{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Statistics coded:* Government Finance Statistics\n",
    "\n",
    "Prepared by [**Bonamino Luca**](luca.bonamino@hotmail.com)\n",
    "\n",
    "This notebook aims at reproducing the figures/graphs the *Statistics Explained* article on [**government finance statistics**](https://ec.europa.eu/eurostat/statistics-explained/index.php/Government_finance_statistics)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings required\n",
    "\n",
    "### Built-il libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Costum libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "costum_lib_locations = ['../../src/lib/python']\n",
    "for location in costum_lib_locations:\n",
    "    if location not in sys.path:\n",
    "        sys.path.append(location)\n",
    "import requestLib\n",
    "import dataDealer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables needed in the notebook\n",
    "\n",
    "Default variables that are used throughout the notebook\n",
    "* `noCountry`: countries that are not shown in the plots -> type: list\n",
    "* `clean_country`: countries that are renamed in the plots in a contracted form -> type: dict\n",
    "* `host_url`: base url for the data extraction -> type: string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "noCountry = ['European Union - 27 countries (2007-2013)' ,\n",
    "             'European Union - 25 countries (2004-2006)',\n",
    "             'European Union - 15 countries (1995-2004)',\n",
    "             'Euro area - 11 countries (1999-2000)',\n",
    "             'Euro area - 12 countries (2001-2006)',\n",
    "             'Euro area - 13 countries (2007)',\n",
    "             'Euro area - 15 countries (2008)',\n",
    "             'Euro area - 16 countries (2009-2010)',\n",
    "             'Euro area - 18 countries (2014)',\n",
    "             'Euro area - 17 countries (2011-2013)',\n",
    "             'Switzerland'\n",
    "            ]\n",
    "\n",
    "clean_country = {'Germany': 'Germany',\n",
    "                 'Euro area - 19' : 'Euro Area (EA-19)', \n",
    "                 'European Union - 27 countries (from ': 'EU-27', \n",
    "                 'European Union - 28' : 'EU-28'}\n",
    "\n",
    "host_url = \"http://ec.europa.eu/eurostat/wdds/rest/data/v2.1/json/en/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions needed in the notebook\n",
    "\n",
    "* `assign_reference`: set lines and columns to prepare the dataFrame creation\n",
    "* `response_fun`: main function that extracts the data from the server and returns the response object whose attributes are the data in dictionary, string and dataframe datatype\n",
    "* `prepare_for_plot`: return plottable values from the data used in the last two sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_reference(data, multiplicity='time'):\n",
    "    '''\n",
    "    set lines and columns to prepare the dataFrame creation\n",
    "        \n",
    "        params: data: dict: dictionary of data\n",
    "        params: multiciplity: string: multiple key argument\n",
    "        \n",
    "        return: {'lines' : lines, 'columns': columns} : dict: dataFramestructure\n",
    "    '''\n",
    "    lines = list(list(data['dimension']['geo']['category'].values())[1].values())\n",
    "    columns= list(list(data['dimension'][multiplicity]['category'].values())[1].values())\n",
    "    return {'lines' : lines, 'columns': columns}\n",
    "\n",
    "def prepare_for_plot(instances, total, subtraction=False, where=(False,False)):\n",
    "    '''\n",
    "    Prepare data for the bar plot\n",
    "        \n",
    "        params: instances: list: list of instances of the component to request \n",
    "                                    - look at the last sections form more explainations\n",
    "        params: total: dict: total compoment \n",
    "        params: substraction: bollean: set if there is a substraction in the percentage calculations \n",
    "                                            - False by default, can only diffear from the default value if \n",
    "                                                \"where\" is not (False, False)\n",
    "        params: where: tuple: say for which compoments there is the subtraction \n",
    "                                            - (False, False) by default, can only diffear from the default value if \n",
    "                                                \"subtraction\" is set to True\n",
    "        \n",
    "        return: output: dict: data ready for the bar component plot\n",
    "    '''\n",
    "    if where != (False, False) and not subtraction:\n",
    "        print('[-] Bad arguments !\\n Cannot give the wehe argument of no subtraction is present')\n",
    "        return False\n",
    "    elif subtraction and where == (False,False):\n",
    "        print('[-] Bad arguments !\\n Need to tell where the substraction is')\n",
    "        \n",
    "    output = {}\n",
    "    for item in instances:\n",
    "        countries = item.response.lines\n",
    "        values = item.response.values\n",
    "        name = item.name\n",
    "        if len(values.keys()) > 1:\n",
    "            tmp = {}\n",
    "            for item in countries:\n",
    "                tmp[item] = []\n",
    "            for key in values:\n",
    "                for c in values[key]:\n",
    "                    tmp[c].append(values[key][c])\n",
    "            temp = {}\n",
    "            for key in tmp:\n",
    "                \n",
    "                if subtraction:\n",
    "                    if where[0] in list(values.keys()):\n",
    "                        s = 0\n",
    "                        for i in range(len(tmp[key])):\n",
    "                            if i == where[1]:\n",
    "                                s += -tmp[key][i]\n",
    "                            else:\n",
    "                                s += tmp[key][i]\n",
    "                        if total[key] != 0:\n",
    "                            temp[key] = s / total[key] * 100\n",
    "                        else:\n",
    "                            temp[key] = 0.\n",
    "                    else:    \n",
    "                        if total[key] != 0:\n",
    "                            temp[key] = sum(tmp[key]) / total[key] * 100\n",
    "                        else:\n",
    "                            temp[key] = 0.\n",
    "                    output[name] = temp\n",
    "                else:    \n",
    "                    if total[key] != 0:\n",
    "                        temp[key] = sum(tmp[key]) / total[key] * 100\n",
    "                    else:\n",
    "                        temp[key] = 0.\n",
    "            output[name] = temp\n",
    "        else:\n",
    "            temp = {}\n",
    "            label = list(values.keys())[0]\n",
    "            for key in values[label]:\n",
    "                if total[key] != 0:\n",
    "                    temp[key] = values[label][key] / total[key] * 100\n",
    "                else:\n",
    "                    temp[key] = 0.\n",
    "            output[name] = temp\n",
    "    return output\n",
    "\n",
    "def response_fun(client, args={}, remove_list=[], clean_dict={}, void_item=False, multiplicity='time', display_success=False, toZero=True):\n",
    "    '''\n",
    "    Extracts the data from the server and returns the response object whose attributes are the data in dictionary, \n",
    "        string and dataframe datatype\n",
    "        \n",
    "        param: client: requestLib.RequestHandeler_Object: object that performs the GET request tothe eurostat \n",
    "                                                                server\n",
    "        param: args: dict: arguments for the url to request - empty by default\n",
    "        param: remove_list: list: countries that are not shown in the plots - empty by default\n",
    "        param: clean_dict: dict: countries that are renamed in the plots in a contracted form - empty by default\n",
    "        param: void_item: boolean: void labels for not shown countries - False by default\n",
    "        param: multiciplity: string: multiple key argument - \"time\" by default\n",
    "        param: display_success: boolean: print success status - False by default\n",
    "        \n",
    "        return: response: requestLib.Response_Object\n",
    "    '''\n",
    "    if args != {}:\n",
    "        response = client.get_request(args, display_success=display_success)\n",
    "    else:\n",
    "        response = client.get_request(client.args)\n",
    "    frame = assign_reference(response.data['dict'], multiplicity=multiplicity)\n",
    "    country = frame['lines']\n",
    "    times = frame['columns']\n",
    "    if 'status' not in list(response.data['dict'].keys()):\n",
    "        v = list(response.data['dict']['value'].values())\n",
    "    else:\n",
    "        if toZero:\n",
    "            v = dataDealer.check_data_by_status(response.data['dict'], toZero=True)\n",
    "        else:\n",
    "            v = dataDealer.check_data_by_status(response.data['dict'], toZero=False)\n",
    "    response.values, response.lines = dataDealer.json_to_data(lines=frame['lines'], \n",
    "                                         columns=frame['columns'], \n",
    "                                         values=v, \n",
    "                                         remove_list=remove_list, \n",
    "                                         void_item=void_item, \n",
    "                                         clean_dict=clean_dict,\n",
    "                                         multiple_key=multiplicity)\n",
    "    \n",
    "    response.DF = dataDealer.subjason_to_DataFrame(lines=response.lines, columns=times, subDict=response.values)\n",
    "    response.x_labels = dataDealer.clean_label(lines=response.lines, remove_list=remove_list)\n",
    "    \n",
    "    return response\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instances\n",
    "\n",
    "Two main instances are presentin this notebook\n",
    "* `client`: object that performs the _GET_ request tothe eurostat server -> only instancied once\n",
    "* `response`: response object returned by the return_fun, containig the whole data -> instanciated at each _GET_ request by the client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = requestLib.RequestHandeler(host_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Procedure\n",
    "\n",
    "For each visualisation we perform the following steps:\n",
    "\n",
    "1. Prepare the GET request url arguments\n",
    "  * If not prepared before or if the request is not directly related to the one just before, the `args_to_dict` method of the `requestLib` module is used.\n",
    "  * If only few arguments need to be chaged from the privious request, the `update_args` method of the client instance is used.\n",
    "2. Run the `response_fun` function with the proper arguments (look at the description in the function definition).\n",
    "3. If more calculations are needed, do them. If not, jump to the next point.\n",
    "4. Plot the data or print the subdataframe depending on what has to be shown.\n",
    "\n",
    "The last two sections [_Main components of government revenu, 2019_](#main-revenu) and [_Main components of government expenditures, 2019_](#main-expenditures) need a different approach that is explained under the former section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Public Balance 2018 and 2019\n",
    "\n",
    "* Arguments for the _GET_ request:\n",
    "  * `sector = 'S13'` - general government\n",
    "  * `na_item = 'B9'` - Net borrowing or lending\n",
    "  * `time = 2018, 2019` - 2018 & 2019\n",
    "  * `unit = 'PC_GDP'` - percentage of GDP\n",
    "\n",
    "* Arguments to `response_fun` and plot filters:\n",
    "  * `clean_dict = clean_country`\n",
    "  * `remove_list = noCountry`\n",
    "  * `void_item = True` - void labels for non shown countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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HrBD3hz/8Ic985jPZeeedWWONNfi3f/s3brzxRs4+++zGxm4SLEmSpEbcfPPN/OY3v2GbbbbhiiuuYLvttlt63cyZM9lqq6244ooreoqVmcv9nplcfvnljY3VJFiSJEkDu//++znssMM4+OCD2XrrrbnrrruYPXv2cn8ze/Zs7rzzznFj7bbbbpx99tnMnz+f++67jw9+8IPcd9993HPPPY2N1yRYkiRJA3nwwQc56KCDWHPNNTnuuOMAmDVrFosWLVru7xYtWsS6645/KN/WW2/Nl7/8ZV7/+tez6aabcsstt/DEJz6RzTbbrLExmwRLkiSpb5nJoYceyl/+8hdOPPFE1lxzTQC22WYbLr300qV/d/fdd3PttdeyzTbb9BT3JS95CZdffjm33nor733ve7n++uvZcccdGxu3SbAkSZL6dsQRR3DVVVdx+umnM2PGjKWX77PPPlx++eWceuqpLF68mGOOOYZtt92WrbfeGoAHHniAxYsXs2TJEh588EEWL17M/fffv3T7BQsW8MADD/DXv/6Vww8/nL333nvptk0wCZYkSVJfbrjhBj7zmc+wcOFCHv7wh7Ppppsya9YsTjrpJDbeeGNOPfVUjj76aDbYYAPOP/98TjnllKXbnnDCCcyYMYMjjjiCc845hxkzZvCqV71q6fVvetObWH/99Xn84x/PBhtswOc+97lGx+5iGZIkDcGWR50x5nXH7z5zEkeiaalrMYs777yzpzrblek3xhZbbLFcF4eRcXbbbbelLdFGOuSQQzjkkEPGjH3uuefWHk8dzgRLkiSpdUyCJUmS1DomwZIkSWodk2BJkiS1jkmwJEnSFNd98JlW9OCDD9bexiRYkiRpCltnnXW49dZbTYRHkZncd9993HjjjcycWa+rii3SJEmSprDNNtuMP/7xj/z1r38d9frFixezzjrrDPQ/mojRZJw61lhjDWbPns1GG21Ub7sJGo8kSZIasOaaazJnzpwxr58/fz5PfvKTB/ofTcRoMs5ksBxCkiRJrWMSLEmSpNYxCZYkSVLrmARLkiSpdUyCJUmS1DomwZIkSWodk2BJkiS1jkmwJEmSWsckWJIkSa1jEixJkqTWMQmWJElS65gES5IkqXVMgiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklpnjWEPQJIkjfCnhTDvhStePu+OSR+KtKpyJliSJEmtYxIsSZKk1jEJliRJUuuYBEuSJKl1TIIlSZLUOibBkiRJah2TYEmSJLWOSbAkSZJaxyRYkiRJrWMSLEmSpNYZOAmOiM0j4qyIuDIiroiINzUxMEmSJGmirNFAjCXAWzPz4ohYF1gQET/OzCsbiC1JkiQ1buCZ4Mz8U2ZeXP1+J3AV8MhB40qSJEkTpdGa4IjYEngycH6TcSVJkqQmRWY2EyhiFnA28IHM/OYo1x8OHA6wySab7HDKKac08n+bctdddzFr1qxJi3HZjXeMed2c2atP6lgmOk5jY7ntZmbde9OKV2y6/eSPZSrdL6vYWJqK41gcy1Qfy0o/B2bcO6nvd34mOZapNJam7brrrgsyc+7IyxtJgiNiTeC7wA8z82Pj/f3cuXPzoosuGvj/Nmn+/PnssssukxZjy6POGPO643efOaljmeg4jY3l5GPZ5dfvWfGKeWO/eU/YWKbS/bKKjaWpOI7FsUz1saz0c2C7ayb1/c7PJMcylcbStIgYNQluojtEAF8AruolAZYkSZKGrYnuEM8ADgIui4iF1WXvzMzvNRC7nf60EOa9cMXLa8wASJIkaWwDJ8GZeS4QDYxFkiRJmhSuGCdJkqTWMQmWJElS6zRREywBYx9dfPzuMyd5JJIkSSvnTLAkSZJaxyRYkiRJrWMSLEmSpNaxJliSJE0s+99rCnImWJIkSa1jEixJkqTWMQmWJElS65gES5IkqXVMgiVJktQ6JsGSJElqHVukraqmUjuaqTQWjW6sxwh8nCRJqyRngiVJktQ6JsGSJElqHcshJElSI7Y86oxRLz9+u0keiNQDZ4IlSZLUOibBkiRJah2TYEmSJLWOSbAkSZJaxyRYkiRJrWMSLEmSpNYxCZYkSVLrmARLkiSpdUyCJUmS1DomwZIkSWodk2BJkiS1jkmwJEmSWsckWJIkSa1jEixJkqTWWWPYA5A0ebY86oxRLz9+u0keiCRJQ+ZMsCRJklrHJFiSJEmtYxIsSZKk1rEmWMBKakV3nznJI5EkSZp4zgRLkiSpdUyCJUmS1DqWQ0iSJE0gSw6nJmeCJUmS1DomwZIkSWodk2BJkiS1jjXBHX9aCPNeuOLl8+6Y9KG0nUv7SpK0ImuLm+VMsCRJklrHJFiSJEmtYzmEJEk1uVtamv6cCZYkSVLrmARLkiSpdUyCJUmS1DrWBEuSpjdbXE4s79+J09R962PUF2eCJUmS1DomwZIkSWodk2BJkiS1jjXBkqT6rEGUNM05EyxJkqTWMQmWJElS61gOoZVzl6ck9c73TGnacCZYkiRJrWMSLEmSpNYxCZYkSVLrtKomeMujzhjzuuO3m8SBSNI0Mdb7pu+ZkqY7Z4IlSZLUOibBkiRJap1GkuCI2D0ifh0Rv42Io5qIKUmSJE2UgWuCI2J14JPA84A/AhdGxHcy88pBY0vqgX1J1RLWJ6sRvmeq0sRM8FOB32bm7zLzPuAUYJRnlyRJkjQ1NJEEPxL4Q9f5P1aXSZIkSVNSZOZgASJeAuyemYdV5w8CdsrM14/4u8OBwwE22WSTHU455ZSB/m/T7rrrLmbNmtXT31524+i7TObMuJdZ99604hWbbj/5Y5m9es8xmhrLRMaYzmOp/XyBUZ8zE/lY171fptJroKkYTdympu6XqTSWsQzjNbCqjaWpOFPpsW4qxoS+Bmq+Z07k/Tstni8wlPeYJu26664LMnPuyMubSIKfDszLzOdX598BkJn/MdY2c+fOzYsuumig/9u0+fPns8suu/T0t2PXpV3DLr9+z4pX1KwzamQsu8/sOUZTY5nIGNN5LLWfLzDqc2YiH+u698tUeg00FaOJ29TU/TKVxjKWYbwGVrWxNBVnKj3WTcWY0NdAzffMibx/p8XzBYbyHtOkiBg1CW6iHOJC4LERMSci1gJeBnyngbiSJEnShBi4O0RmLomI1wM/BFYHvpiZVww8MkmSJGmCNLJscmZ+D/heE7EkSZKkieaKcZIkSWodk2BJkiS1TiPlEJIkSU25/kMvGPXy+ScfO7kD0SrNmWBJkiS1jkmwJEmSWsckWJIkSa1jTbAkSVrljFlXPH/+5A5EU5YzwZIkSWodk2BJkiS1jkmwJEmSWseaYEnCvqSS1DbOBEuSJKl1TIIlSZLUOpZDSBOsqd3stvsZkk23hwPuGPYoJEkNcyZYkiRJrWMSLEmSpNYxCZYkSVLrWBMsDYu1ppImku8x0ko5EyxJkqTWMQmWJElS61gOIUkNcdU5aYJZ4qEGORMsSZKk1jEJliRJUuuYBEuSJKl1rAnug3V/kmqxjlETyCXVpf44EyxJkqTWMQmWJElS65gES5IkqXWsCZakKcbjDiRp4jkTLEmSpNYxCZYkSVLrWA4hSdOFrdYkDchyq2WcCZYkSVLrmARLkiSpdUyCJUmS1DomwZIkSWodk2BJkiS1jkmwJEmSWsckWJIkSa1jEixJkqTWMQmWJElS65gES5IkqXVMgiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklpnjWEPQJIkSUO26fZwwB3DHsWkciZYkiRJrWMSLEmSpNYxCZYkSVLrmARLkiSpdUyCJUmS1DomwZIkSWodW6RJktTC9lBS2zkTLEmSpNYxCZYkSVLrmARLkiSpdUyCJUmS1DomwZIkSWodk2BJkiS1jkmwJEmSWsckWJIkSa1jEixJkqTWMQmWJElS6wy0bHJE/CewF3AfcC3wr5l5ewPjmp5cdlOSJGlaGHQm+MfAkzJzW+A3wDsGH5IkSZI0sQZKgjPzR5m5pDp7HrDZ4EOSJEmSJlZkZjOBIk4HvpaZJ45x/eHA4QCbbLLJDqecckoj/7cpd911F7NmzRp6jLpxLrtx9PKLObNXn/SxTGQMxzK1xjLm827Gvcy696YVr9h0+wkdz0TFmM5jmUqPUe2x1BzPqvae2VQcx9JMjIl8LXm/TI5dd911QWbOHXn5uElwRJwJPHyUq47OzG9Xf3M0MBfYN3vIqufOnZsXXXRRTwOfLPPnz2eXXXYZeoy6cbY86oxRLz9+95mTPpaJjOFYptZYxnzebXcNu/z6PSteMa9erXzb798m4kylx6j2WGqOZ1V7z2wqjmNpJsZEvpa8XyZHRIyaBI97YFxm7jZO4EOAPYHn9pIAS5IkScM2aHeI3YG3A8/OzHuaGZIkSZI0sQZKgoHjgLWBH0cEwHmZ+ZqBRyVJWmVc/6EXjHr5/PnzbSspaWgGSoIz8zFNDUSSJEmaLK4YJ0mSpNYxCZYkSVLrDFoTLEnStLHS+mRJPRvztXTysZM7kAE4EyxJkqTWMQmWJElS61gOIUmroFVhV6WkaWjT7adN60NngiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklrHJFiSJEmtYxIsSZKk1jEJliRJUuvYJ1hSbfaglSRNd84ES5IkqXVMgiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklrHJFiSJEmtYxIsSZKk1jEJliRJUuuYBEuSJKl1TIIlSZLUOibBkiRJah2TYEmSJLWOSbAkSZJaZ41hD0DSKmTT7eGAO4Y9CklqzPUfesGol88/+djJHYga50ywJEmSWsckWJIkSa1jEixJkqTWsSZYktrEum1JApwJliRJUguZBEuSJKl1TIIlSZLUOibBkiRJah2TYEmSJLWOSbAkSZJaxyRYkiRJrWMSLEmSpNYxCZYkSVLrmARLkiSpdUyCJUmS1DomwZIkSWodk2BJkiS1jkmwJEmSWsckWJIkSa1jEixJkqTWMQmWJElS66wx7AFoMNd/6AWjXj5//vzJHYgkSdI04kywJEmSWsckWJIkSa1jEixJkqTWMQmWJElS65gES5IkqXVMgiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklrHJFiSJEmtYxIsSZKk1jEJliRJUuuYBEuSJKl1GkmCI+KtEZERsVET8SRJkqSJNHASHBGbA/8E/H7w4UiSJEkTr4mZ4P8G3g5kA7EkSZKkCTdQEhwRLwRuzMxLGxqPJEmSNOEic+UTuBFxJvDwUa46Gngn8E+ZeUdEXA/MzcxbxohzOHA4wCabbLLDKaecMsi4G3fXXXcxa9asocdwLI6l7WNpKo5jcSzTdSxNxXEsEzyW225m1r03rXjFpttP/lim0v3SUJwm7brrrgsyc+4KV2RmXyfgH4Cbgeur0xJKXfDDx9t2hx12yKnmrLPOmhIxmorjWCYuRlNxHMvExnEsExejqTiOZWLjOJaJi5GZedZX/zvzPeuteBrGWKbS/dJQnCYBF+Uo+ega/WbVmXkZ8LDO+fFmgiVJkqSpwj7BkiRJap2+Z4JHyswtm4olSZIkTSRngiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklrHJFiSJEmtYxIsSZKk1jEJliRJUuuYBEuSJKl1TIIlSZLUOo2tGCdJktQam24PB9wx7FFoAM4ES5IkqXVMgiVJktQ6JsGSJElqHZNgSZIktY5JsCRJklrHJFiSJEmtYxIsSZKk1jEJliRJUuuYBEuSJKl1TIIlSZLUOibBkiRJah2TYEmSJLWOSbAkSZJaxyRYkiRJrROZOfn/NOKvwA2T/o9XbiPglikQw7E4lraPpak4jsWxTNexNBXHsTiW6TqWpm2RmRuvcGlmeipfBC6aCjEci2Np+1hWxdvkWBxL22+TY3EsU/FkOYQkSZJaxyRYkiRJrWMSvMxnp0iMpuI4lomL0VQcxzKxcRzLxMVoKo5jmdg4jmXiYjQVx7EM0VAOjJMkSZKGyZlgSZIktY5JsCRJklrHJFiSJEmts8awB7AqiIhtgS3puj8z85tDGxAQERsAm2fmr/rc/mHAOp3zmfn7mtu/ATgxM//Wz//virNHZn5/xGWvycxPDxJ3GCLi6cArgGcCmwJ/By4HzqDcV3cMcXgCImL9zLy9gThrZOaS6vdZwNbA7zLzthoxNlzZ9XViTSURsRdwRmY+OAXG8g+Zedmwx9EREU8Z5eI7gBs6z6fpJCKeASzMzLsj4hXAU4CPZ+ZUWyxLA4iI1YEXsGIe9LFhjalXrT0wLiLWAQ4FtmH5ZO+VNeN8EdgWuALovKlnH3EeC/wH8MQR43l0jRjzgb0pT8IFwM3AzzPzLTVi7A18FHhEtf0WwFWZuU2vMao47wdeBlwMfBH4YfbxZIuIXwDvysyfVuffDuyamXvUjTVMEfF94Cbg28BFlPt2HeBxwK7AXsDHMvM7PcZr4vnyOOBTwCaZ+aTqy9zemfn+XmNUcfYFPgw8DIjqlJm5Xp04U0FELAHmAycDp/aTEEfEIZTX0K3Am4BPAtdRHuu3Z+bJPca5DkjK/fko4G/V7+sDv8/MOTXG9BDgrcCjMvNV1fPn8Zn53RoxBn7OVXFOBJ4OnAp8MTOvrrn96ZT7ZVSZuXeNWOcAawPHAyf1+0W0wc+T8yiJ4q8oj/WTKJ8ts4EjMvNHPcZ5wShjOabmWDYG/o0VH+/n1IjxK2A7ymfk8cDngf0z89k9br/Sz65ekqwmYoyIN/BjHRFrAy9mxaSx7mPUxFieBnwCeAKwFrA6cHed9++I+B6wGLiMZXkQmfneXmMMS5vLIU4AHg48Hzgb2Ay4s484T8vMuZl5cGb+a3Wq9cZX+RIlIVlCSYq+ApxYM8bszFwE7At8JTN3AnarGeN9wNOA31Qfss8FzqsZg8x8F/BY4AvAIcA1EfHBiNiqZqi9gQ9GxDMj4gPATsAL645nCjgoMw/NzO9k5k2ZuSQz78rMizPzo5m5C/CLGvGaeL58DngHcD9AtdfgZTVjAHyEkjzPzsz1MnPd6ZgAV64CjgWeA1wbEd+OiJdFxIwaMd4KPJ7y3vI14HmZ+VxgLuX+7klmzqkSzDOBvTJzo8x8KLAn0FMy1OVLwL2U5BPgRqDWlx2aec6Rma8AngxcCxwfEb+MiMMjYt0eQ/wX5UvGdZS9KZ+rTndVMeuM5ZnAgcDmwIKI+GpEPK9OjEpTnyc3AU+uPlN2oNxPvwOeR3mdjSsiPg28FHgDJZHejzKZUddJlNfDHOC9wPXAhTVjLKkmP14IHJeZnwR6fZyp/nZdymvnCOCR1ek1lC8LdWKMdaqricf625T7ZAlwd9dpGGM5DjgAuAaYARxG+eJex2aZuW9mvicz39s51YwxHMNYpm4qnIBLqp+/qn6uCZzXR5wvAE9sYDwLqp+XjbysRozLKLvZfwTs2H37asS4qPp5KbBa5/cBbtd2lKTiasoH6CXAR2rGeBhlZuRLVHsvpvMJ2ABYbwo8Xy6sfl7SddnCPsby82Hfpw0+Nhd3/T4D2B/4JmVW96s9xljY9ftNI66r9Xoc+Riv7LJxYnRe192Pda3XdRPPuRHxHgocSUmuvk/5EH5D3ds03mU9xlqdMjN3IyXxuxrYt8b2l3Q/vgN8nlw+1mW9vja7xtD5OQs4p4+xLOiOU/1+Yc0YZ1O++P2GkqytVve5W8X5GbBu1/l1gZ/1+9wb5NTEYz3a4zzEsVzUHaM7bo0YHwb+aRiPx6CnNtcE31/9vD0ingT8mZJs1fUV4JcR8WfKTEtnV/C2NePcGxGrUWZMX095M55VM8YxwA+BczPzwoh4NOWDpY7bqxrGnwEnRcTN9PENNSLeBPwLcAtlF9j/y8z7O7cRePs429/J8rs81wIeDbwkIjKn2UxjRDwC+BDl2/8s4MaIgFIq8oHMvH8lm4+miefLLdXMfFZjfAnwp5oxAC6KiK8B36K8BoDh18X3KTq/ZObfga8DX4+I2cCLeozx+4j4D8oH9dUR8VFKIr0b/d2/N0XEu1g263ogZcawjvuq2ezOY70VXY9Vj5p4zhERL6TsHXoM5f3zqZl5c1WycSVl12wvZkbEozPzd1XcOcDMmmPZFvhXSj3jjykz7hdXr9dfUh63XjT1eXJlRHwKOKU6/9LqsrW7/sd4/l79vKe6HbdSJkfq6vy/P1XlFTcBK61TH8VLgZcDh2bmnyPiUcB/9jGWTYD7us7fV13Ws6ZKVmjmsf5FQ/XoTYzlnohYC1gYER+hvEfVrRI4Dziten+4n+lUEjfsLHxYJ8qU/wbAsym7m24GXtNHnN9SdtnPoexy2gLYoo84O1I+UDajzHh+k1JqMdn3y0zKrMgawMHAG4GH9hHnvWPdD8ATeowRlBrGoT9fGrhffwrsUv2+L/Df1X39fuCzw3i+UL5UnAncQ0lozgW27GMsXxrl9MVh3+d9Pk5vayDGepTZr6Oqx+jFwHcpuxg37SPehsDHKXtRLql+37BmjOdRZuX+StnNfX3n+TiZz7kqzvHAs8a47rk14uwO/J5Sw312dZueX3MsZ1O+rM8Y5bqDasRp6vNkBqWc5rTq9DbgIZSkZFaPMd5NqRt/MSUp+hPwvj7GsielFvlJwFmU40z2qhuniRNwNGXv5LzqtBB4Z80Y36CU+11L+Wz7EeUgvbpjGfixpnzZuw/4NWUv52X0t5eoibFsUT3v1gPeA3wMeEzNGNdR6r6n3Z7a1h4Y15SI+GVmPn38v5x4EfElRjlgJPurUR5YdcToJixf+F+3y8RlmfkPTY9tskXEpZm5Xdf5BVlq/oiIqzNz6yGObSal9KWfGkZNExHxUEq9f1B2md4yhDGsDpyZmbs2FG9tSucNgKszs+7s9pTR9H1TxVwbWCf7OOAvIp6RmT8f77Ixth25J2852ccMYUTsAOxcnf1ZZl5Sc/tLMvPJEfGrzNw2ItaklIk8re5YBhURW4x2eU7TrhkR8TPKl+qhd3ypq3XlEBHxisw8cawjRrN+S49LIuKrwOn0sSs4Io7NzCPHOuI5axzpTJlt6lgH2Iced5tGxLmZufMob1597daodpfOA/5CV9cMyrfFOi6OiB0zs+4BGVPNX6sWQWdRZoKvB4hSE9Hzrqcmny8R8UFKffbt1fkNgLdmOaixl+3fnpkfiYhPjDGWN/Y6lqmiSkQOo8x2/qD7Az8i3pU9dM5oIsaIeI+jzAhuyfJfKOscpb8P8NPMPKM6v35EvCgzv9XDto095zLzgYh4MCJm95OYjWIHlt0v20UEmfmV8TaKiMsYPUmrVc7W5OfJoPdNRDwnM38apVvLyOt6/kzq8glWPPhstMtWkJnrVv/3fZSZ6BMo9+2B9FeaAWX2909Ur4GIeFTNSZWBSgcafqxviIidgcdm5peidOLoubSoibFExNczc/+xXgu9vgYqvwPmR+mC1J0HTfkWaa1LgllWM9bPUaGjmUF50P+p67Kk91qyE6qf/zXoQDLz1O7zEXEyZRd3L9vuXP1s6n45ktKC6dYB4+wEHBgRN1Bqk/utuR62V1Ie46Mob+avry7fkBodA2jw+QLskZnv7JzJzL9FxD8DPSXBlAOIoLR8W1V8hrL7+QLgfyLi7FzWYnBfeuuo0ESMbt8APk2prX+g5rYd78nM0zpnMvP2iHgPpY57PE0+56B0cbgsIn5M1/EGdb80RcQJwFaU11PnfklKnfF49qzzv1ai6c+TQe6bZ1PKrvYa5bqeP5Oi9DP/R2DjEUnWepRSuTr27t4DBnwqIi4F/r1OkCh9599DmVR5gOpzgHqTKp+tvui/G/gOJemsM47GHuvqtTeX0kXmS5QD2k4EnjGJY3lT9bOJ18J11Wmt6jRttLIcopqpeWNm/vewxwJLx/OVzDyw4biPpzSlf0wf4xm0jOEsSmuogRq8r2q7jaaSKD08d+zsQq4OnLooa/aEXpV0dpVWv68B/C+wEaWF0HmZ+eTJiDEi3tLSmX51j6nrslqlRhHxpsz8+HiX9RDn4NEuz8wv14xzFaUzzyrzIdbEfRMRq2dmv1+WiIhnA7tQ2pB1L0p0J3B6ZvZ8sHWUPu+fpBzol5TXwOsy8x9rjum3wE4NTKpMCRGxkNL+7uLO+8For9FJGktjC1JFOaiezLyrqfFNtDbOBHd2Ox1AOThpIE3U4Vbj2SIi1srM+8bfYsyxjCxl+DOl2XmdGN3fuAcpY+jsHjmDAXaPdJLdGLGC3aokIv496zdJfwal3GQLyuu4M0NeZ+GCk4CfVM9hKEfJ10pEqrEM3FR/Clk6i1F9gTs8Iv6dMsPW6+7KJmJ0Oz0iXks5UKr7tVRnxbiLIuJjLOv/+TrKgU51HEw5KK/bIaNctlJ1k92VuJzSdqufjhtAMwsFVHE2Bl7FiiUrtY7HaOi+uS4ifkDpUf3Tul8SMvNs4OyIOL6ByYaXU54fH6d8jvy8uqyuP1BWzuvbGKUDd1BawS2sEaeJx/q+zMyI6HRrqdXVpOGxvDsi7s0RC1Kx/Beg8cbxJMoeow2r87cA/5KZV9QYx1C0Mgmu/DwijqO8UXTvdrq4Zpy+63BH+F01pu+MGE9PSWNVW7pN3RnbUbyJZsoYfl+dBto9EmOsYEdpc7OqOIzS3q6OLwBvpiQyfc36ZOaHq12TnQVV3peZP+wj1EmU19ELKLNHB1O6EExHF0XE7pn5g84FmXlMRNxE6XM9WTG6dWYH/1/XZUnp7tGrN1B2A3+tOv9jSiI8rmrC4OXAnOr9qWNdoPbSzdHQynOU2fUrI+IClv9yUOc4iuMoC8R8g7J7+l8oK/vV9W3gHEq3lUFmYa9j9EmVOvfN1pRd3K8DvhAR3wVOycyeSuO6a7/Lx8oKY6lTA349zSxu1MSkytzqdHp1fk9KZ4bXRMQ3MrOnxUho5rH+ekR8Blg/Il5FKZf7XB9xmhjL3sB3I+L/UTqubE39x+yzwFsy8yyAiNiFcntqzfgPQyvLIWDp7vqRctDZqyh98s7tY3fPe0a7PGusulJ39+YYMRopY2hKlaQ9h3LU9JMjYlfgFZl56JCHVktELBrrKkp7plpfSCPi/CwrAg46ri0oB2ecGaVP6+pZs0tEZ3f9iDKACzNzx0HHp+GrniNzKInrUV1X3Ulp61TrvSIizqXsbfpvSv3qv1K6k9StE332aJdXM5m9xrgoM+eOeO5e0kfJysLM3L7ONmPEeWjX2XUoq71tWPe+6Yq3AWUW9sDM7Kmed6z7taPm/dvIDHlDn48/A/65s6u+2nV/BiXxW5CZT+wxTlOP9fMoxxIF8MPM/HEfMZoay8MoifQC4JV19x7EiO5HY102FbV5JvjQrJqsd0RZXGJQj6WPJul1Xswr0Xcnha5dRY2UMVRvfm9nxcbkdb9k3J+Zt0bEahGxWmaeFRHH1owxFdxOqb/9y8grIuIPfcQ7KyL+k3KwS/fj1POejGoG4nDKLqytKMuRfpqyVHYdTTTVn7Ii4rOZefgwY1S7G0fOnPZyAFhn+747TFS7xG9g2ZLLg5qRmT+JiKhiz4uIBdQ8WKpOMrYSTSwUAGUm7Z8z83uDDGaUPXDH9nPfVInsSykJ3kWUlQ97HUMT92tHIzPkDX0+PozlF4i5H9gkM/8eEXVa6zX1WP+YskdmEH2PZZTyyUEWpPpdRLybZQfRvoKSS0x5bU6C/48VW718g9Jyp2ddT6TO0aq163CrOE0kjYN0UugcZdpIGQPLdpHvyWC7yG+PBlawmwK+QinlWCEJBr7aR7zOLPDcrsuSMmveq9cBTwXOB8jMa6oZgbreH2VFtbdS6ivXo5RqrCrmjv8nExejmgXbhZIEfw/Yg9L1peckmAY6TDRVP0tzK881MZ6DKEnv6ynP2c0pHTzqehPwziqZ6nvFrIjo/kxajfK8qbuX6HrKoipfp6zU2df7ZUNlKw/JzNqfh6OMpYnPx5OA8yPi29X5vYCvVvW4V9aI0/djPUriuZw+XkudsdxHWXyj57Fkc52goJRzvJdlHUjOqS6b8lpXDhERW1NeSB9h+Rq79ShvGEOpNY2IH1GSxrfRlTTWeQOJKdRJoald5NUb1GJY2mNyNnBSAzXLrdcpqYhlTeTXoBytPN3az02oiPhBZu4+rBhR+nhuB1ySmdtFxCbAiZn5vBoxmugwcRGj1M9mZp0Wf0TEjpS6/vUpK3jNpvSrPm+yxxMNdbxoyogyvSWUfuL/lZm/rhHjWZn5sxGX9bTIxYhtBi5biYj3A78YdNa0ic/HKs5clrUh+3lmDqW9Y4zRP7nfspcGxrMBZS929xeMn429xaqjjUnwC4EXUYrBuw/yuJNy8MAveoyzdWZePeKb+1J1dktX8ZpKGp9CWVUnKS/yuuP4MbBfLr+AwimZ+fyacc7LzKdFxA+B/6HsIv+/zNyqTpyueOux/G7c2gfkTDURMS8z5/W57WzKB9SzqovOBo7JGk32q92/t1OShzcArwWuzMyja47l0ZS6w6dTOor8EnjzyHIj9SciLsjMp1a7xXelvFddlTVWGYyIeZQDS/vuMNFU/WxTmhhPRFycmU8ZcVlft2mqJBJj3KYVLushTuczaemxJnW/TFUznzMpz7lBZsgH/nyMiEeNdnn2cTD5oI91UzW0EdFJoOdk5vsiYnNKMn1BjRiHUWaUN6P03H4a8MteZtljjAV0OrLeQapD0bpyiMz8NvDtiHh6Zv5ygFBvodRTfnS0f0O93dLQQF1llDZM+7Fsl8SXohz1Wqc5/8adBBiWLqAwtF3kEfFqym6WxZQEq1N20kT99rDtTWlz1o8vUlpEdWr9DqI0Xa+zK/co4FDKuvWvpuxq/3wfY/kqpfXWPtX5lwEns6xkY9oY5U09gVuAszLzxMmKMcJFEbE+5WjrBZQFFeq+dzXRYaKR+tkxPjjvoNSufiYzF0/0eKL5jhejJhLU/BwY5MttNLvIBTRQttLgLvcmjjs4g2XPuxmUgz1/Tc1OQw091ndHxIEs3z+5n7KV/6V8Lj6HslflLsp7cZ3JszdVf39eZu5a7S3/YI/bdhbQ2ZfSrrDz/nYAo5f+TTmtmwnuqN403w/8HfgBpQ/um/v8kGpiPHtS6mg2Z1nS+N7M/M5KN1w+xq+B7TofIlEWP1iYmY+vEWMBsE/n23FVYnFa3VmEpkTENcDTM/OWYfz/iTTILFqMclTwaJf1EGcG8Kg6u1tHiTHaQgzT4sjgkWL0I+M3pBzocU1mHjXK9Y3HWEnsLYH1MvNX/cYY4H93atrXonyhnQ38b2b+tmacjwMbU74oQTmAaxElGVgvMw+qMZ6bKatt1RpPNN/x4jKWJRLbdxKJzKxVXxwRp1K+3Hb6BR9EeU8fN040uMhFFa+pspWBZ8ib+HwcJeZTgNdm5mE1txv4sa5exx+nlGZ0+icfmaWlXJ2xXJyZT+n+LKn73tuZUY+ygMdOmXlvRFyRNUpDO3tlxrtsKmpzErywegLvQzl46y3Az/rYHTHaE/8O4LLMvLmBodYZy1mUBPb26vz6wDd72a3RFWN3Ss+/symzrs8EDs+a/WMjYg5lF/uWLF/GUGv3SJSm7/tm5j11tpsOqlmWRwIvy8z/rLntLyk17OdW559BqR3s+Qj+KD2Y/xNYKzPnRMT2lFmnuo/Rh4G/sWxW46XABlXsVaV0ZXVKG6XtJytGjFFq1dFHqdNAHSaqGGuxrI/urzPz/pX9/RgxVtiN3fVBXOvDd6poIpGo4gz85TYitsgpsqLmILvaJ0P00Va0qce6CRFxPmX2/8IqGd4Y+FHNkqDTKPXeR1JmlP8GrJmZ/1wjxlXAC7Iqgas+/7+XmU/o+cYMSevKIbqsWf18AfCNzLwjRmkM3oNDKbWQnQMadqHsspwTEcdk5gljbdgtIv5nlIvvoCxj++1RrhvNHcAVUep6E3gecEEndvaw/nxm/qD68H1addGRfc7CfouyoMPpLFt5rh/vAH5Rvdi7axnHvS1TVfVGtR9ll9EjKHWadb0G+Eq1+xTKG9eoS66uxHso3SHmA2TmwurNq65OSUanBVjnhfQyVpHSlSyrOk52jNFKrZaGo8bu12igw0SUBvhfphysFcDmEXFwH3WvsyLiUV17mx7Fst3s466YWc3ErawOcdwDO2Pso/T7qlkF/lhNOnwL+HFE/I3SVq6uv0fEziO+3P69Zox7orRP7KuTwojykBXU/JI8yK52IuITrPyx7vlzYESJyGqU7lD9LGw18GMdpWXhpygt2p4UEdsCe9csXYRyvM1pwMMi4gPAS4B31QmQmZ0ytnnVRNpsyt7xOt5Maa36O8praAtKid2U1+Yk+PSIuJryBnNElZj0WovWbQ3gCVn1f41y5PZXKPWQP2NZ37zxrENZqeUb1fkXA9cB20XErpl5ZA8xTmP5hGp+j/97qSif0rsDj86yytWjIuKpdQrtK4szc7TEvq7PUJabvYzBkumhioh1KXVTL6fMpH2TcjDDZn2GXJSlU8B6AJm5qI8E9v5Rvvz1vGuo2l36h8ycU50/mPK8vR6YNx1ngCNitDrDDSgHD/a0BGgTMQAyc9de/7YHL2FZh4l/rd6n6pZ+fRT4p07pTPVBfjI120pSjhM4NyKupXxgzgFeG6UTTC/LBu9Z8/+toMFa1U68JhIJgCOAL1dfboNSn1z3y+2g7SmfTlmm+GRK+8RBvv0tzszFEUFErJ3lYPKey/ModeJN6X7Ml1BqhE+tG6Shx/pzlPr8z1QxfxURX6WUaNYZy0lRShifS3mcXpSZV9WJUU2SnZKZv8g+e0RXk2ePpeQwAFdnZp3ey0PT2nIIWPphdUc1Q/MQSj3an2vGuDK7VpqpksgrMvOJUaPmMyLOA56RmQ9U59eg1EDtTCmtGHc1m4jYCzgjM/tOFiPiU1SF9pn5hCj1XD8aufuyhzgvp9SB/Yg+F3Oo4vR8H05lEfF34ALKt/RzMzMj4ndZf6nYTrzRjgCve+T2F4CfUGoiXwy8kbIb7DW9jgHYLTNvi4hnUcoh3gBsT/li+JJexzJVxLJlazsf/J2D2uYD78/MsVb+azTGKDH/kRVLi+rM4jbRYWK02u8VLusx1tos+8D8dfZ+MNzION0rHs4A1siaKx42IRrsPFDFW/rlto9tB+qkEKVs53mUPVXbUpLFkzOz5y9wXbEG3tU+It4sgKxWfau57X6Z+Y3xLusx1gaU+uTu12OdhYo6JRVLP9/qlr1U2yxNYOtsNyLGwZQStsdTJtFOyZqt42IKlYXW1dqZ4Ij4l67fu6+qVSNH2QXwXZafwZ1fzWrcXiPOBpRdgp2jgGdSlst8IHpfzeallBWGTgW+mJlX1/j/HTtVtUWXwNLuEP0smvEPlIM6nsOyGdx+umZ8PyIOp5RV9NXaaYp4B6U84H+BkyPia/0EiWV9rmePeONZj65dnz16A3A05X79KvBD6s1ErN71OLwU+GxmngqcGqVebtrpzGoPO0a3iDiBsqLfQpYtdJHUe69qosPEgoj4PMtmkA+k/5m6HViW1G8XEf3UJ49c8XAz+lvxsAmdzgNBeR3223ngoZQypZ2BjNKr95is1xd9oE4K1UTMD4AfVF9WDqB8pr03M4+rMY7GZsij1LOfQLkdERF/Bf6lZmL+DpZ9Tq/ssvHG8j7gEMqKaP1+tt0SEVtV2xERL6F0N6lrAfCuana9rwQ2M79M2fuwISV/+XCUcqXH1gjTKQv9KeU1sAt9lIUORWa28kQ5wrRz+hzlCf1/fcQJyq7G/65OL6GaYa8Z51BK+cOXgOOr8RxGSYb/s0ac9Si1OOdRPuQOB9atsf35lHY6F1fnN6bsQq17e35LOeBq0MfpulFOvxv282eA2/No4J2U8o7FlNUFH1dj+xdWz5Fbq5+d0/8A/1gjzuqUll2D3JbLKTNvAFcDz+q+btj3dZ+36e1dv+834roPTlaMEdtc1c97Stf2AWzedX5LYNs+4qxNOYD4m9XpzcDafcQ5AfgF5Qth5z34f/qIs5DSqeKSrssuG/ZzqBrHU4DP97Hdj4F3U5LoOZQ9R2fWjLEnJdl8EuVYlQXAXn081vtSEsQLqzE9ssb2G67s1Mf98gtg167zu1AW4ehl2z2q59hfqvfJzul44II+xvJrBvxsqz4HzgTuobSeOxfYYoB4GwKvouzZu6bPGE+llDz9ltJNpM62P6TUN3fOb1JdtuFU/ywY+gCmyonSBuYHQx7DppQk54XAIwaI81DK7qfrge8D1wBv6HHbAymLiPwR+ED1gt+vjzF8C3jYsB/XqXyqPqQ+APy2j22f3sD//wkwe4Dtj6a09vk2ZZnWTnnVYygLtQz9Pu7jNl082u+jnZ/IGCO2+QalAf4gt2ug5JDypenqhu7jgZL6rjjnVz8vqX6uQWlvNvTnUb/3+WgJw6CPXRXjyBp/+xXgYspeoSf1+f+uo0zkLDd5QZ+TGMClvVw2xrbbUeqib6h+dk77Ahv0MZZTm/pso0xy9TxJtZI4gySwH6lyhB9QSlfW7+P/XznifHQuo49JtMk8tbYcYhR3U+Mo9mjo6OJYsQ3SH6qfD4+Ih2e9OqO9KU/ix1DeyJ6amTdX9c5XUr4Nr1Q2UGhfWR+4OiIuZPkyhtoryAxaDzkVRLXCYPX72pl5b2ZeDhxdldPUtU9EXMFgfa7vAi6L0k1kaaP27PGI68z8QET8hPLl7UdZveNRjrx+Q41xTCUxxu+jnZ/IGN02Aq6MiAvo/7V0cUTsmJkX9vH/yVKW9evo6uowgMspjfX72f3b7eyIeCcwIyKeR1nx8PQBY/Ylmus88KOIeBnw9er8SygzaoN6C3Bsj3/7Csr7wZuAN3aVC/b82Zal5WJnD8SgzxeA30XEu1l2oPkrKEn1uDLz0oi4HHh+ll3/g/oP4JIqZl+vx+qg0PMox/2cQ40DZkfE+QhlkaJrKQdDvi+7Frvq0bUM3ou/qbLQSdfaA+Ni+VWLVgeeAHw9B2hk3+c4zlrJ1Zm9LV34GMqHyquAL2TVsihKe50/Z+a1EfHczPxJD7G+AHwiMxd2XTYvay7vG6MvGEDWPPp0rHrIXhO1qaL7QLaRB7WNdpBbD/EW5oB9rqsDIjo6r4Vo6INiWmricZqAx3rg11KUTjiPpewduptlCU3PB7VFxM+AJ1MO8Oz+0lS3r/RZlIMnB0nqOwchHwb8E+X2/JBSgjDpH2pRWtB1LKHcz6dmjwf8dU2qBGV2sFNruhpwV6+TKiuJ/4fM3HyQGH3+39p9eMeIswFl5dCdKffTOZTFMv5WI8Y5wHMzc9w2fOPEuYLS1WG5jkU1X49rUzpIPZOyYMbjKXsx9lnphivGeTXleTbQYlLVBNrSVQozs9aXyeq1+GLKbYGyh/DUYbwW62rzTPB/df2+hJIIv7SfQBGxM+UI5S9FxEaU3RvX9bJtNtMG6VjgHZl58IjLF1XX7dVLAlx5PjA3Ij7aNdtaa3nf6ujiz2SNI89XYi7wxOnwYhpH07ODffe5jogXAptl5ier8xdQar+TUqPcZttFxCLKYzKj+h2WHfA0WTGWqvvFsVvXzO3zB4jxGEqN37tHXPVM+pvNndfvWLrGtDqlC8/WlGM6hioz3zvg9o22bBvtX0xw/LEMtAciItahtHl7DCXpfGv2sUBL5Trg51H6IHd/iftYzTj35ODtPx+gHMD4ACWRvrk61ZKZn4mIvaN054H+Etj/oJRTnFRd9MaIeHpmvrPGOBL4v+o0rbQ2Cc7MsyPiyZS+rftRXiC1ewZWMwBzKd/kvkQ5UONEln0j6jXOv4x2eY+7/TfJzMtG2fayKMsz1nEzpYXSiRGxE2WXWK0kbYruOh22HOP30c73YpA+12+ndKroWItytP4synO4dsugVUVmrj4VYgBExLmZufMopVd1Sq6+BTwlM2+IiFMz88V9DOVYypfs5d5jIuI2ysIHX6gTbJCkvitGk+8xA4vSM/ltrFi2VXtltCgLJ4yM880etltZid6MuuNoyE7AgRFxA/3tgfgyJVk8h3KA2xMox7v049rqtBrL9wyu65wqcfwO/bf/XERJ6j8GfC7rdf9YqokEljKRsn1WrVUj4suUYzx6jhGlU9GHgYdRHuN+F52ZdK1Lgqs3qwOq0y2UOpoYYEZ2H8ouwosBMvOmKAsj1NXdw3EdSk3uxfTWBmn9lVxX980vMvMOYK+ImEc5unh2zRhQWr5dUc0y9r3rlGbqIaeCzaL0dIyu36nOP7JusMw8qqoH6/S5vodyQGUv1srMP3SdPzdLq7PbqhouTQ0HwsCzhN1fYPtdua/JL9kjk7W1KHs17u7jA7Op95gmfIPSnu3zLCvbqi0ivkip77+C5dtvjZsET8Jscj/63gNReWKnnKIq1au7aNNSndn6GKDXcKXTt/5pXZcl9VqkHUAp7XgtcFhE/IJSztbrHtuOgRPYyvqUhVmgv8/7j1D2OPdz/NBQtS4JprRyOgfYMzN/CxARbx4g3n2ZmRGRVay+kojMXO5Aoig9PU/pcfOLIuJVmbncbsEo67Yv6CVAV13x0iUzM3NeRDxAaUdT18hdp/2a11CcYft/Xb+P7OPYc1/HiHh7Zn6kOvvcrBq9Z+bdEXE0vb35bdB9JjNf33V2417Hogl3GuUAKwaYxV3ZHoherb+S62rPMHYna1Ut4QtZPqHoVVPvMU1YkpmfaiDO07KHhZGmi2oPRHe54MYsWyK7F0tLHzJzSa8lX6OJ5XsNExG3UL/XcCMljJn5beDbUfq+70GZ3X47/c3Yr89gCWznQL+zKF+an0VZQKmOv0zHBBhaeGBcRLyIsiv4GZSj6k+hHEzRV4P7iHgb5YCT51GeTK8EvpqZ43ZiGCfumpR2OeMuMRllCdTTgPtYlvTOpcyy7JM9rIIX5cjO0XZ5/gOlt+leNW/CyNWcHkJZXKH2ak5NxVkVNHTg1knA/FG+NL0a2CUzD2h63Kovll9NaunvNWM8wLLd0DMofUmhxu7KiDgZ+OkYX7Kfl5l9HUsxIlZft2/YYtkS2W+klJKdxgCL+lSznR/NzCsbG+QQdZcLZubjIuIRlGMYeioX7Hr+wvLP4dq726vZ1qMz86zq/C6Uz7Z/7DVGtd1syoImS+twKQua3DH2VivEOJXSuu1a4GeUibkLsubKiRFxAPAhyh7bpQlsZtZajCkiNmXZ3ugLeskZRmz/ccok2rdY/vk/7h6MYWtdEtxRzdi+kLJb4jmUsoPTMvNHfcR6Hl1HKGfmj/uI0d2tYjXgidTsVhERu1J6z0I5aOSnNbYdc1nN6OMI3+hazSkzt4qyrvinM7PWak5NxRm2iPgc8PEsbdFGXjeTclDmvZl50gobL/+3YyZGvSYSEfEwlr1ZderYdqA0yH9RZv6lt1ulibSyLzyTPI6Bv2SPiNe90uFqVaxnZ+bTe9y+kfaUTYgVl8julllzWfQonUC+A/yZ8vqs3cVjKomycuSTKf2xO+9bfS213cBYLs0R3XNGu6yHOKdSjlXpdNE5CNguM0dbOnjktjtS2qBuRilbeAWlq8L1wLy6X5qqmH0lsLFie9bl1KlxjogvjR4iX9lrjGFpbRLcLUr7lf2Alw6SXEXpDHFr9nGnxvJtkJYAN2TmH/sdSx///5ocY5nEiPhtZj6mZryFlIL987ve/PpJphuJM2wRsT2lVOEfKG+gf6XUfj+WssrfFynJ/UqXyG5iJrjr75/DsmVda31p0sRrYha34fH0/SV7RJzuD8xOO7HPZWbto+OniohYZ+Qs3miX9RDnt5R2hyPbb93QyEAnWURckJlP7bw3VV/4fzmkJPg0ypf+7l7DO2T9tmQLM3P78S4bY9uLgd0y87YoHR1OofRU3x54Qma+pMcxDJzAVuUP3V/glstbso+DOqejNtYEryBLr8HPVqeeRMTTKLshbgPeR3lhbQSsFhH/kpm11kfPriOmO8l0ne0bMHBd8Qj3ZuZ9nRquiFiD/moSm4ozVFn6Lu8f5aCMuZQFJv4OXJWZv64RqrH2W1USY+I7RWVDXSaaUu1GXllf817j/GsDw5lqfkFVvz3OZeP5a2Z+Z/w/mza+HhGfAdav9uq9kuG1tHslpdfwN1nWa7ifmcq/R8TOmXkuQJR+/H/vcdvVu2Z7Xwp8NjNPBU6tJnx69VFWksDS20F6/wb8ITP/BBClb/zSWeleBtE5RiUiPjHKGHpeeGmYTIL7dxxlZm82JZHYIzPPqwrdT6bUG4+r6WR6AEcCp0XEgYyyy7OPeGdHM6s5NRVnSshyRPL8AbafUomR1KuxPig7psMH5kgR8XBKd5cZUVpudpKS9YCH9BHykoj4KuU9blrVVo4mM/+ret9eRGkj+u/9lAsOIprtNQxwBPDlqjY4KJ/dI3v0j2X1iFgjM5dQOkAd3nVdnXxs4ASW0s1kt2r7Z1GOaerMSn+WslrheDoHw/V8cPdUYzlEn7p3f0TEVZn5hK7rej7IIyIuYlky/VlGJNOTfbBIg7s8VwMOpdRKQ6mV/vyAcYa6KpSk/sXyKxS+l3Jw0VI5DVcqrG7TIZQJg+5E4E7g+LrJ63SurZyqIuJrLN9r+PrMPLKBuJ1ypLuBl413PEe1zdHAP1Pasz6K0sM7o3Rn+nL2fsDgwGUV3fXQEfFJyl6IedX5Xss7tsvMS8e47ohspmPKhDIJ7lNTtZlNJdNTRax8NbK3Z2btFWWitNUhM//a5FglDcd0fG9bmYh4cbVbW11iCiyi0H0MSVVOd0GdYye64qwHvI4y8/9t4Mzq/FspSx731Ke92vu7KfCjzLy7uuxxwKxeD0ZrKIG9nNJjeEmUhZcOz8yfda7LzCetPAJExO+A/TJzwYjL30vpGzyUg3nrsByif03VZj7Y9fvIuqLp+A1lvNXIekqCoxQBvwd4PeUI8s6BQp/IzGOaHLCkSTcd39tWEBGvyMwTgS0j4i0jr8+aS/JWydCnKAuUPCnK6nF7Z+b7mxnxpJsKiyg01Wv4BOBvwC+BVwFHUz7v96mO+ehJZp43ymW/qTmWJsoqTqaUG95CyT3OAahmpXtt97Yf8I2IODAzf1l9bn+KUvqyS48xhsokuE8N1mY2dqDTFNHUamRvpvRy3jEzrwOIiEcDn4qIN2fmfzc35MlTzWr/G6UF3tLHty1H4kqrmM572mgLQPST6H+OsrDOZwAy81dVjfB0TYKnwiIK2434XJ3R9ZlbZ1b60V0zyp8H/gQ8qm4HkIYMnMBm5gci4icsm5XubtH6hrG3XC7GgihrL5wWEa+jfDkA2D3H6XQ0VVgOoUbFStqpRcS1mblVj3EuoTTiv2XE5RtTXrDTcldqRPyIslT32ygHaxxM2ZX1b0MdmDTBYvn+vg9hyC3fJlpEHJmZx9bc5sLM3DGW7wfe0+7tqSSW9YJ+NtN0EYWRBil7nKDxDFxW0cAYOovFPJHyGJ9J2Xv7INRfLGYYnAlW086P0VutvZp6676vOTIBhlIXHGU1venqoZn5hYh4U5a2eGdHxIXDHpQ00bJrueSWeAtwbM1tbomIrai+LETESygzjtNN9wqj97DsAGkot23aJcE0N6PciIbKKga1gGVfbO8EdqJ8zkd1ea3FYobBJFhNezPwrYh4OaOsRlYjzn19XjfVderT/hQRLwBuolrLXtIqpZ/i09dRugRtHRE3AtdRFnWYVrLqBR0Rz8jMn3dfF6Wv7rRje8oVZeacYY9hUJZDaELEgKuRxfJrxi93FbBOZk7L2eCI2JNSv7U58AlKP9H35qrVIF9qvYj4fWY+quY262Tm4ur4idUy886I2Gi0vWLTwWglA8MuI5C6mQRLktSHEXXOy10FzMjMWntbI+Iy4FWdXd0R8WLgPzLzcQMPdhJFxNOBf6QswtR9EPN6lG4K2w1jXNJIlkNIk2gVbIEktdYE1Dm/HPhiRMwHHgE8lN6WwJ1q1qJ0zFgD6L6PFtHbSmTSpHAmWJpEEXE2VQukrqO/e2pMLmnVV7WcOoFyoNGzMvO3wx1R/yJii8y8ISIekpn3jL+FppOu7hCjsjuEpJEekpkXjGjYvmRYg5E0dUTEF4CtgG2BxwHfjYhPdFbgnIYeERHfp8wKPyoitgNenZmvHfK41IxOd4igLAP9t+r39YHfA1P+wLnVhj0AqWVWlRZIkpp3GbBrZl6XmT+ktJyazgeRHQs8H7gVIDMvBZ41zAGpOZk5JzMfTekPvFdmbpSZDwX2BH403NH1xnIIaRJVq959lnLQyN8oLZAOzMwbhjowSWpYRJyfmTuNWPzjUg+MW7VExGWd1fRWdtlUZDmENEkiYnXgtZm5W3cLpGGPS9LUEBHXMUq3iWq2bTr6Q0T8I5DVIkdvAoa9jLKad1NEvAs4sTp/IKUH/pRnEixNksx8ICJ2rn4frQeypHab2/X7OsB+TO/FdF4DfBx4JHAjZRf564Y6Ik2EA4D3AKdRvsT9rLpsyrMcQppEEfEpygfCN+haDCQzp+MyopImWEQsyMwdhj0OaTwRMXO6TfA4EyxNrnUoB4l09/5MwCRYarmI6D4IbjXKzPC0+5yOiH9fydWZme+btMFowlUlL59nGnYBcSZYGrKI2DEzLxz2OCQNV0Sc1XV2CeXA2f/KzN8MaUh9iYi3jnLxTOBQ4KGZOWuSh6QJFBHnUxZB+c50638/7b5hSquCiHgipWbqAOB2lq8FlNRCmbnryMsi4khgWiXBmfnRzu8RsS7lgLh/BU4BPjrWdpq+MvMPI/rfPzCssdRhEixNkojYkmWJ7/3AFsDczLx+iMOSNLW9hdJvd1qpVhN7C6VTwJeBp2Tm34Y7Kk2QadsFxMUypEkQEb8EzqB88XxxdaDLnSbAksYR4//J1BIR/wlcSFn6+R8yc54J8CrtNZSuH50uINsDU74eGEyCpcnyF2BdYBNg4+oyC/IljWc6vk+8FXgE8C5KD9lF1enOiFg05LGpeY/PzAMzc5PMfFhmvgJ4wrAH1QsPjJMmSUTMBvallEM8lrK++vMz84JhjkvScEXEnYye7AYwIzMtXdSUFREXZ+ZTxrtsKjIJloYgIh4G7E9JiB+VmZsPeUiSJPUsIp4O/CNwJPDfXVetB+wzHZbH9tulNASZeTNwHHBcRGwx7PFIklTTWpTewGtQyv06FlFapk15zgRLkiSpLxGxRWbeMOxx9MMkWJIkSbVExLGZeWREnM4oNe2ZufcQhlWL5RCSJEmq64Tq538NdRQDcCZYmkQRsRnwCWBnyjfnc4A3ZeYfhzowSZJaxj7B0uT6EvAdYFNKH83Tq8skSZp2IuIZEfHjiPhNRPwuIq6LiN8Ne1y9cCZYmkQRsTAztx/vMkmSpoOIuBp4M7AAeKBzeWbeOrRB9ciaYGly3RoRrwBOrs4fAEz5NwpJksZwR2Z+f9iD6IczwdIkqnoCfwJ4OqUm+BfAGzPz90MdmCRJfYiIDwGrA98E7u1cnpkXD21QPTIJliZJRKwOfCUzDxz2WCRJakJEnDXKxZmZz5n0wdRkOYQ0STLzgYjYIiLWysz7hj0eSZIGlZm7DnsM/TIJlibX74CfR8R3gLs7F2bmx4Y3JEmS6omIt4y4KIFbgHMz87ohDKk2W6RJk+ta4LuU1966XSdJkqaTdUec1gPmAt+PiJcNc2C9siZYkiRJjYiIDYEzM/Mpwx7LeCyHkCZRdQDBaGusT/kDCCRJGk9m3hYRMexx9MIkWJpcb+v6fR3gxcCSIY1FkqRGRcSuwN+GPY5eWA4hDVlEXJCZTx32OCRJ6lVEXMaKezY3BG4C/iUzr578UdXjTLA0iapaqY7VgB2A2UMajiRJ/dpzxPkEbs3Mu0f746nIJFiaXAsobxRBKYO4Djh0qCOSJKmmzLxh2GMYlOUQkiRJah37BEuTICLe3vX7fiOu++Dkj0iSpHYzCZYmR3fj8HeMuG73yRyIJEkyCZYmS4zx+2jnJUnSBDMJliZHjvH7aOclSdIE88A4aRJExAPA3ZRZ3xnAPZ2rgHUyc81hjU2SpDYyCZYkSVLrWA4hSZKk1jEJliRJUuuYBEuSJKl1TIIlSZLUOibBkiRJah2TYEmSJLXO/wcbIh3Jkbrm/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib inline\n",
    "#args = requestLib.args_to_dict('table= gov_10dd_edpt1', 'na_item=B9', 'precision=1', 'unit=PC_GDP', 'time=2018,2019','sector=S13')\n",
    "args = requestLib.args_to_dict_fun(table='gov_10dd_edpt1', na_item='B9', unit = 'PC_GDP', time='2018,2019', sector='S13')\n",
    "r = response_fun(client,  clean_dict=clean_country, remove_list=noCountry,args=args, void_item=True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.title(\"Public balance, 2018 and 2019\")\n",
    "pos = np.arange(len(r.x_labels))\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(r.x_labels, rotation = 90)\n",
    "width = 0.35\n",
    "\n",
    "ax.bar(pos -  width/2., list(r.values['2018'].values()),  width, label='2018')\n",
    "ax.bar(pos +  width/2., list(r.values['2019'].values()),  width, label='2019')\n",
    "fig.set_size_inches(12, 6)\n",
    "plt.legend(loc='upper right', prop={'size':12})\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Public Balance and General Governement Debt, 2016-2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>0.7</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro area - 17 countries (2011-2013)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro area - 18 countries (2014)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Euro Area (EA-19)</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Union - 25 countries (2004-2006)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Union - 27 countries (2007-2013)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU-27</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU-28</th>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>-0.9</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>-2.3</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>-2.1</td>\n",
       "      <td>-2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>-2.2</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>3.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latvia</th>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>-2.9</td>\n",
       "      <td>-4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovakia</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>-2.2</td>\n",
       "      <td>-2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           2018  2019\n",
       "Austria                                     0.2   0.7\n",
       "Belgium                                    -0.8  -1.9\n",
       "Bulgaria                                    2.0   1.9\n",
       "Cyprus                                     -3.5   1.5\n",
       "Czechia                                     0.9   0.3\n",
       "Germany                                     1.8   1.5\n",
       "Denmark                                     0.7   3.8\n",
       "Euro area - 17 countries (2011-2013)        0.0   0.0\n",
       "Euro area - 18 countries (2014)             0.0   0.0\n",
       "Euro Area (EA-19)                          -0.5  -0.6\n",
       "Estonia                                    -0.5   0.1\n",
       "Greece                                      1.0   1.5\n",
       "Spain                                      -2.5  -2.9\n",
       "European Union - 25 countries (2004-2006)   0.0   0.0\n",
       "European Union - 27 countries (2007-2013)   0.0   0.0\n",
       "EU-27                                      -0.4  -0.5\n",
       "EU-28                                      -0.7  -0.8\n",
       "Finland                                    -0.9  -1.0\n",
       "France                                     -2.3  -3.0\n",
       "Croatia                                     0.2   0.4\n",
       "Hungary                                    -2.1  -2.1\n",
       "Ireland                                     0.1   0.5\n",
       "Italy                                      -2.2  -1.6\n",
       "Lithuania                                   0.6   0.3\n",
       "Luxembourg                                  3.1   2.4\n",
       "Latvia                                     -0.8  -0.6\n",
       "Malta                                       2.0   0.5\n",
       "Netherlands                                 1.4   1.7\n",
       "Poland                                     -0.2  -0.7\n",
       "Portugal                                   -0.3   0.1\n",
       "Romania                                    -2.9  -4.4\n",
       "Sweden                                      0.8   0.5\n",
       "Slovenia                                    0.7   0.5\n",
       "Slovakia                                   -1.0  -1.4\n",
       "United Kingdom                             -2.2  -2.3"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = response_fun(client,  clean_dict=clean_country, remove_list=noCountry,args=args, void_item=True)\n",
    "debt = r.DF\n",
    "debt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General government debt, 2019 and 2018\n",
    "\n",
    "* Arguments for the _GET_ request:\n",
    "  * `sector = 'S13'` - general government\n",
    "  * `na_item = 'GD'` - conslidated gross\n",
    "  * `time = '2018,2019` - 2018 & 2019\n",
    "  * `unit = 'PC_GDP'` - percentage of GDP\n",
    "\n",
    "* Arguments to `response_fun` and plot filters:\n",
    "  * `clean_dict = clean_country`\n",
    "  * `remove_list = noCountry`\n",
    "  * `void_item = True` - void labels for non shown countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args = requestLib.args_to_dict_fun(table='gov_10dd_edpt1', na_item='GD', unit = 'PC_GDP', time='2018,2019', sector='S13')\n",
    "\n",
    "r = response_fun(client,  clean_dict=clean_country, remove_list=noCountry, void_item=True, args=args)\n",
    "\n",
    "pos = np.arange(len(r.x_labels))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.title(\"General government debt, 2018 and 2019\")\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(r.x_labels, rotation = 90)\n",
    "width = 0.35\n",
    "\n",
    "ax.bar(pos -  width/2., list(r.values['2018'].values()),  width, label='2018')\n",
    "ax.bar(pos +  width/2., list(r.values['2019'].values()),  width, label='2019')\n",
    "fig.set_size_inches(12, 6)\n",
    "plt.legend(loc='upper right', prop={'size':12})\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Government revenu and expenditure 2009 - 2019 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Development of total expenditure and total revenue 2009-2019 (% of GDP)\n",
    "\n",
    "* Arguments for the _GET_ request\n",
    "  1. First request\n",
    "    * `sector = 'S13'` - general government\n",
    "    * `na_item = 'TE'` - total expenditure\n",
    "    * `time = 2009,2010,2011,2012,2013,2014,2015,2016,2017,1018,2019` - from 2009 to 2019\n",
    "    * `unit = 'PC_GDP'` - percentage of GDP\n",
    "\n",
    "  2. Second request\n",
    "    * `sector = 'S13'` - general government\n",
    "    * `na_item = 'TR'` - total ereveue\n",
    "    * `time = 2009,2010,2011,2012,2013,2014,2015,2016,2017,1018,2019 - from 2009 to 2019\n",
    "    * `unit = 'PC_GDP'` - percentage of GDP\n",
    "\n",
    "* Arguments to `response_fun` and filter countries\n",
    "  * `clean_dict = clean_country` (defined above)\n",
    "  * Only the countries `'EU-27'` and Euro Area (`'EA-19'`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = {'total expenditure': {'EU-27':[],'Euro Area (EA-19)' :[] }, \n",
    "     'total revenue': {'EU-27':[],'Euro Area (EA-19)' :[] }\n",
    "     }\n",
    "args = requestLib.args_to_dict_fun(table = 'gov_10a_main',\n",
    "                                   na_item = 'TE',\n",
    "                                   unit = 'PC_GDP',\n",
    "                                   time = '2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019',\n",
    "                                sector = 'S13')\n",
    "expenditures = response_fun(client,  clean_dict=clean_country,args=args)\n",
    "for country in y['total expenditure']:\n",
    "    for year in expenditures.values:\n",
    "        y['total expenditure'][country].append(expenditures.values[year][country])\n",
    "\n",
    "args = client.update_args('na_item=TR')\n",
    "revenues = response_fun(client,  clean_dict=clean_country, args = args)\n",
    "\n",
    "for country in y['total revenue']:\n",
    "    for year in revenues.values:\n",
    "        y['total revenue'][country].append(revenues.values[year][country])\n",
    "\n",
    "##### PLOT\n",
    "plt.figure(figsize=(8,4))\n",
    "plot_colors = ['C0', 'C1']\n",
    "plt.title(\"Development of total expenditure and total revenue 2009-2019 (% of GDP)\")\n",
    "x = list(revenues.values.keys())\n",
    "for measure in y:\n",
    "    i = 0\n",
    "    for country in y[measure]:\n",
    "        if measure == 'total revenue':\n",
    "            line_style = '--'\n",
    "        else:\n",
    "            line_style = '-'   \n",
    "        plt.plot(x,y[measure][country],ls=line_style, marker='x', label='%s: %s' % (country, measure), color=plot_colors[i])\n",
    "        i += 1\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(0.8, -0.1), prop={'size':12})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Development of total expenditure and total revenue 2009-2019 (Billion EUR)\n",
    "\n",
    "* Arguments for the _GET_ request:\n",
    "  1. First request\n",
    "    * `sector = 'S13'` - general government\n",
    "    * `na_item = 'TE'` - total expenditure\n",
    "    * `time = 2009,2010,2011,2012,2013,2014,2015,2016,2017,1018,2019` - from 2009 to 2019\n",
    "    * `unit = 'MIO_EUR '`- billion EUR\n",
    "  2. Second request\n",
    "    * `sector = S13` - general government\n",
    "    * `na_item = TR` - total ereveue\n",
    "    * `time = 2009,2010,2011,2012,2013,2014,2015,2016,2017,1018,2019 - from 2009 to 2019\n",
    "    * `unit = 'MIO_EUR'` - billion EUR\n",
    "\n",
    "* Arguments to `response_fun` and filter countries\n",
    "  * `clean_dict = clean_country` (defined above)\n",
    "  * Only the countries `'EU-27'` and Euro Area (`'EA-19'`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = {'total expenditure': {'EU-27':[],'Euro Area (EA-19)' :[] }, \n",
    "     'total revenue': {'EU-27':[],'Euro Area (EA-19)' :[] }\n",
    "     }\n",
    "\n",
    "args = requestLib.args_to_dict('table=gov_10a_main', \n",
    "                    'na_item=TE', \n",
    "                    'precision=1', \n",
    "                    'unit=MIO_EUR', \n",
    "                    'time= 2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019',\n",
    "                    'sector=S13')\n",
    "\n",
    "expenditures = response_fun(client,  clean_dict=clean_country, args = args)\n",
    "\n",
    "for country in y['total expenditure']:\n",
    "    for year in expenditures.values:\n",
    "        y['total expenditure'][country].append(expenditures.values[year][country])\n",
    "        \n",
    "args = client.update_args('na_item=TR')\n",
    "revenues = response_fun(client,  clean_dict=clean_country, args = args)\n",
    "\n",
    "for country in y['total revenue']:\n",
    "    for year in revenues.values:\n",
    "        y['total revenue'][country].append(revenues.values[year][country])\n",
    "\n",
    "##### PLOT\n",
    "plt.figure(figsize=(9,5))\n",
    "plot_colors = ['C0', 'C1']\n",
    "plt.title(\"Development of total expenditure and total revenue 2009-2019 (% of GDP)\")\n",
    "x = list(revenues.values.keys())\n",
    "for measure in y:\n",
    "    i = 0\n",
    "    for country in y[measure]:\n",
    "        if measure == 'total revenue':\n",
    "            line_style = '--'\n",
    "        else:\n",
    "            line_style = '-'   \n",
    "        plt.plot(x,y[measure][country],ls=line_style, marker='x', label='%s: %s' % (country, measure), color=plot_colors[i])\n",
    "        i += 1\n",
    "plt.grid()\n",
    "\n",
    "plt.legend(bbox_to_anchor=(0.8, -0.1), prop={'size':12})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Governement revenue and expenditure, 2019\n",
    "\n",
    "* Arguments for the GET request:\n",
    "  * `sector = 'S13'` - general goverment\n",
    "  * `na_item = 'TE','TR'` - total expenditures & total revenues\n",
    "  * `time = 2019`\n",
    "\n",
    "* Arguments to **response_fun** and plot filters\n",
    "  * `remove_list = noCountry` (defined above)\n",
    "  * `clean_dict = clean_countries` (defined above)\n",
    "  * `void_item = True` - void labels for not shown countries\n",
    "  * `multiciplity = na_item`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "args = requestLib.args_to_dict('table=gov_10a_main', \n",
    "                    'na_item=TE,TR', \n",
    "                    'precision=1', \n",
    "                    'unit=PC_GDP', \n",
    "                    'time= 2019',\n",
    "                    'sector=S13')\n",
    "\n",
    "r = response_fun(client,  clean_dict=clean_country, remove_list=noCountry,void_item=False,multiplicity='na_item' ,args = args)\n",
    "\n",
    "pos = np.arange(len(r.x_labels))\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.title(\"Government revenue and expenditure\")\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(r.x_labels, rotation = 90)\n",
    "width = 0.35\n",
    "a = pos - width/2.\n",
    "\n",
    "ax.bar(pos - width/2., list(r.values['Total general government expenditure'].values()),  width, label='Total general government expenditure')\n",
    "ax.bar(pos +  width/2., list(r.values['Total general government revenue'].values()),  width, label='Total general government revenue')\n",
    "plt.grid()\n",
    "fig.set_size_inches(12, 6)\n",
    "plt.legend(bbox_to_anchor=(0.8, -0.35), prop={'size': 16})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main components of government revenu, 2019<a id=\"main-revenu\"></a>\n",
    "\n",
    "Values use and calculations:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align*}\n",
    "\\text{Total expenditure (TR)} {}={} & \\ \\ \\ \\ \\ \\ \\text{total taxes} & \\text{D2PAY} + \\text{D5PAY} + \\text{D91PAY}\\\\\n",
    "                                  & + \\ \\text{total social contributions} & \\text{D61PAY}\\\\\n",
    "                                  & + \\ \\text{total sales of goods and services} & \\text{P11_P12_P131}\\\\\n",
    "                                  & + \\ \\text{other current revenu} & \\text{D39PAY} + \\text{D4PAY} + \\text{D7PAY}\\\\\n",
    "                                  & + \\ \\text{other capital revenue} & \\text{D92PAY} + \\text{D99PAY}\\\\\n",
    "\\end{align*}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not all countries in the databse present all the components searched. Therefore only all the countries presenting these componets are plotted. \n",
    "\n",
    "In a first step all the countries are extracted in all the requests, then after a filter, a second sequence of extraction is done with oly the relevent onces.\n",
    "\n",
    "The filter is implemented by checking the request with the smaller number of countries returned, from which we are able to find then countries that have to be neglected for the plot.\n",
    "\n",
    "The implementation of the secrion is done using the `Component` class and the `return_response` function defined below\n",
    "\n",
    "**`Component` class**\n",
    "\n",
    "Each component of the total rvenu/expenditure is an instance of the `Component` class\n",
    "\n",
    "* Arguments to the construction function:\n",
    "  * `name`: name of the component\n",
    "  * `na_item`: the correponding na_item/s\n",
    "  * `label`: the label to show in the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Component:\n",
    "    def __init__(self,name, na_item, label):\n",
    "        self.name = name\n",
    "        self.na_item = na_item\n",
    "        self.label = label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`return_response` function**\n",
    "\n",
    "Take as inputs the list of instances, the default `remove_list` defined in the section above and the `client` object instanced above. It returns the lenghts of the returns countries and the `Compoment` instaces updated by the response of the _GET_ request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_response(istances, remove_list, client):\n",
    "    lens = []\n",
    "    for instance in istances:\n",
    "        client.update_args('na_item=%s' % (instance.na_item))\n",
    "        instance.response = response_fun(client, clean_dict=clean_country, remove_list=remove_list, void_item=False, multiplicity='na_item')\n",
    "        lens.append(len(instance.response.lines))\n",
    "    return lens, instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxes = Component('taxes','D2REC,D5REC,D91REC', 'Taxes')\n",
    "social_contribution = Component('social contributions','D61REC', 'Net social contributions')\n",
    "sales = Component('sales', 'P11_P12_P131', 'Market output,output for own final user and payments for non-market ptoduction')\n",
    "proprety_income = Component('proprety income','D92REC,D99REC', 'Proprety income')\n",
    "other = Component('other','D39REC,D4REC,D7REC', 'other')\n",
    "\n",
    "instances = [taxes, social_contribution,sales, proprety_income, other]\n",
    "args = requestLib.args_to_dict_fun(table='gov_10a_main', unit = 'PC_GDP', time='2019', sector='S13', na_item='')\n",
    "\n",
    "lens, instances = return_response(instances, noCountry, client)\n",
    "\n",
    "min_len_countries = [item.response.lines for item in instances if len(item.response.lines) == min(lens)][0]\n",
    "diffCountry = [item for item in other.response.lines if item not in min_len_countries]\n",
    "avoidCountry = noCountry+diffCountry\n",
    "   \n",
    "lens, instances = return_response(instances, avoidCountry, client)\n",
    "\n",
    "client.update_args('na_item=TR')\n",
    "r = response_fun(client, clean_dict=clean_country, remove_list=avoidCountry, void_item=False, multiplicity='na_item')\n",
    "\n",
    "revenu = r.values['Total general government revenue']\n",
    "\n",
    "output = prepare_for_plot(instances, revenu)\n",
    "plot_x_labels = taxes.response.x_labels\n",
    "fig, ax = plt.subplots()\n",
    "pos = np.arange(len(plot_x_labels))\n",
    "plt.title(\"Main components of government revenu, 2019\")\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(plot_x_labels, rotation = 90)\n",
    "width = 0.35\n",
    "\n",
    "b = np.zeros(len(list(output['taxes'].values())))\n",
    "for i in range(len(instances)):\n",
    "    a = np.array(list(output[instances[i].name].values()))\n",
    "    if i == 0:\n",
    "        ax.bar(pos,a,width, label=instances[i].label)\n",
    "    else:\n",
    "        ax.bar(pos,a,width, bottom=b, label=instances[i].label)\n",
    "    b += a\n",
    "fig.set_size_inches(9, 4)\n",
    "plt.legend(bbox_to_anchor=(1.05, -0.35), prop={'size': 12})\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main components of government expenditures, 2019<a id=\"main-expenditures\"></a>\n",
    "\n",
    "Values used and calculations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align*}\n",
    "\\text{Total expenditure (TE)} {}={} & \\ \\ \\ \\ \\ \\ \\text{intermediate consumption} & \\text{P2}\\\\\n",
    "                                  & + \\ \\text{compensation of employees} & \\text{D1PAY}\\\\\n",
    "                                  & + \\ \\text{interest} & \\text{D41PAY}\\\\\n",
    "                                  & + \\ \\text{social benefits other than social transfers in kind} & \\text{D62PAY}\\\\\n",
    "                                  & + \\ \\text{social transfers in kind via market producers} & \\text{D632PAY}\\\\\n",
    "                                  & + \\ \\text{subsides} & \\text{D3PAY}\\\\\n",
    "                                  & + \\ \\text{other current expenditure} & \\text{D29PAY} + \\text{(D4PAY}-\\text{D41PAY)}+\\text{D5PAY} + \\text{D7PAY} + \\text{D8PAY}\\\\\n",
    "                                  & + \\ \\text{capital expenditure} & \\text{P5} + \\text{NP} + \\text{D92PAY} + \\text{D99PAY}\n",
    "\\end{align*}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "args = requestLib.args_to_dict_fun(table='gov_10a_main', unit = 'PC_GDP', time='2019', sector='S13', na_item='')\n",
    "\n",
    "social_tranfers = Component('social tranfers','D632PAY', 'Social tranfers')\n",
    "compensation_emp = Component('compensation of employees','D1PAY', 'compensation of employees, payable')\n",
    "intermediate_consuption = Component('intermediate consumption','P2', 'Intermediate consumption')\n",
    "capital_expenditure = Component('capital expenditure', 'P5,NP,P92PAY,P99PAY', 'Cital expenditures')\n",
    "social_benefits = Component('social benefits','D62PAY', 'Social benefits other than social tranfers in kind')\n",
    "other = Component('Other current expenditure','D29PAY,D4PAY,D41PAY,D5PAY,D7PAY,D8PAY', 'Other current expenditure')\n",
    "subsices = Component('subsides','D3PAY', 'Subsidies, payable')\n",
    "interest = Component('Other', 'D41PAY', 'Other')\n",
    "\n",
    "instances = [\n",
    "    social_benefits,\n",
    "    compensation_emp,\n",
    "    intermediate_consuption,\n",
    "    social_tranfers, \n",
    "    capital_expenditure,\n",
    "    other,\n",
    "    subsices,\n",
    "    interest\n",
    "]\n",
    "\n",
    "lens, instances = return_response(instances, noCountry, client)\n",
    "\n",
    "min_len_countries = [item.response.lines for item in instances if len(item.response.lines) == min(lens)][0]\n",
    "diffCountry = [item for item in other.response.lines if item not in min_len_countries]\n",
    "avoidCountry = noCountry+diffCountry\n",
    "   \n",
    "lens, instances = return_response(instances, avoidCountry, client)\n",
    "\n",
    "client.update_args('na_item=TE')\n",
    "r = response_fun(client, clean_dict=clean_country, remove_list=avoidCountry, void_item=False, multiplicity='na_item')\n",
    "\n",
    "expenditure = r.values['Total general government expenditure']\n",
    "\n",
    "output = prepare_for_plot(instances, expenditure, subtraction=True ,where=('Property income, payable', 2))\n",
    "\n",
    "plot_x_labels = instances[0].response.x_labels\n",
    "fig, ax = plt.subplots()\n",
    "pos = np.arange(len(plot_x_labels))\n",
    "plt.title(\"Main components of government expenditures, 2019\")\n",
    "ax.set_xticks(pos)\n",
    "ax.set_xticklabels(plot_x_labels, rotation = 90)\n",
    "width = 0.35\n",
    "\n",
    "b = np.zeros(len(list(output['subsides'].values())))\n",
    "for i in range(len(instances)):\n",
    "    a = np.array(list(output[instances[i].name].values()))\n",
    "    if i == 0:\n",
    "        ax.bar(pos,a,width, label=instances[i].label)\n",
    "    else:\n",
    "        ax.bar(pos,a,width, bottom=b, label=instances[i].label)\n",
    "    b += a\n",
    "    \n",
    "fig.set_size_inches(12, 4)\n",
    "plt.legend(bbox_to_anchor=(0.8, -0.40), prop={'size': 12})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restore import settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lanch as last commend to restore the previous python import settings\n",
    "for location in costum_lib_locations:\n",
    "    try:\n",
    "        sys.path.remove(location)\n",
    "    except ValueError:\n",
    "        print(\"path already removed\")"
   ]
  }
 ],
 "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",
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