{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/rob/data/OpenDataPortalDresden/csv\n"
     ]
    }
   ],
   "source": [
    "%cd /home/rob/data/OpenDataPortalDresden/csv/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "steuerstatistik_dresden_quartale_md1.csv\r\n"
     ]
    }
   ],
   "source": [
    "%ls steuerstatistik_dresden_quartale_md1.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./steuerstatistik_dresden_quartale_md1.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     934\n",
       "1    4636\n",
       "2     824\n",
       "3    1052\n",
       "4    2615\n",
       "Name: Grundsteuer A und B, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = df['Jahr']\n",
    "tax   = df['Grundsteuer A und B']\n",
    "taxdict = dict(zip(df[\"Jahr\"], df['Grundsteuer A und B']))\n",
    "taxlist = sorted(taxdict.items()) # sorted by key, return a list of tuples\n",
    "tax.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert to some usefull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.relplot(y=\"Jahr\", x=\"Grundsteuer A und B\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}