03 Dataset env_wasmun.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "from utils import *\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (3.4.2)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in /usr/local/lib/python3.8/dist-packages (0.12.2)\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (2.4.7)\r\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (8.2.0)\r\n",
      "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (1.19.5)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/lib/python3/dist-packages (from matplotlib) (0.10.0)\r\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/lib/python3/dist-packages (from matplotlib) (1.0.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (2.8.1)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)\r\n",
      "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.8/dist-packages (from statsmodels) (0.5.1)\r\n",
      "Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.8/dist-packages (from statsmodels) (1.2.4)\r\n",
      "Requirement already satisfied: scipy>=1.1 in /usr/local/lib/python3.8/dist-packages (from statsmodels) (1.6.3)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.21->statsmodels) (2019.3)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade matplotlib statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Siedlungsabfälle nach Abfallbewirtschaftungsmaßnahmen (env_wasmun)\n",
    "\n",
    "[Data Explorere](https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_wasmun&lang=de)\n",
    "\n",
    "[Abfallwirtschaft in der EU: Zahlen und Fakten](https://www.europarl.europa.eu/news/de/headlines/society/20180328STO00751/abfallwirtschaft-in-der-eu-zahlen-und-fakten)\n",
    "\n",
    "[Municipal waste statistics](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Municipal_waste_statistics#Municipal_waste_generation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "file_generation = \"data/env_wasgen.xls\"\n",
    "file_waste_municipal = \"data/env_wasmun.xls\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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 - Total\n",
      "NHAZ: Non-hazardous\n",
      "\n",
      "Category:  WASTE\n",
      "---------\n",
      "W06_07A: Recyclable wastes (subtotal, W06+W07 except W077)\n",
      "SEC: Secondary waste (W033+W103+W128_13)\n",
      "TOTAL: Total waste\n",
      "PRIM: Primary waste (TOTAL minus SEC)\n",
      "W09: Animal and vegetal wastes (subtotal, W091+W092+W093)\n",
      "W11: Common sludges\n",
      "W01-05: Chemical and medical wastes (subtotal)\n",
      "W10: Mixed ordinary wastes (subtotal, W101+W102+W103)\n",
      "W077_08: Equipment (subtotal, W077+W08A+W081+W0841)\n",
      "TOT_X_MIN: Waste excluding major mineral wastes\n",
      "W12-13: Mineral and solidified wastes (subtotal)\n",
      "\n",
      "Category:  NACE_R2\n",
      "---------\n",
      "G4677: Wholesale of waste and scrap\n",
      "TOTAL_HH: All NACE activities plus households\n",
      "B: Mining and quarrying\n",
      "D: Electricity, gas, steam and air conditioning supply\n",
      "E: Water supply; sewerage, waste management and remediation activities\n",
      "C: Manufacturing\n",
      "EP_HH: Households\n",
      "G-U_X_G4677: Services (except wholesale of waste and scrap)\n",
      "F: Construction\n",
      "A: Agriculture, forestry and fishing\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_codes(file_generation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category:  WST_OPER\n",
      "---------\n",
      "DSP_I: Beseitigung - Verbrennung (D10)\n",
      "GEN: Erzeugter Abfall\n",
      "RCV_E: Verwertung - energetische Verwertung (R1)\n",
      "TRT: Abfallbehandlung\n",
      "DSP_I_RCV_E: Beseitigung - Verbrennung (D10) und Verwertung - energetische Verwertung (R1)\n",
      "RCY_M: Recycling - werkstofflich\n",
      "DSP_L_OTH: Beseitigung - Deponien und sonstiges (D1-D7, D12)\n",
      "RCY_C_D: Recycling - Kompostierung und Vergärung\n",
      "\n",
      "Category:  UNIT\n",
      "---------\n",
      "T: Tonne\n",
      "THS_T: Tausend Tonnen\n",
      "KG_HAB: Kilogramm pro Kopf\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_codes(file_waste_municipal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df = get_data_from_excel(file_waste_municipal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>wst_oper</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>1995</td>\n",
       "      <td>467.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>1995</td>\n",
       "      <td>470.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>1995</td>\n",
       "      <td>455.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>1995</td>\n",
       "      <td>694.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>1995</td>\n",
       "      <td>302.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2019</td>\n",
       "      <td>125.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year  value wst_oper    unit\n",
       "geo                                    \n",
       "EU27_2020  1995  467.0      GEN  KG_HAB\n",
       "EU28       1995  470.0      GEN  KG_HAB\n",
       "BE         1995  455.0      GEN  KG_HAB\n",
       "BG         1995  694.0      GEN  KG_HAB\n",
       "CZ         1995  302.0      GEN  KG_HAB\n",
       "...         ...    ...      ...     ...\n",
       "AL         2019    NaN  RCY_C_D   THS_T\n",
       "RS         2019    NaN  RCY_C_D   THS_T\n",
       "TR         2019  125.0  RCY_C_D   THS_T\n",
       "BA         2019    NaN  RCY_C_D   THS_T\n",
       "XK         2019    NaN  RCY_C_D   THS_T\n",
       "\n",
       "[24000 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df.to_parquet(\"data/env_wasmun_combined.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df_waste_generation = pd.read_parquet(\"data/env_wasgen_combined.parquet\")\n",
    "df_waste_municipal = pd.read_parquet(\"data/env_wasmun_combined.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>waste</th>\n",
       "      <th>nace_r2</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>TOTAL</td>\n",
       "      <td>A</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>TOTAL</td>\n",
       "      <td>A</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>TOTAL</td>\n",
       "      <td>A</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>TOTAL</td>\n",
       "      <td>A</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOT_X_MIN</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2018</td>\n",
       "      <td>11780914.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOT_X_MIN</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2018</td>\n",
       "      <td>74786520.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOT_X_MIN</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2018</td>\n",
       "      <td>5610790.0</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOT_X_MIN</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>T</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>TOT_X_MIN</td>\n",
       "      <td>TOTAL_HH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year       value    unit    hazard      waste   nace_r2\n",
       "geo                                                               \n",
       "EU27_2020  2004       146.0  KG_HAB  HAZ_NHAZ      TOTAL         A\n",
       "EU28       2004       130.0  KG_HAB  HAZ_NHAZ      TOTAL         A\n",
       "BE         2004       114.0  KG_HAB  HAZ_NHAZ      TOTAL         A\n",
       "BG         2004        94.0  KG_HAB  HAZ_NHAZ      TOTAL         A\n",
       "CZ         2004       122.0  KG_HAB  HAZ_NHAZ      TOTAL         A\n",
       "...         ...         ...     ...       ...        ...       ...\n",
       "AL         2018         NaN       T      NHAZ  TOT_X_MIN  TOTAL_HH\n",
       "RS         2018  11780914.0       T      NHAZ  TOT_X_MIN  TOTAL_HH\n",
       "TR         2018  74786520.0       T      NHAZ  TOT_X_MIN  TOTAL_HH\n",
       "BA         2018   5610790.0       T      NHAZ  TOT_X_MIN  TOTAL_HH\n",
       "XK         2018         NaN       T      NHAZ  TOT_X_MIN  TOTAL_HH\n",
       "\n",
       "[211200 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_waste_generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>wst_oper</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>1995</td>\n",
       "      <td>467.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>1995</td>\n",
       "      <td>470.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>1995</td>\n",
       "      <td>455.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>1995</td>\n",
       "      <td>694.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>1995</td>\n",
       "      <td>302.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2019</td>\n",
       "      <td>125.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>THS_T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year  value wst_oper    unit\n",
       "geo                                    \n",
       "EU27_2020  1995  467.0      GEN  KG_HAB\n",
       "EU28       1995  470.0      GEN  KG_HAB\n",
       "BE         1995  455.0      GEN  KG_HAB\n",
       "BG         1995  694.0      GEN  KG_HAB\n",
       "CZ         1995  302.0      GEN  KG_HAB\n",
       "...         ...    ...      ...     ...\n",
       "AL         2019    NaN  RCY_C_D   THS_T\n",
       "RS         2019    NaN  RCY_C_D   THS_T\n",
       "TR         2019  125.0  RCY_C_D   THS_T\n",
       "BA         2019    NaN  RCY_C_D   THS_T\n",
       "XK         2019    NaN  RCY_C_D   THS_T\n",
       "\n",
       "[24000 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_waste_municipal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005,\n",
       "       2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,\n",
       "       2017, 2018, 2019])"
      ]
     },
     "execution_count": 8,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_waste_municipal.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>wst_oper</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24000</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>DSP_I</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3000</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       wst_oper    unit\n",
       "count     24000   24000\n",
       "unique        8       3\n",
       "top       DSP_I  KG_HAB\n",
       "freq       3000    8000"
      ]
     },
     "execution_count": 9,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_waste_municipal.describe(include=[\"category\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Waste treatment 2017\n",
    "\n",
    "codes:\n",
    "- GEN: Erzeugter Abfall\n",
    "- RCY_C_D: Recycling - Kompostierung und Vergärung\n",
    "- RCY_M: Recycling - werkstofflich\n",
    "- DSP_L_OTH: Beseitigung - Deponien und sonstiges (D1-D7, D12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 1: Anteil Siedlungsabfälle am Gesamtabfallaufkommen\n",
    "Siedlungsabfälle machen weniger als ein Zehntel des Gesamtabfallaufkommens von 2,5 Milliarden Tonnen aus, das pro Jahr in der EU anfällt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2,618,970,000.0'"
      ]
     },
     "execution_count": 8,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waste_total = df_waste_generation.query(\"year == 2018 and unit == 'T' and hazard == 'HAZ_NHAZ' and waste == 'TOTAL' and nace_r2 == 'TOTAL_HH'\").loc[\"EU28\"].value\n",
    "\n",
    "f\"{waste_total:,}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'252,396,000.0'"
      ]
     },
     "execution_count": 9,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waste_municipal = df_waste_municipal.query(\"year == 2018 and wst_oper =='GEN' and unit == 'THS_T'\").loc[\"EU28\"].value * 1000\n",
    "\n",
    "f\"{waste_municipal:,}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Siedlungsabfall macht im Jahr 2018 insgesamt 10 Prozent des gesamten Abfallaufkommens in der EU aus'"
      ]
     },
     "execution_count": 11,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"Siedlungsabfall macht im Jahr 2018 insgesamt {round(waste_municipal / waste_total * 100)} Prozent des gesamten Abfallaufkommens in der EU aus\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 2: Welche Trends sind für den Siedlungsabfall zu beobachten?\n",
    "2018 wurden neue, ehrgeizige EU-Ziele verabschiedet, um Wiederverwendung und Recycling zu fördern, Verpackungsabfälle zu verringern und die Deponieablagerung in der Union einzuschränken. Die Vorgaben sollen zur Schaffung einer nachhaltigen Kreislaufwirtschaft in der EU beitragen.\n",
    "\n",
    "Im Zeitraum von 2005 bis 2018 ging die durchschnittlich von einer Person in der EU produzierte Menge an Siedlungsabfällen zurück. Dennoch sind für die einzelnen EU-Mitgliedsländer unterschiedliche Trends erkennbar: So gibt es in Dänemark, Deutschland, Griechenland, Malta und Tschechien Zuwächse. Geringere Abfallmengen verzeichnen Bulgarien, Spanien, Ungarn, Rumänien und die Niederlande.\n",
    "\n",
    "![](https://www.umweltwirtschaft.com/news/media/8/EU-FB-MUNICIPAL-WASTE-01.jpg-74840-detailpp.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "waste_gen_for_eu28_over_time = df_waste_municipal.query(\"wst_oper == 'GEN' and unit == 'KG_HAB'\").loc[\"EU28\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-2.1.0.min.js\"></script>                <div id=\"9acded5e-fe46-417e-8870-13237d40c257\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9acded5e-fe46-417e-8870-13237d40c257\")) {                    Plotly.newPlot(                        \"9acded5e-fe46-417e-8870-13237d40c257\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"year=%{x}<br>Kilograms per person=%{text}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"text\":[\"470.0\",\"482.0\",\"496.0\",\"494.0\",\"508.0\",\"521.0\",\"519.0\",\"525.0\",\"513.0\",\"512.0\",\"515.0\",\"522.0\",\"524.0\",\"521.0\",\"511.0\",\"504.0\",\"498.0\",\"486.0\",\"479.0\",\"478.0\",\"481.0\",\"489.0\",\"492.0\",\"492.0\",\"nan\"],\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"xaxis\":\"x\",\"y\":[470.0,482.0,496.0,494.0,508.0,521.0,519.0,525.0,513.0,512.0,515.0,522.0,524.0,521.0,511.0,504.0,498.0,486.0,479.0,478.0,481.0,489.0,492.0,492.0,null],\"yaxis\":\"y\"}],                        {\"barmode\":\"relative\",\"legend\":{\"tracegroupgap\":0},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Municipal waste generation for EU28 from 1995 to 2018\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"year\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Kilograms per person\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('9acded5e-fe46-417e-8870-13237d40c257');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 14,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.bar(\n",
    "    waste_gen_for_eu28_over_time,\n",
    "    x = \"year\",\n",
    "    y=\"value\",\n",
    "    title=\"Municipal waste generation for EU28 from 1995 to 2018\", labels={\"value\": \"Kilograms per person\"},\n",
    "    text=\"value\"\n",
    ")\n",
    "\n",
    "# zoom in for 2005 - noww"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "waste_gen_for_2018 = df_waste_municipal.query(\"year == 2018 and wst_oper == 'GEN' and unit == 'KG_HAB' and geo != 'EU27_2020'\").reset_index()\n",
    "eu28_value = waste_gen_for_2018.query(\"geo == 'EU28'\").value.values[0]\n",
    "waste_gen_for_2018 = waste_gen_for_2018.assign(below_average=waste_gen_for_2018.value <= eu28_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-2.1.0.min.js\"></script>                <div id=\"8e3c6e4d-1b9f-42c2-b622-6c6e474b0f6d\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"8e3c6e4d-1b9f-42c2-b622-6c6e474b0f6d\")) {                    Plotly.newPlot(                        \"8e3c6e4d-1b9f-42c2-b622-6c6e474b0f6d\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Below EU28 average=True<br>geo=%{x}<br>Kilogram per person=%{y}<extra></extra>\",\"legendgroup\":\"True\",\"marker\":{\"color\":\"green\",\"pattern\":{\"shape\":\"\"}},\"name\":\"True\",\"offsetgroup\":\"True\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"EU28\",\"BE\",\"BG\",\"EE\",\"ES\",\"HR\",\"LV\",\"LT\",\"HU\",\"PL\",\"RO\",\"SI\",\"SK\",\"SE\",\"UK\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\"],\"xaxis\":\"x\",\"y\":[492.0,409.0,407.0,405.0,475.0,432.0,407.0,464.0,381.0,329.0,272.0,486.0,414.0,434.0,463.0,412.0,462.0,319.0,424.0,356.0,226.0],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Below EU28 average=False<br>geo=%{x}<br>Kilogram per person=%{y}<extra></extra>\",\"legendgroup\":\"False\",\"marker\":{\"color\":\"red\",\"pattern\":{\"shape\":\"\"}},\"name\":\"False\",\"offsetgroup\":\"False\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"CZ\",\"DK\",\"DE\",\"IE\",\"EL\",\"FR\",\"IT\",\"CY\",\"LU\",\"MT\",\"NL\",\"AT\",\"PT\",\"FI\",\"IS\",\"NO\",\"CH\",\"ME\"],\"xaxis\":\"x\",\"y\":[494.0,814.0,606.0,598.0,515.0,535.0,499.0,646.0,803.0,663.0,511.0,579.0,507.0,551.0,702.0,739.0,706.0,530.0],\"yaxis\":\"y\"}],                        {\"barmode\":\"relative\",\"legend\":{\"title\":{\"text\":\"Below EU28 average\"},\"tracegroupgap\":0},\"shapes\":[{\"line\":{\"color\":\"green\",\"width\":2},\"type\":\"line\",\"x0\":0,\"x1\":1,\"xref\":\"x domain\",\"y0\":492.0,\"y1\":492.0,\"yref\":\"y\"}],\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Municipal waste generation for EU28 in 2018\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"geo\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Kilogram per person\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('8e3c6e4d-1b9f-42c2-b622-6c6e474b0f6d');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 20,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = px.bar(\n",
    "    waste_gen_for_2018,\n",
    "    x=\"geo\",\n",
    "    y=\"value\",\n",
    "    title=\"Municipal waste generation for EU28 in 2018\",\n",
    "    labels={\"value\": \"Kilogram per person\", \"below_average\": \"Below EU28 average\"},\n",
    "    color=waste_gen_for_2018.below_average,\n",
    "    color_discrete_map={True: \"green\", False: \"red\"}\n",
    ")\n",
    "\n",
    "fig.add_hline(y=eu28_value, line_width=2, line_color=\"green\")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 3: Erreichen wir die EU Ziele für 2025?\n",
    "![](https://www.europarl.europa.eu/resources/library/images/20201201PHT92822/20201201PHT92822_original.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# store each operation in its own df\n",
    "df_household_generation = df_waste_municipal.query(\"year == 2017 and unit == 'KG_HAB' and wst_oper == 'GEN'\")\n",
    "df_household_recycling_compost = df_waste_municipal.query(\"year == 2017 and unit == 'KG_HAB' and wst_oper == 'RCY_C_D'\")\n",
    "df_household_recycling_material = df_waste_municipal.query(\"year == 2017 and unit == 'KG_HAB' and wst_oper == 'RCY_M'\")\n",
    "df_household_landfill = df_waste_municipal.query(\"year == 2017 and unit == 'KG_HAB' and wst_oper == 'DSP_L_OTH'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>wst_oper</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>2017</td>\n",
       "      <td>496.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2017</td>\n",
       "      <td>492.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2017</td>\n",
       "      <td>435.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2017</td>\n",
       "      <td>489.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>2017</td>\n",
       "      <td>820.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>2017</td>\n",
       "      <td>627.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>2017</td>\n",
       "      <td>390.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>2017</td>\n",
       "      <td>576.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>2017</td>\n",
       "      <td>504.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>2017</td>\n",
       "      <td>473.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>2017</td>\n",
       "      <td>535.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>2017</td>\n",
       "      <td>416.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>2017</td>\n",
       "      <td>488.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>2017</td>\n",
       "      <td>625.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>2017</td>\n",
       "      <td>455.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>2017</td>\n",
       "      <td>798.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>2017</td>\n",
       "      <td>385.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>2017</td>\n",
       "      <td>666.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>2017</td>\n",
       "      <td>513.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>2017</td>\n",
       "      <td>570.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>2017</td>\n",
       "      <td>315.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>2017</td>\n",
       "      <td>486.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>2017</td>\n",
       "      <td>272.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>2017</td>\n",
       "      <td>471.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>2017</td>\n",
       "      <td>378.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>2017</td>\n",
       "      <td>510.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>2017</td>\n",
       "      <td>452.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>2017</td>\n",
       "      <td>656.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>2017</td>\n",
       "      <td>748.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>2017</td>\n",
       "      <td>709.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>2017</td>\n",
       "      <td>468.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>2017</td>\n",
       "      <td>509.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2017</td>\n",
       "      <td>436.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2017</td>\n",
       "      <td>306.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2017</td>\n",
       "      <td>425.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2017</td>\n",
       "      <td>352.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2017</td>\n",
       "      <td>229.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year  value wst_oper    unit\n",
       "geo                                    \n",
       "EU27_2020  2017  496.0      GEN  KG_HAB\n",
       "EU28       2017  492.0      GEN  KG_HAB\n",
       "BE         2017  411.0      GEN  KG_HAB\n",
       "BG         2017  435.0      GEN  KG_HAB\n",
       "CZ         2017  489.0      GEN  KG_HAB\n",
       "DK         2017  820.0      GEN  KG_HAB\n",
       "DE         2017  627.0      GEN  KG_HAB\n",
       "EE         2017  390.0      GEN  KG_HAB\n",
       "IE         2017  576.0      GEN  KG_HAB\n",
       "EL         2017  504.0      GEN  KG_HAB\n",
       "ES         2017  473.0      GEN  KG_HAB\n",
       "FR         2017  535.0      GEN  KG_HAB\n",
       "HR         2017  416.0      GEN  KG_HAB\n",
       "IT         2017  488.0      GEN  KG_HAB\n",
       "CY         2017  625.0      GEN  KG_HAB\n",
       "LV         2017  411.0      GEN  KG_HAB\n",
       "LT         2017  455.0      GEN  KG_HAB\n",
       "LU         2017  798.0      GEN  KG_HAB\n",
       "HU         2017  385.0      GEN  KG_HAB\n",
       "MT         2017  666.0      GEN  KG_HAB\n",
       "NL         2017  513.0      GEN  KG_HAB\n",
       "AT         2017  570.0      GEN  KG_HAB\n",
       "PL         2017  315.0      GEN  KG_HAB\n",
       "PT         2017  486.0      GEN  KG_HAB\n",
       "RO         2017  272.0      GEN  KG_HAB\n",
       "SI         2017  471.0      GEN  KG_HAB\n",
       "SK         2017  378.0      GEN  KG_HAB\n",
       "FI         2017  510.0      GEN  KG_HAB\n",
       "SE         2017  452.0      GEN  KG_HAB\n",
       "IS         2017  656.0      GEN  KG_HAB\n",
       "NO         2017  748.0      GEN  KG_HAB\n",
       "CH         2017  709.0      GEN  KG_HAB\n",
       "UK         2017  468.0      GEN  KG_HAB\n",
       "ME         2017  509.0      GEN  KG_HAB\n",
       "MK         2017    NaN      GEN  KG_HAB\n",
       "AL         2017  436.0      GEN  KG_HAB\n",
       "RS         2017  306.0      GEN  KG_HAB\n",
       "TR         2017  425.0      GEN  KG_HAB\n",
       "BA         2017  352.0      GEN  KG_HAB\n",
       "XK         2017  229.0      GEN  KG_HAB"
      ]
     },
     "execution_count": 22,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_household_generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>wst_oper</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>2017</td>\n",
       "      <td>85.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2017</td>\n",
       "      <td>84.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2017</td>\n",
       "      <td>81.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2017</td>\n",
       "      <td>50.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>2017</td>\n",
       "      <td>154.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>2017</td>\n",
       "      <td>114.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>2017</td>\n",
       "      <td>14.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>2017</td>\n",
       "      <td>51.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>2017</td>\n",
       "      <td>21.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>2017</td>\n",
       "      <td>84.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>2017</td>\n",
       "      <td>106.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>2017</td>\n",
       "      <td>9.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>2017</td>\n",
       "      <td>98.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>2017</td>\n",
       "      <td>9.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>2017</td>\n",
       "      <td>29.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>2017</td>\n",
       "      <td>109.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>2017</td>\n",
       "      <td>153.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>2017</td>\n",
       "      <td>144.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>2017</td>\n",
       "      <td>182.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>2017</td>\n",
       "      <td>22.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>2017</td>\n",
       "      <td>83.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>2017</td>\n",
       "      <td>18.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>2017</td>\n",
       "      <td>73.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>2017</td>\n",
       "      <td>33.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>2017</td>\n",
       "      <td>67.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>2017</td>\n",
       "      <td>70.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>2017</td>\n",
       "      <td>75.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>2017</td>\n",
       "      <td>154.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>2017</td>\n",
       "      <td>79.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year  value wst_oper    unit\n",
       "geo                                    \n",
       "EU27_2020  2017   85.0  RCY_C_D  KG_HAB\n",
       "EU28       2017   84.0  RCY_C_D  KG_HAB\n",
       "BE         2017   81.0  RCY_C_D  KG_HAB\n",
       "BG         2017   34.0  RCY_C_D  KG_HAB\n",
       "CZ         2017   50.0  RCY_C_D  KG_HAB\n",
       "DK         2017  154.0  RCY_C_D  KG_HAB\n",
       "DE         2017  114.0  RCY_C_D  KG_HAB\n",
       "EE         2017   14.0  RCY_C_D  KG_HAB\n",
       "IE         2017   51.0  RCY_C_D  KG_HAB\n",
       "EL         2017   21.0  RCY_C_D  KG_HAB\n",
       "ES         2017   84.0  RCY_C_D  KG_HAB\n",
       "FR         2017  106.0  RCY_C_D  KG_HAB\n",
       "HR         2017    9.0  RCY_C_D  KG_HAB\n",
       "IT         2017   98.0  RCY_C_D  KG_HAB\n",
       "CY         2017    9.0  RCY_C_D  KG_HAB\n",
       "LV         2017   29.0  RCY_C_D  KG_HAB\n",
       "LT         2017  109.0  RCY_C_D  KG_HAB\n",
       "LU         2017  153.0  RCY_C_D  KG_HAB\n",
       "HU         2017   32.0  RCY_C_D  KG_HAB\n",
       "MT         2017    0.0  RCY_C_D  KG_HAB\n",
       "NL         2017  144.0  RCY_C_D  KG_HAB\n",
       "AT         2017  182.0  RCY_C_D  KG_HAB\n",
       "PL         2017   22.0  RCY_C_D  KG_HAB\n",
       "PT         2017   83.0  RCY_C_D  KG_HAB\n",
       "RO         2017   18.0  RCY_C_D  KG_HAB\n",
       "SI         2017   73.0  RCY_C_D  KG_HAB\n",
       "SK         2017   33.0  RCY_C_D  KG_HAB\n",
       "FI         2017   67.0  RCY_C_D  KG_HAB\n",
       "SE         2017   70.0  RCY_C_D  KG_HAB\n",
       "IS         2017    0.0  RCY_C_D  KG_HAB\n",
       "NO         2017   75.0  RCY_C_D  KG_HAB\n",
       "CH         2017  154.0  RCY_C_D  KG_HAB\n",
       "UK         2017   79.0  RCY_C_D  KG_HAB\n",
       "ME         2017    1.0  RCY_C_D  KG_HAB\n",
       "MK         2017    NaN  RCY_C_D  KG_HAB\n",
       "AL         2017    NaN  RCY_C_D  KG_HAB\n",
       "RS         2017    0.0  RCY_C_D  KG_HAB\n",
       "TR         2017    2.0  RCY_C_D  KG_HAB\n",
       "BA         2017    0.0  RCY_C_D  KG_HAB\n",
       "XK         2017    NaN  RCY_C_D  KG_HAB"
      ]
     },
     "execution_count": 23,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_household_recycling_compost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>GEN</th>\n",
       "      <th>RCY</th>\n",
       "      <th>DSP_L</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>2017</td>\n",
       "      <td>496.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2017</td>\n",
       "      <td>492.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2017</td>\n",
       "      <td>435.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2017</td>\n",
       "      <td>489.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>2017</td>\n",
       "      <td>820.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>2017</td>\n",
       "      <td>627.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EE</th>\n",
       "      <td>2017</td>\n",
       "      <td>390.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>2017</td>\n",
       "      <td>576.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>2017</td>\n",
       "      <td>504.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>2017</td>\n",
       "      <td>473.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>2017</td>\n",
       "      <td>535.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HR</th>\n",
       "      <td>2017</td>\n",
       "      <td>416.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>2017</td>\n",
       "      <td>488.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>2017</td>\n",
       "      <td>625.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LV</th>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>267.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>2017</td>\n",
       "      <td>455.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>2017</td>\n",
       "      <td>798.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>2017</td>\n",
       "      <td>385.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>2017</td>\n",
       "      <td>666.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>2017</td>\n",
       "      <td>513.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>2017</td>\n",
       "      <td>570.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PL</th>\n",
       "      <td>2017</td>\n",
       "      <td>315.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>2017</td>\n",
       "      <td>486.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>2017</td>\n",
       "      <td>272.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>2017</td>\n",
       "      <td>471.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK</th>\n",
       "      <td>2017</td>\n",
       "      <td>378.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>2017</td>\n",
       "      <td>510.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>2017</td>\n",
       "      <td>452.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>2017</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>2017</td>\n",
       "      <td>748.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>2017</td>\n",
       "      <td>709.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>2017</td>\n",
       "      <td>468.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME</th>\n",
       "      <td>2017</td>\n",
       "      <td>509.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2017</td>\n",
       "      <td>436.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2017</td>\n",
       "      <td>306.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2017</td>\n",
       "      <td>425.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2017</td>\n",
       "      <td>352.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2017</td>\n",
       "      <td>229.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year    GEN    RCY  DSP_L\n",
       "geo                                 \n",
       "EU27_2020  2017  496.0  233.0  120.0\n",
       "EU28       2017  492.0  229.0  115.0\n",
       "BE         2017  411.0  221.0    4.0\n",
       "BG         2017  435.0  151.0  269.0\n",
       "CZ         2017  489.0  157.0  222.0\n",
       "DK         2017  820.0  390.0    8.0\n",
       "DE         2017  627.0  421.0    5.0\n",
       "EE         2017  390.0  110.0   75.0\n",
       "IE         2017  576.0  232.0  130.0\n",
       "EL         2017  504.0   95.0  403.0\n",
       "ES         2017  473.0  171.0  242.0\n",
       "FR         2017  535.0  236.0  115.0\n",
       "HR         2017  416.0   98.0  301.0\n",
       "IT         2017  488.0  234.0  114.0\n",
       "CY         2017  625.0  101.0  492.0\n",
       "LV         2017  411.0  102.0  267.0\n",
       "LT         2017  455.0  219.0  149.0\n",
       "LU         2017  798.0  390.0   36.0\n",
       "HU         2017  385.0  135.0  186.0\n",
       "MT         2017  666.0   74.0  530.0\n",
       "NL         2017  513.0  280.0    7.0\n",
       "AT         2017  570.0  329.0   12.0\n",
       "PL         2017  315.0  106.0  132.0\n",
       "PT         2017  486.0  142.0  227.0\n",
       "RO         2017  272.0   38.0  220.0\n",
       "SI         2017  471.0  272.0   48.0\n",
       "SK         2017  378.0  113.0  229.0\n",
       "FI         2017  510.0  207.0    5.0\n",
       "SE         2017  452.0  212.0    2.0\n",
       "IS         2017  656.0    0.0  416.0\n",
       "NO         2017  748.0  291.0   26.0\n",
       "CH         2017  709.0  372.0    0.0\n",
       "UK         2017  468.0  205.0   79.0\n",
       "ME         2017  509.0   19.0  473.0\n",
       "MK         2017    NaN    NaN  344.0\n",
       "AL         2017  436.0    NaN  346.0\n",
       "RS         2017  306.0    1.0  256.0\n",
       "TR         2017  425.0   39.0  359.0\n",
       "BA         2017  352.0    0.0  270.0\n",
       "XK         2017  229.0    NaN  228.0"
      ]
     },
     "execution_count": 24,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats = pd.DataFrame(\n",
    "    {\n",
    "        \"year\": 2017,\n",
    "        \"GEN\": df_household_generation.value,\n",
    "        \"RCY\": df_household_recycling_compost.value + df_household_recycling_material.value,\n",
    "        \"DSP_L\": df_household_landfill.value\n",
    "    },\n",
    "    index=df_household_generation.index\n",
    ")\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>wst_oper</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo</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>2017</td>\n",
       "      <td>496.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>2017</td>\n",
       "      <td>492.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>2017</td>\n",
       "      <td>435.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2017</td>\n",
       "      <td>489.0</td>\n",
       "      <td>GEN</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XK</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RCY_C_D</td>\n",
       "      <td>KG_HAB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           year  value wst_oper    unit\n",
       "geo                                    \n",
       "EU27_2020  2017  496.0      GEN  KG_HAB\n",
       "EU28       2017  492.0      GEN  KG_HAB\n",
       "BE         2017  411.0      GEN  KG_HAB\n",
       "BG         2017  435.0      GEN  KG_HAB\n",
       "CZ         2017  489.0      GEN  KG_HAB\n",
       "...         ...    ...      ...     ...\n",
       "AL         2017    NaN  RCY_C_D  KG_HAB\n",
       "RS         2017    0.0  RCY_C_D  KG_HAB\n",
       "TR         2017    2.0  RCY_C_D  KG_HAB\n",
       "BA         2017    0.0  RCY_C_D  KG_HAB\n",
       "XK         2017    NaN  RCY_C_D  KG_HAB\n",
       "\n",
       "[120 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternatively, get df_stats directly by query...but this long format is not very good for adding new features\n",
    "# it would be possible but you have to think about it...a new feature aka column would be needed to be appended at the end\n",
    "# so adding a new feature requires to create a dataframe first and append it to df_stats\n",
    "df_stats = df_waste_municipal.query(\"year == 2017 and unit == 'KG_HAB' and wst_oper in ['GEN', 'RCY_C_D', 'RCY_M', 'DSP_L']\")\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df_stats = df_stats.assign(recycling_rate=df_stats.RCY / df_stats.GEN, disposal_rate=df_stats.DSP_L / df_stats.GEN)\n",
    "df_stats.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>geo</th>\n",
       "      <th>year</th>\n",
       "      <th>GEN</th>\n",
       "      <th>RCY</th>\n",
       "      <th>DSP_L</th>\n",
       "      <th>recycling_rate</th>\n",
       "      <th>disposal_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EU27_2020</td>\n",
       "      <td>2017</td>\n",
       "      <td>496.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.469758</td>\n",
       "      <td>0.241935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2017</td>\n",
       "      <td>492.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.465447</td>\n",
       "      <td>0.233740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.537713</td>\n",
       "      <td>0.009732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BG</td>\n",
       "      <td>2017</td>\n",
       "      <td>435.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>0.347126</td>\n",
       "      <td>0.618391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ</td>\n",
       "      <td>2017</td>\n",
       "      <td>489.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.321063</td>\n",
       "      <td>0.453988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DK</td>\n",
       "      <td>2017</td>\n",
       "      <td>820.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.475610</td>\n",
       "      <td>0.009756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017</td>\n",
       "      <td>627.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.671451</td>\n",
       "      <td>0.007974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EE</td>\n",
       "      <td>2017</td>\n",
       "      <td>390.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.282051</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IE</td>\n",
       "      <td>2017</td>\n",
       "      <td>576.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.402778</td>\n",
       "      <td>0.225694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EL</td>\n",
       "      <td>2017</td>\n",
       "      <td>504.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>0.188492</td>\n",
       "      <td>0.799603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ES</td>\n",
       "      <td>2017</td>\n",
       "      <td>473.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.361522</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FR</td>\n",
       "      <td>2017</td>\n",
       "      <td>535.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.441121</td>\n",
       "      <td>0.214953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HR</td>\n",
       "      <td>2017</td>\n",
       "      <td>416.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.235577</td>\n",
       "      <td>0.723558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IT</td>\n",
       "      <td>2017</td>\n",
       "      <td>488.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0.479508</td>\n",
       "      <td>0.233607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CY</td>\n",
       "      <td>2017</td>\n",
       "      <td>625.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>0.161600</td>\n",
       "      <td>0.787200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LV</td>\n",
       "      <td>2017</td>\n",
       "      <td>411.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>0.248175</td>\n",
       "      <td>0.649635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LT</td>\n",
       "      <td>2017</td>\n",
       "      <td>455.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.481319</td>\n",
       "      <td>0.327473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LU</td>\n",
       "      <td>2017</td>\n",
       "      <td>798.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.488722</td>\n",
       "      <td>0.045113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HU</td>\n",
       "      <td>2017</td>\n",
       "      <td>385.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.350649</td>\n",
       "      <td>0.483117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MT</td>\n",
       "      <td>2017</td>\n",
       "      <td>666.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.795796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NL</td>\n",
       "      <td>2017</td>\n",
       "      <td>513.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.545809</td>\n",
       "      <td>0.013645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AT</td>\n",
       "      <td>2017</td>\n",
       "      <td>570.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.577193</td>\n",
       "      <td>0.021053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PL</td>\n",
       "      <td>2017</td>\n",
       "      <td>315.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.336508</td>\n",
       "      <td>0.419048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PT</td>\n",
       "      <td>2017</td>\n",
       "      <td>486.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>0.292181</td>\n",
       "      <td>0.467078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>RO</td>\n",
       "      <td>2017</td>\n",
       "      <td>272.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.139706</td>\n",
       "      <td>0.808824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SI</td>\n",
       "      <td>2017</td>\n",
       "      <td>471.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.577495</td>\n",
       "      <td>0.101911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SK</td>\n",
       "      <td>2017</td>\n",
       "      <td>378.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.298942</td>\n",
       "      <td>0.605820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>FI</td>\n",
       "      <td>2017</td>\n",
       "      <td>510.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.405882</td>\n",
       "      <td>0.009804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SE</td>\n",
       "      <td>2017</td>\n",
       "      <td>452.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.469027</td>\n",
       "      <td>0.004425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>IS</td>\n",
       "      <td>2017</td>\n",
       "      <td>656.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.634146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NO</td>\n",
       "      <td>2017</td>\n",
       "      <td>748.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.389037</td>\n",
       "      <td>0.034759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CH</td>\n",
       "      <td>2017</td>\n",
       "      <td>709.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524683</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>UK</td>\n",
       "      <td>2017</td>\n",
       "      <td>468.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.438034</td>\n",
       "      <td>0.168803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ME</td>\n",
       "      <td>2017</td>\n",
       "      <td>509.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>0.037328</td>\n",
       "      <td>0.929273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MK</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>344.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>AL</td>\n",
       "      <td>2017</td>\n",
       "      <td>436.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>346.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.793578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RS</td>\n",
       "      <td>2017</td>\n",
       "      <td>306.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.836601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TR</td>\n",
       "      <td>2017</td>\n",
       "      <td>425.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>0.091765</td>\n",
       "      <td>0.844706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>BA</td>\n",
       "      <td>2017</td>\n",
       "      <td>352.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.767045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>XK</td>\n",
       "      <td>2017</td>\n",
       "      <td>229.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.995633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          geo  year    GEN    RCY  DSP_L  recycling_rate  disposal_rate\n",
       "0   EU27_2020  2017  496.0  233.0  120.0        0.469758       0.241935\n",
       "1        EU28  2017  492.0  229.0  115.0        0.465447       0.233740\n",
       "2          BE  2017  411.0  221.0    4.0        0.537713       0.009732\n",
       "3          BG  2017  435.0  151.0  269.0        0.347126       0.618391\n",
       "4          CZ  2017  489.0  157.0  222.0        0.321063       0.453988\n",
       "5          DK  2017  820.0  390.0    8.0        0.475610       0.009756\n",
       "6          DE  2017  627.0  421.0    5.0        0.671451       0.007974\n",
       "7          EE  2017  390.0  110.0   75.0        0.282051       0.192308\n",
       "8          IE  2017  576.0  232.0  130.0        0.402778       0.225694\n",
       "9          EL  2017  504.0   95.0  403.0        0.188492       0.799603\n",
       "10         ES  2017  473.0  171.0  242.0        0.361522       0.511628\n",
       "11         FR  2017  535.0  236.0  115.0        0.441121       0.214953\n",
       "12         HR  2017  416.0   98.0  301.0        0.235577       0.723558\n",
       "13         IT  2017  488.0  234.0  114.0        0.479508       0.233607\n",
       "14         CY  2017  625.0  101.0  492.0        0.161600       0.787200\n",
       "15         LV  2017  411.0  102.0  267.0        0.248175       0.649635\n",
       "16         LT  2017  455.0  219.0  149.0        0.481319       0.327473\n",
       "17         LU  2017  798.0  390.0   36.0        0.488722       0.045113\n",
       "18         HU  2017  385.0  135.0  186.0        0.350649       0.483117\n",
       "19         MT  2017  666.0   74.0  530.0        0.111111       0.795796\n",
       "20         NL  2017  513.0  280.0    7.0        0.545809       0.013645\n",
       "21         AT  2017  570.0  329.0   12.0        0.577193       0.021053\n",
       "22         PL  2017  315.0  106.0  132.0        0.336508       0.419048\n",
       "23         PT  2017  486.0  142.0  227.0        0.292181       0.467078\n",
       "24         RO  2017  272.0   38.0  220.0        0.139706       0.808824\n",
       "25         SI  2017  471.0  272.0   48.0        0.577495       0.101911\n",
       "26         SK  2017  378.0  113.0  229.0        0.298942       0.605820\n",
       "27         FI  2017  510.0  207.0    5.0        0.405882       0.009804\n",
       "28         SE  2017  452.0  212.0    2.0        0.469027       0.004425\n",
       "29         IS  2017  656.0    0.0  416.0        0.000000       0.634146\n",
       "30         NO  2017  748.0  291.0   26.0        0.389037       0.034759\n",
       "31         CH  2017  709.0  372.0    0.0        0.524683       0.000000\n",
       "32         UK  2017  468.0  205.0   79.0        0.438034       0.168803\n",
       "33         ME  2017  509.0   19.0  473.0        0.037328       0.929273\n",
       "34         MK  2017    NaN    NaN  344.0             NaN            NaN\n",
       "35         AL  2017  436.0    NaN  346.0             NaN       0.793578\n",
       "36         RS  2017  306.0    1.0  256.0        0.003268       0.836601\n",
       "37         TR  2017  425.0   39.0  359.0        0.091765       0.844706\n",
       "38         BA  2017  352.0    0.0  270.0        0.000000       0.767045\n",
       "39         XK  2017  229.0    NaN  228.0             NaN       0.995633"
      ]
     },
     "execution_count": 26,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>geo</th>\n",
       "      <th>year</th>\n",
       "      <th>GEN</th>\n",
       "      <th>RCY</th>\n",
       "      <th>DSP_L</th>\n",
       "      <th>recycling_rate</th>\n",
       "      <th>disposal_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017</td>\n",
       "      <td>627.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.671451</td>\n",
       "      <td>0.007974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geo  year    GEN    RCY  DSP_L  recycling_rate  disposal_rate\n",
       "6  DE  2017  627.0  421.0    5.0        0.671451       0.007974"
      ]
     },
     "execution_count": 27,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats.query(\"geo == 'DE'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "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",
       "\u001b[0;34m\u001b[0m    \u001b[0merror_y_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[0manimation_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[0manimation_group\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[0mcategory_orders\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[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": 12,
     "metadata": {
     },
     "output_type": "execute_result",
     "start": 0
    }
   ],
   "source": [
    "?px.bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-2.1.0.min.js\"></script>                <div id=\"bf95c5ac-8613-452b-b493-bb902f0898fa\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"bf95c5ac-8613-452b-b493-bb902f0898fa\")) {                    Plotly.newPlot(                        \"bf95c5ac-8613-452b-b493-bb902f0898fa\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=GEN<br>geo=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"GEN\",\"marker\":{\"color\":\"blue\",\"opacity\":0.6,\"pattern\":{\"shape\":\"\"}},\"name\":\"GEN\",\"offsetgroup\":\"GEN\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"EU27_2020\",\"EU28\",\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"IS\",\"NO\",\"CH\",\"UK\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\"],\"xaxis\":\"x\",\"y\":[496.0,492.0,411.0,435.0,489.0,820.0,627.0,390.0,576.0,504.0,473.0,535.0,416.0,488.0,625.0,411.0,455.0,798.0,385.0,666.0,513.0,570.0,315.0,486.0,272.0,471.0,378.0,510.0,452.0,656.0,748.0,709.0,468.0,509.0,null,436.0,306.0,425.0,352.0,229.0],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=RCY<br>geo=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"RCY\",\"marker\":{\"color\":\"green\",\"opacity\":0.6,\"pattern\":{\"shape\":\"\"}},\"name\":\"RCY\",\"offsetgroup\":\"RCY\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"EU27_2020\",\"EU28\",\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"IS\",\"NO\",\"CH\",\"UK\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\"],\"xaxis\":\"x\",\"y\":[233.0,229.0,221.0,151.0,157.0,390.0,421.0,110.0,232.0,95.0,171.0,236.0,98.0,234.0,101.0,102.0,219.0,390.0,135.0,74.0,280.0,329.0,106.0,142.0,38.0,272.0,113.0,207.0,212.0,0.0,291.0,372.0,205.0,19.0,null,null,1.0,39.0,0.0,null],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=DSP_L<br>geo=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"DSP_L\",\"marker\":{\"color\":\"red\",\"opacity\":0.6,\"pattern\":{\"shape\":\"\"}},\"name\":\"DSP_L\",\"offsetgroup\":\"DSP_L\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"EU27_2020\",\"EU28\",\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"IS\",\"NO\",\"CH\",\"UK\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\"],\"xaxis\":\"x\",\"y\":[120.0,115.0,4.0,269.0,222.0,8.0,5.0,75.0,130.0,403.0,242.0,115.0,301.0,114.0,492.0,267.0,149.0,36.0,186.0,530.0,7.0,12.0,132.0,227.0,220.0,48.0,229.0,5.0,2.0,416.0,26.0,0.0,79.0,473.0,344.0,346.0,256.0,359.0,270.0,228.0],\"yaxis\":\"y\"}],                        {\"barmode\":\"group\",\"legend\":{\"title\":{\"text\":\"variable\"},\"tracegroupgap\":0},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Municipal waste generation vs recycling vs landfill for EU in 2017\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"geo\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"value\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('bf95c5ac-8613-452b-b493-bb902f0898fa');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 31,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.bar(\n",
    "    df_stats,\n",
    "    x=\"geo\",\n",
    "    y=[\"GEN\", \"RCY\", \"DSP_L\"],\n",
    "    barmode=\"group\",\n",
    "    color_discrete_map={\"GEN\": \"blue\", \"RCY\": \"green\", \"DSP_L\": \"red\"},\n",
    "    title=\"Municipal waste generation vs recycling vs landfill for EU in 2017\",\n",
    "    opacity=0.6\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# task: find all countries with a recycling rate worse than the EU average (or landfill rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-2.1.0.min.js\"></script>                <div id=\"5ba16ab0-f637-4161-8d8e-4ecfba4cda42\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"5ba16ab0-f637-4161-8d8e-4ecfba4cda42\")) {                    Plotly.newPlot(                        \"5ba16ab0-f637-4161-8d8e-4ecfba4cda42\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=recycling_rate<br>Countries=%{x}<br>Kilograms per capita=%{y}<extra></extra>\",\"legendgroup\":\"recycling_rate\",\"marker\":{\"color\":\"green\",\"opacity\":0.6,\"pattern\":{\"shape\":\"\"}},\"name\":\"recycling_rate\",\"offsetgroup\":\"recycling_rate\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"EU27_2020\",\"EU28\",\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"IS\",\"NO\",\"CH\",\"UK\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\"],\"xaxis\":\"x\",\"y\":[0.46975806451612906,0.4654471544715447,0.537712895377129,0.3471264367816092,0.3210633946830266,0.47560975609756095,0.671451355661882,0.28205128205128205,0.4027777777777778,0.1884920634920635,0.36152219873150104,0.4411214953271028,0.23557692307692307,0.47950819672131145,0.1616,0.24817518248175183,0.48131868131868133,0.48872180451127817,0.35064935064935066,0.1111111111111111,0.5458089668615984,0.5771929824561404,0.33650793650793653,0.29218106995884774,0.13970588235294118,0.5774946921443737,0.29894179894179895,0.40588235294117647,0.4690265486725664,0.0,0.3890374331550802,0.5246826516220028,0.43803418803418803,0.03732809430255403,null,null,0.0032679738562091504,0.09176470588235294,0.0,null],\"yaxis\":\"y\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=disposal_rate<br>Countries=%{x}<br>Kilograms per capita=%{y}<extra></extra>\",\"legendgroup\":\"disposal_rate\",\"marker\":{\"color\":\"red\",\"opacity\":0.6,\"pattern\":{\"shape\":\"\"}},\"name\":\"disposal_rate\",\"offsetgroup\":\"disposal_rate\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"type\":\"bar\",\"x\":[\"EU27_2020\",\"EU28\",\"BE\",\"BG\",\"CZ\",\"DK\",\"DE\",\"EE\",\"IE\",\"EL\",\"ES\",\"FR\",\"HR\",\"IT\",\"CY\",\"LV\",\"LT\",\"LU\",\"HU\",\"MT\",\"NL\",\"AT\",\"PL\",\"PT\",\"RO\",\"SI\",\"SK\",\"FI\",\"SE\",\"IS\",\"NO\",\"CH\",\"UK\",\"ME\",\"MK\",\"AL\",\"RS\",\"TR\",\"BA\",\"XK\"],\"xaxis\":\"x\",\"y\":[0.24193548387096775,0.23373983739837398,0.009732360097323601,0.6183908045977011,0.4539877300613497,0.00975609756097561,0.007974481658692184,0.19230769230769232,0.22569444444444445,0.7996031746031746,0.5116279069767442,0.21495327102803738,0.7235576923076923,0.2336065573770492,0.7872,0.6496350364963503,0.3274725274725275,0.045112781954887216,0.4831168831168831,0.7957957957957958,0.01364522417153996,0.021052631578947368,0.41904761904761906,0.4670781893004115,0.8088235294117647,0.10191082802547771,0.6058201058201058,0.00980392156862745,0.004424778761061947,0.6341463414634146,0.034759358288770054,0.0,0.16880341880341881,0.9292730844793713,null,0.7935779816513762,0.8366013071895425,0.8447058823529412,0.7670454545454546,0.9956331877729258],\"yaxis\":\"y\"}],                        {\"barmode\":\"group\",\"legend\":{\"title\":{\"text\":\"variable\"},\"tracegroupgap\":0},\"shapes\":[{\"line\":{\"color\":\"green\",\"dash\":\"dash\",\"width\":3},\"type\":\"line\",\"x0\":0,\"x1\":1,\"xref\":\"x domain\",\"y0\":0.55,\"y1\":0.55,\"yref\":\"y\"},{\"line\":{\"color\":\"red\",\"dash\":\"dash\",\"width\":3},\"type\":\"line\",\"x0\":0,\"x1\":1,\"xref\":\"x domain\",\"y0\":0.1,\"y1\":0.1,\"yref\":\"y\"}],\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Siedlungsabfälle: EU-Ziele 2025 Stand 2017\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Countries\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Kilograms per capita\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('5ba16ab0-f637-4161-8d8e-4ecfba4cda42');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 36,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = px.bar(\n",
    "    df_stats,\n",
    "    x=\"geo\",\n",
    "    y=[\"recycling_rate\", \"disposal_rate\"],\n",
    "    barmode=\"group\",\n",
    "    title=\"Siedlungsabfälle: EU-Ziele 2025 Stand 2017\",\n",
    "    labels={\"geo\": \"Countries\", \"value\": \"Kilograms per capita\"},\n",
    "    color_discrete_map={\"recycling_rate\": \"green\", \"disposal_rate\": \"red\"},\n",
    "    opacity=0.6\n",
    ")\n",
    "fig.add_hline(y=0.55, line_width=3, line_dash=\"dash\", line_color=\"green\")\n",
    "fig.add_hline(y=0.10,  line_width=3, line_dash=\"dash\", line_color=\"red\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Question 4: Prognose für die EU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# store each operation in its own df\n",
    "df_household_generation = df_waste_municipal.query(\"geo == 'EU28' and unit == 'KG_HAB' and wst_oper == 'GEN'\")\n",
    "df_household_recycling_compost = df_waste_municipal.query(\"geo == 'EU28' and unit == 'KG_HAB' and wst_oper == 'RCY_C_D'\")\n",
    "df_household_recycling_material = df_waste_municipal.query(\"geo == 'EU28' and unit == 'KG_HAB' and wst_oper == 'RCY_M'\")\n",
    "df_household_landfill = df_waste_municipal.query(\"geo == 'EU28' and unit == 'KG_HAB' and wst_oper == 'DSP_L_OTH'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>geo</th>\n",
       "      <th>year</th>\n",
       "      <th>GEN</th>\n",
       "      <th>RCY</th>\n",
       "      <th>DSP_L</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>1995</td>\n",
       "      <td>470.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>1996</td>\n",
       "      <td>482.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>1997</td>\n",
       "      <td>496.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>1998</td>\n",
       "      <td>494.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>1999</td>\n",
       "      <td>508.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2000</td>\n",
       "      <td>521.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2001</td>\n",
       "      <td>519.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2002</td>\n",
       "      <td>525.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2003</td>\n",
       "      <td>513.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2004</td>\n",
       "      <td>512.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2005</td>\n",
       "      <td>515.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2006</td>\n",
       "      <td>522.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2007</td>\n",
       "      <td>524.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2008</td>\n",
       "      <td>521.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2009</td>\n",
       "      <td>511.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2010</td>\n",
       "      <td>504.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2011</td>\n",
       "      <td>498.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2012</td>\n",
       "      <td>486.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2013</td>\n",
       "      <td>479.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2014</td>\n",
       "      <td>478.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2015</td>\n",
       "      <td>481.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2016</td>\n",
       "      <td>489.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2017</td>\n",
       "      <td>492.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2018</td>\n",
       "      <td>492.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>EU28</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       geo  year    GEN    RCY  DSP_L\n",
       "geo                                  \n",
       "EU28  EU28  1995  470.0   81.0  302.0\n",
       "EU28  EU28  1996  482.0   92.0  295.0\n",
       "EU28  EU28  1997  496.0  102.0  298.0\n",
       "EU28  EU28  1998  494.0  109.0  289.0\n",
       "EU28  EU28  1999  508.0  121.0  287.0\n",
       "EU28  EU28  2000  521.0  131.0  287.0\n",
       "EU28  EU28  2001  519.0  138.0  277.0\n",
       "EU28  EU28  2002  525.0  148.0  268.0\n",
       "EU28  EU28  2003  513.0  150.0  254.0\n",
       "EU28  EU28  2004  512.0  156.0  239.0\n",
       "EU28  EU28  2005  515.0  163.0  223.0\n",
       "EU28  EU28  2006  522.0  171.0  220.0\n",
       "EU28  EU28  2007  524.0  183.0  214.0\n",
       "EU28  EU28  2008  521.0  190.0  202.0\n",
       "EU28  EU28  2009  511.0  192.0  194.0\n",
       "EU28  EU28  2010  504.0  193.0  185.0\n",
       "EU28  EU28  2011  498.0  195.0  171.0\n",
       "EU28  EU28  2012  486.0  200.0  156.0\n",
       "EU28  EU28  2013  479.0  200.0  145.0\n",
       "EU28  EU28  2014  478.0  208.0  134.0\n",
       "EU28  EU28  2015  481.0  215.0  125.0\n",
       "EU28  EU28  2016  489.0  226.0  118.0\n",
       "EU28  EU28  2017  492.0  229.0  115.0\n",
       "EU28  EU28  2018  492.0  230.0  113.0\n",
       "EU28  EU28  2019    NaN    NaN    NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats = pd.DataFrame(\n",
    "    {\n",
    "        \"geo\": 'EU28',\n",
    "        \"year\": df_household_generation.year,\n",
    "        \"GEN\": df_household_generation.value,\n",
    "        \"RCY\": df_household_recycling_compost.value + df_household_recycling_material.value,\n",
    "        \"DSP_L\": df_household_landfill.value\n",
    "    },\n",
    "    index=df_household_generation.index\n",
    ")\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df_stats = df_stats.query(\"year != 2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-2.1.0.min.js\"></script>                <div id=\"7a3ebc45-3472-432f-a9ed-e0543da51789\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"7a3ebc45-3472-432f-a9ed-e0543da51789\")) {                    Plotly.newPlot(                        \"7a3ebc45-3472-432f-a9ed-e0543da51789\",                        [{\"hovertemplate\":\"variable=GEN<br>year=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"GEN\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"mode\":\"lines\",\"name\":\"GEN\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],\"xaxis\":\"x\",\"y\":[470.0,482.0,496.0,494.0,508.0,521.0,519.0,525.0,513.0,512.0,515.0,522.0,524.0,521.0,511.0,504.0,498.0,486.0,479.0,478.0,481.0,489.0,492.0,492.0],\"yaxis\":\"y\"},{\"hovertemplate\":\"variable=DSP_L<br>year=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"DSP_L\",\"line\":{\"color\":\"#EF553B\",\"dash\":\"solid\"},\"mode\":\"lines\",\"name\":\"DSP_L\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],\"xaxis\":\"x\",\"y\":[302.0,295.0,298.0,289.0,287.0,287.0,277.0,268.0,254.0,239.0,223.0,220.0,214.0,202.0,194.0,185.0,171.0,156.0,145.0,134.0,125.0,118.0,115.0,113.0],\"yaxis\":\"y\"},{\"hovertemplate\":\"variable=RCY<br>year=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"RCY\",\"line\":{\"color\":\"#00cc96\",\"dash\":\"solid\"},\"mode\":\"lines\",\"name\":\"RCY\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],\"xaxis\":\"x\",\"y\":[81.0,92.0,102.0,109.0,121.0,131.0,138.0,148.0,150.0,156.0,163.0,171.0,183.0,190.0,192.0,193.0,195.0,200.0,200.0,208.0,215.0,226.0,229.0,230.0],\"yaxis\":\"y\"}],                        {\"legend\":{\"title\":{\"text\":\"variable\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"year\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"value\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('7a3ebc45-3472-432f-a9ed-e0543da51789');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 41,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.line(df_stats, x=\"year\", y=[\"GEN\", \"DSP_L\", \"RCY\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "df_stats = df_stats.assign(recycling_rate=df_stats.RCY / df_stats.GEN, disposal_rate=df_stats.DSP_L / df_stats.GEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": "<html>\n<head><meta charset=\"utf-8\" /></head>\n<body>\n    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n        <script src=\"https://cdn.plot.ly/plotly-2.1.0.min.js\"></script>                <div id=\"b01861d2-c32a-401c-8183-1a249a27c04a\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"b01861d2-c32a-401c-8183-1a249a27c04a\")) {                    Plotly.newPlot(                        \"b01861d2-c32a-401c-8183-1a249a27c04a\",                        [{\"hovertemplate\":\"variable=recycling_rate<br>year=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"recycling_rate\",\"line\":{\"color\":\"green\",\"dash\":\"solid\"},\"mode\":\"lines\",\"name\":\"recycling_rate\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],\"xaxis\":\"x\",\"y\":[0.1723404255319149,0.1908713692946058,0.2056451612903226,0.22064777327935223,0.23818897637795275,0.2514395393474088,0.2658959537572254,0.2819047619047619,0.29239766081871343,0.3046875,0.31650485436893205,0.3275862068965517,0.34923664122137404,0.3646833013435701,0.37573385518590996,0.38293650793650796,0.39156626506024095,0.411522633744856,0.4175365344467641,0.4351464435146444,0.446985446985447,0.4621676891615542,0.4654471544715447,0.46747967479674796],\"yaxis\":\"y\"},{\"hovertemplate\":\"variable=disposal_rate<br>year=%{x}<br>value=%{y}<extra></extra>\",\"legendgroup\":\"disposal_rate\",\"line\":{\"color\":\"red\",\"dash\":\"solid\"},\"mode\":\"lines\",\"name\":\"disposal_rate\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018],\"xaxis\":\"x\",\"y\":[0.6425531914893617,0.6120331950207469,0.6008064516129032,0.5850202429149798,0.5649606299212598,0.5508637236084453,0.5337186897880539,0.5104761904761905,0.4951267056530214,0.466796875,0.4330097087378641,0.421455938697318,0.4083969465648855,0.3877159309021113,0.3796477495107632,0.36706349206349204,0.3433734939759036,0.32098765432098764,0.302713987473904,0.2803347280334728,0.2598752598752599,0.24130879345603273,0.23373983739837398,0.22967479674796748],\"yaxis\":\"y\"}],                        {\"legend\":{\"title\":{\"text\":\"variable\"},\"tracegroupgap\":0},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Recycling vs disposal rate for EU\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"year\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"value\"}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('b01861d2-c32a-401c-8183-1a249a27c04a');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                            </script>        </div>\n</body>\n</html>"
     },
     "execution_count": 53,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "px.line(df_stats, \n",
    "        x=\"year\",\n",
    "        y=[\"recycling_rate\", \"disposal_rate\"],\n",
    "        color_discrete_map={\"recycling_rate\": \"green\", \"disposal_rate\": \"red\"},\n",
    "        title=\"Recycling vs disposal rate for EU\",\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Analyse statistischer Zusammenhänge mit statsmodels\n",
    "\n",
    "https://www.statsmodels.org/stable/gettingstarted.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# handelt es sich bei recycling_rate um eine annährend lineare Beziehung?\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from patsy import dmatrices\n",
    "\n",
    "y, X = dmatrices('recycling_rate ~ year', data=df_stats, return_type='dataframe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(      Intercept    year\n",
       " geo                    \n",
       " EU28        1.0  1995.0\n",
       " EU28        1.0  1996.0\n",
       " EU28        1.0  1997.0\n",
       " EU28        1.0  1998.0\n",
       " EU28        1.0  1999.0\n",
       " EU28        1.0  2000.0\n",
       " EU28        1.0  2001.0\n",
       " EU28        1.0  2002.0\n",
       " EU28        1.0  2003.0\n",
       " EU28        1.0  2004.0\n",
       " EU28        1.0  2005.0\n",
       " EU28        1.0  2006.0\n",
       " EU28        1.0  2007.0\n",
       " EU28        1.0  2008.0\n",
       " EU28        1.0  2009.0\n",
       " EU28        1.0  2010.0\n",
       " EU28        1.0  2011.0\n",
       " EU28        1.0  2012.0\n",
       " EU28        1.0  2013.0\n",
       " EU28        1.0  2014.0\n",
       " EU28        1.0  2015.0\n",
       " EU28        1.0  2016.0\n",
       " EU28        1.0  2017.0\n",
       " EU28        1.0  2018.0,\n",
       "       recycling_rate\n",
       " geo                 \n",
       " EU28        0.172340\n",
       " EU28        0.190871\n",
       " EU28        0.205645\n",
       " EU28        0.220648\n",
       " EU28        0.238189\n",
       " EU28        0.251440\n",
       " EU28        0.265896\n",
       " EU28        0.281905\n",
       " EU28        0.292398\n",
       " EU28        0.304688\n",
       " EU28        0.316505\n",
       " EU28        0.327586\n",
       " EU28        0.349237\n",
       " EU28        0.364683\n",
       " EU28        0.375734\n",
       " EU28        0.382937\n",
       " EU28        0.391566\n",
       " EU28        0.411523\n",
       " EU28        0.417537\n",
       " EU28        0.435146\n",
       " EU28        0.446985\n",
       " EU28        0.462168\n",
       " EU28        0.465447\n",
       " EU28        0.467480)"
      ]
     },
     "execution_count": 56,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         recycling_rate   R-squared:                       0.995\n",
      "Model:                            OLS   Adj. R-squared:                  0.995\n",
      "Method:                 Least Squares   F-statistic:                     4625.\n",
      "Date:                Thu, 24 Jun 2021   Prob (F-statistic):           4.51e-27\n",
      "Time:                        18:18:41   Log-Likelihood:                 87.731\n",
      "No. Observations:                  24   AIC:                            -171.5\n",
      "Df Residuals:                      22   BIC:                            -169.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -25.9534      0.387    -67.141      0.000     -26.755     -25.152\n",
      "year           0.0131      0.000     68.008      0.000       0.013       0.014\n",
      "==============================================================================\n",
      "Omnibus:                        7.779   Durbin-Watson:                   0.595\n",
      "Prob(Omnibus):                  0.020   Jarque-Bera (JB):                5.569\n",
      "Skew:                          -1.023   Prob(JB):                       0.0618\n",
      "Kurtosis:                       4.175   Cond. No.                     5.82e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.82e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "mod = sm.OLS(y, X)    # Describe model\n",
    "\n",
    "res = mod.fit()       # Fit model\n",
    "\n",
    "print(res.summary())   # Summarize model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -25.953370\n",
       "year          0.013102\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.422894536394035, 0.012513616717338954)"
      ]
     },
     "execution_count": 59,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Admittedly, the output produced above is not very verbose, but we know from reading the docstring (also, print(sm.stats.linear_rainbow.__doc__)) that the first number is an F-statistic and that the second is the p-value.\n",
    "\n",
    "sm.stats.linear_rainbow(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 4 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "image/png": {
       "height": 496,
       "width": 856
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statsmodels also provides graphics functions. For example, we can draw a plot of partial regression for a set of regressors by:\n",
    "sm.graphics.plot_regress_exog(res, 'year')\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-26.755030</td>\n",
       "      <td>-25.151710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.012702</td>\n",
       "      <td>0.013501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0          1\n",
       "Intercept -26.755030 -25.151710\n",
       "year        0.012702   0.013501"
      ]
     },
     "execution_count": 18,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wird die EU ihr Ziel, die Recyclingrate bis 2025 auf über 55% zu bringen, erreichen?')"
      ]
     },
     "execution_count": 19,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "image/png": {
       "height": 426,
       "width": 719
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = sm.graphics.abline_plot(model_results=res, label=\"Modellvorhersage\")\n",
    "\n",
    "ax = fig.axes[0]\n",
    "ax.axhline(0.55, color=\"green\", linewidth=2)\n",
    "ax.axvline(2025, color=\"red\")\n",
    "ax.scatter(df_stats.year, df_stats.recycling_rate, label=\"data\")\n",
    "\n",
    "ax.legend()\n",
    "ax.margins(.1)\n",
    "ax.set_xlim(1995, 2035)\n",
    "ax.set_title(\"Wird die EU ihr Ziel, die Recyclingrate bis 2025 auf über 55% zu bringen, erreichen?\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Wird die EU ihr Ziel, die Recyclingrate bis 2025 auf über 55% zu bringen, erreichen?')"
      ]
     },
     "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": 426,
       "width": 719
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.OLS(y, X)    # Describe model\n",
    "res = mod.fit()       # Fit model\n",
    "fig = sm.graphics.abline_plot(model_results=res, label=\"Modellvorhersage\")\n",
    "\n",
    "quantiles = np.arange(.05, .96, .1)\n",
    "ax = fig.axes[0]\n",
    "for q in quantiles:\n",
    "    mod = smf.quantreg('recycling_rate ~ year', df_stats)\n",
    "    res = mod.fit(q=q)\n",
    "    #fig = sm.graphics.abline_plot(model_results=res, label=\"Modellvorhersage\")\n",
    "    X_q = np.array(range(1995, 2035))\n",
    "    Y_q = res.params[\"year\"] * X_q + res.params[\"Intercept\"]\n",
    "    ax.plot(X_q, Y_q, color=\"grey\", linestyle=\"dashed\", linewidth=1)\n",
    "\n",
    "ax = fig.axes[0]\n",
    "ax.axhline(0.55, color=\"green\", linewidth=2)\n",
    "ax.axvline(2025, color=\"red\")\n",
    "ax.scatter(df_stats.year, df_stats.recycling_rate, label=\"data\")\n",
    "\n",
    "ax.legend()\n",
    "#ax.margins(.1)\n",
    "ax.set_xlim(1995, 2035)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_title(\"Wird die EU ihr Ziel, die Recyclingrate bis 2025 auf über 55% zu bringen, erreichen?\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (system-wide)",
   "language": "python",
   "metadata": {
    "cocalc": {
     "description": "Python 3 programming language",
     "priority": 100,
     "url": "https://www.python.org/"
    }
   },
   "name": "python3",
   "resource_dir": "/ext/jupyter/kernels/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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}