lectures/pandas_3_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://pandas.pydata.org/_static/pandas_logo.png\" width=\"400\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [PANDAS: 3. Pivot Table](#1)\n",
    "* [PANDAS: 4. Group By](#2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PANDAS: 3. Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/ODFpGo7UomA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1094297f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://www.youtube.com/embed/ODFpGo7UomA', width=640, height=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit,hazard,wst_oper,waste,geo\\time\t2016 \t2014 \t2012 \t2010 \t2008 \t2006 \t2004 \r\n",
      "KG_HAB,HAZ,DSP_I,DSP_OTH,IS\t: \t: b\t: \t: \t: \t: \t: \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,AT\t: \t: \t: \t: \t: c\t2 \t2 \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,BE\t: \t: \t: \t: \t2 \t0 \t: \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,BG\t: \t: \t: \t: \t0 \t: \t0 \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,CY\t: \t: \t: \t: \t0 \t0 \t: \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,CZ\t: \t: \t: \t: \t0 \t0 \t: c\r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,DE\t: \t: \t: \t: \t2 \t2 \t1 \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,DK\t: \t: \t: \t: \t0 \t: \t: \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,EE\t: \t: \t: \t: \t0 \t0 \t0 \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,EL\t: \t: \t: \t: \t0 \t: \t0 \r\n",
      "KG_HAB,HAZ,DSP_I,INC_OTH,ES\t: \t: \t: \t: \t0 \t1 \t0 \r\n"
     ]
    }
   ],
   "source": [
    "!head -12 data/env_wastrt.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('data/env_wastrt.tsv',header=0,sep=',|\\t',engine='python',\n",
    "                  names=['unit','hazard','operation','waste','country',2016,2014,2012,\n",
    "                        2010,2008,2006,2004])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.waste=='W074','waste']='Plastic'\n",
    "df.loc[df.waste=='W071','waste']='Glass'\n",
    "df.loc[df.waste=='W06_07A','waste']='Recyclable'\n",
    "df.loc[df.waste=='W12-13','waste']='Mineral'\n",
    "df.loc[df.waste=='W10','waste']='Mixed ordinary wastes'\n",
    "df.loc[df.waste=='W073','waste']='Rubber'\n",
    "df.loc[df.waste=='W072','waste']='Paper and cardboard'\n",
    "df.loc[df.waste=='W081','waste']='Vehicles'\n",
    "df.loc[df.waste=='W01-05','waste']='Chemical&Medical'\n",
    "df.loc[df.waste=='W075','waste']='Wood'\n",
    "df.loc[df.waste=='W076','waste']='Textile'\n",
    "df.loc[df.waste=='W063','waste']='Metal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df.operation=='DSP_L','operation']='Landfill'\n",
    "df.loc[df.operation=='DSP_I','operation']='Incineration'\n",
    "df.loc[df.operation=='RCV_R','operation']='Recycling'\n",
    "df.loc[df.operation=='TRT','operation']='Waste treatment'\n",
    "df.loc[df.operation=='DSP_L_OTH','operation']='Landfill&Other'\n",
    "df.loc[df.operation=='DSP_OTH','operation']='Other'\n",
    "df.loc[df.operation=='RCV_E','operation']='Energy recovery'\n",
    "df.loc[df.operation=='RCV_R_B','operation']='Recycling&Backfilling'\n",
    "df.loc[df.operation=='RCV_B','operation']='Backfilling'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,5:] = df.iloc[:,5:].applymap(lambda x: x.rstrip('bcdefinprsuz')) \n",
    "df.iloc[:,5:] = df.iloc[:,5:].apply(pd.to_numeric,errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>operation</th>\n",
       "      <th>waste</th>\n",
       "      <th>country</th>\n",
       "      <th>2016</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2008</th>\n",
       "      <th>2006</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>DSP_OTH</td>\n",
       "      <td>IS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>INC_OTH</td>\n",
       "      <td>AT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>INC_OTH</td>\n",
       "      <td>BE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>INC_OTH</td>\n",
       "      <td>BG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>HAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>INC_OTH</td>\n",
       "      <td>CY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     unit hazard     operation    waste country 2016 2014 2012 2010 2008 2006  \\\n",
       "0  KG_HAB    HAZ  Incineration  DSP_OTH      IS  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  KG_HAB    HAZ  Incineration  INC_OTH      AT  NaN  NaN  NaN  NaN  NaN    2   \n",
       "2  KG_HAB    HAZ  Incineration  INC_OTH      BE  NaN  NaN  NaN  NaN    2    0   \n",
       "3  KG_HAB    HAZ  Incineration  INC_OTH      BG  NaN  NaN  NaN  NaN    0  NaN   \n",
       "4  KG_HAB    HAZ  Incineration  INC_OTH      CY  NaN  NaN  NaN  NaN    0    0   \n",
       "\n",
       "  2004  \n",
       "0  NaN  \n",
       "1    2  \n",
       "2  NaN  \n",
       "3    0  \n",
       "4  NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>operation</th>\n",
       "      <th>waste</th>\n",
       "      <th>country</th>\n",
       "      <th>2016</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2008</th>\n",
       "      <th>2006</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28967</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28968</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>AT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>182</td>\n",
       "      <td>159</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28969</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>BE</td>\n",
       "      <td>136</td>\n",
       "      <td>125</td>\n",
       "      <td>228</td>\n",
       "      <td>167</td>\n",
       "      <td>348</td>\n",
       "      <td>210</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28970</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>BG</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28971</th>\n",
       "      <td>KG_HAB</td>\n",
       "      <td>NHAZ</td>\n",
       "      <td>Incineration</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>CY</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         unit hazard     operation  waste country 2016 2014 2012 2010 2008  \\\n",
       "28967  KG_HAB   NHAZ  Incineration  TOTAL      AL  NaN   13  NaN  NaN  NaN   \n",
       "28968  KG_HAB   NHAZ  Incineration  TOTAL      AT  NaN    2    1  189  182   \n",
       "28969  KG_HAB   NHAZ  Incineration  TOTAL      BE  136  125  228  167  348   \n",
       "28970  KG_HAB   NHAZ  Incineration  TOTAL      BG    1    2    1    0    1   \n",
       "28971  KG_HAB   NHAZ  Incineration  TOTAL      CY    0    0    8    8   18   \n",
       "\n",
       "      2006 2004  \n",
       "28967  NaN  NaN  \n",
       "28968  159  209  \n",
       "28969  210  228  \n",
       "28970   16   13  \n",
       "28971   19    9  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.unit=='KG_HAB')&(df.waste=='TOTAL')&(df.hazard=='NHAZ')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/wastrt.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>261.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>14173.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>1523.0</td>\n",
       "      <td>1784.0</td>\n",
       "      <td>16728.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>5.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>2074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>2844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>499.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5293.0</td>\n",
       "      <td>5441.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>6145.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Backfilling  Energy recovery  Incineration  Landfill  \\\n",
       "country                                                           \n",
       "SE               261.0            770.0           0.0    8773.0   \n",
       "IT                 5.0             41.0         103.0     319.0   \n",
       "DK                 0.0            595.0           0.0     456.0   \n",
       "EL               499.0             12.0           2.0    5293.0   \n",
       "\n",
       "operation  Landfill&Other   Other  Recycling  Recycling&Backfilling  \\\n",
       "country                                                               \n",
       "SE                14173.0  5400.0     1523.0                 1784.0   \n",
       "IT                  319.0     0.0     1605.0                 1610.0   \n",
       "DK                  471.0    16.0     1778.0                 1778.0   \n",
       "EL                 5441.0   148.0      191.0                  690.0   \n",
       "\n",
       "operation  Waste treatment  \n",
       "country                     \n",
       "SE                 16728.0  \n",
       "IT                  2074.0  \n",
       "DK                  2844.0  \n",
       "EL                  6145.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[(df.unit=='KG_HAB')&(df.waste=='TOTAL')&(df.hazard=='NHAZ')]\n",
    "  .pivot_table(index='country',columns='operation',values=2014,aggfunc='first')\n",
    "  ).loc[['SE','IT','DK','EL']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>261.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8712.0</td>\n",
       "      <td>14112.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>15304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>783.0</td>\n",
       "      <td>893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>1819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>499.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4863.0</td>\n",
       "      <td>5008.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>5539.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Backfilling  Energy recovery  Incineration  Landfill  \\\n",
       "country                                                           \n",
       "SE               261.0              2.0           0.0    8712.0   \n",
       "IT                 5.0              0.0           0.0     109.0   \n",
       "DK                 0.0             26.0           0.0     436.0   \n",
       "EL               499.0              0.0           0.0    4863.0   \n",
       "\n",
       "operation  Landfill&Other   Other  Recycling  Recycling&Backfilling  \\\n",
       "country                                                               \n",
       "SE                14112.0  5400.0      929.0                 1190.0   \n",
       "IT                  109.0     0.0      778.0                  783.0   \n",
       "DK                  436.0     0.0     1358.0                 1358.0   \n",
       "EL                 5008.0   145.0       33.0                  532.0   \n",
       "\n",
       "operation  Waste treatment  \n",
       "country                     \n",
       "SE                 15304.0  \n",
       "IT                   893.0  \n",
       "DK                  1819.0  \n",
       "EL                  5539.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[(df.unit=='KG_HAB')&(df.waste=='Mineral')&(df.hazard=='NHAZ')]\n",
    "  .pivot_table(index='country',columns='operation',values=2014,aggfunc='first')\n",
    "  ).loc[['SE','IT','DK','EL']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Backfilling  Energy recovery  Incineration  Landfill  \\\n",
       "country                                                           \n",
       "SE                 0.0              7.0           0.0       0.0   \n",
       "IT                 0.0              1.0           0.0       1.0   \n",
       "DK                 0.0              1.0           0.0       0.0   \n",
       "EL                 0.0              0.0           0.0       6.0   \n",
       "\n",
       "operation  Landfill&Other  Other  Recycling  Recycling&Backfilling  \\\n",
       "country                                                              \n",
       "SE                    0.0    0.0        8.0                    8.0   \n",
       "IT                    1.0    0.0       34.0                   34.0   \n",
       "DK                    0.0    0.0       11.0                   11.0   \n",
       "EL                    6.0    0.0        3.0                    3.0   \n",
       "\n",
       "operation  Waste treatment  \n",
       "country                     \n",
       "SE                    16.0  \n",
       "IT                    37.0  \n",
       "DK                    12.0  \n",
       "EL                    10.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[(df.unit=='KG_HAB')&(df.waste=='Plastic')&(df.hazard=='NHAZ')]\n",
    "  .pivot_table(index='country',columns='operation',values=2014,aggfunc='first')\n",
    "  ).loc[['SE','IT','DK','EL']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Backfilling  Energy recovery  Incineration  Landfill  \\\n",
       "country                                                           \n",
       "SE                 0.0              3.0           0.0       0.0   \n",
       "IT                 0.0              1.0           0.0       0.0   \n",
       "DK                 0.0              0.0           0.0       0.0   \n",
       "EL                 0.0              0.0           0.0       0.0   \n",
       "\n",
       "operation  Landfill&Other  Other  Recycling  Recycling&Backfilling  \\\n",
       "country                                                              \n",
       "SE                    0.0    0.0        5.0                    5.0   \n",
       "IT                    0.0    0.0        2.0                    2.0   \n",
       "DK                    0.0    0.0       10.0                   10.0   \n",
       "EL                    0.0    0.0        1.0                    1.0   \n",
       "\n",
       "operation  Waste treatment  \n",
       "country                     \n",
       "SE                     8.0  \n",
       "IT                     3.0  \n",
       "DK                    10.0  \n",
       "EL                     1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[(df.unit=='KG_HAB')&(df.waste=='Rubber')&(df.hazard=='NHAZ')]\n",
    "  .pivot_table(index='country',columns='operation',values=2014,aggfunc='first')\n",
    "  ).loc[['SE','IT','DK','EL']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": 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>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Backfilling  Energy recovery  Incineration  Landfill  \\\n",
       "country                                                           \n",
       "SE                 0.0             10.0           0.0       0.0   \n",
       "IT                 0.0              2.0           0.0       1.0   \n",
       "DK                 0.0              1.0           0.0       0.0   \n",
       "EL                 0.0              0.0           0.0       6.0   \n",
       "\n",
       "operation  Landfill&Other  Other  Recycling  Recycling&Backfilling  \\\n",
       "country                                                              \n",
       "SE                    0.0    0.0       13.0                   13.0   \n",
       "IT                    1.0    0.0       36.0                   36.0   \n",
       "DK                    0.0    0.0       21.0                   21.0   \n",
       "EL                    6.0    0.0        4.0                    4.0   \n",
       "\n",
       "operation  Waste treatment  \n",
       "country                     \n",
       "SE                    24.0  \n",
       "IT                    40.0  \n",
       "DK                    22.0  \n",
       "EL                    11.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[(df.unit=='KG_HAB')&((df.waste=='Plastic')|(df.waste=='Rubber'))&(df.hazard=='NHAZ')]\n",
    "  .pivot_table(index='country',columns='operation',values=2014,aggfunc='sum')\n",
    "  ).loc[['SE','IT','DK','EL']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PANDAS: 4. Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"400\"\n",
       "            src=\"https://www.youtube.com/embed/oh8UijClQoE\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x111784f60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://www.youtube.com/embed/oh8UijClQoE', width=640, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MultiIndex Creation (see previous lecture)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2008</th>\n",
       "      <th>2006</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>waste</th>\n",
       "      <th>operation</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">KG_HAB</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">HAZ</th>\n",
       "      <th>DSP_OTH</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>IS</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">INC_OTH</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">Incineration</th>\n",
       "      <th>AT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           2016 2014 2012 2010 2008 2006 2004\n",
       "unit   hazard waste   operation    country                                   \n",
       "KG_HAB HAZ    DSP_OTH Incineration IS       NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "              INC_OTH Incineration AT       NaN  NaN  NaN  NaN  NaN    2    2\n",
       "                                   BE       NaN  NaN  NaN  NaN    2    0  NaN\n",
       "                                   BG       NaN  NaN  NaN  NaN    0  NaN    0\n",
       "                                   CY       NaN  NaN  NaN  NaN    0    0  NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(keys=['unit','hazard','waste','operation','country'],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2008</th>\n",
       "      <th>2006</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Incineration</th>\n",
       "      <th>AT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     2016 2014 2012 2010 2008 2006 2004\n",
       "operation    country                                   \n",
       "Incineration AT       NaN  NaN  NaN    0  NaN  NaN  NaN\n",
       "             BE         2    1    0    0  NaN  NaN  NaN\n",
       "             BG         0    0    0    0  NaN  NaN  NaN\n",
       "             CY         0    0    0    0  NaN  NaN  NaN\n",
       "             CZ         0    0  NaN    0  NaN  NaN  NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs(['KG_HAB','NHAZ','Plastic'],level=[0,1,2]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unstack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">2016</th>\n",
       "      <th>2014</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th colspan=\"9\" halign=\"left\">2004</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>...</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 2016                                                       \\\n",
       "operation Backfilling Energy recovery Incineration Landfill Landfill&Other   \n",
       "country                                                                      \n",
       "AT                  0             NaN          NaN        0              0   \n",
       "BE                  0               1            2        3              3   \n",
       "BG                  0               2            0        0              0   \n",
       "CY                  0               0            0        0              0   \n",
       "CZ                  7               6            0        5              5   \n",
       "\n",
       "                                                                       2014  \\\n",
       "operation Other Recycling Recycling&Backfilling Waste treatment Backfilling   \n",
       "country                                                                       \n",
       "AT            0        32                    32              37           0   \n",
       "BE            0        23                    23              28           0   \n",
       "BG            0        30                    30              32         NaN   \n",
       "CY            0         0                     0               0           0   \n",
       "CZ            0        18                    24              35           4   \n",
       "\n",
       "                ...                  2006        2004                  \\\n",
       "operation       ...       Waste treatment Backfilling Energy recovery   \n",
       "country         ...                                                     \n",
       "AT              ...                    20         NaN             NaN   \n",
       "BE              ...                    12         NaN             NaN   \n",
       "BG              ...                     3         NaN             NaN   \n",
       "CY              ...                     5         NaN             NaN   \n",
       "CZ              ...                     9         NaN             NaN   \n",
       "\n",
       "                                                                \\\n",
       "operation Incineration Landfill Landfill&Other Other Recycling   \n",
       "country                                                          \n",
       "AT                 NaN      NaN            NaN   NaN       NaN   \n",
       "BE                 NaN      NaN            NaN   NaN       NaN   \n",
       "BG                 NaN      NaN            NaN   NaN       NaN   \n",
       "CY                 NaN      NaN            NaN   NaN       NaN   \n",
       "CZ                 NaN      NaN            NaN   NaN       NaN   \n",
       "\n",
       "                                                 \n",
       "operation Recycling&Backfilling Waste treatment  \n",
       "country                                          \n",
       "AT                           43              43  \n",
       "BE                          NaN              30  \n",
       "BG                            1               1  \n",
       "CY                           14              14  \n",
       "CZ                            6               6  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs(['KG_HAB','NHAZ','Plastic'],level=[0,1,2]).unstack(0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation Backfilling Energy recovery Incineration Landfill Landfill&Other  \\\n",
       "country                                                                      \n",
       "SE                  0               7            0        0              0   \n",
       "IT                  0               1            0        1              1   \n",
       "DK                  0               1            0        0              0   \n",
       "EL                  0               0            0        6              6   \n",
       "\n",
       "operation Other Recycling Recycling&Backfilling Waste treatment  \n",
       "country                                                          \n",
       "SE            0         8                     8              16  \n",
       "IT            0        34                    34              37  \n",
       "DK            0        11                    11              12  \n",
       "EL            0         3                     3              10  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs(['KG_HAB','NHAZ','Plastic'],level=[0,1,2]).unstack(0).loc[['SE','IT','DK','EL'],2014]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">2014</th>\n",
       "      <th colspan=\"4\" halign=\"left\">2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              2014                                               2010  \\\n",
       "operation Landfill Energy recovery Recycling Waste treatment Landfill   \n",
       "country                                                                 \n",
       "DK               0               1        11              12        0   \n",
       "EL               6               0         3              10       12   \n",
       "IT               1               1        34              37        1   \n",
       "SE               0               7         8              16        0   \n",
       "\n",
       "                                                     \n",
       "operation Energy recovery Recycling Waste treatment  \n",
       "country                                              \n",
       "DK                      0        12              13  \n",
       "EL                      0         2              14  \n",
       "IT                      0        28              30  \n",
       "SE                      9         5              14  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "( df.xs(['KG_HAB','NHAZ','Plastic'],level=[0,1,2])\n",
    " .loc[(['Energy recovery','Landfill','Recycling','Waste treatment']\n",
    "       ,['SE','IT','DK','EL']),(2014,2010)]\n",
    ").unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AL <class 'pandas.core.series.Series'> operation        country\n",
      "Waste treatment  AL         0\n",
      "Name: 2014, dtype: object\n",
      "AT <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           AT            NaN\n",
      "Landfill               AT              0\n",
      "Landfill&Other         AT              0\n",
      "Other                  AT              0\n",
      "Backfilling            AT              0\n",
      "Energy recovery        AT            NaN\n",
      "Recycling              AT         187436\n",
      "Recycling&Backfilling  AT         187436\n",
      "Waste treatment        AT         230008\n",
      "Name: 2014, dtype: object\n",
      "BE <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           BE          16136\n",
      "Landfill               BE          22120\n",
      "Landfill&Other         BE          22120\n",
      "Other                  BE              0\n",
      "Backfilling            BE              0\n",
      "Energy recovery        BE          11624\n",
      "Recycling              BE         204032\n",
      "Recycling&Backfilling  BE         204032\n",
      "Waste treatment        BE         253912\n",
      "Name: 2014, dtype: object\n",
      "BG <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           BG            132\n",
      "Landfill               BG            587\n",
      "Landfill&Other         BG            NaN\n",
      "Other                  BG            NaN\n",
      "Backfilling            BG            NaN\n",
      "Energy recovery        BG            877\n",
      "Recycling              BG         194530\n",
      "Recycling&Backfilling  BG            NaN\n",
      "Waste treatment        BG         196126\n",
      "Name: 2014, dtype: object\n",
      "CY <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           CY         0\n",
      "Landfill               CY         0\n",
      "Landfill&Other         CY         0\n",
      "Other                  CY         0\n",
      "Backfilling            CY         0\n",
      "Energy recovery        CY         0\n",
      "Recycling              CY         0\n",
      "Recycling&Backfilling  CY         0\n",
      "Waste treatment        CY         0\n",
      "Name: 2014, dtype: object\n",
      "CZ <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           CZ            557\n",
      "Landfill               CZ          41594\n",
      "Landfill&Other         CZ          41594\n",
      "Other                  CZ              0\n",
      "Backfilling            CZ          41556\n",
      "Energy recovery        CZ          45094\n",
      "Recycling              CZ         136857\n",
      "Recycling&Backfilling  CZ         178413\n",
      "Waste treatment        CZ         265658\n",
      "Name: 2014, dtype: object\n",
      "DE <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           DE               14714\n",
      "Landfill               DE                1354\n",
      "Landfill&Other         DE                1354\n",
      "Other                  DE                   0\n",
      "Backfilling            DE                   0\n",
      "Energy recovery        DE              505751\n",
      "Recycling              DE         1.72892e+06\n",
      "Recycling&Backfilling  DE         1.72892e+06\n",
      "Waste treatment        DE         2.25074e+06\n",
      "Name: 2014, dtype: object\n",
      "DK <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           DK             0\n",
      "Landfill               DK          1514\n",
      "Landfill&Other         DK          1514\n",
      "Other                  DK             0\n",
      "Backfilling            DK             0\n",
      "Energy recovery        DK          3514\n",
      "Recycling              DK         60423\n",
      "Recycling&Backfilling  DK         60423\n",
      "Waste treatment        DK         65451\n",
      "Name: 2014, dtype: object\n",
      "EE <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           EE             0\n",
      "Landfill               EE           285\n",
      "Landfill&Other         EE           285\n",
      "Other                  EE             0\n",
      "Backfilling            EE             0\n",
      "Energy recovery        EE           506\n",
      "Recycling              EE         10059\n",
      "Recycling&Backfilling  EE         10059\n",
      "Waste treatment        EE         10851\n",
      "Name: 2014, dtype: object\n",
      "EL <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           EL              0\n",
      "Landfill               EL          68455\n",
      "Landfill&Other         EL          68455\n",
      "Other                  EL              0\n",
      "Backfilling            EL              0\n",
      "Energy recovery        EL            783\n",
      "Recycling              EL          38113\n",
      "Recycling&Backfilling  EL          38113\n",
      "Waste treatment        EL         107351\n",
      "Name: 2014, dtype: object\n",
      "ES <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           ES                   0\n",
      "Landfill               ES              150775\n",
      "Landfill&Other         ES              150775\n",
      "Other                  ES                   0\n",
      "Backfilling            ES                   0\n",
      "Energy recovery        ES                6362\n",
      "Recycling              ES              948414\n",
      "Recycling&Backfilling  ES              948414\n",
      "Waste treatment        ES         1.10555e+06\n",
      "Name: 2014, dtype: object\n",
      "EU28 <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           EU28           90000\n",
      "Landfill               EU28          680000\n",
      "Landfill&Other         EU28             NaN\n",
      "Other                  EU28             NaN\n",
      "Energy recovery        EU28        1.73e+06\n",
      "Recycling              EU28        8.71e+06\n",
      "Recycling&Backfilling  EU28             NaN\n",
      "Waste treatment        EU28       1.124e+07\n",
      "Name: 2014, dtype: object\n",
      "FI <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           FI         17975\n",
      "Landfill               FI          1142\n",
      "Landfill&Other         FI          1142\n",
      "Other                  FI             0\n",
      "Backfilling            FI             0\n",
      "Energy recovery        FI         56205\n",
      "Recycling              FI          7948\n",
      "Recycling&Backfilling  FI          7948\n",
      "Waste treatment        FI         83270\n",
      "Name: 2014, dtype: object\n",
      "FR <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           FR                   0\n",
      "Landfill               FR              223604\n",
      "Landfill&Other         FR              223604\n",
      "Other                  FR                   0\n",
      "Backfilling            FR                   0\n",
      "Energy recovery        FR              800377\n",
      "Recycling              FR              286327\n",
      "Recycling&Backfilling  FR              286327\n",
      "Waste treatment        FR         1.31031e+06\n",
      "Name: 2014, dtype: object\n",
      "HR <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           HR             0\n",
      "Landfill               HR          1219\n",
      "Landfill&Other         HR          1219\n",
      "Other                  HR             0\n",
      "Backfilling            HR             0\n",
      "Energy recovery        HR             0\n",
      "Recycling              HR         44952\n",
      "Recycling&Backfilling  HR         44952\n",
      "Waste treatment        HR         46171\n",
      "Name: 2014, dtype: object\n",
      "HU <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           HU           1844\n",
      "Landfill               HU          12728\n",
      "Landfill&Other         HU          12728\n",
      "Other                  HU              0\n",
      "Backfilling            HU              0\n",
      "Energy recovery        HU           9813\n",
      "Recycling              HU         116527\n",
      "Recycling&Backfilling  HU         116527\n",
      "Waste treatment        HU         140912\n",
      "Name: 2014, dtype: object\n",
      "IE <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           IE             0\n",
      "Landfill               IE           292\n",
      "Landfill&Other         IE           292\n",
      "Other                  IE             0\n",
      "Backfilling            IE             0\n",
      "Energy recovery        IE             0\n",
      "Recycling              IE         67135\n",
      "Recycling&Backfilling  IE         67135\n",
      "Waste treatment        IE         67427\n",
      "Name: 2014, dtype: object\n",
      "IS <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           IS          428\n",
      "Landfill               IS          451\n",
      "Landfill&Other         IS          451\n",
      "Other                  IS            0\n",
      "Backfilling            IS            0\n",
      "Energy recovery        IS            0\n",
      "Recycling              IS         4671\n",
      "Recycling&Backfilling  IS         4671\n",
      "Waste treatment        IS         5550\n",
      "Name: 2014, dtype: object\n",
      "IT <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           IT               29592\n",
      "Landfill               IT               43391\n",
      "Landfill&Other         IT               43391\n",
      "Other                  IT                   0\n",
      "Backfilling            IT                   0\n",
      "Energy recovery        IT               87578\n",
      "Recycling              IT         2.07616e+06\n",
      "Recycling&Backfilling  IT         2.07616e+06\n",
      "Waste treatment        IT         2.23672e+06\n",
      "Name: 2014, dtype: object\n",
      "LT <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           LT             0\n",
      "Landfill               LT          5357\n",
      "Landfill&Other         LT          5357\n",
      "Other                  LT             0\n",
      "Backfilling            LT             0\n",
      "Energy recovery        LT             0\n",
      "Recycling              LT         26677\n",
      "Recycling&Backfilling  LT         26677\n",
      "Waste treatment        LT         32034\n",
      "Name: 2014, dtype: object\n",
      "LU <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           LU             0\n",
      "Landfill               LU             0\n",
      "Landfill&Other         LU             0\n",
      "Other                  LU             0\n",
      "Backfilling            LU             0\n",
      "Energy recovery        LU           NaN\n",
      "Recycling              LU           NaN\n",
      "Recycling&Backfilling  LU         22722\n",
      "Waste treatment        LU         27592\n",
      "Name: 2014, dtype: object\n",
      "LV <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           LV             0\n",
      "Landfill               LV           319\n",
      "Landfill&Other         LV           319\n",
      "Other                  LV             0\n",
      "Backfilling            LV             0\n",
      "Energy recovery        LV             0\n",
      "Recycling              LV         39910\n",
      "Recycling&Backfilling  LV         39910\n",
      "Waste treatment        LV         40230\n",
      "Name: 2014, dtype: object\n",
      "ME <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           ME           0\n",
      "Landfill               ME          12\n",
      "Landfill&Other         ME         NaN\n",
      "Other                  ME         NaN\n",
      "Backfilling            ME         NaN\n",
      "Energy recovery        ME         NaN\n",
      "Recycling              ME         NaN\n",
      "Recycling&Backfilling  ME         NaN\n",
      "Waste treatment        ME          12\n",
      "Name: 2014, dtype: object\n",
      "MK <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           MK          503\n",
      "Landfill               MK         3985\n",
      "Landfill&Other         MK         3985\n",
      "Other                  MK            0\n",
      "Backfilling            MK            0\n",
      "Energy recovery        MK            0\n",
      "Recycling              MK            0\n",
      "Recycling&Backfilling  MK            0\n",
      "Waste treatment        MK         4488\n",
      "Name: 2014, dtype: object\n",
      "MT <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           MT          0\n",
      "Landfill               MT         37\n",
      "Landfill&Other         MT         37\n",
      "Other                  MT          0\n",
      "Backfilling            MT          0\n",
      "Energy recovery        MT          0\n",
      "Recycling              MT          0\n",
      "Recycling&Backfilling  MT          0\n",
      "Waste treatment        MT         37\n",
      "Name: 2014, dtype: object\n",
      "NL <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           NL           3380\n",
      "Landfill               NL            967\n",
      "Landfill&Other         NL            967\n",
      "Other                  NL              0\n",
      "Backfilling            NL              0\n",
      "Energy recovery        NL          45660\n",
      "Recycling              NL         350182\n",
      "Recycling&Backfilling  NL         350182\n",
      "Waste treatment        NL         400189\n",
      "Name: 2014, dtype: object\n",
      "NO <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           NO             32\n",
      "Landfill               NO           2592\n",
      "Landfill&Other         NO           3754\n",
      "Other                  NO           1162\n",
      "Backfilling            NO              0\n",
      "Energy recovery        NO         129214\n",
      "Recycling              NO         120930\n",
      "Recycling&Backfilling  NO         120930\n",
      "Waste treatment        NO         253930\n",
      "Name: 2014, dtype: object\n",
      "PL <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           PL                  68\n",
      "Landfill               PL                7516\n",
      "Landfill&Other         PL                7516\n",
      "Other                  PL                   0\n",
      "Backfilling            PL                   0\n",
      "Energy recovery        PL               15840\n",
      "Recycling              PL         1.01948e+06\n",
      "Recycling&Backfilling  PL         1.01948e+06\n",
      "Waste treatment        PL          1.0429e+06\n",
      "Name: 2014, dtype: object\n",
      "PT <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           PT              5\n",
      "Landfill               PT           7280\n",
      "Landfill&Other         PT           7280\n",
      "Other                  PT              0\n",
      "Energy recovery        PT           4365\n",
      "Recycling              PT         118996\n",
      "Recycling&Backfilling  PT            NaN\n",
      "Waste treatment        PT         130646\n",
      "Name: 2014, dtype: object\n",
      "RO <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           RO            614\n",
      "Landfill               RO           5413\n",
      "Landfill&Other         RO           5413\n",
      "Other                  RO              0\n",
      "Backfilling            RO              0\n",
      "Energy recovery        RO          13583\n",
      "Recycling              RO         260074\n",
      "Recycling&Backfilling  RO         260074\n",
      "Waste treatment        RO         279684\n",
      "Name: 2014, dtype: object\n",
      "RS <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           RS             0\n",
      "Landfill               RS           545\n",
      "Landfill&Other         RS           545\n",
      "Other                  RS             0\n",
      "Backfilling            RS           NaN\n",
      "Energy recovery        RS             0\n",
      "Recycling              RS         20164\n",
      "Recycling&Backfilling  RS           NaN\n",
      "Waste treatment        RS         20709\n",
      "Name: 2014, dtype: object\n",
      "SE <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           SE            230\n",
      "Landfill               SE             21\n",
      "Landfill&Other         SE             21\n",
      "Other                  SE              0\n",
      "Backfilling            SE              0\n",
      "Energy recovery        SE          71417\n",
      "Recycling              SE          81063\n",
      "Recycling&Backfilling  SE          81063\n",
      "Waste treatment        SE         152731\n",
      "Name: 2014, dtype: object\n",
      "SI <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           SI            16\n",
      "Landfill               SI          2838\n",
      "Landfill&Other         SI          2838\n",
      "Other                  SI             0\n",
      "Backfilling            SI             0\n",
      "Energy recovery        SI          1650\n",
      "Recycling              SI         40572\n",
      "Recycling&Backfilling  SI         40572\n",
      "Waste treatment        SI         45076\n",
      "Name: 2014, dtype: object\n",
      "SK <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           SK           215\n",
      "Landfill               SK         11653\n",
      "Landfill&Other         SK         11653\n",
      "Other                  SK             0\n",
      "Backfilling            SK             0\n",
      "Energy recovery        SK          1214\n",
      "Recycling              SK         57047\n",
      "Recycling&Backfilling  SK         57047\n",
      "Waste treatment        SK         70129\n",
      "Name: 2014, dtype: object\n",
      "TR <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           TR            NaN\n",
      "Landfill               TR            NaN\n",
      "Other                  TR            NaN\n",
      "Backfilling            TR            NaN\n",
      "Energy recovery        TR          12820\n",
      "Recycling              TR         756546\n",
      "Recycling&Backfilling  TR            NaN\n",
      "Waste treatment        TR         784069\n",
      "Name: 2014, dtype: object\n",
      "UK <class 'pandas.core.series.Series'> operation              country\n",
      "Incineration           UK              0\n",
      "Landfill               UK          70022\n",
      "Landfill&Other         UK          70022\n",
      "Other                  UK              0\n",
      "Backfilling            UK              7\n",
      "Energy recovery        UK              0\n",
      "Recycling              UK         581258\n",
      "Recycling&Backfilling  UK         581265\n",
      "Waste treatment        UK         651288\n",
      "Name: 2014, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for name, group in df.xs(['T','NHAZ','Plastic'],level=[0,1,2])[2014].groupby('country'):\n",
    "    print(name,type(group),group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "('float division by zero', 'occurred at index 2014')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36msafe_na_op\u001b[0;34m(lvalues, rvalues)\u001b[0m\n\u001b[1;32m   1032\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\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;32m-> 1033\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1034\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   1011\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1012\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, op_str, a, b, use_numexpr, **eval_kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0meval_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b, **eval_kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\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;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-634a7aa9541c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m ( df.xs(['T','NHAZ','Plastic'],level=[0,1,2]).groupby('country')\n\u001b[0;32m----> 2\u001b[0;31m  .transform(lambda x : x / x[-1] * 100) )\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4432\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4433\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4434\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4436\u001b[0m         \u001b[0;31m# a reduction transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_transform_general\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4383\u001b[0m                 \u001b[0;31m# Try slow path and fast path.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4384\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4385\u001b[0;31m                     \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_choose_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfast_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslow_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4386\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4387\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform_item_by_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfast_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_choose_path\u001b[0;34m(self, fast_path, slow_path, group)\u001b[0m\n\u001b[1;32m   4480\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_choose_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfast_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslow_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4481\u001b[0m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslow_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4482\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslow_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4484\u001b[0m         \u001b[0;31m# if we make it here, test if we can use the fast path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(group)\u001b[0m\n\u001b[1;32m   4475\u001b[0m             \u001b[0mfast_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4476\u001b[0m             slow_path = lambda group: group.apply(\n\u001b[0;32m-> 4477\u001b[0;31m                 lambda x: func(x, *args, **kwargs), axis=self.axis)\n\u001b[0m\u001b[1;32m   4478\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfast_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslow_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)\u001b[0m\n\u001b[1;32m   6012\u001b[0m                          \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6013\u001b[0m                          kwds=kwds)\n\u001b[0;32m-> 6014\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6016\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\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;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m                                       *self.args, **self.kwds)\n\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFrameRowApply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\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;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\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;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m         \u001b[0;31m# compute the result using the series generator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\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;32m--> 277\u001b[0;31m                     \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   4475\u001b[0m             \u001b[0mfast_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4476\u001b[0m             slow_path = lambda group: group.apply(\n\u001b[0;32m-> 4477\u001b[0;31m                 lambda x: func(x, *args, **kwargs), axis=self.axis)\n\u001b[0m\u001b[1;32m   4478\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfast_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslow_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-24-634a7aa9541c>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m ( df.xs(['T','NHAZ','Plastic'],level=[0,1,2]).groupby('country')\n\u001b[0;32m----> 2\u001b[0;31m  .transform(lambda x : x / x[-1] * 100) )\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m   1067\u001b[0m             \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafe_na_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1070\u001b[0m         return construct_result(left, result,\n\u001b[1;32m   1071\u001b[0m                                 index=left.index, name=res_name, dtype=None)\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36msafe_na_op\u001b[0;34m(lvalues, rvalues)\u001b[0m\n\u001b[1;32m   1035\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m                 return libalgos.arrmap_object(lvalues,\n\u001b[0;32m-> 1037\u001b[0;31m                                               lambda x: op(x, rvalues))\n\u001b[0m\u001b[1;32m   1038\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/algos_common_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.algos.arrmap_object\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/LUcompute/lib/python3.7/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1035\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m                 return libalgos.arrmap_object(lvalues,\n\u001b[0;32m-> 1037\u001b[0;31m                                               lambda x: op(x, rvalues))\n\u001b[0m\u001b[1;32m   1038\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1039\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: ('float division by zero', 'occurred at index 2014')"
     ]
    }
   ],
   "source": [
    "( df.xs(['T','NHAZ','Plastic'],level=[0,1,2]).groupby('country')\n",
    " .transform(lambda x : x / x[-1] * 100) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">2014</th>\n",
       "      <th colspan=\"3\" halign=\"left\">2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>DK</th>\n",
       "      <td>2.31318</td>\n",
       "      <td>5.3689</td>\n",
       "      <td>92.3179</td>\n",
       "      <td>3.29567</td>\n",
       "      <td>1.76776</td>\n",
       "      <td>94.9366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>63.7675</td>\n",
       "      <td>0.729383</td>\n",
       "      <td>35.5032</td>\n",
       "      <td>82.5777</td>\n",
       "      <td>0</td>\n",
       "      <td>17.4223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>6.04982</td>\n",
       "      <td>15.3915</td>\n",
       "      <td>77.4911</td>\n",
       "      <td>10.2062</td>\n",
       "      <td>15.6701</td>\n",
       "      <td>72.4742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>1.93994</td>\n",
       "      <td>3.91547</td>\n",
       "      <td>92.8216</td>\n",
       "      <td>4.14758</td>\n",
       "      <td>0.676479</td>\n",
       "      <td>93.8621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.0137497</td>\n",
       "      <td>46.76</td>\n",
       "      <td>53.0757</td>\n",
       "      <td>0.311207</td>\n",
       "      <td>64.9379</td>\n",
       "      <td>34.7509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2014                                2010                  \\\n",
       "operation   Landfill Energy recovery Recycling  Landfill Energy recovery   \n",
       "country                                                                    \n",
       "DK           2.31318          5.3689   92.3179   3.29567         1.76776   \n",
       "EL           63.7675        0.729383   35.5032   82.5777               0   \n",
       "EU28         6.04982         15.3915   77.4911   10.2062         15.6701   \n",
       "IT           1.93994         3.91547   92.8216   4.14758        0.676479   \n",
       "SE         0.0137497           46.76   53.0757  0.311207         64.9379   \n",
       "\n",
       "                     \n",
       "operation Recycling  \n",
       "country              \n",
       "DK          94.9366  \n",
       "EL          17.4223  \n",
       "EU28        72.4742  \n",
       "IT          93.8621  \n",
       "SE          34.7509  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "( df.xs(['T','NHAZ','Plastic'],level=[0,1,2]).groupby('country')\n",
    " .transform(lambda x : x / x[-1] * 100 if x[-1]>0 else x*np.nan) \n",
    " .loc[(['Energy recovery','Landfill','Recycling'],['SE','IT','DK','EL','EU28']),\n",
    "     ([2014,2010])] ).unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recalculating the Aggregate EU28\n",
    "https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Country_codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>operation</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>EU28</th>\n",
       "      <th>EU28</th>\n",
       "      <th>EU28</th>\n",
       "      <th>EU28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1.73e+06</td>\n",
       "      <td>680000</td>\n",
       "      <td>8.71e+06</td>\n",
       "      <td>1.124e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.52e+06</td>\n",
       "      <td>990000</td>\n",
       "      <td>7.03e+06</td>\n",
       "      <td>9.7e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation Energy recovery Landfill Recycling Waste treatment\n",
       "country              EU28     EU28      EU28            EU28\n",
       "2014             1.73e+06   680000  8.71e+06       1.124e+07\n",
       "2010             1.52e+06   990000  7.03e+06         9.7e+06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.xs(['T','NHAZ','Plastic'],level=[0,1,2])\n",
    " .loc[(['Energy recovery','Landfill','Recycling','Waste treatment'],['EU28']),\n",
    "     ([2014,2010])]).sort_index().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1682213.0</td>\n",
       "      <td>680483.0</td>\n",
       "      <td>8683088.0</td>\n",
       "      <td>11242992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1470385.0</td>\n",
       "      <td>987780.0</td>\n",
       "      <td>7030405.0</td>\n",
       "      <td>9701209.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Energy recovery  Landfill  Recycling  Waste treatment\n",
       "2014             1682213.0  680483.0  8683088.0       11242992.0\n",
       "2010             1470385.0  987780.0  7030405.0        9701209.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.xs(['T','NHAZ','Plastic'],level=[0,1,2])\n",
    " .drop(labels=['EU28','IS','LI','NO','CH','ME','MK','AL','RS','TR','XK','HA'],level=1)\n",
    " .groupby('operation').sum()\n",
    " .loc[['Energy recovery','Landfill','Recycling','Waste treatment'],[2014,2010]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>64700.500000</td>\n",
       "      <td>24302.964286</td>\n",
       "      <td>321595.851852</td>\n",
       "      <td>401535.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>54458.703704</td>\n",
       "      <td>35277.857143</td>\n",
       "      <td>251085.892857</td>\n",
       "      <td>346471.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation  Energy recovery      Landfill      Recycling  Waste treatment\n",
       "2014          64700.500000  24302.964286  321595.851852    401535.428571\n",
       "2010          54458.703704  35277.857143  251085.892857    346471.750000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.xs(['T','NHAZ','Plastic'],level=[0,1,2])\n",
    " .drop(labels=['EU28','IS','LI','NO','CH','ME','MK','AL','RS','TR','XK','HA'],level=1)\n",
    " .groupby('operation')\n",
    " .agg( lambda x : x.mean() if x.count()>0 else np.nan )\n",
    " .loc[['Energy recovery','Landfill','Recycling','Waste treatment'],[2014,2010]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>operation</th>\n",
       "      <th>Backfilling</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Incineration</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Landfill&amp;Other</th>\n",
       "      <th>Other</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Recycling&amp;Backfilling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>mean</th>\n",
       "      <td>1598.576923</td>\n",
       "      <td>64700.500000</td>\n",
       "      <td>3165.851852</td>\n",
       "      <td>24302.964286</td>\n",
       "      <td>25181.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>321595.851852</td>\n",
       "      <td>324378.730769</td>\n",
       "      <td>401535.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard deviation</th>\n",
       "      <td>7991.484729</td>\n",
       "      <td>176323.513987</td>\n",
       "      <td>7253.816700</td>\n",
       "      <td>50130.970498</td>\n",
       "      <td>50838.859210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>517927.899710</td>\n",
       "      <td>528460.742516</td>\n",
       "      <td>614415.675541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>54458.703704</td>\n",
       "      <td>4260.714286</td>\n",
       "      <td>35277.857143</td>\n",
       "      <td>35431.928571</td>\n",
       "      <td>154.071429</td>\n",
       "      <td>251085.892857</td>\n",
       "      <td>276375.080000</td>\n",
       "      <td>346471.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard deviation</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>149979.671491</td>\n",
       "      <td>9721.797625</td>\n",
       "      <td>69192.006370</td>\n",
       "      <td>69129.917756</td>\n",
       "      <td>800.578627</td>\n",
       "      <td>445222.826384</td>\n",
       "      <td>464940.585347</td>\n",
       "      <td>539147.684052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation                Backfilling  Energy recovery  Incineration  \\\n",
       "2014 mean                1598.576923     64700.500000   3165.851852   \n",
       "     standard deviation  7991.484729    176323.513987   7253.816700   \n",
       "2010 mean                   0.000000     54458.703704   4260.714286   \n",
       "     standard deviation     0.000000    149979.671491   9721.797625   \n",
       "\n",
       "operation                    Landfill  Landfill&Other       Other  \\\n",
       "2014 mean                24302.964286    25181.333333    0.000000   \n",
       "     standard deviation  50130.970498    50838.859210    0.000000   \n",
       "2010 mean                35277.857143    35431.928571  154.071429   \n",
       "     standard deviation  69192.006370    69129.917756  800.578627   \n",
       "\n",
       "operation                    Recycling  Recycling&Backfilling  Waste treatment  \n",
       "2014 mean                321595.851852          324378.730769    401535.428571  \n",
       "     standard deviation  517927.899710          528460.742516    614415.675541  \n",
       "2010 mean                251085.892857          276375.080000    346471.750000  \n",
       "     standard deviation  445222.826384          464940.585347    539147.684052  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_mean = lambda x : x.mean() if x.count()>0 else np.nan\n",
    "f_mean.__name__ = 'mean'\n",
    "f_std = lambda x : np.std(x) if x.count()>0 else np.nan\n",
    "f_std.__name__ = 'standard deviation'\n",
    "\n",
    "(df.xs(['T','NHAZ','Plastic'],level=[0,1,2])\n",
    " .drop(labels=['EU28','IS','LI','NO','CH','ME','MK','AL','RS','TR','XK','HA'],level=1)\n",
    " .groupby('operation')\n",
    " .agg( { 2014 : [f_mean,f_std], 2010 : [f_mean,f_std] } ) ).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>operation</th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>1514</td>\n",
       "      <td>3514</td>\n",
       "      <td>60423</td>\n",
       "      <td>65451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>68455</td>\n",
       "      <td>783</td>\n",
       "      <td>38113</td>\n",
       "      <td>107351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>43391</td>\n",
       "      <td>87578</td>\n",
       "      <td>2.07616e+06</td>\n",
       "      <td>2.23672e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>21</td>\n",
       "      <td>71417</td>\n",
       "      <td>81063</td>\n",
       "      <td>152731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "operation Landfill Energy recovery    Recycling Waste treatment\n",
       "country                                                        \n",
       "DK            1514            3514        60423           65451\n",
       "EL           68455             783        38113          107351\n",
       "IT           43391           87578  2.07616e+06     2.23672e+06\n",
       "SE              21           71417        81063          152731"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trt_2014 = (df.xs(['T','NHAZ','Plastic'],level=[0,1,2])\n",
    " .loc[(['Energy recovery','Landfill','Recycling','Waste treatment'],['SE','IT','DK','EL']),2014]\n",
    ").unstack(0)\n",
    "trt_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2016</th>\n",
       "      <th>2014</th>\n",
       "      <th>2012</th>\n",
       "      <th>2010</th>\n",
       "      <th>2008</th>\n",
       "      <th>2006</th>\n",
       "      <th>2004</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit</th>\n",
       "      <th>hazard</th>\n",
       "      <th>waste</th>\n",
       "      <th>activity</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">KG_HAB</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">HAZ</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">PRIM</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">A</th>\n",
       "      <th>AT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     2016 2014 2012 2010 2008 2006 2004\n",
       "unit   hazard waste activity country                                   \n",
       "KG_HAB HAZ    PRIM  A        AT       NaN    1    1    9  NaN  NaN  NaN\n",
       "                             BE         2    2    2    2  NaN  NaN  NaN\n",
       "                             BG         0    0    0    0  NaN  NaN  NaN\n",
       "                             CY         1    1    1    1  NaN  NaN  NaN\n",
       "                             CZ         1    0    0    1  NaN  NaN  NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table('data/waste.tsv',header=0,sep=',|\\t',engine='python',\n",
    "                  names=['unit','hazard','activity','waste','country',2016,2014,2012,\n",
    "                        2010,2008,2006,2004])\n",
    "df.loc[df.waste=='W074','waste']='Plastic'\n",
    "df.loc[df.activity=='G-U_X_G4677','activity']='Services'\n",
    "df.loc[df.activity=='C','activity']='Manufacturing'\n",
    "df.loc[df.activity=='F','activity']='Construction'\n",
    "df.loc[df.activity=='EP_HH','activity']='Households'\n",
    "df.loc[df.activity=='TOTAL_HH','activity']='Total'\n",
    "df.iloc[:,5:] = df.iloc[:,5:].applymap(lambda x: x.rstrip('bcdefinprsuz')) \n",
    "df.iloc[:,5:] = df.iloc[:,5:].apply(pd.to_numeric,errors='coerce')\n",
    "df = df.set_index(keys=['unit','hazard','waste','activity','country'],inplace=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>activity</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Households</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>4798</td>\n",
       "      <td>0</td>\n",
       "      <td>29575</td>\n",
       "      <td>192248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>4.73e+06</td>\n",
       "      <td>2.74e+06</td>\n",
       "      <td>920000</td>\n",
       "      <td>1.735e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>716131</td>\n",
       "      <td>1.01254e+06</td>\n",
       "      <td>28232</td>\n",
       "      <td>3.21597e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>46085</td>\n",
       "      <td>96035</td>\n",
       "      <td>500</td>\n",
       "      <td>301187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "activity Manufacturing   Households Construction        Total\n",
       "country                                                      \n",
       "EL                4798            0        29575       192248\n",
       "EU28          4.73e+06     2.74e+06       920000    1.735e+07\n",
       "IT              716131  1.01254e+06        28232  3.21597e+06\n",
       "SE               46085        96035          500       301187"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_2014 = (df.xs(['T','NHAZ','Plastic'],level=[0,1,2])\n",
    " .loc[(['Manufacturing','Construction','Households','Total'],['SE','IT','EL','EU28']),2014]\n",
    ").unstack(0)\n",
    "gen_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Households</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>1514</td>\n",
       "      <td>3514</td>\n",
       "      <td>60423</td>\n",
       "      <td>65451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>68455</td>\n",
       "      <td>783</td>\n",
       "      <td>38113</td>\n",
       "      <td>107351</td>\n",
       "      <td>4798</td>\n",
       "      <td>0</td>\n",
       "      <td>29575</td>\n",
       "      <td>192248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.73e+06</td>\n",
       "      <td>2.74e+06</td>\n",
       "      <td>920000</td>\n",
       "      <td>1.735e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>43391</td>\n",
       "      <td>87578</td>\n",
       "      <td>2.07616e+06</td>\n",
       "      <td>2.23672e+06</td>\n",
       "      <td>716131</td>\n",
       "      <td>1.01254e+06</td>\n",
       "      <td>28232</td>\n",
       "      <td>3.21597e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>21</td>\n",
       "      <td>71417</td>\n",
       "      <td>81063</td>\n",
       "      <td>152731</td>\n",
       "      <td>46085</td>\n",
       "      <td>96035</td>\n",
       "      <td>500</td>\n",
       "      <td>301187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Landfill Energy recovery    Recycling Waste treatment Manufacturing  \\\n",
       "country                                                                       \n",
       "DK          1514            3514        60423           65451           NaN   \n",
       "EL         68455             783        38113          107351          4798   \n",
       "EU28         NaN             NaN          NaN             NaN      4.73e+06   \n",
       "IT         43391           87578  2.07616e+06     2.23672e+06        716131   \n",
       "SE            21           71417        81063          152731         46085   \n",
       "\n",
       "          Households Construction        Total  \n",
       "country                                         \n",
       "DK               NaN          NaN          NaN  \n",
       "EL                 0        29575       192248  \n",
       "EU28        2.74e+06       920000    1.735e+07  \n",
       "IT       1.01254e+06        28232  3.21597e+06  \n",
       "SE             96035          500       301187  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(trt_2014,gen_2014,how='outer',left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Households</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>68455</td>\n",
       "      <td>783</td>\n",
       "      <td>38113</td>\n",
       "      <td>107351</td>\n",
       "      <td>4798</td>\n",
       "      <td>0</td>\n",
       "      <td>29575</td>\n",
       "      <td>192248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>43391</td>\n",
       "      <td>87578</td>\n",
       "      <td>2.07616e+06</td>\n",
       "      <td>2.23672e+06</td>\n",
       "      <td>716131</td>\n",
       "      <td>1.01254e+06</td>\n",
       "      <td>28232</td>\n",
       "      <td>3.21597e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>21</td>\n",
       "      <td>71417</td>\n",
       "      <td>81063</td>\n",
       "      <td>152731</td>\n",
       "      <td>46085</td>\n",
       "      <td>96035</td>\n",
       "      <td>500</td>\n",
       "      <td>301187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Landfill Energy recovery    Recycling Waste treatment Manufacturing  \\\n",
       "country                                                                       \n",
       "EL         68455             783        38113          107351          4798   \n",
       "IT         43391           87578  2.07616e+06     2.23672e+06        716131   \n",
       "SE            21           71417        81063          152731         46085   \n",
       "\n",
       "          Households Construction        Total  \n",
       "country                                         \n",
       "EL                 0        29575       192248  \n",
       "IT       1.01254e+06        28232  3.21597e+06  \n",
       "SE             96035          500       301187  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(trt_2014,gen_2014,how='inner',left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Households</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>1514</td>\n",
       "      <td>3514</td>\n",
       "      <td>60423</td>\n",
       "      <td>65451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>68455</td>\n",
       "      <td>783</td>\n",
       "      <td>38113</td>\n",
       "      <td>107351</td>\n",
       "      <td>4798</td>\n",
       "      <td>0</td>\n",
       "      <td>29575</td>\n",
       "      <td>192248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>43391</td>\n",
       "      <td>87578</td>\n",
       "      <td>2.07616e+06</td>\n",
       "      <td>2.23672e+06</td>\n",
       "      <td>716131</td>\n",
       "      <td>1.01254e+06</td>\n",
       "      <td>28232</td>\n",
       "      <td>3.21597e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>21</td>\n",
       "      <td>71417</td>\n",
       "      <td>81063</td>\n",
       "      <td>152731</td>\n",
       "      <td>46085</td>\n",
       "      <td>96035</td>\n",
       "      <td>500</td>\n",
       "      <td>301187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Landfill Energy recovery    Recycling Waste treatment Manufacturing  \\\n",
       "country                                                                       \n",
       "DK          1514            3514        60423           65451           NaN   \n",
       "EL         68455             783        38113          107351          4798   \n",
       "IT         43391           87578  2.07616e+06     2.23672e+06        716131   \n",
       "SE            21           71417        81063          152731         46085   \n",
       "\n",
       "          Households Construction        Total  \n",
       "country                                         \n",
       "DK               NaN          NaN          NaN  \n",
       "EL                 0        29575       192248  \n",
       "IT       1.01254e+06        28232  3.21597e+06  \n",
       "SE             96035          500       301187  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(trt_2014,gen_2014,how='left',left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Landfill</th>\n",
       "      <th>Energy recovery</th>\n",
       "      <th>Recycling</th>\n",
       "      <th>Waste treatment</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Households</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EL</th>\n",
       "      <td>68455</td>\n",
       "      <td>783</td>\n",
       "      <td>38113</td>\n",
       "      <td>107351</td>\n",
       "      <td>4798</td>\n",
       "      <td>0</td>\n",
       "      <td>29575</td>\n",
       "      <td>192248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.73e+06</td>\n",
       "      <td>2.74e+06</td>\n",
       "      <td>920000</td>\n",
       "      <td>1.735e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>43391</td>\n",
       "      <td>87578</td>\n",
       "      <td>2.07616e+06</td>\n",
       "      <td>2.23672e+06</td>\n",
       "      <td>716131</td>\n",
       "      <td>1.01254e+06</td>\n",
       "      <td>28232</td>\n",
       "      <td>3.21597e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>21</td>\n",
       "      <td>71417</td>\n",
       "      <td>81063</td>\n",
       "      <td>152731</td>\n",
       "      <td>46085</td>\n",
       "      <td>96035</td>\n",
       "      <td>500</td>\n",
       "      <td>301187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Landfill Energy recovery    Recycling Waste treatment Manufacturing  \\\n",
       "country                                                                       \n",
       "EL         68455             783        38113          107351          4798   \n",
       "EU28         NaN             NaN          NaN             NaN      4.73e+06   \n",
       "IT         43391           87578  2.07616e+06     2.23672e+06        716131   \n",
       "SE            21           71417        81063          152731         46085   \n",
       "\n",
       "          Households Construction        Total  \n",
       "country                                         \n",
       "EL                 0        29575       192248  \n",
       "EU28        2.74e+06       920000    1.735e+07  \n",
       "IT       1.01254e+06        28232  3.21597e+06  \n",
       "SE             96035          500       301187  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(trt_2014,gen_2014,how='right',left_index=True,right_index=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}