kiel_number_of_cars.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status 200, OK\n",
      "[{'cars': '54347', 'year': 1970},\n",
      " {'cars': '61394', 'year': 1971},\n",
      " {'cars': '64803', 'year': 1972},\n",
      " {'cars': '67261', 'year': 1973},\n",
      " {'cars': '68046', 'year': 1974},\n",
      " {'cars': '67532', 'year': 1975},\n",
      " {'cars': '69776', 'year': 1976},\n",
      " {'cars': '73169', 'year': 1977},\n",
      " {'cars': '77022', 'year': 1978},\n",
      " {'cars': '80537', 'year': 1979},\n",
      " {'cars': '83606', 'year': 1980},\n",
      " {'cars': '84003', 'year': 1981},\n",
      " {'cars': '83828', 'year': 1982},\n",
      " {'cars': '84225', 'year': 1983},\n",
      " {'cars': '85131', 'year': 1984},\n",
      " {'cars': '85940', 'year': 1985},\n",
      " {'cars': '87574', 'year': 1986},\n",
      " {'cars': '89127', 'year': 1987},\n",
      " {'cars': '92974', 'year': 1988},\n",
      " {'cars': '95176', 'year': 1989},\n",
      " {'cars': '99194', 'year': 1990},\n",
      " {'cars': '100075', 'year': 1991},\n",
      " {'cars': '101065', 'year': 1992},\n",
      " {'cars': '103731', 'year': 1993},\n",
      " {'cars': '105335', 'year': 1994},\n",
      " {'cars': '105894', 'year': 1995},\n",
      " {'cars': '106254', 'year': 1996},\n",
      " {'cars': '106187', 'year': 1997},\n",
      " {'cars': '104839', 'year': 1998},\n",
      " {'cars': '102856', 'year': 1999},\n",
      " {'cars': '100737', 'year': 2000},\n",
      " {'cars': '102988', 'year': 2001},\n",
      " {'cars': '104223', 'year': 2002},\n",
      " {'cars': '104350', 'year': 2003},\n",
      " {'cars': '104262', 'year': 2004},\n",
      " {'cars': '104418', 'year': 2005},\n",
      " {'cars': '106642', 'year': 2006},\n",
      " {'cars': '107472', 'year': 2007},\n",
      " {'cars': '94561', 'year': 2008},\n",
      " {'cars': '94713', 'year': 2009},\n",
      " {'cars': '96294', 'year': 2010},\n",
      " {'cars': '97966', 'year': 2011},\n",
      " {'cars': '99725', 'year': 2012},\n",
      " {'cars': '101101', 'year': 2013},\n",
      " {'cars': '102299', 'year': 2014},\n",
      " {'cars': '103889', 'year': 2015},\n",
      " {'cars': '105759', 'year': 2016},\n",
      " {'cars': '107982', 'year': 2017},\n",
      " {'cars': '109050', 'year': 2018}]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pprint import pprint\n",
    "\n",
    "URL = \"https://xern-statistic.de/api/cars\"\n",
    "\n",
    "### fetch data using requests ###\n",
    "\n",
    "# This part is not really necessary.\n",
    "# It's only here to show a different way to fetch json data.\n",
    "\n",
    "def fetchJsonData(url):\n",
    "    try:\n",
    "        response = requests.get(url)\n",
    "    except OSError:\n",
    "        print('No connection to the server!')\n",
    "        return None\n",
    "\n",
    "    # check if the request is successful\n",
    "    if response.status_code == 200:\n",
    "        print('Status 200, OK')\n",
    "        return response.json()\n",
    "    else:\n",
    "        print('JSON data request not successful!.')\n",
    "        return None\n",
    "\n",
    "json_data = fetchJsonData(URL)\n",
    "pprint(json_data)\n",
    "\n",
    "### fetch data using pandas ###\n",
    "\n",
    "# Fetch JSON data from a given url\n",
    "df = pd.read_json(URL, orient=\"columns\")\n",
    "\n",
    "# View the first ten rows\n",
    "df.head(10)\n",
    "\n",
    "### plot the received data ###\n",
    "\n",
    "# create a subplot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# drop not available (na) data\n",
    "df_cleaned = df.dropna(how='all')\n",
    "\n",
    "x = df_cleaned[\"year\"].values\n",
    "y = df_cleaned[\"cars\"].values\n",
    "\n",
    "plt.title(\"Kiel - Number of Cars\", size=\"x-large\")\n",
    "plt.ylabel(\"\", size=\"x-large\")\n",
    "plt.xlabel(\"Year\", size=\"x-large\")\n",
    "\n",
    "plt.plot(y, \"*-\", markersize=6, linewidth=1, color=\"r\", label=\"Number of Cars\")\n",
    "\n",
    "plt.legend(loc=(0.4, 0.2))\n",
    "\n",
    "ax.set_xticks(range(len(x)))\n",
    "ax.set_xticklabels(x, rotation=\"vertical\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}