZ       B  TOTAL\n",
       "BA         2020     NaN  KG_HAB  HAZ_NHAZ       B  TOTAL\n",
       "XK         2020     NaN  KG_HAB  HAZ_NHAZ       B  TOTAL\n",
       "\n",
       "[720 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data_from_excel(file, [(\"KG_HAB\", \"HAZ_NHAZ\", \"A\", \"TOTAL\"), (\"KG_HAB\", \"HAZ_NHAZ\", \"B\", \"TOTAL\")])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\tools\\miniconda3\\lib\\site-packages\\openpyxl\\styles\\stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "df = get_data_from_excel(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "CategoricalIndex: 21600 entries, EU27_2020 to XK\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype   \n",
      "---  ------   --------------  -----   \n",
      " 0   year     21600 non-null  int32   \n",
      " 1   value    18626 non-null  float64 \n",
      " 2   unit     21600 non-null  category\n",
      " 3   hazard   21600 non-null  category\n",
      " 4   nace_r2  21600 non-null  category\n",
      " 5   waste    21600 non-null  category\n",
      "dtypes: category(4), float64(1), int32(1)\n",
      "memory usage: 360.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>nace_r2</th>\n",
       "      <th>waste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>EU27_2020</th>\n",
       "      <td>2004</td>\n",
       "      <td>146.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2004</td>\n",
       "      <td>130.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2004</td>\n",
       "      <td>114.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2004</td>\n",
       "      <td>94.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2004</td>\n",
       "      <td>122.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2020</td>\n",
       "      <td>47307595.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2020</td>\n",
       "      <td>76949950.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2020</td>\n",
       "      <td>6743515.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2020</td>\n",
       "      <td>2592826.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year       value    unit    hazard   nace_r2  waste\n",
       "geo                                                           \n",
       "EU27_2020  2004       146.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "EU28       2004       130.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "BE         2004       114.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "BG         2004        94.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "CZ         2004       122.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "...         ...         ...     ...       ...       ...    ...\n",
       "AL         2020         NaN       T      NHAZ  TOTAL_HH  TOTAL\n",
       "RS         2020  47307595.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "TR         2020  76949950.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "BA         2020   6743515.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "XK         2020   2592826.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "\n",
       "[21600 rows x 6 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='geo'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.year == 2016) & (df.unit == \"KG_HAB\") & (df.hazard == \"HAZ_NHAZ\") & (df.waste == \"TOTAL\") & (df.nace_r2 == \"TOTAL_HH\")].value.plot.bar(figsize=(20,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Storing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"data/env_wasgen_combined.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/env_wasgen_combined.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 211200 entries, 0 to 211199\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   geo      211200 non-null  object \n",
      " 1   year     211200 non-null  int64  \n",
      " 2   value    95562 non-null   float64\n",
      " 3   unit     211200 non-null  object \n",
      " 4   hazard   211200 non-null  object \n",
      " 5   waste    211200 non-null  object \n",
      " 6   nace_r2  211200 non-null  object \n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 11.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[df.select_dtypes(\"object\").columns] = df.select_dtypes(\"object\").apply(lambda x: pd.Series(x).astype(\"category\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 211200 entries, 0 to 211199\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count   Dtype   \n",
      "---  ------   --------------   -----   \n",
      " 0   geo      211200 non-null  category\n",
      " 1   year     211200 non-null  int64   \n",
      " 2   value    95562 non-null   float64 \n",
      " 3   unit     211200 non-null  category\n",
      " 4   hazard   211200 non-null  category\n",
      " 5   waste    211200 non-null  category\n",
      " 6   nace_r2  211200 non-null  category\n",
      "dtypes: category(5), float64(1), int64(1)\n",
      "memory usage: 4.2 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.set_index(\"geo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "CategoricalIndex: 211200 entries, EU27_2020 to XK\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count   Dtype   \n",
      "---  ------   --------------   -----   \n",
      " 0   year     211200 non-null  int64   \n",
      " 1   value    95562 non-null   float64 \n",
      " 2   unit     211200 non-null  category\n",
      " 3   hazard   211200 non-null  category\n",
      " 4   waste    211200 non-null  category\n",
      " 5   nace_r2  211200 non-null  category\n",
      "dtypes: category(4), float64(1), int64(1)\n",
      "memory usage: 4.2 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_parquet(\"data/env_wasgen_combined.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_parquet(\"data/env_wasgen_combined.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "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>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>nace_r2</th>\n",
       "      <th>waste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>EU27_2020</th>\n",
       "      <td>2004</td>\n",
       "      <td>146.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2004</td>\n",
       "      <td>130.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2004</td>\n",
       "      <td>114.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2004</td>\n",
       "      <td>94.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2004</td>\n",
       "      <td>122.0</td>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ_NHAZ</td>\n",
       "      <td>A</td>\n",
       "      <td>TOTAL</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2020</td>\n",
       "      <td>47307595.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2020</td>\n",
       "      <td>76949950.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2020</td>\n",
       "      <td>6743515.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2020</td>\n",
       "      <td>2592826.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "      <td>TOTAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21600 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year       value    unit    hazard   nace_r2  waste\n",
       "geo                                                           \n",
       "EU27_2020  2004       146.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "EU28       2004       130.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "BE         2004       114.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "BG         2004        94.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "CZ         2004       122.0  KG_HAB  HAZ_NHAZ         A  TOTAL\n",
       "...         ...         ...     ...       ...       ...    ...\n",
       "AL         2020         NaN       T      NHAZ  TOTAL_HH  TOTAL\n",
       "RS         2020  47307595.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "TR         2020  76949950.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "BA         2020   6743515.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "XK         2020   2592826.0       T      NHAZ  TOTAL_HH  TOTAL\n",
       "\n",
       "[21600 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "CategoricalIndex: 21600 entries, EU27_2020 to XK\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype   \n",
      "---  ------   --------------  -----   \n",
      " 0   year     21600 non-null  int32   \n",
      " 1   value    18626 non-null  float64 \n",
      " 2   unit     21600 non-null  category\n",
      " 3   hazard   21600 non-null  category\n",
      " 4   nace_r2  21600 non-null  category\n",
      " 5   waste    21600 non-null  category\n",
      "dtypes: category(4), float64(1), int32(1)\n",
      "memory usage: 360.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='geo'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.year == 2016) & (df.unit == \"T\") & (df.hazard == \"HAZ_NHAZ\") & (df.waste == \"TOTAL\") & (df.nace_r2 == \"TOTAL_HH\")].value.plot.bar(figsize=(20,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_code_all_hidden": false,
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "3247f7d4635bb288d9e06d3deacee818856115b2677ccfdf5a578edab993fe5f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}