envir/environmental_economy_py.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Statistics coded:* Environmental Economy – statistics on employment and growth\n",
    "\n",
    "Prepared by [**Rezvan Derayati**](r.derayati@studenti.unipi.it) ([EMOS](https://ec.europa.eu/eurostat/web/european-statistical-system/emos)).\n",
    "\n",
    "The objective of this notebook is to reproduce the figures of the *Statistics Expplained* article on [**development of key indicators for the environmental economy and the overall economy, EU-27, 2000–2017**](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Environmental_economy_%E2%80%93_statistics_on_employment_and_growth#Development_of_key_indicators_for_the_environmental_economy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings\n",
    "\n",
    "First of all, we start by importing required packages  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: eurostatapiclient in c:\\users\\vahid\\appdata\\roaming\\python\\python37\\site-packages (0.2.5)\n",
      "Requirement already satisfied: requests==2.21.0 in c:\\users\\vahid\\appdata\\roaming\\python\\python37\\site-packages (from eurostatapiclient) (2.21.0)\n",
      "Requirement already satisfied: pandas==0.23.4 in c:\\programdata\\anaconda3\\lib\\site-packages (from eurostatapiclient) (0.23.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests==2.21.0->eurostatapiclient) (2019.11.28)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests==2.21.0->eurostatapiclient) (2.8)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in c:\\users\\vahid\\appdata\\roaming\\python\\python37\\site-packages (from requests==2.21.0->eurostatapiclient) (1.24.3)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests==2.21.0->eurostatapiclient) (3.0.4)\n",
      "Requirement already satisfied: numpy>=1.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas==0.23.4->eurostatapiclient) (1.18.1)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas==0.23.4->eurostatapiclient) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas==0.23.4->eurostatapiclient) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas==0.23.4->eurostatapiclient) (1.14.0)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "!pip install --user eurostatapiclient\n",
    "from eurostatapiclient import EurostatAPIClient\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.legend_handler import HandlerPatch\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The API parameters for `EurostatAPIClient` are configured. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "VERSION = 'v2.1'\n",
    "FORMAT = 'json'\n",
    "LANGUAGE = 'en'\n",
    "client = EurostatAPIClient(VERSION, FORMAT, LANGUAGE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Development of key indicators for the environmental economy\n",
    "\n",
    "In this section, the Development of key indicators for the environmental economy and overall economy are investigated. \n",
    "\n",
    "In order to regenerate [Figure 1](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Development_of_key_indicators_for_the_environmental_economy_and_the_overall_economy,_EU-27,_2000%E2%80%9317_(2000_%3D_100).png), we retrieve the required datasets: \n",
    "* [`env_ac_egss1`](https://ec.europa.eu/eurostat/databrowser/view/env_ac_egss1/default/table?lang=en): Employment in the environmental goods and services sector \n",
    "* [`env_ac_egss2`](https://ec.europa.eu/eurostat/databrowser/view/env_ac_egss2/default/table?lang=en): Production, value added and exports in the environmental goods and services sector\n",
    "* [`nama_10_gdp`](https://ec.europa.eu/eurostat/databrowser/view/nama_10_gdp/default/table?lang=en): GDP and main components (output, expenditure and income)\n",
    "* [`nama_10_a10_e`](https://ec.europa.eu/eurostat/databrowser/view/nama_10_a10_e/default/table?lang=en): Employment by A*10 industry breakdowns\n",
    "\n",
    "We define a list which contains datasets `env_ac_egss1`, `env_ac_egss2`, `nama_10_gdp`, `nama_10_a10_e`. Then for each dataset, separetely, define a dictionary with filtered parameters. These parameters are defined for time period of 2000-2017. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ['env_ac_egss1', 'env_ac_egss2', 'nama_10_gdp', 'nama_10_a10_e']\n",
    "\n",
    "params = dict.fromkeys(ds, {})\n",
    "params.update({\n",
    "    'env_ac_egss1': {\n",
    "        'ceparema': 'TOTAL',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2': 'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit': 'FTE',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'env_ac_egss2': {\n",
    "        'ceparema': 'TOTAL',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2': 'TOTAL',\n",
    "        'na_item': 'B1G',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit': 'MEUR_CLV10',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'nama_10_gdp' : {\n",
    "        'GEO': 'EU27_2020',\n",
    "        'na_item': 'B1GQ',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'unit': 'CLV10_MEUR',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'nama_10_a10_e': {\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2': 'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'unit': 'THS_PER',\n",
    "        'precision': 1\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, the required data, according to the defined filter, are collected by using `EurostatAPIClient`.\n",
    "By using the inline for loop, we get the data and keep them in a dictionary with dataset name as key and collected dataframe as value. \n",
    "\n",
    "In next step, we filter the dataframe to contain only years up to 2017. Also we need to have data for whole EU so we filter only `EU27_2020` as `geo`. Then we manipulate data to be in line with original paper and divide values of each year by initial year value for each dataset value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dict.fromkeys(ds)\n",
    "[df.update({ds: client.get_dataset(ds, params = params[ds]).to_dataframe()}) \n",
    " for ds in df.keys()];\n",
    "\n",
    "[params[lbl].update({'lastTimePeriod': '2017'}) for lbl in params]\n",
    "\n",
    "lastTimePeriod = 2017\n",
    "geoPlace = 'EU27_2020'\n",
    "for ds in df.keys():\n",
    "    data = df[ds].copy() \n",
    "    data = data[data.time.astype(int) <= lastTimePeriod] \n",
    "    df[ds] = data.loc[data['geo' ]== geoPlace]\n",
    "    \n",
    "baseYear = 2000\n",
    "basevalues, values = dict.fromkeys(ds), dict.fromkeys(ds)\n",
    "for ds in df.keys():\n",
    "    data = df[ds].copy()\n",
    "    basevalues[ds] = data[data.time.astype(int) == baseYear]['values'].squeeze()\n",
    "    values[ds] = 100 * data['values']/basevalues[ds]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next part, we plot Figure 1 of the article by using the `matplotlib` library. We have defined dictionaries for plot lables and color of each line. Then the `ax.plot` line plots the figure by using dates (year) as x-values and already filled list of `values` as y-value. Beside that, there are some lines of code to adjust the size, title and legend of original plot in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = dict.fromkeys(ds, {})\n",
    "labels.update({\n",
    "    'env_ac_egss1': \"Environmental economy: empolyment\",\n",
    "    'env_ac_egss2': \"Environmental economy: gross value added\",\n",
    "    'nama_10_gdp' : \"Overall economy: employment\",\n",
    "    'nama_10_a10_e': \"Overall economy: gross domestic production\"\n",
    "})\n",
    "\n",
    "kw_plot = dict.fromkeys(ds, {})\n",
    "kw_plot.update({'env_ac_egss1': {'color': 'lightseagreen'},\n",
    "                'env_ac_egss2': {'linestyle': 'dashed', 'color':'mediumorchid'},\n",
    "                'nama_10_gdp' : {'color': 'blue'},\n",
    "                'nama_10_a10_e': {'linestyle': 'dashed', 'color': 'firebrick'}\n",
    "               })\n",
    "fig, ax = plt.subplots()\n",
    "plt.margins(x = 0.01, y = 0.01)\n",
    "[ax.plot(df[ds].time, values[ds], label = labels[ds], **kw_plot[ds]) for ds in df.keys()];\n",
    "ax.yaxis.grid()\n",
    "plt.title('Development of key indicators for the environmental economy and the overall economy, EU-27, 2000-2017 \\n(2000=100)', \n",
    "          fontweight = 'bold')\n",
    "plt.legend(loc = 'lower center', fancybox = False, shadow = True,bbox_to_anchor = (0.5, -0.45))\n",
    "ax.xaxis.set_tick_params(labelsize = 'small')\n",
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*2, Size[1], forward=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Employment by environmental domain\n",
    "\n",
    "Employment in the environmental economy can be broken down by environmental protection and resource management activities. This two major parts are seprated into three environmental protection activities (wastewater management; waste management; other environmental protection activities) and two resource management activities (water saving; renewable energy and energy efficiency) respectively. \n",
    "\n",
    "First we define a list of labels:\n",
    "* `params_df1_pro_water` = > wastewater management;\n",
    "* `params_df1_pro_waste` = > waste management;\n",
    "* `params_df1_pro_other` = > other environmental protection activities;\n",
    "* `params_df1_manage_watersaving` = > water saving ;\n",
    "* `params_df1_manage_renewable`  = > renewable energy and energy efficiency.\n",
    "\n",
    "Each label is representing a view of dataset (`env_ac_egss1`). The `params` variable is actually a nested dictionary with label as key and the corresponding parameters as value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['params_df1_pro_water','params_df1_pro_waste','params_df1_pro_other',\n",
    "          'params_df1_manage_watersaving','params_df1_manage_renewable']\n",
    "\n",
    "params = dict.fromkeys(labels, {})\n",
    "\n",
    "params.update({\n",
    "    'params_df1_pro_water' : {\n",
    "        'ceparema': 'CEPA2',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'FTE',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_pro_waste' : {\n",
    "        'ceparema': 'CEPA3',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'FTE',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_pro_other' : {\n",
    "        'ceparema': ('CEPA1','CEPA4','CEPA5','CEPA6','CEPA7','CEPA7-9','CEPA8','CEPA812','CEPA9'),\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'FTE',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_manage_watersaving' : {\n",
    "        'ceparema': 'CREMA10',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'FTE',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_manage_renewable' : {\n",
    "        'ceparema': ('CREMA13A','CREMA13B'),\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'EMP_DC',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'FTE',\n",
    "        'precision': 1\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We retrieve the dataframes corresponding to each label with the `EurostatApiClient`. For this plot we filter dates to be either in year 2000 or 2017. Then, we filter dataframe to contain only data for the whole EU (`EU27_2020`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dict.fromkeys(labels);\n",
    "\n",
    "[df.update({ds: client.get_dataset('env_ac_egss1', params = params[ds]).to_dataframe()}) \n",
    " for ds in df.keys()];\n",
    "\n",
    "[params[lbl].update({'lastTimePeriod': '2017'}) for lbl in params]\n",
    "\n",
    "[df.update({lbl: df[lbl][df[lbl].time.isin(['2000','2017'])]}) \n",
    " for lbl in df.keys()];\n",
    "\n",
    "[df.update({lbl: df[lbl].loc[df[lbl]['geo']=='EU27_2020']}) \n",
    " for lbl in df.keys()];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to compute the y-values for plotting. In order to regenerate the exact plot for the labels of `params_df1_pro_water`, `params_df1_pro_waste` and `params_df1_manage_watersaving`, we simply divide the values by 1000000, but for the other two labels since these two constitue several activities, we need to first calculate sum of those activities (in `CEPAREMA` field in dataset) and then divide by 1000000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = dict.fromkeys(labels)\n",
    "for lbl in labels:\n",
    "    if lbl in ['params_df1_pro_water', 'params_df1_pro_waste','params_df1_manage_watersaving']:\n",
    "        values.update({lbl: list(df[lbl]['values']/1000000)\n",
    "                      })\n",
    "    else:\n",
    "        values.update({lbl: (df[lbl][df[lbl].time=='2000']\n",
    "                             .sum()['values']/1000000,df[lbl][df[lbl].time=='2017']\n",
    "                             .sum()['values']/1000000)\n",
    "                      })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In following section, [Figure 2](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Env_econ_empl_2017data_update-01.jpg) is plotted. Colors corresponding to each label is difined in `colors` dictionary. To draw the plot in correct order, we have defined the rearranged_labels list to dictate `plt.bar` to regenerate exact figure in paper. Then we plot the `params_df1_manage_renewable` as base and add other bars on top of that to have the exact similar stacked bar plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = [0, 0.05]\n",
    "\n",
    "plt.figure(figsize=(6, 8))\n",
    "ax = plt.axes()\n",
    "\n",
    "# Names of group and bar width\n",
    "names = ['2000', '2017']\n",
    "barWidth = 0.028\n",
    "\n",
    "colors = dict.fromkeys(labels, \"\")\n",
    "colors.update({'params_df1_pro_water' : 'royalblue',\n",
    "                'params_df1_pro_waste': 'hotpink',\n",
    "                'params_df1_pro_other': 'lightpink',\n",
    "                'params_df1_manage_watersaving': 'cornflowerblue',\n",
    "                'params_df1_manage_renewable' : 'teal'\n",
    "               })\n",
    "\n",
    "rearranged_labels = ['params_df1_manage_renewable','params_df1_pro_waste',\n",
    "                     'params_df1_pro_other','params_df1_pro_water',\n",
    "                     'params_df1_manage_watersaving']\n",
    "\n",
    "plotted = []\n",
    "for lbl in rearranged_labels:\n",
    "    if lbl == 'params_df1_manage_renewable':\n",
    "        plt.bar(r, values[lbl],align='center', color=colors[lbl], edgecolor='white', \n",
    "                width=barWidth)\n",
    "        plotted.append(values[lbl])\n",
    "    else:\n",
    "        plt.bar(r, values[lbl], align='center', \n",
    "                bottom=[sum([item[0] for item in plotted]),sum([item[1] for item in plotted])], \n",
    "                color=colors[lbl],\n",
    "                edgecolor='white', width=barWidth)\n",
    "        plotted.append(values[lbl])\n",
    "\n",
    "plt.title('Environmental economy in the EU\\nEmployment by domain\\n(in million full-time equivalents)',\n",
    "          fontweight='bold')\n",
    "plt.xticks(r, names, fontweight='bold')\n",
    "ax.yaxis.grid(linestyle='dashed') # horizontal lines\n",
    "plt.legend(['Renewable energy and energy efficiency','Waste management', 'Other environmental protection', 'Waste water management','Water saving']\n",
    "           ,loc='center right',bbox_to_anchor=(1.8, 0.7), handlelength=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Employment, production and value added in the environmental economy\n",
    "\n",
    "The environmental economy can also be analysed with a view on production units. Since we wanted to analyze environmental economy through different indices, namely: employment, output and gross value added, first we define a list of labels: \n",
    "* `params_df1_employment` = > employment;\n",
    "* `params_df1_output` = > output;\n",
    "* `params_df1_GVA` = > Gross Value Added.\n",
    "\n",
    "Each label is representing a view of dataset(`env_ac_egss3`). Like in the previous section, the `params` variable is actually a nested dictionary with label as key and the corresponding parameters as value.\n",
    "\n",
    "Moreover, the value for each indicator - the employment, Output and Gross Value Added - in different sectors, namely: \"Agriculture, forestry and fishing\",  \"Mining, quarrying and manufacturing\", \"Energy and water supply, sewerage and waste services\", \"Construction; Services and Total\" should be defined. Selecting these sectors is done through the  `nace_r2` parameter.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['params_df1_employment','params_df1_output', 'params_df1_GVA']\n",
    "\n",
    "params = dict.fromkeys(labels, {})\n",
    "\n",
    "params.update({\n",
    "    'params_df1_employment' : {\n",
    "        'ceparema': 'TOTAL',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':('A','B_C','D_E','F','G-U','TOTAL'),\n",
    "        'na_item': 'EMP_DC',\n",
    "        'time':'2017',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'FTE',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_output' : {\n",
    "        'ceparema': 'TOTAL',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':('A','B_C','D_E','F','G-U','TOTAL'),\n",
    "        'na_item': 'P1',\n",
    "        'time':'2017',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_GVA' : {\n",
    "        'ceparema': 'TOTAL',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':('A','B_C','D_E','F','G-U','TOTAL'),\n",
    "        'na_item': 'B1G',\n",
    "        'time':'2017',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We retrieve the dataframes from the `env_ac_egss3` dataset, corresponding to each label, with the `EurostatApiClient` api. Here, we only filter dataframe to contain only data for the EU (`EU27_2020`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We retrieve the info with the EurostatApiClient\n",
    "df = dict.fromkeys(labels)\n",
    "\n",
    "[df.update({ds: client.get_dataset('env_ac_egss3', params = params[ds]).to_dataframe()}) \n",
    " for ds in df.keys()]\n",
    "\n",
    "[df.update({lbl: df[lbl].loc[df[lbl]['geo']=='EU27_2020']}) \n",
    " for lbl in df.keys()]\n",
    "\n",
    "values = dict.fromkeys(labels)\n",
    "[values.update({lbl: df[lbl]['values']}) for lbl in df.keys()];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We represent the [table 1](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Employment,_production_and_value_added_in_the_environmental_economy,_by_activity,_EU-27,_2017.png) of the article. To do that, we first create a new dataframe (`data`) by concatenating all values for different labels. In `data` variable the `\"TOTAL\"` values appear in the last row, so we need to shift it to first row to represent exact table in the paper. The rest of the code are adjusting font size, height of the cells and also the table size to reproduce the exact table in the paper.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([values[lbl]/1000 for lbl in values],axis=1,ignore_index=True)\n",
    "\n",
    "cell_text = data.apply(np.roll, shift=1) \n",
    "\n",
    "row_labels = ['Total', 'Agriculture, forestry and fishing', 'Mining, quarrying and manufacturing',\n",
    "              'Energy and water supply, sewerage and waste services', 'Construction', 'Services']\n",
    "col_labels = ['Employment\\n(thousand full-time \\nequivalents', 'Output\\n(EUR billion)',\n",
    "              'Gross value added\\n(EUR billion)']\n",
    "\n",
    "fig_border = 'steelblue'\n",
    "\n",
    "fig = plt.figure(linewidth=2,\n",
    "                 edgecolor=fig_border,\n",
    "                 tight_layout={'pad': 1})\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ccolors = plt.cm.BuPu(np.full(len(col_labels), 0.1))\n",
    "plt.axis(\"off\")\n",
    "\n",
    "table = plt.table(cell_text.values,\n",
    "                  colWidths=[1] * 3,\n",
    "                  rowLabels=row_labels, colLabels=col_labels, loc=\"center\", colColours=ccolors)\n",
    "\n",
    "table.set_fontsize(20)\n",
    "\n",
    "cellDict = table.get_celld()\n",
    "for i in range(0, len(col_labels)):\n",
    "    cellDict[(0, i)].set_height(.2)\n",
    "\n",
    "for key, cell in cellDict.items():\n",
    "    cell.set_edgecolor('grey')\n",
    "\n",
    "table.scale(1.5, 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a better understanding of labor productivity in different production units, after defining the paramethers of gross value added in different sectors in 2017, the pie chart of Gross value added of enviornmental economy by activity is plotted - as it is demonstrated in [Figure 3](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Gross_value_added_of_the_environmental_economy,_by_activity,_EU-27,_2017_2.png).\n",
    " \n",
    "For this plot we only need one set of parameters to query from `env_ac_egss3` dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_df1_GVA = {\n",
    "    'ceparema': 'TOTAL',\n",
    "    'GEO': 'EU27_2020',\n",
    "    'nace_r2':('A','B_C','D_E','F','G-U'),\n",
    "    'na_item': 'B1G',\n",
    "    'time':'2017',\n",
    "    #'sinceTimePeriod': '2000',\n",
    "    'ty': 'TOT_EGSS',\n",
    "    'unit':'MIO_EUR',\n",
    "    'precision': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a dataframe with defined parameters and then filter it in order to only contain EU27 values for the year 2017.  \n",
    "\n",
    "Then we plot the pie chart according to the defined labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe_1 = client.get_dataset('env_ac_egss3', params=params_df1_GVA).to_dataframe()\n",
    "dataframe_1 = dataframe_1.loc[dataframe_1['geo']=='EU27_2020']\n",
    "\n",
    "labels=['Agriculture, forestry and fishing','Mining and quarrying; manufacturing ',\n",
    "        'Energy and water supply, sewerage and waste treatment','Construction','Services']\n",
    "plt.pie(dataframe_1['values'], startangle=90, counterclock=False, labels=labels,\n",
    "        autopct='%1.0f%%',colors=['#13bbaf','#7bf2da','#d767ad','#fe83cc','#5170d7'], \n",
    "        labeldistance=1.3)\n",
    "plt.title('Gross value added of environmental economy, by activity, EU-27, 2017\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evolution of gross value added of the environmental economy\n",
    "\n",
    "In this section, the evolution of GVA of environmental economy in time period of 2000 until 2017 in different sections  including: `Waste management`, `Wastewater management`, `Other environmental protection`, `Renewable energy and energy efficiency` and `Water saving`, is analysed.\n",
    "\n",
    "First, the list of labels are defined:\n",
    "* `params_df1_pro_water` = > wastewater management;\n",
    "* `params_df1_pro_waste` = > waste management;\n",
    "* `params_df1_pro_other` = > other environmental protection activities;\n",
    "* `params_df1_manage_watersaving` = > water saving ;\n",
    "* `params_df1_manage_renewable` = > renewable energy and energy efficiency.\n",
    "\n",
    "Each label is representing a view of dataset `env_ac_egss2`. The `params` variable is actually a nested dictionary with label as key and the corresponding parameters as value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['params_df1_pro_water','params_df1_pro_waste', 'params_df1_pro_other',\n",
    "          'params_df1_manage_watersaving','params_df1_manage_renewable']\n",
    "\n",
    "params = dict.fromkeys(labels, {})\n",
    "params.update({\n",
    "    'params_df1_pro_water' : {\n",
    "        'ceparema': 'CEPA2',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'B1G',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_pro_waste' : {\n",
    "        'ceparema': 'CEPA3',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'B1G',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_pro_other' : {\n",
    "        'ceparema': ('CEPA1','CEPA4','CEPA5','CEPA6','CEPA7','CEPA7-9','CEPA8','CEPA812','CEPA9'),\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'B1G',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_manage_watersaving': {\n",
    "        'ceparema': 'CREMA10',\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'B1G',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    },\n",
    "    'params_df1_manage_renewable': {\n",
    "        'ceparema': ('CREMA13A','CREMA13B'),\n",
    "        'GEO': 'EU27_2020',\n",
    "        'nace_r2':'TOTAL',\n",
    "        'na_item': 'B1G',\n",
    "        'sinceTimePeriod': '2000',\n",
    "        'ty': 'TOT_EGSS',\n",
    "        'unit':'MIO_EUR',\n",
    "        'precision': 1\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We retrieve the dataframes corresponding to each label with the `EurostatApiClient` api. Then, we filter dates to be before year 2017. Then, we filter dataframe to contain only data for the whole EU (`EU27_2020`).\n",
    "\n",
    "To get the y-values of the [Figure 4](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Gross_value_added_of_the_environmental_economy,_by_domain,_EU-27,_2000%E2%80%9317_(EUR_billion)_2.png),  we have divided the values for params_df1_pro_water, `params_df1_pro_waste` and `params_df1_manage_watersaving` directly by 1000 to reach the values in plot. for other labels, we needed to sum over all dates to reach one value and then divide it by 1000. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dict.fromkeys(labels)\n",
    "\n",
    "[df.update({ds: client.get_dataset('env_ac_egss2', params = params[ds]).to_dataframe()}) \n",
    " for ds in df.keys()]\n",
    "\n",
    "[params[lbl].update({'lastTimePeriod': '2017'}) \n",
    " for lbl in params]\n",
    "\n",
    "[df.update({lbl: df[lbl][df[lbl].time <= params[lbl]['lastTimePeriod']]}) \n",
    " for lbl in df.keys()]\n",
    "\n",
    "[df.update({lbl: df[lbl].loc[df[lbl]['geo']=='EU27_2020']}) \n",
    " for lbl in df.keys()]\n",
    "\n",
    "values = dict.fromkeys(labels)\n",
    "labels = ['params_df1_pro_water','params_df1_pro_waste', 'params_df1_pro_other',\n",
    "          'params_df1_manage_watersaving','params_df1_manage_renewable']\n",
    "\n",
    "for lbl in labels:\n",
    "    if lbl in ['params_df1_pro_water', 'params_df1_pro_waste','params_df1_manage_watersaving']:\n",
    "        values.update({lbl: list(df[lbl]['values']/1000)\n",
    "                      })\n",
    "    else:\n",
    "        values.update({lbl: list(df[lbl].groupby(['time'])['values']\n",
    "                                 .sum()/1000)\n",
    "                      })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use `stackplt` to draw the Figure.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def swapPositions(list, pos1, pos2):\n",
    "    list[pos1], list[pos2] = list[pos2], list[pos1]\n",
    "    return list\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.xaxis.set_tick_params(labelsize='small')\n",
    "\n",
    "plt.margins(x=0,y=0)\n",
    "ax.yaxis.grid(zorder=0)\n",
    "\n",
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*2, Size[1])\n",
    "\n",
    "plt_labels=['Waste management','Wastewater management','Other environmental protection',\n",
    "            'Renewable energy and energy efficiency','Water saving']\n",
    "plt.stackplot(df['params_df1_pro_water'].time , tuple(swapPositions(list(values.values()),-1,-2)),\n",
    "              labels=plt_labels,colors=['#5170d7','#d767ad','#13bbaf','#a2cffe','#acc2d9'], zorder=3)\n",
    "\n",
    "plt.title('Gross value added of environmental economy, by domain, EU-27, 2000-2017\\n(EUR billion)',\n",
    "          fontweight='bold')\n",
    "plt.legend(loc='lower center',fancybox=False, shadow=True,bbox_to_anchor=(0.5, -0.5),\n",
    "           handlelength=0.7)\n",
    "plt.show()"
   ]
  }
 ],
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