autos-vs-inwoners.ipynb

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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%config InlineBackend.figure_formats=['svg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
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      "RangeIndex: 29 entries, 0 to 28\n",
      "Data columns (total 9 columns):\n",
      "jaar                        27 non-null float64\n",
      "Aantal auto’s               29 non-null int64\n",
      "aantal inwoners             29 non-null float64\n",
      "Aantal auto’s tov 2000      29 non-null int64\n",
      "+/- auto’s                  29 non-null int64\n",
      "+/- inwoners                21 non-null float64\n",
      "ratio                       21 non-null float64\n",
      "Per 1000 inw                22 non-null float64\n",
      "Aantal inwoners tov 2000    1 non-null float64\n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 2.1 KB\n"
     ]
    }
   ],
   "source": [
    "data_Gent = pd.read_csv('datasets/Gent_autos_inwoners.csv', decimal=',')\n",
    "data_Gent['aantal inwoners'] = data_Gent['aantal inwoners'].fillna(0).apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Gent['Per 1000 inw'] = data_Gent['Per 1000 inw'].apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Gent.round({'Per 1000 inw': 0, 'jaar': 0})\n",
    "#data_Gent['jaar'] = data_Gent['jaar'].fillna(0).apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Gent.info()"
   ]
  },
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   "execution_count": 314,
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27 entries, 0 to 26\n",
      "Data columns (total 8 columns):\n",
      "jaar                      27 non-null int64\n",
      "Aantal auto’s             27 non-null int64\n",
      "aantal inwoners           24 non-null float64\n",
      "Aantal auto’s tov 2000    27 non-null int64\n",
      "+/- auto’s                26 non-null float64\n",
      "+/- inwoners              23 non-null float64\n",
      "ratio                     23 non-null float64\n",
      "Per 1000 inw              23 non-null float64\n",
      "dtypes: float64(5), int64(3)\n",
      "memory usage: 1.8 KB\n"
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   "source": [
    "data_Gron = pd.read_csv('datasets/Gron_autos_inwoners.csv', decimal=',')\n",
    "data_Gron['Per 1000 inw'] = data_Gron['Per 1000 inw'].apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Gron.round({'Per 1000 inw': 0, 'jaar': 0})\n",
    "data_Gron['jaar'] = data_Gron['jaar'].fillna(0).apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Gron.info()"
   ]
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   "execution_count": 315,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
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      "Data columns (total 8 columns):\n",
      "jaar                      20 non-null int64\n",
      "Aantal auto’s             19 non-null float64\n",
      "aantal inwoners           20 non-null int64\n",
      "Aantal auto’s tov 2000    19 non-null float64\n",
      "+/- auto’s                19 non-null float64\n",
      "+/- inwoners              20 non-null int64\n",
      "ratio                     19 non-null float64\n",
      "Per 1000 inw              19 non-null float64\n",
      "dtypes: float64(5), int64(3)\n",
      "memory usage: 1.3 KB\n"
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   "source": [
    "data_Lpzg = pd.read_csv('datasets/Leipzig_autos_inwoners.csv', decimal=',')\n",
    "data_Lpzg['Per 1000 inw'] = data_Lpzg['Per 1000 inw'].apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Lpzg.round({'Per 1000 inw': 0, 'jaar': 0})\n",
    "data_Lpzg['jaar'] = data_Lpzg['jaar'].fillna(0).apply(pd.to_numeric, downcast = 'signed')\n",
    "data_Lpzg.info()"
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   "source": [
    "x = data_Gent['jaar']\n",
    "y = data_Gent['Aantal auto’s tov 2000']\n",
    "color = 'tab:red'\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.set_xlabel('jaar')\n",
    "ax.set_ylabel('Aantal auto’s t.o.v. 2000 (2000 = 100)', color=color)\n",
    "#select number of cars in 2000 in Ghent\n",
    "count_2000 = data_Gent.loc[data_Gent['jaar'] == 2000]['Aantal auto’s']\n",
    "\n",
    "label='Aantal auto’s tov 2000 in Gent {0}'.format(count_2000)\n",
    "ax.plot(x, y, label=label, color=color)\n",
    "\n",
    "x = data_Gron['jaar']\n",
    "y = data_Gron['Aantal auto’s tov 2000']\n",
    "color = 'tab:blue'\n",
    "ax.plot(x, y, label='Aantal auto’s tov 2000 in Groningen', color=color)\n",
    "\n",
    "x = data_Lpzg['jaar']\n",
    "y = data_Lpzg['Aantal auto’s tov 2000']\n",
    "color = 'tab:green'\n",
    "ax.plot(x, y, label='Aantal auto’s tov 2000 in Leipzig', color=color)\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "x = data_Gent['jaar']\n",
    "y = data_Gent['Per 1000 inw']\n",
    "color = 'tab:red'\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.set_xlabel('jaar')\n",
    "ax.set_ylabel('Per 1000 inw', color=color)\n",
    "label='Aantal auto\\'s per 1000 inw in Gent'\n",
    "ax.plot(x, y, label=label, color=color)\n",
    "\n",
    "x = data_Gron['jaar']\n",
    "y = data_Gron['Per 1000 inw']\n",
    "color = 'tab:blue'\n",
    "ax.plot(x, y, label='Aantal auto\\'s per 1000 inw in Groningen', color=color)\n",
    "\n",
    "x = data_Lpzg['jaar']\n",
    "y = data_Lpzg['Per 1000 inw']\n",
    "color = 'tab:green'\n",
    "ax.plot(x, y, label='Aantal auto\\'s per 1000 inw in Leipzig', color=color)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 341,
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   "source": [
    "#x = data_Gent['jaar']\n",
    "#y = data_Gent['aantal inwoners']\n",
    "# select records without Null values\n",
    "first_year = data_Gent.loc[data_Gent['aantal inwoners'] > 0]['jaar'].min()\n",
    "x = data_Gent.loc[data_Gent['jaar'] >= first_year]['jaar']\n",
    "y = data_Gent.loc[data_Gent['aantal inwoners'] > 0]['aantal inwoners']\n",
    "color = 'tab:red'\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.set_xlabel('jaar')\n",
    "ax.set_ylabel('aantal inwoners', color=color)\n",
    "label='Aantal inwoners in Gent'\n",
    "ax.plot(x, y, label=label, color=color)\n",
    "\n",
    "x = data_Gron['jaar']\n",
    "y = data_Gron['aantal inwoners']\n",
    "color = 'tab:blue'\n",
    "ax.plot(x, y, label='aantal inwoners in Groningen', color=color)\n",
    "\n",
    "x = data_Lpzg['jaar']\n",
    "y = data_Lpzg['aantal inwoners']\n",
    "color = 'tab:green'\n",
    "ax.plot(x, y, label='aantal inwoners in Leipzig', color=color)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## --- --- --- --- ##\n",
    "fig, (axes, axes3) = plt.subplots(2,1)\n",
    "\n",
    "#fig, axes = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "\n",
    "x = data['jaar']\n",
    "y = data['Aantal auto’s tov 2000']\n",
    "\n",
    "axes.set_xlabel('jaar')\n",
    "axes.set_ylabel('Aantal auto’s tov 2000', color=color)\n",
    "axes.set_title('Gent');\n",
    "axes.plot(x, y, label='Aantal auto’s tov 2000', color=color)\n",
    "axes.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "axes2 = axes.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "axes2.set_ylabel('Auto\\'s Per 1000 inw', color=color)  # we already handled the x-label with ax1\n",
    "\n",
    "z = data['Per 1000 inw'][:22]\n",
    "xx = x[:22]\n",
    "\n",
    "#axes2.plot(xx, z, 'r--')\n",
    "axes2.plot(xx, z, color=color)\n",
    "axes2.tick_params(axis='y', labelcolor=color)\n",
    "#axes2.set_yticks([410, 415, 420])\n",
    "axes.legend()\n",
    "axes2.legend()\n",
    "#fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "\n",
    "\n",
    "color = 'tab:red'\n",
    "\n",
    "#x = data['jaar']\n",
    "y = data['Aantal auto’s']\n",
    "z = data['aantal inwoners']\n",
    "\n",
    "axes3.set_xlabel('jaar')\n",
    "axes3.set_ylabel('Aantal auto’s', color=color)\n",
    "axes3.set_title('Auto\\'s en inwoners in Gent');\n",
    "axes3.plot(x, y, label='Aantal auto’s', color=color)\n",
    "axes3.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "axes4 = axes3.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "axes4.set_ylabel('aantal inwoners', color=color)  # we already handled the x-label with ax1\n",
    "\n",
    "z = data['aantal inwoners']\n",
    "#xx = x[:22]\n",
    "\n",
    "#axes2.plot(xx, z, 'r--')\n",
    "axes4.plot(x, z, color=color)\n",
    "axes4.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "axes3.legend()\n",
    "axes4.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11eb19198>]"
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     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data2['jaar']\n",
    "y = data2['Aantal auto’s tov 2000']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data3['jaar']\n",
    "y = data3['Aantal personenauto’s']\n",
    "fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(x, y, color='r')\n",
    "x = data3['Per 1000 inw']\n",
    "ax.plot(x, y, 'k--')\n",
    "ax.set_xticks([2000, 2005, 2007, 2010, 2015, 2017])\n",
    "ax.set_xlabel('Years')\n",
    "#ax.set_yticks([185000, 200000, 210000, 220000, 230000])\n",
    "ax.set_ylabel('Number of cars in the city')\n",
    "ax.set_title('Numbers of cars in Leipzig')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4 = pd.read_csv('datasets/Verkehr_Kraftfahrzeugbestand.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Kennziffer', 'Einheit', '2001', '2002', '2003', '2004', '2005', '2006',\n",
       "       '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',\n",
       "       '2016', '2017'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "my = data4.loc[:,'2005':'2010']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a43c2b0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my.T.plot(legend = 'best', label = 'type', style='--') #T = swap x and y axes (transpose)"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}