pyscbwrapper_en.ipynb

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 "metadata": {
  "colab": {
   "name": "pyscbwrapper_en.ipynb",
   "version": "0.3.2",
   "provenance": [],
   "collapsed_sections": [],
   "include_colab_link": true
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/kirajcg/pyscbwrapper/blob/master/pyscbwrapper_en.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "metadata": {
    "id": "va1gxHmdk_BF",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Introduction"
   ]
  },
  {
   "metadata": {
    "id": "CnlRg-XwlDtd",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "We want to construct a graph over the population density in each county ('län') in Sweden over time. To help with this we have Statistics Sweden's data and Python, and our goal is to not have to leave the Python environment to not even fetch the data. Luckily there is a custom made package for this purpose, called pyscbwrapper."
   ]
  },
  {
   "metadata": {
    "id": "8OsouNVGRyAY",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Installation"
   ]
  },
  {
   "metadata": {
    "id": "wuvWj0t8PBgU",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "pyscbwrapper is a kind of interface to the Statistical Database, written in Python 3. To run this interface we first need to install and import it. (If you do not have access to Python you can run this via Colab by clicking the button above.)"
   ]
  },
  {
   "metadata": {
    "id": "ZYGzbK0yPFQ6",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "!pip install -q pyscbwrapper\n",
    "from pyscbwrapper import SCB"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "KJ95Mz5ZR54O",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Initialisation"
   ]
  },
  {
   "metadata": {
    "id": "lVc42ouPPfpd",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "SCB is now a class we have imported, containing functions for navigating in the Statistical Database and fetch metadata as well as data from it. To use the SCB class we first need to initialise an object from it. Then we need one mandatory argument: language for the data and metadata. English and Swedish are supported. We choose English: "
   ]
  },
  {
   "metadata": {
    "id": "o6TVUjq-Pnzu",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "scb = SCB('en')"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "n5ZmYMkpR8wd",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Navigation and metadata"
   ]
  },
  {
   "metadata": {
    "id": "haLTV0PrQBEE",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now we can look at the top node in the tree that is the Statistical Database's metadata: "
   ]
  },
  {
   "metadata": {
    "id": "HyrSZ-Z_RFEN",
    "colab_type": "code",
    "outputId": "ce32e1dd-929f-4d89-9593-450dc894e100",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    }
   },
   "cell_type": "code",
   "source": [
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'AM', 'text': 'Labour market', 'type': 'l'},\n",
       " {'id': 'BE', 'text': 'Population', 'type': 'l'},\n",
       " {'id': 'BO', 'text': 'Housing, construction and building', 'type': 'l'},\n",
       " {'id': 'EN', 'text': 'Energy', 'type': 'l'},\n",
       " {'id': 'FM', 'text': 'Financial markets', 'type': 'l'},\n",
       " {'id': 'HA', 'text': 'Trade in goods and services', 'type': 'l'},\n",
       " {'id': 'HE', 'text': 'Household finances', 'type': 'l'},\n",
       " {'id': 'JO', 'text': 'Agriculture, forestry and fishery', 'type': 'l'},\n",
       " {'id': 'LE', 'text': 'Living conditions', 'type': 'l'},\n",
       " {'id': 'ME', 'text': 'Democracy', 'type': 'l'},\n",
       " {'id': 'MI', 'text': 'Environment', 'type': 'l'},\n",
       " {'id': 'NR', 'text': 'National accounts', 'type': 'l'},\n",
       " {'id': 'NV', 'text': 'Business activities', 'type': 'l'},\n",
       " {'id': 'OE', 'text': 'Public finances', 'type': 'l'},\n",
       " {'id': 'PR', 'text': 'Prices and Consumption', 'type': 'l'},\n",
       " {'id': 'TK', 'text': 'Transport and communications', 'type': 'l'},\n",
       " {'id': 'UF', 'text': 'Education and research', 'type': 'l'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 3
    }
   ]
  },
  {
   "metadata": {
    "id": "xQmiT-H1RJw9",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "We can now go down in the tree by using the id tag in the metadata we fetched. Let's say we are interested in the population statistics. "
   ]
  },
  {
   "metadata": {
    "id": "h8KkMIGjQCFj",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "scb.go_down('BE')"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "CFtjJLmwQI7D",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "To fetch the metadata about the population statistics, we once again run the function info():"
   ]
  },
  {
   "metadata": {
    "id": "kNgjhvyAQGU8",
    "colab_type": "code",
    "outputId": "0e9834ba-b93f-451b-8c8a-8393896c6980",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    }
   },
   "cell_type": "code",
   "source": [
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'BE0001', 'text': 'Name statistics', 'type': 'l'},\n",
       " {'id': 'BE0401', 'text': 'Population projections', 'type': 'l'},\n",
       " {'id': 'BE0101', 'text': 'Population statistics', 'type': 'l'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 5
    }
   ]
  },
  {
   "metadata": {
    "id": "O6ywgfC2S5Ju",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "We can keep going down in the tree: "
   ]
  },
  {
   "metadata": {
    "id": "Pwb2Zl9lS7l_",
    "colab_type": "code",
    "outputId": "1edec823-e141-4c65-b98a-7ac7652adbc7",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    }
   },
   "cell_type": "code",
   "source": [
    "scb.go_down('BE0001')\n",
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'BE0001D', 'text': 'Newborn', 'type': 'l'},\n",
       " {'id': 'BE0001G', 'text': 'All registered persons in Sweden', 'type': 'l'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 6
    }
   ]
  },
  {
   "metadata": {
    "id": "XlhEs7ZzTAce",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Whoops! We did not want the name statistics, but the population statistics. We go up one step and back down to the correct node: "
   ]
  },
  {
   "metadata": {
    "id": "3ESMKrs0TX1u",
    "colab_type": "code",
    "outputId": "dde53426-e538-44f4-b6e9-7b4ab9debd50",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    }
   },
   "cell_type": "code",
   "source": [
    "scb.go_up()\n",
    "scb.go_down('BE0101')\n",
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'BE0101A', 'text': 'Number of inhabitants', 'type': 'l'},\n",
       " {'id': 'BE0101B', 'text': 'Average age', 'type': 'l'},\n",
       " {'id': 'BE0101C', 'text': 'Population density', 'type': 'l'},\n",
       " {'id': 'BE0101D', 'text': 'Mean population', 'type': 'l'},\n",
       " {'id': 'BE0101E', 'text': 'Foreign-born persons', 'type': 'l'},\n",
       " {'id': 'BE0101F', 'text': 'Foreign citizens', 'type': 'l'},\n",
       " {'id': 'BE0101G', 'text': 'Summary of vital events', 'type': 'l'},\n",
       " {'id': 'BE0101H', 'text': 'Live births', 'type': 'l'},\n",
       " {'id': 'BE0101I', 'text': 'Deaths', 'type': 'l'},\n",
       " {'id': 'BE0101J', 'text': 'Migration - internal and external', 'type': 'l'},\n",
       " {'id': 'BE0101S', 'text': 'Household', 'type': 'l'},\n",
       " {'id': 'BE0101L', 'text': 'Married, divorced and widowed', 'type': 'l'},\n",
       " {'id': 'BE0101N', 'text': 'Changes of citizenship', 'type': 'l'},\n",
       " {'id': 'BE0101O', 'text': 'Registered partnership', 'type': 'l'},\n",
       " {'id': 'BE0101P', 'text': 'Asylum-seekers', 'type': 'l'},\n",
       " {'id': 'BE0101Q', 'text': 'Foreign/Swedish background', 'type': 'l'},\n",
       " {'id': 'BE0101K', 'text': 'Adoptees', 'type': 'l'},\n",
       " {'id': 'BE0101M', 'text': 'Circular migration', 'type': 'l'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 7
    }
   ]
  },
  {
   "metadata": {
    "id": "u1uplOemT6dB",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Direct route to specific nodes"
   ]
  },
  {
   "metadata": {
    "id": "QzXXWBFITanH",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    " If we know where in the tree we want to go, we can initialise an object using the id tags as extra variables:"
   ]
  },
  {
   "metadata": {
    "id": "9jguJXCcTrWQ",
    "colab_type": "code",
    "outputId": "e673c517-7d4b-4a68-8d49-1265eaf8aaf0",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    }
   },
   "cell_type": "code",
   "source": [
    "scb = SCB('en', 'BE', 'BE0101')\n",
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'BE0101A', 'text': 'Number of inhabitants', 'type': 'l'},\n",
       " {'id': 'BE0101B', 'text': 'Average age', 'type': 'l'},\n",
       " {'id': 'BE0101C', 'text': 'Population density', 'type': 'l'},\n",
       " {'id': 'BE0101D', 'text': 'Mean population', 'type': 'l'},\n",
       " {'id': 'BE0101E', 'text': 'Foreign-born persons', 'type': 'l'},\n",
       " {'id': 'BE0101F', 'text': 'Foreign citizens', 'type': 'l'},\n",
       " {'id': 'BE0101G', 'text': 'Summary of vital events', 'type': 'l'},\n",
       " {'id': 'BE0101H', 'text': 'Live births', 'type': 'l'},\n",
       " {'id': 'BE0101I', 'text': 'Deaths', 'type': 'l'},\n",
       " {'id': 'BE0101J', 'text': 'Migration - internal and external', 'type': 'l'},\n",
       " {'id': 'BE0101S', 'text': 'Household', 'type': 'l'},\n",
       " {'id': 'BE0101L', 'text': 'Married, divorced and widowed', 'type': 'l'},\n",
       " {'id': 'BE0101N', 'text': 'Changes of citizenship', 'type': 'l'},\n",
       " {'id': 'BE0101O', 'text': 'Registered partnership', 'type': 'l'},\n",
       " {'id': 'BE0101P', 'text': 'Asylum-seekers', 'type': 'l'},\n",
       " {'id': 'BE0101Q', 'text': 'Foreign/Swedish background', 'type': 'l'},\n",
       " {'id': 'BE0101K', 'text': 'Adoptees', 'type': 'l'},\n",
       " {'id': 'BE0101M', 'text': 'Circular migration', 'type': 'l'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 8
    }
   ]
  },
  {
   "metadata": {
    "id": "Dw-GxQQlTwQA",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "As you can see, we end up directly in the population statistics. \n",
    "\n",
    "---\n",
    "\n",
    "The specific initialisation of the object does not stop us from navigating in the tree: "
   ]
  },
  {
   "metadata": {
    "id": "Ks_6hseiW2CL",
    "colab_type": "code",
    "outputId": "06554ff9-fce0-48ea-c176-b5723c5137ea",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    }
   },
   "cell_type": "code",
   "source": [
    "scb.go_up()\n",
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'BE0001', 'text': 'Name statistics', 'type': 'l'},\n",
       " {'id': 'BE0401', 'text': 'Population projections', 'type': 'l'},\n",
       " {'id': 'BE0101', 'text': 'Population statistics', 'type': 'l'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 9
    }
   ]
  },
  {
   "metadata": {
    "id": "a89QseBbeyIn",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Anyway, we go directly back to population density: "
   ]
  },
  {
   "metadata": {
    "id": "3VMWQ8rEe50_",
    "colab_type": "code",
    "outputId": "9f61231c-1a65-483b-de72-eb3ffc84f6d7",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    }
   },
   "cell_type": "code",
   "source": [
    "scb.go_down('BE0101', 'BE0101C')\n",
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'id': 'BefArealTathetKon',\n",
       "  'text': 'Population density per sq. km, population and land area by region and sex. Year 1991 - 2018',\n",
       "  'type': 't',\n",
       "  'updated': '2019-03-07T09:30:00'}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 10
    }
   ]
  },
  {
   "metadata": {
    "id": "9KwTQmUcDXwC",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now there is only one nod to go to, so we do that:"
   ]
  },
  {
   "metadata": {
    "id": "VukEugEKDex8",
    "colab_type": "code",
    "outputId": "3fcaa728-bab9-4464-8816-79d29c7df3d0",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 12581
    }
   },
   "cell_type": "code",
   "source": [
    "scb.go_down('BefArealTathetKon')\n",
    "scb.info()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'title': 'Population density per sq. km by region, sex, observations and year',\n",
       " 'variables': [{'code': 'Region',\n",
       "   'elimination': True,\n",
       "   'text': 'region',\n",
       "   'valueTexts': ['Sweden',\n",
       "    'Stockholm county',\n",
       "    'Upplands Väsby',\n",
       "    'Vallentuna',\n",
       "    'Österåker',\n",
       "    'Värmdö',\n",
       "    'Järfälla',\n",
       "    'Ekerö',\n",
       "    'Huddinge',\n",
       "    'Botkyrka',\n",
       "    'Salem',\n",
       "    'Haninge',\n",
       "    'Tyresö',\n",
       "    'Upplands-Bro',\n",
       "    'Nykvarn',\n",
       "    'Täby',\n",
       "    'Danderyd',\n",
       "    'Sollentuna',\n",
       "    'Stockholm',\n",
       "    'Södertälje',\n",
       "    'Nacka',\n",
       "    'Sundbyberg',\n",
       "    'Solna',\n",
       "    'Lidingö',\n",
       "    'Vaxholm',\n",
       "    'Norrtälje',\n",
       "    'Sigtuna',\n",
       "    'Nynäshamn',\n",
       "    'Uppsala county',\n",
       "    'Håbo',\n",
       "    'Älvkarleby',\n",
       "    'Knivsta',\n",
       "    'Heby',\n",
       "    'Tierp',\n",
       "    'Uppsala',\n",
       "    'Enköping',\n",
       "    'Östhammar',\n",
       "    'Södermanland county',\n",
       "    'Vingåker',\n",
       "    'Gnesta',\n",
       "    'Nyköping',\n",
       "    'Oxelösund',\n",
       "    'Flen',\n",
       "    'Katrineholm',\n",
       "    'Eskilstuna',\n",
       "    'Strängnäs',\n",
       "    'Trosa',\n",
       "    'Östergötland county',\n",
       "    'Ödeshög',\n",
       "    'Ydre',\n",
       "    'Kinda',\n",
       "    'Boxholm',\n",
       "    'Åtvidaberg',\n",
       "    'Finspång',\n",
       "    'Valdemarsvik',\n",
       "    'Linköping',\n",
       "    'Norrköping',\n",
       "    'Söderköping',\n",
       "    'Motala',\n",
       "    'Vadstena',\n",
       "    'Mjölby',\n",
       "    'Jönköping county',\n",
       "    'Aneby',\n",
       "    'Gnosjö',\n",
       "    'Mullsjö',\n",
       "    'Habo',\n",
       "    'Gislaved',\n",
       "    'Vaggeryd',\n",
       "    'Jönköping',\n",
       "    'Nässjö',\n",
       "    'Värnamo',\n",
       "    'Sävsjö',\n",
       "    'Vetlanda',\n",
       "    'Eksjö',\n",
       "    'Tranås',\n",
       "    'Kronoberg county',\n",
       "    'Uppvidinge',\n",
       "    'Lessebo',\n",
       "    'Tingsryd',\n",
       "    'Alvesta',\n",
       "    'Älmhult',\n",
       "    'Markaryd',\n",
       "    'Växjö',\n",
       "    'Ljungby',\n",
       "    'Kalmar county',\n",
       "    'Högsby',\n",
       "    'Torsås',\n",
       "    'Mörbylånga',\n",
       "    'Hultsfred',\n",
       "    'Mönsterås',\n",
       "    'Emmaboda',\n",
       "    'Kalmar',\n",
       "    'Nybro',\n",
       "    'Oskarshamn',\n",
       "    'Västervik',\n",
       "    'Vimmerby',\n",
       "    'Borgholm',\n",
       "    'Gotland county',\n",
       "    'Gotland',\n",
       "    'Blekinge county',\n",
       "    'Olofström',\n",
       "    'Karlskrona',\n",
       "    'Ronneby',\n",
       "    'Karlshamn',\n",
       "    'Sölvesborg',\n",
       "    'Skåne county',\n",
       "    'Svalöv',\n",
       "    'Staffanstorp',\n",
       "    'Burlöv',\n",
       "    'Vellinge',\n",
       "    'Östra Göinge',\n",
       "    'Örkelljunga',\n",
       "    'Bjuv',\n",
       "    'Kävlinge',\n",
       "    'Lomma',\n",
       "    'Svedala',\n",
       "    'Skurup',\n",
       "    'Sjöbo',\n",
       "    'Hörby',\n",
       "    'Höör',\n",
       "    'Tomelilla',\n",
       "    'Bromölla',\n",
       "    'Osby',\n",
       "    'Perstorp',\n",
       "    'Klippan',\n",
       "    'Åstorp',\n",
       "    'Båstad',\n",
       "    'Malmö',\n",
       "    'Lund',\n",
       "    'Landskrona',\n",
       "    'Helsingborg',\n",
       "    'Höganäs',\n",
       "    'Eslöv',\n",
       "    'Ystad',\n",
       "    'Trelleborg',\n",
       "    'Kristianstad',\n",
       "    'Simrishamn',\n",
       "    'Ängelholm',\n",
       "    'Hässleholm',\n",
       "    'Halland county',\n",
       "    'Hylte',\n",
       "    'Halmstad',\n",
       "    'Laholm',\n",
       "    'Falkenberg',\n",
       "    'Varberg',\n",
       "    'Kungsbacka',\n",
       "    'Västra Götaland county',\n",
       "    'Härryda',\n",
       "    'Partille',\n",
       "    'Öckerö',\n",
       "    'Stenungsund',\n",
       "    'Tjörn',\n",
       "    'Orust',\n",
       "    'Sotenäs',\n",
       "    'Munkedal',\n",
       "    'Tanum',\n",
       "    'Dals-Ed',\n",
       "    'Färgelanda',\n",
       "    'Ale',\n",
       "    'Lerum',\n",
       "    'Vårgårda',\n",
       "    'Bollebygd',\n",
       "    'Grästorp',\n",
       "    'Essunga',\n",
       "    'Karlsborg',\n",
       "    'Gullspång',\n",
       "    'Tranemo',\n",
       "    'Bengtsfors',\n",
       "    'Mellerud',\n",
       "    'Lilla Edet',\n",
       "    'Mark',\n",
       "    'Svenljunga',\n",
       "    'Herrljunga',\n",
       "    'Vara',\n",
       "    'Götene',\n",
       "    'Tibro',\n",
       "    'Töreboda',\n",
       "    'Göteborg',\n",
       "    'Mölndal',\n",
       "    'Kungälv',\n",
       "    'Lysekil',\n",
       "    'Uddevalla',\n",
       "    'Strömstad',\n",
       "    'Vänersborg',\n",
       "    'Trollhättan',\n",
       "    'Alingsås',\n",
       "    'Borås',\n",
       "    'Ulricehamn',\n",
       "    'Åmål',\n",
       "    'Mariestad',\n",
       "    'Lidköping',\n",
       "    'Skara',\n",
       "    'Skövde',\n",
       "    'Hjo',\n",
       "    'Tidaholm',\n",
       "    'Falköping',\n",
       "    'Värmland county',\n",
       "    'Kil',\n",
       "    'Eda',\n",
       "    'Torsby',\n",
       "    'Storfors',\n",
       "    'Hammarö',\n",
       "    'Munkfors',\n",
       "    'Forshaga',\n",
       "    'Grums',\n",
       "    'Årjäng',\n",
       "    'Sunne',\n",
       "    'Karlstad',\n",
       "    'Kristinehamn',\n",
       "    'Filipstad',\n",
       "    'Hagfors',\n",
       "    'Arvika',\n",
       "    'Säffle',\n",
       "    'Örebro county',\n",
       "    'Lekeberg',\n",
       "    'Laxå',\n",
       "    'Hallsberg',\n",
       "    'Degerfors',\n",
       "    'Hällefors',\n",
       "    'Ljusnarsberg',\n",
       "    'Örebro',\n",
       "    'Kumla',\n",
       "    'Askersund',\n",
       "    'Karlskoga',\n",
       "    'Nora',\n",
       "    'Lindesberg',\n",
       "    'Västmanland county',\n",
       "    'Skinnskatteberg',\n",
       "    'Surahammar',\n",
       "    'Kungsör',\n",
       "    'Hallstahammar',\n",
       "    'Norberg',\n",
       "    'Västerås',\n",
       "    'Sala',\n",
       "    'Fagersta',\n",
       "    'Köping',\n",
       "    'Arboga',\n",
       "    'Dalarna county',\n",
       "    'Vansbro',\n",
       "    'Malung-Sälen',\n",
       "    'Gagnef',\n",
       "    'Leksand',\n",
       "    'Rättvik',\n",
       "    'Orsa',\n",
       "    'Älvdalen',\n",
       "    'Smedjebacken',\n",
       "    'Mora',\n",
       "    'Falun',\n",
       "    'Borlänge',\n",
       "    'Säter',\n",
       "    'Hedemora',\n",
       "    'Avesta',\n",
       "    'Ludvika',\n",
       "    'Gävleborg county',\n",
       "    'Ockelbo',\n",
       "    'Hofors',\n",
       "    'Ovanåker',\n",
       "    'Nordanstig',\n",
       "    'Ljusdal',\n",
       "    'Gävle',\n",
       "    'Sandviken',\n",
       "    'Söderhamn',\n",
       "    'Bollnäs',\n",
       "    'Hudiksvall',\n",
       "    'Västernorrland county',\n",
       "    'Ånge',\n",
       "    'Timrå',\n",
       "    'Härnösand',\n",
       "    'Sundsvall',\n",
       "    'Kramfors',\n",
       "    'Sollefteå',\n",
       "    'Örnsköldsvik',\n",
       "    'Jämtland county',\n",
       "    'Ragunda',\n",
       "    'Bräcke',\n",
       "    'Krokom',\n",
       "    'Strömsund',\n",
       "    'Åre',\n",
       "    'Berg',\n",
       "    'Härjedalen',\n",
       "    'Östersund',\n",
       "    'Västerbotten county',\n",
       "    'Nordmaling',\n",
       "    'Bjurholm',\n",
       "    'Vindeln',\n",
       "    'Robertsfors',\n",
       "    'Norsjö',\n",
       "    'Malå',\n",
       "    'Storuman',\n",
       "    'Sorsele',\n",
       "    'Dorotea',\n",
       "    'Vännäs',\n",
       "    'Vilhelmina',\n",
       "    'Åsele',\n",
       "    'Umeå',\n",
       "    'Lycksele',\n",
       "    'Skellefteå',\n",
       "    'Norrbotten county',\n",
       "    'Arvidsjaur',\n",
       "    'Arjeplog',\n",
       "    'Jokkmokk',\n",
       "    'Överkalix',\n",
       "    'Kalix',\n",
       "    'Övertorneå',\n",
       "    'Pajala',\n",
       "    'Gällivare',\n",
       "    'Älvsbyn',\n",
       "    'Luleå',\n",
       "    'Piteå',\n",
       "    'Boden',\n",
       "    'Haparanda',\n",
       "    'Kiruna'],\n",
       "   'values': ['00',\n",
       "    '01',\n",
       "    '0114',\n",
       "    '0115',\n",
       "    '0117',\n",
       "    '0120',\n",
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       "    '1907',\n",
       "    '1960',\n",
       "    '1961',\n",
       "    '1962',\n",
       "    '1980',\n",
       "    '1981',\n",
       "    '1982',\n",
       "    '1983',\n",
       "    '1984',\n",
       "    '20',\n",
       "    '2021',\n",
       "    '2023',\n",
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       "    '2029',\n",
       "    '2031',\n",
       "    '2034',\n",
       "    '2039',\n",
       "    '2061',\n",
       "    '2062',\n",
       "    '2080',\n",
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       "    '2082',\n",
       "    '2083',\n",
       "    '2084',\n",
       "    '2085',\n",
       "    '21',\n",
       "    '2101',\n",
       "    '2104',\n",
       "    '2121',\n",
       "    '2132',\n",
       "    '2161',\n",
       "    '2180',\n",
       "    '2181',\n",
       "    '2182',\n",
       "    '2183',\n",
       "    '2184',\n",
       "    '22',\n",
       "    '2260',\n",
       "    '2262',\n",
       "    '2280',\n",
       "    '2281',\n",
       "    '2282',\n",
       "    '2283',\n",
       "    '2284',\n",
       "    '23',\n",
       "    '2303',\n",
       "    '2305',\n",
       "    '2309',\n",
       "    '2313',\n",
       "    '2321',\n",
       "    '2326',\n",
       "    '2361',\n",
       "    '2380',\n",
       "    '24',\n",
       "    '2401',\n",
       "    '2403',\n",
       "    '2404',\n",
       "    '2409',\n",
       "    '2417',\n",
       "    '2418',\n",
       "    '2421',\n",
       "    '2422',\n",
       "    '2425',\n",
       "    '2460',\n",
       "    '2462',\n",
       "    '2463',\n",
       "    '2480',\n",
       "    '2481',\n",
       "    '2482',\n",
       "    '25',\n",
       "    '2505',\n",
       "    '2506',\n",
       "    '2510',\n",
       "    '2513',\n",
       "    '2514',\n",
       "    '2518',\n",
       "    '2521',\n",
       "    '2523',\n",
       "    '2560',\n",
       "    '2580',\n",
       "    '2581',\n",
       "    '2582',\n",
       "    '2583',\n",
       "    '2584']},\n",
       "  {'code': 'Kon',\n",
       "   'elimination': True,\n",
       "   'text': 'sex',\n",
       "   'valueTexts': ['men', 'women', 'total'],\n",
       "   'values': ['1', '2', '1+2']},\n",
       "  {'code': 'ContentsCode',\n",
       "   'text': 'observations',\n",
       "   'valueTexts': ['Population density per sq. km',\n",
       "    'Population',\n",
       "    'Land area sq. km'],\n",
       "   'values': ['BE0101U1', 'BE0101U2', 'BE0101U3']},\n",
       "  {'code': 'Tid',\n",
       "   'text': 'year',\n",
       "   'time': True,\n",
       "   'valueTexts': ['1991',\n",
       "    '1992',\n",
       "    '1993',\n",
       "    '1994',\n",
       "    '1995',\n",
       "    '1996',\n",
       "    '1997',\n",
       "    '1998',\n",
       "    '1999',\n",
       "    '2000',\n",
       "    '2001',\n",
       "    '2002',\n",
       "    '2003',\n",
       "    '2004',\n",
       "    '2005',\n",
       "    '2006',\n",
       "    '2007',\n",
       "    '2008',\n",
       "    '2009',\n",
       "    '2010',\n",
       "    '2011',\n",
       "    '2012',\n",
       "    '2013',\n",
       "    '2014',\n",
       "    '2015',\n",
       "    '2016',\n",
       "    '2017',\n",
       "    '2018'],\n",
       "   'values': ['1991',\n",
       "    '1992',\n",
       "    '1993',\n",
       "    '1994',\n",
       "    '1995',\n",
       "    '1996',\n",
       "    '1997',\n",
       "    '1998',\n",
       "    '1999',\n",
       "    '2000',\n",
       "    '2001',\n",
       "    '2002',\n",
       "    '2003',\n",
       "    '2004',\n",
       "    '2005',\n",
       "    '2006',\n",
       "    '2007',\n",
       "    '2008',\n",
       "    '2009',\n",
       "    '2010',\n",
       "    '2011',\n",
       "    '2012',\n",
       "    '2013',\n",
       "    '2014',\n",
       "    '2015',\n",
       "    '2016',\n",
       "    '2017',\n",
       "    '2018']}]}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 11
    }
   ]
  },
  {
   "metadata": {
    "id": "tgofEk9yELFk",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Note how the metadata differs from previous nodes: The keyword variables is there, which indicates that we are in a leaf node. From here we can therefore fetch the actual data. \n",
    "\n",
    "---\n",
    "\n",
    "It is not necessary to call info() after each go_down() but it is a good idea to do anyway, if you are not very sure of what the database looks like."
   ]
  },
  {
   "metadata": {
    "id": "MbhICJlAEuqy",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Fetch data"
   ]
  },
  {
   "metadata": {
    "id": "vLSmIRoAkNDh",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now that we are in a leaf node we can look at which variables there are, and their respective ranges: "
   ]
  },
  {
   "metadata": {
    "id": "BmRvq7UqkSMw",
    "colab_type": "code",
    "outputId": "a673bfe0-439a-4e29-c927-41ab1cc6fdc6",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 6209
    }
   },
   "cell_type": "code",
   "source": [
    "scb.get_variables()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'observations': ['Population density per sq. km',\n",
       "  'Population',\n",
       "  'Land area sq. km'],\n",
       " 'region': ['Sweden',\n",
       "  'Stockholm county',\n",
       "  'Upplands Väsby',\n",
       "  'Vallentuna',\n",
       "  'Österåker',\n",
       "  'Värmdö',\n",
       "  'Järfälla',\n",
       "  'Ekerö',\n",
       "  'Huddinge',\n",
       "  'Botkyrka',\n",
       "  'Salem',\n",
       "  'Haninge',\n",
       "  'Tyresö',\n",
       "  'Upplands-Bro',\n",
       "  'Nykvarn',\n",
       "  'Täby',\n",
       "  'Danderyd',\n",
       "  'Sollentuna',\n",
       "  'Stockholm',\n",
       "  'Södertälje',\n",
       "  'Nacka',\n",
       "  'Sundbyberg',\n",
       "  'Solna',\n",
       "  'Lidingö',\n",
       "  'Vaxholm',\n",
       "  'Norrtälje',\n",
       "  'Sigtuna',\n",
       "  'Nynäshamn',\n",
       "  'Uppsala county',\n",
       "  'Håbo',\n",
       "  'Älvkarleby',\n",
       "  'Knivsta',\n",
       "  'Heby',\n",
       "  'Tierp',\n",
       "  'Uppsala',\n",
       "  'Enköping',\n",
       "  'Östhammar',\n",
       "  'Södermanland county',\n",
       "  'Vingåker',\n",
       "  'Gnesta',\n",
       "  'Nyköping',\n",
       "  'Oxelösund',\n",
       "  'Flen',\n",
       "  'Katrineholm',\n",
       "  'Eskilstuna',\n",
       "  'Strängnäs',\n",
       "  'Trosa',\n",
       "  'Östergötland county',\n",
       "  'Ödeshög',\n",
       "  'Ydre',\n",
       "  'Kinda',\n",
       "  'Boxholm',\n",
       "  'Åtvidaberg',\n",
       "  'Finspång',\n",
       "  'Valdemarsvik',\n",
       "  'Linköping',\n",
       "  'Norrköping',\n",
       "  'Söderköping',\n",
       "  'Motala',\n",
       "  'Vadstena',\n",
       "  'Mjölby',\n",
       "  'Jönköping county',\n",
       "  'Aneby',\n",
       "  'Gnosjö',\n",
       "  'Mullsjö',\n",
       "  'Habo',\n",
       "  'Gislaved',\n",
       "  'Vaggeryd',\n",
       "  'Jönköping',\n",
       "  'Nässjö',\n",
       "  'Värnamo',\n",
       "  'Sävsjö',\n",
       "  'Vetlanda',\n",
       "  'Eksjö',\n",
       "  'Tranås',\n",
       "  'Kronoberg county',\n",
       "  'Uppvidinge',\n",
       "  'Lessebo',\n",
       "  'Tingsryd',\n",
       "  'Alvesta',\n",
       "  'Älmhult',\n",
       "  'Markaryd',\n",
       "  'Växjö',\n",
       "  'Ljungby',\n",
       "  'Kalmar county',\n",
       "  'Högsby',\n",
       "  'Torsås',\n",
       "  'Mörbylånga',\n",
       "  'Hultsfred',\n",
       "  'Mönsterås',\n",
       "  'Emmaboda',\n",
       "  'Kalmar',\n",
       "  'Nybro',\n",
       "  'Oskarshamn',\n",
       "  'Västervik',\n",
       "  'Vimmerby',\n",
       "  'Borgholm',\n",
       "  'Gotland county',\n",
       "  'Gotland',\n",
       "  'Blekinge county',\n",
       "  'Olofström',\n",
       "  'Karlskrona',\n",
       "  'Ronneby',\n",
       "  'Karlshamn',\n",
       "  'Sölvesborg',\n",
       "  'Skåne county',\n",
       "  'Svalöv',\n",
       "  'Staffanstorp',\n",
       "  'Burlöv',\n",
       "  'Vellinge',\n",
       "  'Östra Göinge',\n",
       "  'Örkelljunga',\n",
       "  'Bjuv',\n",
       "  'Kävlinge',\n",
       "  'Lomma',\n",
       "  'Svedala',\n",
       "  'Skurup',\n",
       "  'Sjöbo',\n",
       "  'Hörby',\n",
       "  'Höör',\n",
       "  'Tomelilla',\n",
       "  'Bromölla',\n",
       "  'Osby',\n",
       "  'Perstorp',\n",
       "  'Klippan',\n",
       "  'Åstorp',\n",
       "  'Båstad',\n",
       "  'Malmö',\n",
       "  'Lund',\n",
       "  'Landskrona',\n",
       "  'Helsingborg',\n",
       "  'Höganäs',\n",
       "  'Eslöv',\n",
       "  'Ystad',\n",
       "  'Trelleborg',\n",
       "  'Kristianstad',\n",
       "  'Simrishamn',\n",
       "  'Ängelholm',\n",
       "  'Hässleholm',\n",
       "  'Halland county',\n",
       "  'Hylte',\n",
       "  'Halmstad',\n",
       "  'Laholm',\n",
       "  'Falkenberg',\n",
       "  'Varberg',\n",
       "  'Kungsbacka',\n",
       "  'Västra Götaland county',\n",
       "  'Härryda',\n",
       "  'Partille',\n",
       "  'Öckerö',\n",
       "  'Stenungsund',\n",
       "  'Tjörn',\n",
       "  'Orust',\n",
       "  'Sotenäs',\n",
       "  'Munkedal',\n",
       "  'Tanum',\n",
       "  'Dals-Ed',\n",
       "  'Färgelanda',\n",
       "  'Ale',\n",
       "  'Lerum',\n",
       "  'Vårgårda',\n",
       "  'Bollebygd',\n",
       "  'Grästorp',\n",
       "  'Essunga',\n",
       "  'Karlsborg',\n",
       "  'Gullspång',\n",
       "  'Tranemo',\n",
       "  'Bengtsfors',\n",
       "  'Mellerud',\n",
       "  'Lilla Edet',\n",
       "  'Mark',\n",
       "  'Svenljunga',\n",
       "  'Herrljunga',\n",
       "  'Vara',\n",
       "  'Götene',\n",
       "  'Tibro',\n",
       "  'Töreboda',\n",
       "  'Göteborg',\n",
       "  'Mölndal',\n",
       "  'Kungälv',\n",
       "  'Lysekil',\n",
       "  'Uddevalla',\n",
       "  'Strömstad',\n",
       "  'Vänersborg',\n",
       "  'Trollhättan',\n",
       "  'Alingsås',\n",
       "  'Borås',\n",
       "  'Ulricehamn',\n",
       "  'Åmål',\n",
       "  'Mariestad',\n",
       "  'Lidköping',\n",
       "  'Skara',\n",
       "  'Skövde',\n",
       "  'Hjo',\n",
       "  'Tidaholm',\n",
       "  'Falköping',\n",
       "  'Värmland county',\n",
       "  'Kil',\n",
       "  'Eda',\n",
       "  'Torsby',\n",
       "  'Storfors',\n",
       "  'Hammarö',\n",
       "  'Munkfors',\n",
       "  'Forshaga',\n",
       "  'Grums',\n",
       "  'Årjäng',\n",
       "  'Sunne',\n",
       "  'Karlstad',\n",
       "  'Kristinehamn',\n",
       "  'Filipstad',\n",
       "  'Hagfors',\n",
       "  'Arvika',\n",
       "  'Säffle',\n",
       "  'Örebro county',\n",
       "  'Lekeberg',\n",
       "  'Laxå',\n",
       "  'Hallsberg',\n",
       "  'Degerfors',\n",
       "  'Hällefors',\n",
       "  'Ljusnarsberg',\n",
       "  'Örebro',\n",
       "  'Kumla',\n",
       "  'Askersund',\n",
       "  'Karlskoga',\n",
       "  'Nora',\n",
       "  'Lindesberg',\n",
       "  'Västmanland county',\n",
       "  'Skinnskatteberg',\n",
       "  'Surahammar',\n",
       "  'Kungsör',\n",
       "  'Hallstahammar',\n",
       "  'Norberg',\n",
       "  'Västerås',\n",
       "  'Sala',\n",
       "  'Fagersta',\n",
       "  'Köping',\n",
       "  'Arboga',\n",
       "  'Dalarna county',\n",
       "  'Vansbro',\n",
       "  'Malung-Sälen',\n",
       "  'Gagnef',\n",
       "  'Leksand',\n",
       "  'Rättvik',\n",
       "  'Orsa',\n",
       "  'Älvdalen',\n",
       "  'Smedjebacken',\n",
       "  'Mora',\n",
       "  'Falun',\n",
       "  'Borlänge',\n",
       "  'Säter',\n",
       "  'Hedemora',\n",
       "  'Avesta',\n",
       "  'Ludvika',\n",
       "  'Gävleborg county',\n",
       "  'Ockelbo',\n",
       "  'Hofors',\n",
       "  'Ovanåker',\n",
       "  'Nordanstig',\n",
       "  'Ljusdal',\n",
       "  'Gävle',\n",
       "  'Sandviken',\n",
       "  'Söderhamn',\n",
       "  'Bollnäs',\n",
       "  'Hudiksvall',\n",
       "  'Västernorrland county',\n",
       "  'Ånge',\n",
       "  'Timrå',\n",
       "  'Härnösand',\n",
       "  'Sundsvall',\n",
       "  'Kramfors',\n",
       "  'Sollefteå',\n",
       "  'Örnsköldsvik',\n",
       "  'Jämtland county',\n",
       "  'Ragunda',\n",
       "  'Bräcke',\n",
       "  'Krokom',\n",
       "  'Strömsund',\n",
       "  'Åre',\n",
       "  'Berg',\n",
       "  'Härjedalen',\n",
       "  'Östersund',\n",
       "  'Västerbotten county',\n",
       "  'Nordmaling',\n",
       "  'Bjurholm',\n",
       "  'Vindeln',\n",
       "  'Robertsfors',\n",
       "  'Norsjö',\n",
       "  'Malå',\n",
       "  'Storuman',\n",
       "  'Sorsele',\n",
       "  'Dorotea',\n",
       "  'Vännäs',\n",
       "  'Vilhelmina',\n",
       "  'Åsele',\n",
       "  'Umeå',\n",
       "  'Lycksele',\n",
       "  'Skellefteå',\n",
       "  'Norrbotten county',\n",
       "  'Arvidsjaur',\n",
       "  'Arjeplog',\n",
       "  'Jokkmokk',\n",
       "  'Överkalix',\n",
       "  'Kalix',\n",
       "  'Övertorneå',\n",
       "  'Pajala',\n",
       "  'Gällivare',\n",
       "  'Älvsbyn',\n",
       "  'Luleå',\n",
       "  'Piteå',\n",
       "  'Boden',\n",
       "  'Haparanda',\n",
       "  'Kiruna'],\n",
       " 'sex': ['men', 'women', 'total'],\n",
       " 'year': ['1991',\n",
       "  '1992',\n",
       "  '1993',\n",
       "  '1994',\n",
       "  '1995',\n",
       "  '1996',\n",
       "  '1997',\n",
       "  '1998',\n",
       "  '1999',\n",
       "  '2000',\n",
       "  '2001',\n",
       "  '2002',\n",
       "  '2003',\n",
       "  '2004',\n",
       "  '2005',\n",
       "  '2006',\n",
       "  '2007',\n",
       "  '2008',\n",
       "  '2009',\n",
       "  '2010',\n",
       "  '2011',\n",
       "  '2012',\n",
       "  '2013',\n",
       "  '2014',\n",
       "  '2015',\n",
       "  '2016',\n",
       "  '2017',\n",
       "  '2018']}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 12
    }
   ]
  },
  {
   "metadata": {
    "id": "yuuDtuMpkWlH",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now that we have these, we can choose what we are interested in and create a json query. Let's say we are interested in the number of inhabitants per square kilometer in Örebro county the latest five years. "
   ]
  },
  {
   "metadata": {
    "id": "bJ6Zcm3ikeHX",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "scb.set_query(region=[\"Örebro county\"], \n",
    "              observations=[\"Population density per sq. km\"], \n",
    "              year=[\"2014\", \"2015\", \"2016\", \"2017\", \"2018\"])"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "1sQ9vvLElAmZ",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now we can check how the query looks: "
   ]
  },
  {
   "metadata": {
    "id": "czNaLMkik_25",
    "colab_type": "code",
    "outputId": "e509ef7f-4863-4118-dbbc-af4828d5566f",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    }
   },
   "cell_type": "code",
   "source": [
    "scb.get_query()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'query': [{'code': 'Region',\n",
       "   'selection': {'filter': 'item', 'values': ['18']}},\n",
       "  {'code': 'ContentsCode',\n",
       "   'selection': {'filter': 'item', 'values': ['BE0101U1']}},\n",
       "  {'code': 'Tid',\n",
       "   'selection': {'filter': 'item',\n",
       "    'values': ['2014', '2015', '2016', '2017', '2018']}}],\n",
       " 'response': {'format': 'json'}}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 14
    }
   ]
  },
  {
   "metadata": {
    "id": "SnSymy9_lOiq",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "The query is automatically formatted in the right way to fetch the data from the API. We fetch the data: "
   ]
  },
  {
   "metadata": {
    "id": "d4r0jNRPlW1b",
    "colab_type": "code",
    "outputId": "e1ace8f4-f742-498d-fe93-719274f7bc0c",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 179
    }
   },
   "cell_type": "code",
   "source": [
    "scb.get_data()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'columns': [{'code': 'Region', 'text': 'region', 'type': 'd'},\n",
       "  {'code': 'Tid', 'text': 'year', 'type': 't'},\n",
       "  {'code': 'BE0101U1', 'text': 'Population density per sq. km', 'type': 'c'}],\n",
       " 'comments': [],\n",
       " 'data': [{'key': ['18', '2014'], 'values': ['33.9']},\n",
       "  {'key': ['18', '2015'], 'values': ['34.2']},\n",
       "  {'key': ['18', '2016'], 'values': ['34.7']},\n",
       "  {'key': ['18', '2017'], 'values': ['35.1']},\n",
       "  {'key': ['18', '2018'], 'values': ['35.5']}]}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 15
    }
   ]
  },
  {
   "metadata": {
    "id": "Y5RtDXhM8tFe",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "This is the same data that one can fetch from the Statistical Database on the website. Via a function in pyscbwrapper we can get the URL to the page with the data: "
   ]
  },
  {
   "metadata": {
    "id": "YWqb_2al83C7",
    "colab_type": "code",
    "outputId": "654e3d3c-06b9-46ea-c284-0cc94678df67",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    }
   },
   "cell_type": "code",
   "source": [
    "scb.get_url()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'http://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__BE__BE0101__BE0101C/BefArealTathetKon'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 16
    }
   ]
  },
  {
   "metadata": {
    "id": "rnl_2GHM8Bhx",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "The data can of course be fetched without pyscbwrapper, by navigating to the URL above. There we select \"Population density per sq. km\", choose region County and select Örebro county, and select the last five years. On the next page we can click \"API for this table\" and get the query and a URL to post it to via e.g. the package requests, but he have to change \"format\": \"px\" to \"format\": \"json\". "
   ]
  },
  {
   "metadata": {
    "id": "em20LRw07x7z",
    "colab_type": "code",
    "outputId": "96a3619e-d958-4af5-8e57-1a2b8f514961",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 179
    }
   },
   "cell_type": "code",
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "session = requests.Session()\n",
    "\n",
    "query = {\n",
    "  \"query\": [\n",
    "    {\n",
    "      \"code\": \"Region\",\n",
    "      \"selection\": {\n",
    "        \"filter\": \"vs:RegionLän99EjAggr\",\n",
    "        \"values\": [\n",
    "          \"18\"\n",
    "        ]\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      \"code\": \"ContentsCode\",\n",
    "      \"selection\": {\n",
    "        \"filter\": \"item\",\n",
    "        \"values\": [\n",
    "          \"BE0101U1\"\n",
    "        ]\n",
    "      }\n",
    "    },\n",
    "    {\n",
    "      \"code\": \"Tid\",\n",
    "      \"selection\": {\n",
    "        \"filter\": \"item\",\n",
    "        \"values\": [\n",
    "          \"2014\",\n",
    "          \"2015\",\n",
    "          \"2016\",\n",
    "          \"2017\",\n",
    "          \"2018\"\n",
    "        ]\n",
    "      }\n",
    "    }\n",
    "  ],\n",
    "  \"response\": {\n",
    "    \"format\": \"json\"\n",
    "  }\n",
    "}\n",
    "\n",
    "url = \"https://api.scb.se/OV0104/v1/doris/en/ssd/START/BE/BE0101/BE0101C/BefArealTathetKon\"\n",
    "\n",
    "response = session.post(url, json=query)\n",
    "response_json = json.loads(response.content.decode('utf-8-sig'))\n",
    "\n",
    "response_json"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'columns': [{'code': 'Region', 'text': 'region', 'type': 'd'},\n",
       "  {'code': 'Tid', 'text': 'year', 'type': 't'},\n",
       "  {'code': 'BE0101U1', 'text': 'Population density per sq. km', 'type': 'c'}],\n",
       " 'comments': [],\n",
       " 'data': [{'key': ['18', '2014'], 'values': ['33.9']},\n",
       "  {'key': ['18', '2015'], 'values': ['34.2']},\n",
       "  {'key': ['18', '2016'], 'values': ['34.7']},\n",
       "  {'key': ['18', '2017'], 'values': ['35.1']},\n",
       "  {'key': ['18', '2018'], 'values': ['35.5']}]}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 2
    }
   ]
  },
  {
   "metadata": {
    "id": "j8W2e51R-uZn",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "As you can see, we get the exact same data. "
   ]
  },
  {
   "metadata": {
    "id": "dRS6-PKl92_6",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# More advanced calls"
   ]
  },
  {
   "metadata": {
    "id": "1vNhyuWrXlAU",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now that we have seen what the data looks like we can fetch more of it to make interesting graphs. Since we already are on the correct place in the API structure we only have to construct a new query. Let's say we want data from every available year for each county. First we can pick out a list over all regions, filter out the counties through regular expressions, and after that use the list in the json query: "
   ]
  },
  {
   "metadata": {
    "id": "wTIDbKQpXjfj",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "import re\n",
    "\n",
    "regions = scb.get_variables()['region']\n",
    "r = re.compile(r'.* county')\n",
    "county = list(filter(r.match, regions))\n",
    "\n",
    "scb.set_query(region=county,\n",
    "              observations=[\"Population density per sq. km\"])"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "QGvN2EagYgBf",
    "colab_type": "code",
    "outputId": "ad6a8bde-aca1-4be0-e187-ff1c61483573",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 485
    }
   },
   "cell_type": "code",
   "source": [
    "scb.get_query()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'query': [{'code': 'Region',\n",
       "   'selection': {'filter': 'item',\n",
       "    'values': ['01',\n",
       "     '03',\n",
       "     '04',\n",
       "     '05',\n",
       "     '06',\n",
       "     '07',\n",
       "     '08',\n",
       "     '09',\n",
       "     '10',\n",
       "     '12',\n",
       "     '13',\n",
       "     '14',\n",
       "     '17',\n",
       "     '18',\n",
       "     '19',\n",
       "     '20',\n",
       "     '21',\n",
       "     '22',\n",
       "     '23',\n",
       "     '24',\n",
       "     '25']}},\n",
       "  {'code': 'ContentsCode',\n",
       "   'selection': {'filter': 'item', 'values': ['BE0101U1']}}],\n",
       " 'response': {'format': 'json'}}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 20
    }
   ]
  },
  {
   "metadata": {
    "id": "IyMmI9_h8tpn",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "This is the exact query we need. We fetch the data and place it in a variable so we can use it later: "
   ]
  },
  {
   "metadata": {
    "id": "oMRduzFwYjED",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "scb_data = scb.get_data()"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "mvT224ir849w",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "As is good practice we look at the data before we do anything else: "
   ]
  },
  {
   "metadata": {
    "id": "euIRGaDK89eI",
    "colab_type": "code",
    "outputId": "6cb93a35-88ad-4ab8-defd-2e5515a93a50",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 10763
    }
   },
   "cell_type": "code",
   "source": [
    "scb_data"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'columns': [{'code': 'Region', 'text': 'region', 'type': 'd'},\n",
       "  {'code': 'Tid', 'text': 'year', 'type': 't'},\n",
       "  {'code': 'BE0101U1', 'text': 'Population density per sq. km', 'type': 'c'}],\n",
       " 'comments': [{'comment': 'Since 1 January 2007, Uppsala county has been expanded to include Heby municipality. Please note that the figures for the county are not comparable with earlier figures.\\r\\n',\n",
       "   'value': '03',\n",
       "   'variable': 'Region'},\n",
       "  {'comment': 'Since 1 January 2007, Västmanland county no longer includes Heby municipality. Please note that the figures for the county are not comparable with earlier figures.\\r\\n',\n",
       "   'value': '19',\n",
       "   'variable': 'Region'}],\n",
       " 'data': [{'key': ['01', '1991'], 'values': ['254.9']},\n",
       "  {'key': ['01', '1992'], 'values': ['257.3']},\n",
       "  {'key': ['01', '1993'], 'values': ['259.8']},\n",
       "  {'key': ['01', '1994'], 'values': ['263.2']},\n",
       "  {'key': ['01', '1995'], 'values': ['265.9']},\n",
       "  {'key': ['01', '1996'], 'values': ['268.8']},\n",
       "  {'key': ['01', '1997'], 'values': ['271.6']},\n",
       "  {'key': ['01', '1998'], 'values': ['274.8']},\n",
       "  {'key': ['01', '1999'], 'values': ['276.6']},\n",
       "  {'key': ['01', '2000'], 'values': ['279.7']},\n",
       "  {'key': ['01', '2001'], 'values': ['282.1']},\n",
       "  {'key': ['01', '2002'], 'values': ['283.8']},\n",
       "  {'key': ['01', '2003'], 'values': ['285.4']},\n",
       "  {'key': ['01', '2004'], 'values': ['287.3']},\n",
       "  {'key': ['01', '2005'], 'values': ['289.9']},\n",
       "  {'key': ['01', '2006'], 'values': ['294.2']},\n",
       "  {'key': ['01', '2007'], 'values': ['299.0']},\n",
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       "  {'key': ['17', '1995'], 'values': ['16.1']},\n",
       "  {'key': ['17', '1996'], 'values': ['16.0']},\n",
       "  {'key': ['17', '1997'], 'values': ['15.9']},\n",
       "  {'key': ['17', '1998'], 'values': ['15.8']},\n",
       "  {'key': ['17', '1999'], 'values': ['15.7']},\n",
       "  {'key': ['17', '2000'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2001'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2002'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2003'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2004'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2005'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2006'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2007'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2008'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2009'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2010'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2011'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2012'], 'values': ['15.5']},\n",
       "  {'key': ['17', '2013'], 'values': ['15.6']},\n",
       "  {'key': ['17', '2014'], 'values': ['15.7']},\n",
       "  {'key': ['17', '2015'], 'values': ['15.7']},\n",
       "  {'key': ['17', '2016'], 'values': ['15.9']},\n",
       "  {'key': ['17', '2017'], 'values': ['16.0']},\n",
       "  {'key': ['17', '2018'], 'values': ['16.1']},\n",
       "  {'key': ['18', '1991'], 'values': ['32.1']},\n",
       "  {'key': ['18', '1992'], 'values': ['32.2']},\n",
       "  {'key': ['18', '1993'], 'values': ['32.3']},\n",
       "  {'key': ['18', '1994'], 'values': ['32.5']},\n",
       "  {'key': ['18', '1995'], 'values': ['32.5']},\n",
       "  {'key': ['18', '1996'], 'values': ['32.4']},\n",
       "  {'key': ['18', '1997'], 'values': ['32.3']},\n",
       "  {'key': ['18', '1998'], 'values': ['32.2']},\n",
       "  {'key': ['18', '1999'], 'values': ['32.0']},\n",
       "  {'key': ['18', '2000'], 'values': ['32.0']},\n",
       "  {'key': ['18', '2001'], 'values': ['32.0']},\n",
       "  {'key': ['18', '2002'], 'values': ['32.0']},\n",
       "  {'key': ['18', '2003'], 'values': ['32.1']},\n",
       "  {'key': ['18', '2004'], 'values': ['32.1']},\n",
       "  {'key': ['18', '2005'], 'values': ['32.1']},\n",
       "  {'key': ['18', '2006'], 'values': ['32.2']},\n",
       "  {'key': ['18', '2007'], 'values': ['32.3']},\n",
       "  {'key': ['18', '2008'], 'values': ['32.5']},\n",
       "  {'key': ['18', '2009'], 'values': ['32.6']},\n",
       "  {'key': ['18', '2010'], 'values': ['32.8']},\n",
       "  {'key': ['18', '2011'], 'values': ['33.1']},\n",
       "  {'key': ['18', '2012'], 'values': ['33.2']},\n",
       "  {'key': ['18', '2013'], 'values': ['33.6']},\n",
       "  {'key': ['18', '2014'], 'values': ['33.9']},\n",
       "  {'key': ['18', '2015'], 'values': ['34.2']},\n",
       "  {'key': ['18', '2016'], 'values': ['34.7']},\n",
       "  {'key': ['18', '2017'], 'values': ['35.1']},\n",
       "  {'key': ['18', '2018'], 'values': ['35.5']},\n",
       "  {'key': ['19', '1991'], 'values': ['41.2']},\n",
       "  {'key': ['19', '1992'], 'values': ['41.3']},\n",
       "  {'key': ['19', '1993'], 'values': ['41.4']},\n",
       "  {'key': ['19', '1994'], 'values': ['41.5']},\n",
       "  {'key': ['19', '1995'], 'values': ['41.4']},\n",
       "  {'key': ['19', '1996'], 'values': ['41.3']},\n",
       "  {'key': ['19', '1997'], 'values': ['41.0']},\n",
       "  {'key': ['19', '1998'], 'values': ['40.9']},\n",
       "  {'key': ['19', '1999'], 'values': ['40.7']},\n",
       "  {'key': ['19', '2000'], 'values': ['40.7']},\n",
       "  {'key': ['19', '2001'], 'values': ['40.8']},\n",
       "  {'key': ['19', '2002'], 'values': ['41.0']},\n",
       "  {'key': ['19', '2003'], 'values': ['41.2']},\n",
       "  {'key': ['19', '2004'], 'values': ['41.3']},\n",
       "  {'key': ['19', '2005'], 'values': ['41.4']},\n",
       "  {'key': ['19', '2006'], 'values': ['48.3']},\n",
       "  {'key': ['19', '2007'], 'values': ['48.4']},\n",
       "  {'key': ['19', '2008'], 'values': ['48.6']},\n",
       "  {'key': ['19', '2009'], 'values': ['48.9']},\n",
       "  {'key': ['19', '2010'], 'values': ['49.1']},\n",
       "  {'key': ['19', '2011'], 'values': ['49.7']},\n",
       "  {'key': ['19', '2012'], 'values': ['50.0']},\n",
       "  {'key': ['19', '2013'], 'values': ['50.6']},\n",
       "  {'key': ['19', '2014'], 'values': ['51.1']},\n",
       "  {'key': ['19', '2015'], 'values': ['51.6']},\n",
       "  {'key': ['19', '2016'], 'values': ['52.3']},\n",
       "  {'key': ['19', '2017'], 'values': ['53.0']},\n",
       "  {'key': ['19', '2018'], 'values': ['53.5']},\n",
       "  {'key': ['20', '1991'], 'values': ['10.3']},\n",
       "  {'key': ['20', '1992'], 'values': ['10.3']},\n",
       "  {'key': ['20', '1993'], 'values': ['10.3']},\n",
       "  {'key': ['20', '1994'], 'values': ['10.3']},\n",
       "  {'key': ['20', '1995'], 'values': ['10.3']},\n",
       "  {'key': ['20', '1996'], 'values': ['10.2']},\n",
       "  {'key': ['20', '1997'], 'values': ['10.1']},\n",
       "  {'key': ['20', '1998'], 'values': ['10.0']},\n",
       "  {'key': ['20', '1999'], 'values': ['10.0']},\n",
       "  {'key': ['20', '2000'], 'values': ['9.9']},\n",
       "  {'key': ['20', '2001'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2002'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2003'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2004'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2005'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2006'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2007'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2008'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2009'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2010'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2011'], 'values': ['9.9']},\n",
       "  {'key': ['20', '2012'], 'values': ['9.8']},\n",
       "  {'key': ['20', '2013'], 'values': ['9.9']},\n",
       "  {'key': ['20', '2014'], 'values': ['10.0']},\n",
       "  {'key': ['20', '2015'], 'values': ['10.0']},\n",
       "  {'key': ['20', '2016'], 'values': ['10.2']},\n",
       "  {'key': ['20', '2017'], 'values': ['10.2']},\n",
       "  {'key': ['20', '2018'], 'values': ['10.2']},\n",
       "  {'key': ['21', '1991'], 'values': ['15.9']},\n",
       "  {'key': ['21', '1992'], 'values': ['15.9']},\n",
       "  {'key': ['21', '1993'], 'values': ['15.9']},\n",
       "  {'key': ['21', '1994'], 'values': ['15.9']},\n",
       "  {'key': ['21', '1995'], 'values': ['15.9']},\n",
       "  {'key': ['21', '1996'], 'values': ['15.8']},\n",
       "  {'key': ['21', '1997'], 'values': ['15.6']},\n",
       "  {'key': ['21', '1998'], 'values': ['15.5']},\n",
       "  {'key': ['21', '1999'], 'values': ['15.4']},\n",
       "  {'key': ['21', '2000'], 'values': ['15.3']},\n",
       "  {'key': ['21', '2001'], 'values': ['15.3']},\n",
       "  {'key': ['21', '2002'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2003'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2004'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2005'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2006'], 'values': ['15.1']},\n",
       "  {'key': ['21', '2007'], 'values': ['15.1']},\n",
       "  {'key': ['21', '2008'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2009'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2010'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2011'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2012'], 'values': ['15.2']},\n",
       "  {'key': ['21', '2013'], 'values': ['15.3']},\n",
       "  {'key': ['21', '2014'], 'values': ['15.5']},\n",
       "  {'key': ['21', '2015'], 'values': ['15.6']},\n",
       "  {'key': ['21', '2016'], 'values': ['15.7']},\n",
       "  {'key': ['21', '2017'], 'values': ['15.8']},\n",
       "  {'key': ['21', '2018'], 'values': ['15.8']},\n",
       "  {'key': ['22', '1991'], 'values': ['12.1']},\n",
       "  {'key': ['22', '1992'], 'values': ['12.0']},\n",
       "  {'key': ['22', '1993'], 'values': ['12.0']},\n",
       "  {'key': ['22', '1994'], 'values': ['12.0']},\n",
       "  {'key': ['22', '1995'], 'values': ['11.9']},\n",
       "  {'key': ['22', '1996'], 'values': ['11.8']},\n",
       "  {'key': ['22', '1997'], 'values': ['11.7']},\n",
       "  {'key': ['22', '1998'], 'values': ['11.6']},\n",
       "  {'key': ['22', '1999'], 'values': ['11.5']},\n",
       "  {'key': ['22', '2000'], 'values': ['11.4']},\n",
       "  {'key': ['22', '2001'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2002'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2003'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2004'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2005'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2006'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2007'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2008'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2009'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2010'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2011'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2012'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2013'], 'values': ['11.2']},\n",
       "  {'key': ['22', '2014'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2015'], 'values': ['11.3']},\n",
       "  {'key': ['22', '2016'], 'values': ['11.4']},\n",
       "  {'key': ['22', '2017'], 'values': ['11.4']},\n",
       "  {'key': ['22', '2018'], 'values': ['11.4']},\n",
       "  {'key': ['23', '1991'], 'values': ['2.8']},\n",
       "  {'key': ['23', '1992'], 'values': ['2.7']},\n",
       "  {'key': ['23', '1993'], 'values': ['2.8']},\n",
       "  {'key': ['23', '1994'], 'values': ['2.8']},\n",
       "  {'key': ['23', '1995'], 'values': ['2.7']},\n",
       "  {'key': ['23', '1996'], 'values': ['2.7']},\n",
       "  {'key': ['23', '1997'], 'values': ['2.7']},\n",
       "  {'key': ['23', '1998'], 'values': ['2.7']},\n",
       "  {'key': ['23', '1999'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2000'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2001'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2002'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2003'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2004'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2005'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2006'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2007'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2008'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2009'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2010'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2011'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2012'], 'values': ['2.5']},\n",
       "  {'key': ['23', '2013'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2014'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2015'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2016'], 'values': ['2.6']},\n",
       "  {'key': ['23', '2017'], 'values': ['2.7']},\n",
       "  {'key': ['23', '2018'], 'values': ['2.7']},\n",
       "  {'key': ['24', '1991'], 'values': ['4.6']},\n",
       "  {'key': ['24', '1992'], 'values': ['4.6']},\n",
       "  {'key': ['24', '1993'], 'values': ['4.7']},\n",
       "  {'key': ['24', '1994'], 'values': ['4.7']},\n",
       "  {'key': ['24', '1995'], 'values': ['4.7']},\n",
       "  {'key': ['24', '1996'], 'values': ['4.7']},\n",
       "  {'key': ['24', '1997'], 'values': ['4.7']},\n",
       "  {'key': ['24', '1998'], 'values': ['4.7']},\n",
       "  {'key': ['24', '1999'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2000'], 'values': ['4.6']},\n",
       "  {'key': ['24', '2001'], 'values': ['4.6']},\n",
       "  {'key': ['24', '2002'], 'values': ['4.6']},\n",
       "  {'key': ['24', '2003'], 'values': ['4.6']},\n",
       "  {'key': ['24', '2004'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2005'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2006'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2007'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2008'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2009'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2010'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2011'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2012'], 'values': ['4.7']},\n",
       "  {'key': ['24', '2013'], 'values': ['4.8']},\n",
       "  {'key': ['24', '2014'], 'values': ['4.8']},\n",
       "  {'key': ['24', '2015'], 'values': ['4.8']},\n",
       "  {'key': ['24', '2016'], 'values': ['4.9']},\n",
       "  {'key': ['24', '2017'], 'values': ['4.9']},\n",
       "  {'key': ['24', '2018'], 'values': ['4.9']},\n",
       "  {'key': ['25', '1991'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1992'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1993'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1994'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1995'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1996'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1997'], 'values': ['2.7']},\n",
       "  {'key': ['25', '1998'], 'values': ['2.6']},\n",
       "  {'key': ['25', '1999'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2000'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2001'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2002'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2003'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2004'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2005'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2006'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2007'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2008'], 'values': ['2.5']},\n",
       "  {'key': ['25', '2009'], 'values': ['2.5']},\n",
       "  {'key': ['25', '2010'], 'values': ['2.5']},\n",
       "  {'key': ['25', '2011'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2012'], 'values': ['2.5']},\n",
       "  {'key': ['25', '2013'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2014'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2015'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2016'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2017'], 'values': ['2.6']},\n",
       "  {'key': ['25', '2018'], 'values': ['2.6']}]}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 22
    }
   ]
  },
  {
   "metadata": {
    "id": "8x4RzrTb9_Pb",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "The actual data we look for is here: "
   ]
  },
  {
   "metadata": {
    "id": "yObUQkJ1-BsK",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "scb_fetch = scb_data['data']"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "CtfDWCaC-cbz",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Once again we check that we have gotten the correct data: "
   ]
  },
  {
   "metadata": {
    "id": "twVrYqVy-gsW",
    "colab_type": "code",
    "outputId": "de2b4dcd-daa9-4c36-f968-8eee055a98da",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 10601
    }
   },
   "cell_type": "code",
   "source": [
    "scb_fetch"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'key': ['01', '1991'], 'values': ['254.9']},\n",
       " {'key': ['01', '1992'], 'values': ['257.3']},\n",
       " {'key': ['01', '1993'], 'values': ['259.8']},\n",
       " {'key': ['01', '1994'], 'values': ['263.2']},\n",
       " {'key': ['01', '1995'], 'values': ['265.9']},\n",
       " {'key': ['01', '1996'], 'values': ['268.8']},\n",
       " {'key': ['01', '1997'], 'values': ['271.6']},\n",
       " {'key': ['01', '1998'], 'values': ['274.8']},\n",
       " {'key': ['01', '1999'], 'values': ['276.6']},\n",
       " {'key': ['01', '2000'], 'values': ['279.7']},\n",
       " {'key': ['01', '2001'], 'values': ['282.1']},\n",
       " {'key': ['01', '2002'], 'values': ['283.8']},\n",
       " {'key': ['01', '2003'], 'values': ['285.4']},\n",
       " {'key': ['01', '2004'], 'values': ['287.3']},\n",
       " {'key': ['01', '2005'], 'values': ['289.9']},\n",
       " {'key': ['01', '2006'], 'values': ['294.2']},\n",
       " {'key': ['01', '2007'], 'values': ['299.0']},\n",
       " {'key': ['01', '2008'], 'values': ['303.9']},\n",
       " {'key': ['01', '2009'], 'values': ['309.7']},\n",
       " {'key': ['01', '2010'], 'values': ['315.1']},\n",
       " {'key': ['01', '2011'], 'values': ['320.5']},\n",
       " {'key': ['01', '2012'], 'values': ['325.9']},\n",
       " {'key': ['01', '2013'], 'values': ['331.4']},\n",
       " {'key': ['01', '2014'], 'values': ['336.9']},\n",
       " {'key': ['01', '2015'], 'values': ['342.0']},\n",
       " {'key': ['01', '2016'], 'values': ['347.8']},\n",
       " {'key': ['01', '2017'], 'values': ['353.8']},\n",
       " {'key': ['01', '2018'], 'values': ['359.9']},\n",
       " {'key': ['03', '1991'], 'values': ['39.2']},\n",
       " {'key': ['03', '1992'], 'values': ['39.9']},\n",
       " {'key': ['03', '1993'], 'values': ['40.5']},\n",
       " {'key': ['03', '1994'], 'values': ['41.0']},\n",
       " {'key': ['03', '1995'], 'values': ['41.3']},\n",
       " {'key': ['03', '1996'], 'values': ['41.4']},\n",
       " {'key': ['03', '1997'], 'values': ['41.6']},\n",
       " {'key': ['03', '1998'], 'values': ['41.7']},\n",
       " {'key': ['03', '1999'], 'values': ['41.6']},\n",
       " {'key': ['03', '2000'], 'values': ['41.8']},\n",
       " {'key': ['03', '2001'], 'values': ['42.2']},\n",
       " {'key': ['03', '2002'], 'values': ['42.4']},\n",
       " {'key': ['03', '2003'], 'values': ['42.7']},\n",
       " {'key': ['03', '2004'], 'values': ['43.0']},\n",
       " {'key': ['03', '2005'], 'values': ['43.3']},\n",
       " {'key': ['03', '2006'], 'values': ['39.0']},\n",
       " {'key': ['03', '2007'], 'values': ['39.4']},\n",
       " {'key': ['03', '2008'], 'values': ['39.9']},\n",
       " {'key': ['03', '2009'], 'values': ['40.4']},\n",
       " {'key': ['03', '2010'], 'values': ['40.9']},\n",
       " {'key': ['03', '2011'], 'values': ['41.3']},\n",
       " {'key': ['03', '2012'], 'values': ['41.7']},\n",
       " {'key': ['03', '2013'], 'values': ['42.2']},\n",
       " {'key': ['03', '2014'], 'values': ['42.6']},\n",
       " {'key': ['03', '2015'], 'values': ['43.2']},\n",
       " {'key': ['03', '2016'], 'values': ['44.1']},\n",
       " {'key': ['03', '2017'], 'values': ['45.1']},\n",
       " {'key': ['03', '2018'], 'values': ['46.0']},\n",
       " {'key': ['04', '1991'], 'values': ['42.4']},\n",
       " {'key': ['04', '1992'], 'values': ['42.5']},\n",
       " {'key': ['04', '1993'], 'values': ['42.8']},\n",
       " {'key': ['04', '1994'], 'values': ['42.9']},\n",
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       " {'key': ['04', '2015'], 'values': ['46.7']},\n",
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       " {'key': ['05', '1997'], 'values': ['39.2']},\n",
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       " {'key': ['19', '1996'], 'values': ['41.3']},\n",
       " {'key': ['19', '1997'], 'values': ['41.0']},\n",
       " {'key': ['19', '1998'], 'values': ['40.9']},\n",
       " {'key': ['19', '1999'], 'values': ['40.7']},\n",
       " {'key': ['19', '2000'], 'values': ['40.7']},\n",
       " {'key': ['19', '2001'], 'values': ['40.8']},\n",
       " {'key': ['19', '2002'], 'values': ['41.0']},\n",
       " {'key': ['19', '2003'], 'values': ['41.2']},\n",
       " {'key': ['19', '2004'], 'values': ['41.3']},\n",
       " {'key': ['19', '2005'], 'values': ['41.4']},\n",
       " {'key': ['19', '2006'], 'values': ['48.3']},\n",
       " {'key': ['19', '2007'], 'values': ['48.4']},\n",
       " {'key': ['19', '2008'], 'values': ['48.6']},\n",
       " {'key': ['19', '2009'], 'values': ['48.9']},\n",
       " {'key': ['19', '2010'], 'values': ['49.1']},\n",
       " {'key': ['19', '2011'], 'values': ['49.7']},\n",
       " {'key': ['19', '2012'], 'values': ['50.0']},\n",
       " {'key': ['19', '2013'], 'values': ['50.6']},\n",
       " {'key': ['19', '2014'], 'values': ['51.1']},\n",
       " {'key': ['19', '2015'], 'values': ['51.6']},\n",
       " {'key': ['19', '2016'], 'values': ['52.3']},\n",
       " {'key': ['19', '2017'], 'values': ['53.0']},\n",
       " {'key': ['19', '2018'], 'values': ['53.5']},\n",
       " {'key': ['20', '1991'], 'values': ['10.3']},\n",
       " {'key': ['20', '1992'], 'values': ['10.3']},\n",
       " {'key': ['20', '1993'], 'values': ['10.3']},\n",
       " {'key': ['20', '1994'], 'values': ['10.3']},\n",
       " {'key': ['20', '1995'], 'values': ['10.3']},\n",
       " {'key': ['20', '1996'], 'values': ['10.2']},\n",
       " {'key': ['20', '1997'], 'values': ['10.1']},\n",
       " {'key': ['20', '1998'], 'values': ['10.0']},\n",
       " {'key': ['20', '1999'], 'values': ['10.0']},\n",
       " {'key': ['20', '2000'], 'values': ['9.9']},\n",
       " {'key': ['20', '2001'], 'values': ['9.8']},\n",
       " {'key': ['20', '2002'], 'values': ['9.8']},\n",
       " {'key': ['20', '2003'], 'values': ['9.8']},\n",
       " {'key': ['20', '2004'], 'values': ['9.8']},\n",
       " {'key': ['20', '2005'], 'values': ['9.8']},\n",
       " {'key': ['20', '2006'], 'values': ['9.8']},\n",
       " {'key': ['20', '2007'], 'values': ['9.8']},\n",
       " {'key': ['20', '2008'], 'values': ['9.8']},\n",
       " {'key': ['20', '2009'], 'values': ['9.8']},\n",
       " {'key': ['20', '2010'], 'values': ['9.8']},\n",
       " {'key': ['20', '2011'], 'values': ['9.9']},\n",
       " {'key': ['20', '2012'], 'values': ['9.8']},\n",
       " {'key': ['20', '2013'], 'values': ['9.9']},\n",
       " {'key': ['20', '2014'], 'values': ['10.0']},\n",
       " {'key': ['20', '2015'], 'values': ['10.0']},\n",
       " {'key': ['20', '2016'], 'values': ['10.2']},\n",
       " {'key': ['20', '2017'], 'values': ['10.2']},\n",
       " {'key': ['20', '2018'], 'values': ['10.2']},\n",
       " {'key': ['21', '1991'], 'values': ['15.9']},\n",
       " {'key': ['21', '1992'], 'values': ['15.9']},\n",
       " {'key': ['21', '1993'], 'values': ['15.9']},\n",
       " {'key': ['21', '1994'], 'values': ['15.9']},\n",
       " {'key': ['21', '1995'], 'values': ['15.9']},\n",
       " {'key': ['21', '1996'], 'values': ['15.8']},\n",
       " {'key': ['21', '1997'], 'values': ['15.6']},\n",
       " {'key': ['21', '1998'], 'values': ['15.5']},\n",
       " {'key': ['21', '1999'], 'values': ['15.4']},\n",
       " {'key': ['21', '2000'], 'values': ['15.3']},\n",
       " {'key': ['21', '2001'], 'values': ['15.3']},\n",
       " {'key': ['21', '2002'], 'values': ['15.2']},\n",
       " {'key': ['21', '2003'], 'values': ['15.2']},\n",
       " {'key': ['21', '2004'], 'values': ['15.2']},\n",
       " {'key': ['21', '2005'], 'values': ['15.2']},\n",
       " {'key': ['21', '2006'], 'values': ['15.1']},\n",
       " {'key': ['21', '2007'], 'values': ['15.1']},\n",
       " {'key': ['21', '2008'], 'values': ['15.2']},\n",
       " {'key': ['21', '2009'], 'values': ['15.2']},\n",
       " {'key': ['21', '2010'], 'values': ['15.2']},\n",
       " {'key': ['21', '2011'], 'values': ['15.2']},\n",
       " {'key': ['21', '2012'], 'values': ['15.2']},\n",
       " {'key': ['21', '2013'], 'values': ['15.3']},\n",
       " {'key': ['21', '2014'], 'values': ['15.5']},\n",
       " {'key': ['21', '2015'], 'values': ['15.6']},\n",
       " {'key': ['21', '2016'], 'values': ['15.7']},\n",
       " {'key': ['21', '2017'], 'values': ['15.8']},\n",
       " {'key': ['21', '2018'], 'values': ['15.8']},\n",
       " {'key': ['22', '1991'], 'values': ['12.1']},\n",
       " {'key': ['22', '1992'], 'values': ['12.0']},\n",
       " {'key': ['22', '1993'], 'values': ['12.0']},\n",
       " {'key': ['22', '1994'], 'values': ['12.0']},\n",
       " {'key': ['22', '1995'], 'values': ['11.9']},\n",
       " {'key': ['22', '1996'], 'values': ['11.8']},\n",
       " {'key': ['22', '1997'], 'values': ['11.7']},\n",
       " {'key': ['22', '1998'], 'values': ['11.6']},\n",
       " {'key': ['22', '1999'], 'values': ['11.5']},\n",
       " {'key': ['22', '2000'], 'values': ['11.4']},\n",
       " {'key': ['22', '2001'], 'values': ['11.3']},\n",
       " {'key': ['22', '2002'], 'values': ['11.3']},\n",
       " {'key': ['22', '2003'], 'values': ['11.3']},\n",
       " {'key': ['22', '2004'], 'values': ['11.3']},\n",
       " {'key': ['22', '2005'], 'values': ['11.2']},\n",
       " {'key': ['22', '2006'], 'values': ['11.3']},\n",
       " {'key': ['22', '2007'], 'values': ['11.2']},\n",
       " {'key': ['22', '2008'], 'values': ['11.2']},\n",
       " {'key': ['22', '2009'], 'values': ['11.2']},\n",
       " {'key': ['22', '2010'], 'values': ['11.2']},\n",
       " {'key': ['22', '2011'], 'values': ['11.2']},\n",
       " {'key': ['22', '2012'], 'values': ['11.2']},\n",
       " {'key': ['22', '2013'], 'values': ['11.2']},\n",
       " {'key': ['22', '2014'], 'values': ['11.3']},\n",
       " {'key': ['22', '2015'], 'values': ['11.3']},\n",
       " {'key': ['22', '2016'], 'values': ['11.4']},\n",
       " {'key': ['22', '2017'], 'values': ['11.4']},\n",
       " {'key': ['22', '2018'], 'values': ['11.4']},\n",
       " {'key': ['23', '1991'], 'values': ['2.8']},\n",
       " {'key': ['23', '1992'], 'values': ['2.7']},\n",
       " {'key': ['23', '1993'], 'values': ['2.8']},\n",
       " {'key': ['23', '1994'], 'values': ['2.8']},\n",
       " {'key': ['23', '1995'], 'values': ['2.7']},\n",
       " {'key': ['23', '1996'], 'values': ['2.7']},\n",
       " {'key': ['23', '1997'], 'values': ['2.7']},\n",
       " {'key': ['23', '1998'], 'values': ['2.7']},\n",
       " {'key': ['23', '1999'], 'values': ['2.6']},\n",
       " {'key': ['23', '2000'], 'values': ['2.6']},\n",
       " {'key': ['23', '2001'], 'values': ['2.6']},\n",
       " {'key': ['23', '2002'], 'values': ['2.6']},\n",
       " {'key': ['23', '2003'], 'values': ['2.6']},\n",
       " {'key': ['23', '2004'], 'values': ['2.6']},\n",
       " {'key': ['23', '2005'], 'values': ['2.6']},\n",
       " {'key': ['23', '2006'], 'values': ['2.6']},\n",
       " {'key': ['23', '2007'], 'values': ['2.6']},\n",
       " {'key': ['23', '2008'], 'values': ['2.6']},\n",
       " {'key': ['23', '2009'], 'values': ['2.6']},\n",
       " {'key': ['23', '2010'], 'values': ['2.6']},\n",
       " {'key': ['23', '2011'], 'values': ['2.6']},\n",
       " {'key': ['23', '2012'], 'values': ['2.5']},\n",
       " {'key': ['23', '2013'], 'values': ['2.6']},\n",
       " {'key': ['23', '2014'], 'values': ['2.6']},\n",
       " {'key': ['23', '2015'], 'values': ['2.6']},\n",
       " {'key': ['23', '2016'], 'values': ['2.6']},\n",
       " {'key': ['23', '2017'], 'values': ['2.7']},\n",
       " {'key': ['23', '2018'], 'values': ['2.7']},\n",
       " {'key': ['24', '1991'], 'values': ['4.6']},\n",
       " {'key': ['24', '1992'], 'values': ['4.6']},\n",
       " {'key': ['24', '1993'], 'values': ['4.7']},\n",
       " {'key': ['24', '1994'], 'values': ['4.7']},\n",
       " {'key': ['24', '1995'], 'values': ['4.7']},\n",
       " {'key': ['24', '1996'], 'values': ['4.7']},\n",
       " {'key': ['24', '1997'], 'values': ['4.7']},\n",
       " {'key': ['24', '1998'], 'values': ['4.7']},\n",
       " {'key': ['24', '1999'], 'values': ['4.7']},\n",
       " {'key': ['24', '2000'], 'values': ['4.6']},\n",
       " {'key': ['24', '2001'], 'values': ['4.6']},\n",
       " {'key': ['24', '2002'], 'values': ['4.6']},\n",
       " {'key': ['24', '2003'], 'values': ['4.6']},\n",
       " {'key': ['24', '2004'], 'values': ['4.7']},\n",
       " {'key': ['24', '2005'], 'values': ['4.7']},\n",
       " {'key': ['24', '2006'], 'values': ['4.7']},\n",
       " {'key': ['24', '2007'], 'values': ['4.7']},\n",
       " {'key': ['24', '2008'], 'values': ['4.7']},\n",
       " {'key': ['24', '2009'], 'values': ['4.7']},\n",
       " {'key': ['24', '2010'], 'values': ['4.7']},\n",
       " {'key': ['24', '2011'], 'values': ['4.7']},\n",
       " {'key': ['24', '2012'], 'values': ['4.7']},\n",
       " {'key': ['24', '2013'], 'values': ['4.8']},\n",
       " {'key': ['24', '2014'], 'values': ['4.8']},\n",
       " {'key': ['24', '2015'], 'values': ['4.8']},\n",
       " {'key': ['24', '2016'], 'values': ['4.9']},\n",
       " {'key': ['24', '2017'], 'values': ['4.9']},\n",
       " {'key': ['24', '2018'], 'values': ['4.9']},\n",
       " {'key': ['25', '1991'], 'values': ['2.7']},\n",
       " {'key': ['25', '1992'], 'values': ['2.7']},\n",
       " {'key': ['25', '1993'], 'values': ['2.7']},\n",
       " {'key': ['25', '1994'], 'values': ['2.7']},\n",
       " {'key': ['25', '1995'], 'values': ['2.7']},\n",
       " {'key': ['25', '1996'], 'values': ['2.7']},\n",
       " {'key': ['25', '1997'], 'values': ['2.7']},\n",
       " {'key': ['25', '1998'], 'values': ['2.6']},\n",
       " {'key': ['25', '1999'], 'values': ['2.6']},\n",
       " {'key': ['25', '2000'], 'values': ['2.6']},\n",
       " {'key': ['25', '2001'], 'values': ['2.6']},\n",
       " {'key': ['25', '2002'], 'values': ['2.6']},\n",
       " {'key': ['25', '2003'], 'values': ['2.6']},\n",
       " {'key': ['25', '2004'], 'values': ['2.6']},\n",
       " {'key': ['25', '2005'], 'values': ['2.6']},\n",
       " {'key': ['25', '2006'], 'values': ['2.6']},\n",
       " {'key': ['25', '2007'], 'values': ['2.6']},\n",
       " {'key': ['25', '2008'], 'values': ['2.5']},\n",
       " {'key': ['25', '2009'], 'values': ['2.5']},\n",
       " {'key': ['25', '2010'], 'values': ['2.5']},\n",
       " {'key': ['25', '2011'], 'values': ['2.6']},\n",
       " {'key': ['25', '2012'], 'values': ['2.5']},\n",
       " {'key': ['25', '2013'], 'values': ['2.6']},\n",
       " {'key': ['25', '2014'], 'values': ['2.6']},\n",
       " {'key': ['25', '2015'], 'values': ['2.6']},\n",
       " {'key': ['25', '2016'], 'values': ['2.6']},\n",
       " {'key': ['25', '2017'], 'values': ['2.6']},\n",
       " {'key': ['25', '2018'], 'values': ['2.6']}]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 24
    }
   ]
  },
  {
   "metadata": {
    "id": "cm3g_49v-lZB",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now we need to understand the structure of the data. We have gotten a list of dictionaries where the first variable 'key' contains the domain (in this case county and year), and the variable 'values' contains the value of the observation variable (in this case inhabitants per square kilometer). To change this into time series that can be used for visualisation we need a few syntactic tricks that are described below. "
   ]
  },
  {
   "metadata": {
    "id": "rM3UQ9Oe_Z3f",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Data processing"
   ]
  },
  {
   "metadata": {
    "id": "FtZYcbB-ARn1",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "What we look for is one time series per county. Therefore we need to restructure the data we have gotten before we can do anything. This is outside the functionality of pyscbwrapper, but we can easily solve it. A good structure would be a dictionary with county as key and another dictionary as value, where the inner dictionary has year as key and variable value as value. To achieve this we take the list of counties that we created earlier and connect it to the county codes, which we can take from their place in get_query(), in this case 0. By comparing these codes, which are now connected to the county names, to the codes in the data, we can connect the county names to the data. \n",
    "\n",
    "This way we create the structure we want, and we take the opportunity to cast the the values as numeric. "
   ]
  },
  {
   "metadata": {
    "id": "AuPqzGOl_ZDM",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "codes = scb.get_query()['query'][0]['selection']['values']\n",
    "\n",
    "countydic = {}\n",
    "for i in range(len(codes)):\n",
    "  countydic[codes[i]] = county[i]\n",
    "\n",
    "\n",
    "countydata = {}\n",
    "\n",
    "for code in countydic:\n",
    "  countydata[countydic[code]] = {}\n",
    "  for i in range(len(scb_fetch)):\n",
    "    if scb_fetch[i]['key'][0] == code:\n",
    "      countydata[countydic[code]][scb_fetch[i]['key'][1]] = \\\n",
    "      float(scb_fetch[i]['values'][0])\n"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "QUn5LPlLFs1H",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "This got a bit hacky, so let's see if we got the structure we wanted: "
   ]
  },
  {
   "metadata": {
    "id": "9ihNQozVFufv",
    "colab_type": "code",
    "outputId": "75ca7754-4c64-42a9-c020-8751e3479825",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 10601
    }
   },
   "cell_type": "code",
   "source": [
    "countydata"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'Blekinge county': {'1991': 51.4,\n",
       "  '1992': 51.4,\n",
       "  '1993': 51.6,\n",
       "  '1994': 52.0,\n",
       "  '1995': 51.9,\n",
       "  '1996': 51.7,\n",
       "  '1997': 51.6,\n",
       "  '1998': 51.5,\n",
       "  '1999': 51.1,\n",
       "  '2000': 51.0,\n",
       "  '2001': 50.9,\n",
       "  '2002': 50.9,\n",
       "  '2003': 50.9,\n",
       "  '2004': 51.0,\n",
       "  '2005': 51.1,\n",
       "  '2006': 51.4,\n",
       "  '2007': 51.5,\n",
       "  '2008': 51.7,\n",
       "  '2009': 51.8,\n",
       "  '2010': 52.0,\n",
       "  '2011': 52.2,\n",
       "  '2012': 51.9,\n",
       "  '2013': 52.1,\n",
       "  '2014': 52.6,\n",
       "  '2015': 53.3,\n",
       "  '2016': 54.1,\n",
       "  '2017': 54.4,\n",
       "  '2018': 54.5},\n",
       " 'Dalarna county': {'1991': 10.3,\n",
       "  '1992': 10.3,\n",
       "  '1993': 10.3,\n",
       "  '1994': 10.3,\n",
       "  '1995': 10.3,\n",
       "  '1996': 10.2,\n",
       "  '1997': 10.1,\n",
       "  '1998': 10.0,\n",
       "  '1999': 10.0,\n",
       "  '2000': 9.9,\n",
       "  '2001': 9.8,\n",
       "  '2002': 9.8,\n",
       "  '2003': 9.8,\n",
       "  '2004': 9.8,\n",
       "  '2005': 9.8,\n",
       "  '2006': 9.8,\n",
       "  '2007': 9.8,\n",
       "  '2008': 9.8,\n",
       "  '2009': 9.8,\n",
       "  '2010': 9.8,\n",
       "  '2011': 9.9,\n",
       "  '2012': 9.8,\n",
       "  '2013': 9.9,\n",
       "  '2014': 10.0,\n",
       "  '2015': 10.0,\n",
       "  '2016': 10.2,\n",
       "  '2017': 10.2,\n",
       "  '2018': 10.2},\n",
       " 'Gotland county': {'1991': 18.3,\n",
       "  '1992': 18.3,\n",
       "  '1993': 18.4,\n",
       "  '1994': 18.5,\n",
       "  '1995': 18.5,\n",
       "  '1996': 18.5,\n",
       "  '1997': 18.4,\n",
       "  '1998': 18.4,\n",
       "  '1999': 18.2,\n",
       "  '2000': 18.2,\n",
       "  '2001': 18.2,\n",
       "  '2002': 18.2,\n",
       "  '2003': 18.3,\n",
       "  '2004': 18.3,\n",
       "  '2005': 18.2,\n",
       "  '2006': 18.2,\n",
       "  '2007': 18.1,\n",
       "  '2008': 18.1,\n",
       "  '2009': 18.2,\n",
       "  '2010': 18.2,\n",
       "  '2011': 18.3,\n",
       "  '2012': 18.2,\n",
       "  '2013': 18.2,\n",
       "  '2014': 18.3,\n",
       "  '2015': 18.3,\n",
       "  '2016': 18.5,\n",
       "  '2017': 18.7,\n",
       "  '2018': 18.9},\n",
       " 'Gävleborg county': {'1991': 15.9,\n",
       "  '1992': 15.9,\n",
       "  '1993': 15.9,\n",
       "  '1994': 15.9,\n",
       "  '1995': 15.9,\n",
       "  '1996': 15.8,\n",
       "  '1997': 15.6,\n",
       "  '1998': 15.5,\n",
       "  '1999': 15.4,\n",
       "  '2000': 15.3,\n",
       "  '2001': 15.3,\n",
       "  '2002': 15.2,\n",
       "  '2003': 15.2,\n",
       "  '2004': 15.2,\n",
       "  '2005': 15.2,\n",
       "  '2006': 15.1,\n",
       "  '2007': 15.1,\n",
       "  '2008': 15.2,\n",
       "  '2009': 15.2,\n",
       "  '2010': 15.2,\n",
       "  '2011': 15.2,\n",
       "  '2012': 15.2,\n",
       "  '2013': 15.3,\n",
       "  '2014': 15.5,\n",
       "  '2015': 15.6,\n",
       "  '2016': 15.7,\n",
       "  '2017': 15.8,\n",
       "  '2018': 15.8},\n",
       " 'Halland county': {'1991': 47.3,\n",
       "  '1992': 47.9,\n",
       "  '1993': 48.5,\n",
       "  '1994': 49.1,\n",
       "  '1995': 49.4,\n",
       "  '1996': 49.5,\n",
       "  '1997': 49.7,\n",
       "  '1998': 50.0,\n",
       "  '1999': 50.1,\n",
       "  '2000': 50.4,\n",
       "  '2001': 50.7,\n",
       "  '2002': 51.0,\n",
       "  '2003': 51.5,\n",
       "  '2004': 52.0,\n",
       "  '2005': 52.3,\n",
       "  '2006': 52.9,\n",
       "  '2007': 53.4,\n",
       "  '2008': 53.8,\n",
       "  '2009': 54.3,\n",
       "  '2010': 54.8,\n",
       "  '2011': 55.6,\n",
       "  '2012': 56.0,\n",
       "  '2013': 56.5,\n",
       "  '2014': 57.2,\n",
       "  '2015': 58.0,\n",
       "  '2016': 59.0,\n",
       "  '2017': 59.9,\n",
       "  '2018': 60.7},\n",
       " 'Jämtland county': {'1991': 2.8,\n",
       "  '1992': 2.7,\n",
       "  '1993': 2.8,\n",
       "  '1994': 2.8,\n",
       "  '1995': 2.7,\n",
       "  '1996': 2.7,\n",
       "  '1997': 2.7,\n",
       "  '1998': 2.7,\n",
       "  '1999': 2.6,\n",
       "  '2000': 2.6,\n",
       "  '2001': 2.6,\n",
       "  '2002': 2.6,\n",
       "  '2003': 2.6,\n",
       "  '2004': 2.6,\n",
       "  '2005': 2.6,\n",
       "  '2006': 2.6,\n",
       "  '2007': 2.6,\n",
       "  '2008': 2.6,\n",
       "  '2009': 2.6,\n",
       "  '2010': 2.6,\n",
       "  '2011': 2.6,\n",
       "  '2012': 2.5,\n",
       "  '2013': 2.6,\n",
       "  '2014': 2.6,\n",
       "  '2015': 2.6,\n",
       "  '2016': 2.6,\n",
       "  '2017': 2.7,\n",
       "  '2018': 2.7},\n",
       " 'Jönköping county': {'1991': 31.2,\n",
       "  '1992': 31.2,\n",
       "  '1993': 31.3,\n",
       "  '1994': 31.4,\n",
       "  '1995': 31.5,\n",
       "  '1996': 31.4,\n",
       "  '1997': 31.3,\n",
       "  '1998': 31.3,\n",
       "  '1999': 31.2,\n",
       "  '2000': 31.2,\n",
       "  '2001': 31.2,\n",
       "  '2002': 31.2,\n",
       "  '2003': 31.3,\n",
       "  '2004': 31.4,\n",
       "  '2005': 31.5,\n",
       "  '2006': 31.6,\n",
       "  '2007': 31.8,\n",
       "  '2008': 31.9,\n",
       "  '2009': 32.0,\n",
       "  '2010': 32.1,\n",
       "  '2011': 32.4,\n",
       "  '2012': 32.4,\n",
       "  '2013': 32.7,\n",
       "  '2014': 33.0,\n",
       "  '2015': 33.3,\n",
       "  '2016': 33.8,\n",
       "  '2017': 34.2,\n",
       "  '2018': 34.6},\n",
       " 'Kalmar county': {'1991': 21.7,\n",
       "  '1992': 21.7,\n",
       "  '1993': 21.7,\n",
       "  '1994': 21.8,\n",
       "  '1995': 21.8,\n",
       "  '1996': 21.7,\n",
       "  '1997': 21.5,\n",
       "  '1998': 21.3,\n",
       "  '1999': 21.1,\n",
       "  '2000': 21.0,\n",
       "  '2001': 20.9,\n",
       "  '2002': 20.9,\n",
       "  '2003': 20.9,\n",
       "  '2004': 20.9,\n",
       "  '2005': 20.9,\n",
       "  '2006': 20.8,\n",
       "  '2007': 20.8,\n",
       "  '2008': 20.8,\n",
       "  '2009': 20.8,\n",
       "  '2010': 20.8,\n",
       "  '2011': 20.9,\n",
       "  '2012': 20.9,\n",
       "  '2013': 21.0,\n",
       "  '2014': 21.1,\n",
       "  '2015': 21.3,\n",
       "  '2016': 21.7,\n",
       "  '2017': 21.8,\n",
       "  '2018': 21.9},\n",
       " 'Kronoberg county': {'1991': 21.1,\n",
       "  '1992': 21.2,\n",
       "  '1993': 21.2,\n",
       "  '1994': 21.4,\n",
       "  '1995': 21.3,\n",
       "  '1996': 21.2,\n",
       "  '1997': 21.2,\n",
       "  '1998': 21.1,\n",
       "  '1999': 20.9,\n",
       "  '2000': 20.9,\n",
       "  '2001': 20.9,\n",
       "  '2002': 20.9,\n",
       "  '2003': 21.0,\n",
       "  '2004': 21.1,\n",
       "  '2005': 21.1,\n",
       "  '2006': 21.2,\n",
       "  '2007': 21.3,\n",
       "  '2008': 21.5,\n",
       "  '2009': 21.6,\n",
       "  '2010': 21.7,\n",
       "  '2011': 21.9,\n",
       "  '2012': 22.0,\n",
       "  '2013': 22.2,\n",
       "  '2014': 22.5,\n",
       "  '2015': 22.7,\n",
       "  '2016': 23.1,\n",
       "  '2017': 23.4,\n",
       "  '2018': 23.7},\n",
       " 'Norrbotten county': {'1991': 2.7,\n",
       "  '1992': 2.7,\n",
       "  '1993': 2.7,\n",
       "  '1994': 2.7,\n",
       "  '1995': 2.7,\n",
       "  '1996': 2.7,\n",
       "  '1997': 2.7,\n",
       "  '1998': 2.6,\n",
       "  '1999': 2.6,\n",
       "  '2000': 2.6,\n",
       "  '2001': 2.6,\n",
       "  '2002': 2.6,\n",
       "  '2003': 2.6,\n",
       "  '2004': 2.6,\n",
       "  '2005': 2.6,\n",
       "  '2006': 2.6,\n",
       "  '2007': 2.6,\n",
       "  '2008': 2.5,\n",
       "  '2009': 2.5,\n",
       "  '2010': 2.5,\n",
       "  '2011': 2.6,\n",
       "  '2012': 2.5,\n",
       "  '2013': 2.6,\n",
       "  '2014': 2.6,\n",
       "  '2015': 2.6,\n",
       "  '2016': 2.6,\n",
       "  '2017': 2.6,\n",
       "  '2018': 2.6},\n",
       " 'Skåne county': {'1991': 97.8,\n",
       "  '1992': 98.5,\n",
       "  '1993': 99.2,\n",
       "  '1994': 100.3,\n",
       "  '1995': 100.8,\n",
       "  '1996': 101.1,\n",
       "  '1997': 101.3,\n",
       "  '1998': 101.6,\n",
       "  '1999': 101.8,\n",
       "  '2000': 102.3,\n",
       "  '2001': 103.0,\n",
       "  '2002': 103.8,\n",
       "  '2003': 104.5,\n",
       "  '2004': 105.2,\n",
       "  '2005': 106.0,\n",
       "  '2006': 107.3,\n",
       "  '2007': 108.7,\n",
       "  '2008': 110.1,\n",
       "  '2009': 111.6,\n",
       "  '2010': 112.7,\n",
       "  '2011': 114.2,\n",
       "  '2012': 115.1,\n",
       "  '2013': 116.2,\n",
       "  '2014': 117.5,\n",
       "  '2015': 118.9,\n",
       "  '2016': 120.8,\n",
       "  '2017': 122.6,\n",
       "  '2018': 124.2},\n",
       " 'Stockholm county': {'1991': 254.9,\n",
       "  '1992': 257.3,\n",
       "  '1993': 259.8,\n",
       "  '1994': 263.2,\n",
       "  '1995': 265.9,\n",
       "  '1996': 268.8,\n",
       "  '1997': 271.6,\n",
       "  '1998': 274.8,\n",
       "  '1999': 276.6,\n",
       "  '2000': 279.7,\n",
       "  '2001': 282.1,\n",
       "  '2002': 283.8,\n",
       "  '2003': 285.4,\n",
       "  '2004': 287.3,\n",
       "  '2005': 289.9,\n",
       "  '2006': 294.2,\n",
       "  '2007': 299.0,\n",
       "  '2008': 303.9,\n",
       "  '2009': 309.7,\n",
       "  '2010': 315.1,\n",
       "  '2011': 320.5,\n",
       "  '2012': 325.9,\n",
       "  '2013': 331.4,\n",
       "  '2014': 336.9,\n",
       "  '2015': 342.0,\n",
       "  '2016': 347.8,\n",
       "  '2017': 353.8,\n",
       "  '2018': 359.9},\n",
       " 'Södermanland county': {'1991': 42.4,\n",
       "  '1992': 42.5,\n",
       "  '1993': 42.8,\n",
       "  '1994': 42.9,\n",
       "  '1995': 42.7,\n",
       "  '1996': 42.5,\n",
       "  '1997': 42.4,\n",
       "  '1998': 42.3,\n",
       "  '1999': 41.9,\n",
       "  '2000': 42.0,\n",
       "  '2001': 42.1,\n",
       "  '2002': 42.4,\n",
       "  '2003': 42.7,\n",
       "  '2004': 42.8,\n",
       "  '2005': 42.9,\n",
       "  '2006': 43.1,\n",
       "  '2007': 43.5,\n",
       "  '2008': 43.8,\n",
       "  '2009': 44.1,\n",
       "  '2010': 44.4,\n",
       "  '2011': 44.9,\n",
       "  '2012': 45.2,\n",
       "  '2013': 45.7,\n",
       "  '2014': 46.2,\n",
       "  '2015': 46.7,\n",
       "  '2016': 47.4,\n",
       "  '2017': 48.0,\n",
       "  '2018': 48.5},\n",
       " 'Uppsala county': {'1991': 39.2,\n",
       "  '1992': 39.9,\n",
       "  '1993': 40.5,\n",
       "  '1994': 41.0,\n",
       "  '1995': 41.3,\n",
       "  '1996': 41.4,\n",
       "  '1997': 41.6,\n",
       "  '1998': 41.7,\n",
       "  '1999': 41.6,\n",
       "  '2000': 41.8,\n",
       "  '2001': 42.2,\n",
       "  '2002': 42.4,\n",
       "  '2003': 42.7,\n",
       "  '2004': 43.0,\n",
       "  '2005': 43.3,\n",
       "  '2006': 39.0,\n",
       "  '2007': 39.4,\n",
       "  '2008': 39.9,\n",
       "  '2009': 40.4,\n",
       "  '2010': 40.9,\n",
       "  '2011': 41.3,\n",
       "  '2012': 41.7,\n",
       "  '2013': 42.2,\n",
       "  '2014': 42.6,\n",
       "  '2015': 43.2,\n",
       "  '2016': 44.1,\n",
       "  '2017': 45.1,\n",
       "  '2018': 46.0},\n",
       " 'Värmland county': {'1991': 16.2,\n",
       "  '1992': 16.2,\n",
       "  '1993': 16.2,\n",
       "  '1994': 16.2,\n",
       "  '1995': 16.1,\n",
       "  '1996': 16.0,\n",
       "  '1997': 15.9,\n",
       "  '1998': 15.8,\n",
       "  '1999': 15.7,\n",
       "  '2000': 15.6,\n",
       "  '2001': 15.6,\n",
       "  '2002': 15.5,\n",
       "  '2003': 15.6,\n",
       "  '2004': 15.6,\n",
       "  '2005': 15.5,\n",
       "  '2006': 15.5,\n",
       "  '2007': 15.6,\n",
       "  '2008': 15.5,\n",
       "  '2009': 15.5,\n",
       "  '2010': 15.5,\n",
       "  '2011': 15.6,\n",
       "  '2012': 15.5,\n",
       "  '2013': 15.6,\n",
       "  '2014': 15.7,\n",
       "  '2015': 15.7,\n",
       "  '2016': 15.9,\n",
       "  '2017': 16.0,\n",
       "  '2018': 16.1},\n",
       " 'Västerbotten county': {'1991': 4.6,\n",
       "  '1992': 4.6,\n",
       "  '1993': 4.7,\n",
       "  '1994': 4.7,\n",
       "  '1995': 4.7,\n",
       "  '1996': 4.7,\n",
       "  '1997': 4.7,\n",
       "  '1998': 4.7,\n",
       "  '1999': 4.7,\n",
       "  '2000': 4.6,\n",
       "  '2001': 4.6,\n",
       "  '2002': 4.6,\n",
       "  '2003': 4.6,\n",
       "  '2004': 4.7,\n",
       "  '2005': 4.7,\n",
       "  '2006': 4.7,\n",
       "  '2007': 4.7,\n",
       "  '2008': 4.7,\n",
       "  '2009': 4.7,\n",
       "  '2010': 4.7,\n",
       "  '2011': 4.7,\n",
       "  '2012': 4.7,\n",
       "  '2013': 4.8,\n",
       "  '2014': 4.8,\n",
       "  '2015': 4.8,\n",
       "  '2016': 4.9,\n",
       "  '2017': 4.9,\n",
       "  '2018': 4.9},\n",
       " 'Västernorrland county': {'1991': 12.1,\n",
       "  '1992': 12.0,\n",
       "  '1993': 12.0,\n",
       "  '1994': 12.0,\n",
       "  '1995': 11.9,\n",
       "  '1996': 11.8,\n",
       "  '1997': 11.7,\n",
       "  '1998': 11.6,\n",
       "  '1999': 11.5,\n",
       "  '2000': 11.4,\n",
       "  '2001': 11.3,\n",
       "  '2002': 11.3,\n",
       "  '2003': 11.3,\n",
       "  '2004': 11.3,\n",
       "  '2005': 11.2,\n",
       "  '2006': 11.3,\n",
       "  '2007': 11.2,\n",
       "  '2008': 11.2,\n",
       "  '2009': 11.2,\n",
       "  '2010': 11.2,\n",
       "  '2011': 11.2,\n",
       "  '2012': 11.2,\n",
       "  '2013': 11.2,\n",
       "  '2014': 11.3,\n",
       "  '2015': 11.3,\n",
       "  '2016': 11.4,\n",
       "  '2017': 11.4,\n",
       "  '2018': 11.4},\n",
       " 'Västmanland county': {'1991': 41.2,\n",
       "  '1992': 41.3,\n",
       "  '1993': 41.4,\n",
       "  '1994': 41.5,\n",
       "  '1995': 41.4,\n",
       "  '1996': 41.3,\n",
       "  '1997': 41.0,\n",
       "  '1998': 40.9,\n",
       "  '1999': 40.7,\n",
       "  '2000': 40.7,\n",
       "  '2001': 40.8,\n",
       "  '2002': 41.0,\n",
       "  '2003': 41.2,\n",
       "  '2004': 41.3,\n",
       "  '2005': 41.4,\n",
       "  '2006': 48.3,\n",
       "  '2007': 48.4,\n",
       "  '2008': 48.6,\n",
       "  '2009': 48.9,\n",
       "  '2010': 49.1,\n",
       "  '2011': 49.7,\n",
       "  '2012': 50.0,\n",
       "  '2013': 50.6,\n",
       "  '2014': 51.1,\n",
       "  '2015': 51.6,\n",
       "  '2016': 52.3,\n",
       "  '2017': 53.0,\n",
       "  '2018': 53.5},\n",
       " 'Västra Götaland county': {'1991': 60.5,\n",
       "  '1992': 60.8,\n",
       "  '1993': 61.2,\n",
       "  '1994': 61.7,\n",
       "  '1995': 61.9,\n",
       "  '1996': 62.0,\n",
       "  '1997': 62.1,\n",
       "  '1998': 62.1,\n",
       "  '1999': 62.1,\n",
       "  '2000': 62.4,\n",
       "  '2001': 62.7,\n",
       "  '2002': 63.0,\n",
       "  '2003': 63.2,\n",
       "  '2004': 63.5,\n",
       "  '2005': 63.8,\n",
       "  '2006': 64.2,\n",
       "  '2007': 64.6,\n",
       "  '2008': 65.0,\n",
       "  '2009': 65.5,\n",
       "  '2010': 66.0,\n",
       "  '2011': 66.8,\n",
       "  '2012': 67.2,\n",
       "  '2013': 67.9,\n",
       "  '2014': 68.6,\n",
       "  '2015': 69.3,\n",
       "  '2016': 70.2,\n",
       "  '2017': 71.0,\n",
       "  '2018': 71.8},\n",
       " 'Örebro county': {'1991': 32.1,\n",
       "  '1992': 32.2,\n",
       "  '1993': 32.3,\n",
       "  '1994': 32.5,\n",
       "  '1995': 32.5,\n",
       "  '1996': 32.4,\n",
       "  '1997': 32.3,\n",
       "  '1998': 32.2,\n",
       "  '1999': 32.0,\n",
       "  '2000': 32.0,\n",
       "  '2001': 32.0,\n",
       "  '2002': 32.0,\n",
       "  '2003': 32.1,\n",
       "  '2004': 32.1,\n",
       "  '2005': 32.1,\n",
       "  '2006': 32.2,\n",
       "  '2007': 32.3,\n",
       "  '2008': 32.5,\n",
       "  '2009': 32.6,\n",
       "  '2010': 32.8,\n",
       "  '2011': 33.1,\n",
       "  '2012': 33.2,\n",
       "  '2013': 33.6,\n",
       "  '2014': 33.9,\n",
       "  '2015': 34.2,\n",
       "  '2016': 34.7,\n",
       "  '2017': 35.1,\n",
       "  '2018': 35.5},\n",
       " 'Östergötland county': {'1991': 38.4,\n",
       "  '1992': 38.7,\n",
       "  '1993': 38.9,\n",
       "  '1994': 39.3,\n",
       "  '1995': 39.4,\n",
       "  '1996': 39.4,\n",
       "  '1997': 39.2,\n",
       "  '1998': 39.0,\n",
       "  '1999': 38.8,\n",
       "  '2000': 38.8,\n",
       "  '2001': 38.9,\n",
       "  '2002': 39.0,\n",
       "  '2003': 39.1,\n",
       "  '2004': 39.2,\n",
       "  '2005': 39.3,\n",
       "  '2006': 39.4,\n",
       "  '2007': 39.7,\n",
       "  '2008': 39.9,\n",
       "  '2009': 40.3,\n",
       "  '2010': 40.5,\n",
       "  '2011': 40.9,\n",
       "  '2012': 41.1,\n",
       "  '2013': 41.5,\n",
       "  '2014': 41.9,\n",
       "  '2015': 42.2,\n",
       "  '2016': 42.8,\n",
       "  '2017': 43.3,\n",
       "  '2018': 43.7}}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 26
    }
   ]
  },
  {
   "metadata": {
    "id": "xTL1IbH0FyPm",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "This looks about right. Now we can loop over the keys and plot the values using key on the x axis and value on the y axis. "
   ]
  },
  {
   "metadata": {
    "id": "R_1L440QF7tG",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "# Data visualisation"
   ]
  },
  {
   "metadata": {
    "id": "V-ijYiU_F-6-",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "We need numpy, pandas, and matplotlib for this. We install and import. "
   ]
  },
  {
   "metadata": {
    "id": "K-fvRDuhGEF3",
    "colab_type": "code",
    "colab": {}
   },
   "cell_type": "code",
   "source": [
    "!pip install -q matplotlib\n",
    "!pip install -q pandas\n",
    "!pip install -q numpy\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "metadata": {
    "id": "rQHNYnXlUW9W",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now we can make a neat graph. "
   ]
  },
  {
   "metadata": {
    "id": "m4WnrQmyUWF6",
    "colab_type": "code",
    "outputId": "c24621a1-1a9b-4cad-db6e-230651df5c49",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    }
   },
   "cell_type": "code",
   "source": [
    "df = pd.DataFrame(countydata)\n",
    "df = df.reset_index()\n",
    "df = df.rename(columns={\"index\":\"Year\"})\n",
    "ax = df.plot(x=df.index, xticks=np.arange(len(df.index)), colormap='hsv')\n",
    "ax.set_xticklabels(df[\"Year\"], rotation=45)\n",
    "plt.title(\"Population density per county\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Inhabitants per square kilometer\")\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "plt.show()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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HhwdxcXGYzeYSF9WoSiQZFEIIUeHO58G2K2pbAphouPELu67KzOM1DPRwNPGI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\nBiUSiUQikUg6MbIwKJFIJBKJRNKJ+X/Hyhu0Cw1LWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ]
  },
  {
   "metadata": {
    "id": "Ha83UCMSUWhe",
    "colab_type": "text"
   },
   "cell_type": "markdown",
   "source": [
    "Now we have what we wanted, and we never left the Python environment!"
   ]
  }
 ]
}