{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing the Textfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import glob\n",
    "plt.style.use('ggplot')\n",
    "import dateutil.parser\n",
    "import re\n",
    "import time\n",
    "from collections import Counter\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing list of file names to iterate through later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "whole_list_of_names = []\n",
    "for name in glob.glob('txtfiles/*'):\n",
    "    name = name.split('/')[-1]\n",
    "    whole_list_of_names.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28876"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(whole_list_of_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [the online database](http://www.bvger.ch/publiws/?lang=de) there are more cases mentioned than we anaylsed. This is due to duplicates in the database and merged cases. We used 'fdupes -dN .' on the command line to locate and remove 300 plus duplicate files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing Regular Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aktennummer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every expression is developed three times, as the verdicts are in three languages, French, German and Italian.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extracting_aktennummer_german(doc):\n",
    "    try:\n",
    "        entscheid = re.search(r'Abteilung [A-Z]+\\n[A-Z]-[0-9]+/[0-9]+', doc)\n",
    "        entscheid = entscheid.group()\n",
    "        entscheid = entscheid.replace('\\n', '')\n",
    "        return entscheid\n",
    "    except:\n",
    "        None\n",
    "        \n",
    "def extracting_aktennummer_french(doc):\n",
    "    try:\n",
    "        entscheid = re.search(r'Cour [A-Z]+\\n[A-Z]-[0-9]+/[0-9]+', doc)\n",
    "        entscheid = entscheid.group()\n",
    "        entscheid = entscheid.replace('\\n', '')\n",
    "        return entscheid\n",
    "    except:\n",
    "        None\n",
    "        \n",
    "def extracting_aktennummer_italian(doc):\n",
    "    try:\n",
    "        entscheid = re.search(r'Corte [A-Z]+\\n[A-Z]-[0-9]+/[0-9]+', doc)\n",
    "        entscheid = entscheid.group()\n",
    "        entscheid = entscheid.replace('\\n', '')\n",
    "        return entscheid\n",
    "    except:\n",
    "        None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entscheide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extracting_entscheid_italian(doc):\n",
    "    try:\n",
    "        doc = doc.replace('\\n1.  \\n', '')\n",
    "        doc = doc.replace('\\n1. \\n1.', '')\n",
    "        doc = doc.replace('\\n1.\\n1.', '')\n",
    "        entscheid = re.findall(r'Tribunale amministrativo federale pronuncia:\\n.*([^.]*)', doc)\n",
    "        entscheid = entscheid[0].replace('Oggetto', '').replace('\\n', '').strip()\n",
    "        entscheid = entscheid[:150]\n",
    "        return entscheid\n",
    "    except:\n",
    "        None\n",
    "\n",
    "def extracting_entscheid_french(doc):\n",
    "    try:\n",
    "        doc = doc.replace('\\n1.  \\n', '')\n",
    "        doc = doc.replace('\\n1. \\n1.', '')\n",
    "        doc = doc.replace('\\n1.\\n1.', '')\n",
    "        doc = doc.replace('\\n1.\\n\\n', '')\n",
    "        doc = doc.replace('\\n', '')\n",
    "        entscheid = re.findall(r'le Tribunal administratif fédéral prononce\\s*:1.([^.]*)', doc)\n",
    "        entscheid = entscheid[:150]\n",
    "        return entscheid\n",
    "    except:\n",
    "        None\n",
    "\n",
    "def extracting_entscheid_german(doc):\n",
    "    try:\n",
    "        #search_date = re.search(r'[0-9]+\\.', doc)\n",
    "        #search_date = search_date.group()\n",
    "        #doc = doc.replace(search_date, '')\n",
    "        doc = doc.replace('4.', '')\n",
    "        doc = doc.replace('6.', '')\n",
    "        doc = doc.replace('13.', '')\n",
    "        doc = doc.replace('8.', '')\n",
    "        doc = doc.replace('24.', '')\n",
    "        doc = doc.replace('25.', '')\n",
    "        doc = doc.replace('18.', '')\n",
    "        doc = doc.replace('30.', '')\n",
    "        doc = doc.replace('\\n1.  \\n', '')\n",
    "        doc = doc.replace('\\n1. \\n1.', '')\n",
    "        doc = doc.replace('\\n1.\\n1.', '')\n",
    "        entscheid = re.findall(r'erkennt das Bundesverwaltungsgericht\\s*:\\s*1.([^.]*)', doc)\n",
    "        entscheid = entscheid[:150]\n",
    "        return entscheid\n",
    "    except:\n",
    "        None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gegenstand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using findall, because the word might occur several times in the document. This way we can just take the first instance, making sure we are extracting correct term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extracting_gegenstand_italian(doc):\n",
    "    try:\n",
    "        gegenstand = re.findall(r'Oggetto.*([^,]*)', doc)\n",
    "        gegenstand = gegenstand[0].replace('Oggetto', '').replace('\\n', '').strip()\n",
    "        gegenstand = gegenstand[:84]\n",
    "        return gegenstand\n",
    "    except:\n",
    "        None\n",
    "        \n",
    "def extracting_gegenstand_french(doc):\n",
    "    try:\n",
    "        gegenstand = re.findall(r'Objet.*([^,]*)', doc)\n",
    "        gegenstand = gegenstand[0].replace('Objet', '').replace('\\n', '').strip()\n",
    "        gegenstand = gegenstand[:84]\n",
    "        return gegenstand\n",
    "    except:\n",
    "        None\n",
    "\n",
    "def extracting_gegenstand_german(doc):\n",
    "    try:\n",
    "        gegenstand = re.findall(r'Gegenstand.*([^,]*)', doc)\n",
    "        gegenstand = gegenstand[0].replace('Gegenstand', '').replace('\\n', '').strip()\n",
    "        gegenstand = gegenstand[:84]\n",
    "        return gegenstand\n",
    "    except:\n",
    "        None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most important thing to keep in mind here is to use the various special characters from [French](http://unicode.e-workers.de/franzoesisch.php), [Italian](http://unicode.e-workers.de/italienisch.php) and German. Same here also applies findall usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extracting_date_french(doc):\n",
    "    Datum = re.findall(r\"Arrêt du [0-9]+[er]* [A-Z]*[éèàâæûa-z]+ 20[0-9]+\", doc)\n",
    "    try:\n",
    "        Datum = Datum[0]\n",
    "    except:\n",
    "        None\n",
    "    Datum = str(Datum).replace(\"['\", '').replace(\"']\", '').replace('Arrêt du', '').strip()\n",
    "    Datum = Datum.replace('1er', '1')\n",
    "    return Datum\n",
    "\n",
    "def extracting_date_german(doc):\n",
    "    Datum = re.findall(r\"Urteil vom [0-9]+.[ ]*[ÄÖÜA-Z][äüöa-z]+ 20[0-9]+\", doc)\n",
    "    try:\n",
    "        Datum = Datum[0]\n",
    "    except:\n",
    "        None\n",
    "    Datum = str(Datum).replace(\"['\", '').replace(\"']\", '').replace('Urteil vom ', '').strip()\n",
    "    Datum = Datum.replace(\".Ap\", '. Ap')\n",
    "    return Datum\n",
    "\n",
    "def extracting_date_italian(doc):\n",
    "    #\n",
    "    Datum = re.findall(r\"Sentenza del[l']*[ ]*[0-9]+[ |°][a-z]+ 20[0-9]+\", doc)\n",
    "    try:\n",
    "        Datum = Datum[0]\n",
    "    except:\n",
    "        None\n",
    "    Datum = str(Datum).replace(\"['\", '').replace(\"']\", '').replace('Sentenza del ', '').replace('°', ' ').strip()\n",
    "    Datum = Datum.replace(\"Sentenza dell'\", '')\n",
    "    return Datum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting list of judges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of judges was pulled of the [Website](http://www.bvger.ch/) with a separate scraper. This gave us the current judges. Judges from earlier years were research by hand from documentation in [Swiss parlament](https://www.parlament.ch/de/ratsbetrieb/amtliches-bulletin/amtliches-bulletin-erkl%C3%A4rt). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_richter = pd.read_csv('data/richter_partei.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the list of judges with their party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "relevant_clean_judges = list(df_richter['Nachname'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with the Lawyers and countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def lawyers_countries(x):\n",
    "    avocat_countries = re.search('Parties\\n*.*\\n*.*', x)\n",
    "    anwalt_countries = re.search('Parteien\\n*.*\\n*.*', x)\n",
    "    avvocato_countries = re.search('Parti\\n*.*\\n*.*', x)\n",
    "    \n",
    "    try:\n",
    "        if anwalt_countries != None:\n",
    "            anwalt_countries = anwalt_countries.group()\n",
    "            x = x.replace(anwalt_countries, '|||')\n",
    "            return x\n",
    "        elif avocat_countries != None:\n",
    "            avocat_countries = avocat_countries.group()\n",
    "            x = x.replace(avocat_countries, '|||')\n",
    "            return text\n",
    "        elif avvocato_countries != None:\n",
    "            avvocato_countries = avvocato_countries.group()\n",
    "            x = x.replace(avvocato_countries, '|||')\n",
    "            return x\n",
    "        else:\n",
    "            return x\n",
    "    except:\n",
    "        None "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with the Gerichtsschreiber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gerichtsschreiber(x):\n",
    "    \n",
    "    gerichtsschreiber = re.search(r'Gerichtsschreiber.*\\.', x)\n",
    "    gerichtsschreiberin = re.search(r'Gerichtsschreiberin.*\\n*', x)\n",
    "    cancelliera = re.search(r'cancellier.*,', x)\n",
    "    greffier = re.search(r'Greffier:.*', x)\n",
    "    \n",
    "    try:\n",
    "        if gerichtsschreiber != None:\n",
    "            gerichtsschreiber = gerichtsschreiber.group()\n",
    "            x = x.replace(gerichtsschreiber, '|||')\n",
    "            return x\n",
    "        elif gerichtsschreiberin != None:\n",
    "            gerichtsschreiberin = gerichtsschreiberin.group()\n",
    "            x = x.replace(gerichtsschreiberin, '|||')\n",
    "            return x\n",
    "        elif cancelliera != None:\n",
    "            cancelliera = cancelliera.group()\n",
    "            x = x.replace(cancelliera, '|||')\n",
    "            return x\n",
    "        elif greffier != None:\n",
    "            greffier = greffier.group()\n",
    "            x = x.replace(greffier, '|||')\n",
    "            return x\n",
    "        \n",
    "        for y in relevant_clean_judges:\n",
    "            y = y + ', greffi'\n",
    "            greffier = re.search(y, x)\n",
    "            if greffier != None:\n",
    "                greffier = greffier.group()\n",
    "                x = x.replace(greffier, '|||')\n",
    "            else:\n",
    "                return x\n",
    "    except:\n",
    "        None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating case list and judge list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#searching for the relevant judges\n",
    "\n",
    "#Lists I already have\n",
    "#whole_list_of_names i my first list\n",
    "#relevant_clean_judges is my second list\n",
    "\n",
    "main_judge_list = []\n",
    "case_list = []\n",
    "vorsitz_list = []\n",
    "\n",
    "for file in whole_list_of_names: #medium_sample_list\n",
    "    \n",
    "    #Importing the texts\n",
    "    file_name = file\n",
    "    file = open('txtfiles/' + file, 'r')\n",
    "    text = file.read()\n",
    "    beginning = text[0:310]\n",
    "    end = text[-2000:]\n",
    "    end = end[0:1815]\n",
    "    text = beginning + end\n",
    "    \n",
    "    #Prepping text files\n",
    "    text = text.replace('E-4432/2006', 'E-4432/20 fsdfasdfaasdfasdfasdfasdfdasfasfasfasdfasfasfasdfsdfasdf')\n",
    "    text = text.replace(';', ',')\n",
    "    text = text.replace('\\n\\n', '\\n')\n",
    "    text = text.replace(':\\n1. Die', ':\\n1.\\nDie')\n",
    "    text = text.replace(':\\n1. Le', ':\\n1.\\nLe')\n",
    "    text = text.replace(':\\n1. Nella', ':\\n1.\\nNella')\n",
    "    text = text.replace('Demnach erkennt das Bundesverwaltungsgericht:             \\n1.\\n', 'Demnach erkennt das Bundesverwaltungsgericht:\\n1.\\n')\n",
    "    text = text.replace(':\\n1. Il', ':\\n1.\\nIl')\n",
    "    \n",
    "    #dealing with Gerichtsschreiber\n",
    "    text = gerichtsschreiber(text)\n",
    "    \n",
    "    #Pulling out lawyer's names, so they don't clash with judges names\n",
    "    text = lawyers_countries(text)\n",
    "    \n",
    "    #Makinging small judge name lists\n",
    "    short_judge_list = []\n",
    "    for judge in relevant_clean_judges:\n",
    "        \n",
    "        try:\n",
    "            judge = re.search(judge, text)\n",
    "            if judge != None:\n",
    "                judge = judge.group()\n",
    "                short_judge_list.append(judge)\n",
    "            else:\n",
    "                continue\n",
    "        except:\n",
    "            None\n",
    "    \n",
    "    #Getting the date\n",
    "    if extracting_date_french(text) == '[]' and extracting_date_italian(text) == '[]':\n",
    "        date = extracting_date_german(text)\n",
    "    elif extracting_date_french(text) == '[]' and extracting_date_german(text) == '[]':\n",
    "        date = extracting_date_italian(text)\n",
    "    else:\n",
    "        date = extracting_date_french(text)\n",
    "    \n",
    "    #Getting Gegenstand\n",
    "    if extracting_gegenstand_german(text) == None and extracting_gegenstand_french(text) == None:\n",
    "        gegenstand = extracting_gegenstand_italian(text)\n",
    "        #print(file_name, gegenstand, date)\n",
    "    elif extracting_gegenstand_french(text) == None and extracting_gegenstand_italian(text) == None:\n",
    "        gegenstand = extracting_gegenstand_german(text)\n",
    "        #print(file_name, gegenstand, date)\n",
    "    else:\n",
    "        gegenstand = extracting_gegenstand_french(text)\n",
    "        #print(file_name, gegenstand, date)\n",
    "        \n",
    "    #Getting Entscheid\n",
    "    if extracting_entscheid_german(text) == None and extracting_entscheid_french(text) == None:\n",
    "        entscheid = extracting_entscheid_italian(text)\n",
    "        #print(file_name, entscheid, date)\n",
    "    elif extracting_entscheid_french(text) == None and extracting_entscheid_italian(text) == None:\n",
    "        entscheid = extracting_entscheid_german(text)\n",
    "        #print(file_name, entscheid, date)\n",
    "    else:\n",
    "        entscheid = extracting_entscheid_french(text)\n",
    "        #print(file_name, entscheid, date)\n",
    "        \n",
    "    #Getting Aktennummer\n",
    "    if extracting_aktennummer_german(text) == None and extracting_aktennummer_french(text) == None:\n",
    "        aktennummer = extracting_aktennummer_italian(text)\n",
    "        #print(file_name, aktennummer, date)\n",
    "    elif extracting_aktennummer_french(text) == None and extracting_aktennummer_italian(text) == None:\n",
    "        aktennummer = extracting_aktennummer_german(text)\n",
    "        #print(file_name, aktennummer, date)\n",
    "    else:\n",
    "        aktennummer = extracting_aktennummer_french(text)\n",
    "        #print(file_name, aktennummer, date)\n",
    "        \n",
    "    #Making small judge dictionaries\n",
    "    small_judge_list = []\n",
    "    \n",
    "    try:\n",
    "        for judge in short_judge_list:\n",
    "            jugdes_small_dicts = {'judge': judge,\n",
    "                              'date': date,\n",
    "                              'gegenstand': gegenstand,\n",
    "                              'decision': entscheid,\n",
    "                              'aktennummer': aktennummer,\n",
    "                              'myfile_number': file_name}\n",
    "            small_judge_list.append(jugdes_small_dicts)\n",
    "                \n",
    "    except:\n",
    "        None\n",
    "    \n",
    "        \n",
    "    #Making separate case file\n",
    "    small_case_list = []\n",
    "    try:\n",
    "        case_dict = {'date': date,\n",
    "                 'gegenstand': gegenstand,\n",
    "                 'decision': entscheid,\n",
    "                 'aktennummer': aktennummer,\n",
    "                 'myfile_number': file_name}\n",
    "        small_case_list.append(case_dict)\n",
    "    except:\n",
    "        None\n",
    "    \n",
    "    \n",
    "    case_list = case_list + small_case_list\n",
    "    main_judge_list = main_judge_list + small_judge_list\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a DF out of the main judge list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_judges = pd.DataFrame(main_judge_list)\n",
    "df_cases = pd.DataFrame(case_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aktennummer</th>\n",
       "      <th>date</th>\n",
       "      <th>decision</th>\n",
       "      <th>gegenstand</th>\n",
       "      <th>judge</th>\n",
       "      <th>myfile_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cour VE-1461/2011</td>\n",
       "      <td>21 mars 2011</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Antonioni</td>\n",
       "      <td>100.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cour VE-1461/2011</td>\n",
       "      <td>21 mars 2011</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>de Coulon Scuntaro</td>\n",
       "      <td>100.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abteilung IVD-3473/2006</td>\n",
       "      <td>20. Februar 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Tellenbach</td>\n",
       "      <td>1000.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abteilung IVD-3473/2006</td>\n",
       "      <td>20. Februar 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Bovier</td>\n",
       "      <td>1000.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abteilung IVD-3473/2006</td>\n",
       "      <td>20. Februar 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Schmid</td>\n",
       "      <td>1000.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cour VE-5900/2010</td>\n",
       "      <td>2 février 2012</td>\n",
       "      <td>Le recours est reje</td>\n",
       "      <td>None</td>\n",
       "      <td>Badoud</td>\n",
       "      <td>1001.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cour VE-5900/2010</td>\n",
       "      <td>2 février 2012</td>\n",
       "      <td>Le recours est reje</td>\n",
       "      <td>None</td>\n",
       "      <td>Schenker Senn</td>\n",
       "      <td>1001.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cour VE-5900/2010</td>\n",
       "      <td>2 février 2012</td>\n",
       "      <td>Le recours est reje</td>\n",
       "      <td>None</td>\n",
       "      <td>Antonioni</td>\n",
       "      <td>1001.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abteilung IVD-3357/2006</td>\n",
       "      <td>9. Juli 2009</td>\n",
       "      <td>Die Beschwerde wird bezüglich des Wegweisungsv...</td>\n",
       "      <td>None</td>\n",
       "      <td>Schürch</td>\n",
       "      <td>1002.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Abteilung IVD-3357/2006</td>\n",
       "      <td>9. Juli 2009</td>\n",
       "      <td>Die Beschwerde wird bezüglich des Wegweisungsv...</td>\n",
       "      <td>None</td>\n",
       "      <td>Marti</td>\n",
       "      <td>1002.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Abteilung IVD-3357/2006</td>\n",
       "      <td>9. Juli 2009</td>\n",
       "      <td>Die Beschwerde wird bezüglich des Wegweisungsv...</td>\n",
       "      <td>None</td>\n",
       "      <td>Spälti Giannakitsas</td>\n",
       "      <td>1002.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Abteilung IVD-3357/2006</td>\n",
       "      <td>9. Juli 2009</td>\n",
       "      <td>Die Beschwerde wird bezüglich des Wegweisungsv...</td>\n",
       "      <td>None</td>\n",
       "      <td>Badoud</td>\n",
       "      <td>1002.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Abteilung IVD-3357/2006</td>\n",
       "      <td>9. Juli 2009</td>\n",
       "      <td>Die Beschwerde wird bezüglich des Wegweisungsv...</td>\n",
       "      <td>None</td>\n",
       "      <td>Zoller</td>\n",
       "      <td>1002.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Abteilung IVD-7620/2009</td>\n",
       "      <td>3. Mai 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Freihofer</td>\n",
       "      <td>1003.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Abteilung IVD-7620/2009</td>\n",
       "      <td>3. Mai 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Bovier</td>\n",
       "      <td>1003.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Abteilung IVD-7620/2009</td>\n",
       "      <td>3. Mai 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Haefeli</td>\n",
       "      <td>1003.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cour VE-6531/2006</td>\n",
       "      <td>28 mai 2008</td>\n",
       "      <td>Le recours est admis</td>\n",
       "      <td>None</td>\n",
       "      <td>Badoud</td>\n",
       "      <td>1004.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cour VE-6531/2006</td>\n",
       "      <td>28 mai 2008</td>\n",
       "      <td>Le recours est admis</td>\n",
       "      <td>None</td>\n",
       "      <td>Brodard</td>\n",
       "      <td>1004.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cour VE-6531/2006</td>\n",
       "      <td>28 mai 2008</td>\n",
       "      <td>Le recours est admis</td>\n",
       "      <td>None</td>\n",
       "      <td>Weber</td>\n",
       "      <td>1004.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cour IVD-1111/2011</td>\n",
       "      <td>24 février 2011</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Scherrer</td>\n",
       "      <td>1005.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Cour IVD-1111/2011</td>\n",
       "      <td>24 février 2011</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Bovier</td>\n",
       "      <td>1005.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cour VE-868/2007</td>\n",
       "      <td>18 octobre 2010</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Freihofer</td>\n",
       "      <td>1006.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Cour VE-868/2007</td>\n",
       "      <td>18 octobre 2010</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Brodard</td>\n",
       "      <td>1006.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cour VE-868/2007</td>\n",
       "      <td>18 octobre 2010</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>de Coulon Scuntaro</td>\n",
       "      <td>1006.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Cour IVD-7461/2009</td>\n",
       "      <td>3 décembre 2009</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Scherrer</td>\n",
       "      <td>1007.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Cour IVD-7461/2009</td>\n",
       "      <td>3 décembre 2009</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Brodard</td>\n",
       "      <td>1007.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Abteilung IVD-5213/2010</td>\n",
       "      <td>26. Juli 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Wespi</td>\n",
       "      <td>1008.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Abteilung IVD-5213/2010</td>\n",
       "      <td>26. Juli 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Cattaneo</td>\n",
       "      <td>1008.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cour VE-1171/2011</td>\n",
       "      <td>30 mars 2011</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Monnet</td>\n",
       "      <td>1009.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Cour VE-1171/2011</td>\n",
       "      <td>30 mars 2011</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Beck Kadima</td>\n",
       "      <td>1009.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73163</th>\n",
       "      <td>Cour IVD-2432/2011</td>\n",
       "      <td>31 mai 2011</td>\n",
       "      <td>La demande de révision est rejetée, dans la me...</td>\n",
       "      <td>None</td>\n",
       "      <td>Cotting-Schalch</td>\n",
       "      <td>9883.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73164</th>\n",
       "      <td>Cour IVD-2432/2011</td>\n",
       "      <td>31 mai 2011</td>\n",
       "      <td>La demande de révision est rejetée, dans la me...</td>\n",
       "      <td>None</td>\n",
       "      <td>Cattaneo</td>\n",
       "      <td>9883.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73165</th>\n",
       "      <td>Abteilung VE-1618/2008</td>\n",
       "      <td>28. Februar 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Schenker Senn</td>\n",
       "      <td>989.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73166</th>\n",
       "      <td>Abteilung VE-1618/2008</td>\n",
       "      <td>28. Februar 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Freihofer</td>\n",
       "      <td>989.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73167</th>\n",
       "      <td>Abteilung VE-1618/2008</td>\n",
       "      <td>28. Februar 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Huber</td>\n",
       "      <td>989.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73168</th>\n",
       "      <td>Abteilung VE-2696/2009</td>\n",
       "      <td>7. Mai 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Schürch</td>\n",
       "      <td>99.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73169</th>\n",
       "      <td>Abteilung VE-2696/2009</td>\n",
       "      <td>7. Mai 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Gysi</td>\n",
       "      <td>99.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73170</th>\n",
       "      <td>Abteilung VE-2040/2011</td>\n",
       "      <td>11. April 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen, soweit darauf ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Freihofer</td>\n",
       "      <td>990.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73171</th>\n",
       "      <td>Abteilung VE-2040/2011</td>\n",
       "      <td>11. April 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen, soweit darauf ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Gysi</td>\n",
       "      <td>990.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73172</th>\n",
       "      <td>Abteilung IVD-3274/2011</td>\n",
       "      <td>21. Juni 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Theis</td>\n",
       "      <td>991.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73173</th>\n",
       "      <td>Abteilung IVD-3274/2011</td>\n",
       "      <td>21. Juni 2011</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Cattaneo</td>\n",
       "      <td>991.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73174</th>\n",
       "      <td>Abteilung VE-5102/2006</td>\n",
       "      <td>31. Mai 2007</td>\n",
       "      <td>Die Beschwerde wird gutgeheissen und die Verfü...</td>\n",
       "      <td>None</td>\n",
       "      <td>Huber</td>\n",
       "      <td>992.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73175</th>\n",
       "      <td>Abteilung VE-5102/2006</td>\n",
       "      <td>31. Mai 2007</td>\n",
       "      <td>Die Beschwerde wird gutgeheissen und die Verfü...</td>\n",
       "      <td>None</td>\n",
       "      <td>Gysi</td>\n",
       "      <td>992.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73176</th>\n",
       "      <td>Abteilung VE-5102/2006</td>\n",
       "      <td>31. Mai 2007</td>\n",
       "      <td>Die Beschwerde wird gutgeheissen und die Verfü...</td>\n",
       "      <td>None</td>\n",
       "      <td>Weber</td>\n",
       "      <td>992.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73177</th>\n",
       "      <td>Corte IVD-2873/2009</td>\n",
       "      <td>8 maggio 2009</td>\n",
       "      <td>Il ricorso è respinto</td>\n",
       "      <td>None</td>\n",
       "      <td>de Coulon Scuntaro</td>\n",
       "      <td>993.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73178</th>\n",
       "      <td>Corte IVD-2873/2009</td>\n",
       "      <td>8 maggio 2009</td>\n",
       "      <td>Il ricorso è respinto</td>\n",
       "      <td>None</td>\n",
       "      <td>Angeli</td>\n",
       "      <td>993.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73179</th>\n",
       "      <td>Abteilung IVD-4839/2010</td>\n",
       "      <td>2. November 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Freihofer</td>\n",
       "      <td>994.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73180</th>\n",
       "      <td>Abteilung IVD-4839/2010</td>\n",
       "      <td>2. November 2010</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Haefeli</td>\n",
       "      <td>994.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73181</th>\n",
       "      <td>Abteilung IVD-3370/2009</td>\n",
       "      <td>28. Mai 2009</td>\n",
       "      <td>Die Beschwerde wird gutgeheissen, soweit sie n...</td>\n",
       "      <td>None</td>\n",
       "      <td>Galliker</td>\n",
       "      <td>995.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73182</th>\n",
       "      <td>Abteilung IVD-3370/2009</td>\n",
       "      <td>28. Mai 2009</td>\n",
       "      <td>Die Beschwerde wird gutgeheissen, soweit sie n...</td>\n",
       "      <td>None</td>\n",
       "      <td>Haefeli</td>\n",
       "      <td>995.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73183</th>\n",
       "      <td>Corte IVD-926/2010</td>\n",
       "      <td>22 febbraio 2010</td>\n",
       "      <td>Nella misura in cui ammissibile, il ricorso è ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Scherrer</td>\n",
       "      <td>996.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73184</th>\n",
       "      <td>Corte IVD-926/2010</td>\n",
       "      <td>22 febbraio 2010</td>\n",
       "      <td>Nella misura in cui ammissibile, il ricorso è ...</td>\n",
       "      <td>None</td>\n",
       "      <td>Angeli</td>\n",
       "      <td>996.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73185</th>\n",
       "      <td>Abteilung VE-5292/2009</td>\n",
       "      <td>19. November 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Lang</td>\n",
       "      <td>997.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73186</th>\n",
       "      <td>Abteilung VE-5292/2009</td>\n",
       "      <td>19. November 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Huber</td>\n",
       "      <td>997.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73187</th>\n",
       "      <td>Abteilung VE-5292/2009</td>\n",
       "      <td>19. November 2009</td>\n",
       "      <td>Die Beschwerde wird abgewiesen</td>\n",
       "      <td>None</td>\n",
       "      <td>Gysi</td>\n",
       "      <td>997.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73188</th>\n",
       "      <td>Cour IVD-8772/2010</td>\n",
       "      <td>5 avril 2011</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Badoud</td>\n",
       "      <td>998.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73189</th>\n",
       "      <td>Cour IVD-8772/2010</td>\n",
       "      <td>5 avril 2011</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Cotting-Schalch</td>\n",
       "      <td>998.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73190</th>\n",
       "      <td>Cour VE-6364/2008</td>\n",
       "      <td>4 novembre 2008</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Monnet</td>\n",
       "      <td>999.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73191</th>\n",
       "      <td>Cour VE-6364/2008</td>\n",
       "      <td>4 novembre 2008</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Bovier</td>\n",
       "      <td>999.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73192</th>\n",
       "      <td>Cour VE-6364/2008</td>\n",
       "      <td>4 novembre 2008</td>\n",
       "      <td>Le recours est rejeté</td>\n",
       "      <td>None</td>\n",
       "      <td>Gysi</td>\n",
       "      <td>999.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73193 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   aktennummer               date  \\\n",
       "0            Cour VE-1461/2011       21 mars 2011   \n",
       "1            Cour VE-1461/2011       21 mars 2011   \n",
       "2      Abteilung IVD-3473/2006   20. Februar 2009   \n",
       "3      Abteilung IVD-3473/2006   20. Februar 2009   \n",
       "4      Abteilung IVD-3473/2006   20. Februar 2009   \n",
       "5            Cour VE-5900/2010     2 février 2012   \n",
       "6            Cour VE-5900/2010     2 février 2012   \n",
       "7            Cour VE-5900/2010     2 février 2012   \n",
       "8      Abteilung IVD-3357/2006       9. Juli 2009   \n",
       "9      Abteilung IVD-3357/2006       9. Juli 2009   \n",
       "10     Abteilung IVD-3357/2006       9. Juli 2009   \n",
       "11     Abteilung IVD-3357/2006       9. Juli 2009   \n",
       "12     Abteilung IVD-3357/2006       9. Juli 2009   \n",
       "13     Abteilung IVD-7620/2009        3. Mai 2010   \n",
       "14     Abteilung IVD-7620/2009        3. Mai 2010   \n",
       "15     Abteilung IVD-7620/2009        3. Mai 2010   \n",
       "16           Cour VE-6531/2006        28 mai 2008   \n",
       "17           Cour VE-6531/2006        28 mai 2008   \n",
       "18           Cour VE-6531/2006        28 mai 2008   \n",
       "19          Cour IVD-1111/2011    24 février 2011   \n",
       "20          Cour IVD-1111/2011    24 février 2011   \n",
       "21            Cour VE-868/2007    18 octobre 2010   \n",
       "22            Cour VE-868/2007    18 octobre 2010   \n",
       "23            Cour VE-868/2007    18 octobre 2010   \n",
       "24          Cour IVD-7461/2009    3 décembre 2009   \n",
       "25          Cour IVD-7461/2009    3 décembre 2009   \n",
       "26     Abteilung IVD-5213/2010      26. Juli 2010   \n",
       "27     Abteilung IVD-5213/2010      26. Juli 2010   \n",
       "28           Cour VE-1171/2011       30 mars 2011   \n",
       "29           Cour VE-1171/2011       30 mars 2011   \n",
       "...                        ...                ...   \n",
       "73163       Cour IVD-2432/2011        31 mai 2011   \n",
       "73164       Cour IVD-2432/2011        31 mai 2011   \n",
       "73165   Abteilung VE-1618/2008   28. Februar 2011   \n",
       "73166   Abteilung VE-1618/2008   28. Februar 2011   \n",
       "73167   Abteilung VE-1618/2008   28. Februar 2011   \n",
       "73168   Abteilung VE-2696/2009        7. Mai 2009   \n",
       "73169   Abteilung VE-2696/2009        7. Mai 2009   \n",
       "73170   Abteilung VE-2040/2011     11. April 2011   \n",
       "73171   Abteilung VE-2040/2011     11. April 2011   \n",
       "73172  Abteilung IVD-3274/2011      21. Juni 2011   \n",
       "73173  Abteilung IVD-3274/2011      21. Juni 2011   \n",
       "73174   Abteilung VE-5102/2006       31. Mai 2007   \n",
       "73175   Abteilung VE-5102/2006       31. Mai 2007   \n",
       "73176   Abteilung VE-5102/2006       31. Mai 2007   \n",
       "73177      Corte IVD-2873/2009      8 maggio 2009   \n",
       "73178      Corte IVD-2873/2009      8 maggio 2009   \n",
       "73179  Abteilung IVD-4839/2010   2. November 2010   \n",
       "73180  Abteilung IVD-4839/2010   2. November 2010   \n",
       "73181  Abteilung IVD-3370/2009       28. Mai 2009   \n",
       "73182  Abteilung IVD-3370/2009       28. Mai 2009   \n",
       "73183       Corte IVD-926/2010   22 febbraio 2010   \n",
       "73184       Corte IVD-926/2010   22 febbraio 2010   \n",
       "73185   Abteilung VE-5292/2009  19. November 2009   \n",
       "73186   Abteilung VE-5292/2009  19. November 2009   \n",
       "73187   Abteilung VE-5292/2009  19. November 2009   \n",
       "73188       Cour IVD-8772/2010       5 avril 2011   \n",
       "73189       Cour IVD-8772/2010       5 avril 2011   \n",
       "73190        Cour VE-6364/2008    4 novembre 2008   \n",
       "73191        Cour VE-6364/2008    4 novembre 2008   \n",
       "73192        Cour VE-6364/2008    4 novembre 2008   \n",
       "\n",
       "                                                decision gegenstand  \\\n",
       "0                                                   None       None   \n",
       "1                                                   None       None   \n",
       "2                         Die Beschwerde wird abgewiesen       None   \n",
       "3                         Die Beschwerde wird abgewiesen       None   \n",
       "4                         Die Beschwerde wird abgewiesen       None   \n",
       "5                                    Le recours est reje       None   \n",
       "6                                    Le recours est reje       None   \n",
       "7                                    Le recours est reje       None   \n",
       "8      Die Beschwerde wird bezüglich des Wegweisungsv...       None   \n",
       "9      Die Beschwerde wird bezüglich des Wegweisungsv...       None   \n",
       "10     Die Beschwerde wird bezüglich des Wegweisungsv...       None   \n",
       "11     Die Beschwerde wird bezüglich des Wegweisungsv...       None   \n",
       "12     Die Beschwerde wird bezüglich des Wegweisungsv...       None   \n",
       "13                        Die Beschwerde wird abgewiesen       None   \n",
       "14                        Die Beschwerde wird abgewiesen       None   \n",
       "15                        Die Beschwerde wird abgewiesen       None   \n",
       "16                                  Le recours est admis       None   \n",
       "17                                  Le recours est admis       None   \n",
       "18                                  Le recours est admis       None   \n",
       "19                                 Le recours est rejeté       None   \n",
       "20                                 Le recours est rejeté       None   \n",
       "21                                 Le recours est rejeté       None   \n",
       "22                                 Le recours est rejeté       None   \n",
       "23                                 Le recours est rejeté       None   \n",
       "24                                 Le recours est rejeté       None   \n",
       "25                                 Le recours est rejeté       None   \n",
       "26                        Die Beschwerde wird abgewiesen       None   \n",
       "27                        Die Beschwerde wird abgewiesen       None   \n",
       "28                                 Le recours est rejeté       None   \n",
       "29                                 Le recours est rejeté       None   \n",
       "...                                                  ...        ...   \n",
       "73163  La demande de révision est rejetée, dans la me...       None   \n",
       "73164  La demande de révision est rejetée, dans la me...       None   \n",
       "73165                     Die Beschwerde wird abgewiesen       None   \n",
       "73166                     Die Beschwerde wird abgewiesen       None   \n",
       "73167                     Die Beschwerde wird abgewiesen       None   \n",
       "73168                     Die Beschwerde wird abgewiesen       None   \n",
       "73169                     Die Beschwerde wird abgewiesen       None   \n",
       "73170  Die Beschwerde wird abgewiesen, soweit darauf ...       None   \n",
       "73171  Die Beschwerde wird abgewiesen, soweit darauf ...       None   \n",
       "73172                     Die Beschwerde wird abgewiesen       None   \n",
       "73173                     Die Beschwerde wird abgewiesen       None   \n",
       "73174  Die Beschwerde wird gutgeheissen und die Verfü...       None   \n",
       "73175  Die Beschwerde wird gutgeheissen und die Verfü...       None   \n",
       "73176  Die Beschwerde wird gutgeheissen und die Verfü...       None   \n",
       "73177                              Il ricorso è respinto       None   \n",
       "73178                              Il ricorso è respinto       None   \n",
       "73179                     Die Beschwerde wird abgewiesen       None   \n",
       "73180                     Die Beschwerde wird abgewiesen       None   \n",
       "73181  Die Beschwerde wird gutgeheissen, soweit sie n...       None   \n",
       "73182  Die Beschwerde wird gutgeheissen, soweit sie n...       None   \n",
       "73183  Nella misura in cui ammissibile, il ricorso è ...       None   \n",
       "73184  Nella misura in cui ammissibile, il ricorso è ...       None   \n",
       "73185                     Die Beschwerde wird abgewiesen       None   \n",
       "73186                     Die Beschwerde wird abgewiesen       None   \n",
       "73187                     Die Beschwerde wird abgewiesen       None   \n",
       "73188                              Le recours est rejeté       None   \n",
       "73189                              Le recours est rejeté       None   \n",
       "73190                              Le recours est rejeté       None   \n",
       "73191                              Le recours est rejeté       None   \n",
       "73192                              Le recours est rejeté       None   \n",
       "\n",
       "                     judge myfile_number  \n",
       "0                Antonioni       100.txt  \n",
       "1       de Coulon Scuntaro       100.txt  \n",
       "2               Tellenbach      1000.txt  \n",
       "3                   Bovier      1000.txt  \n",
       "4                   Schmid      1000.txt  \n",
       "5                   Badoud      1001.txt  \n",
       "6            Schenker Senn      1001.txt  \n",
       "7                Antonioni      1001.txt  \n",
       "8                  Schürch      1002.txt  \n",
       "9                    Marti      1002.txt  \n",
       "10     Spälti Giannakitsas      1002.txt  \n",
       "11                  Badoud      1002.txt  \n",
       "12                  Zoller      1002.txt  \n",
       "13               Freihofer      1003.txt  \n",
       "14                  Bovier      1003.txt  \n",
       "15                 Haefeli      1003.txt  \n",
       "16                  Badoud      1004.txt  \n",
       "17                 Brodard      1004.txt  \n",
       "18                   Weber      1004.txt  \n",
       "19                Scherrer      1005.txt  \n",
       "20                  Bovier      1005.txt  \n",
       "21               Freihofer      1006.txt  \n",
       "22                 Brodard      1006.txt  \n",
       "23      de Coulon Scuntaro      1006.txt  \n",
       "24                Scherrer      1007.txt  \n",
       "25                 Brodard      1007.txt  \n",
       "26                   Wespi      1008.txt  \n",
       "27                Cattaneo      1008.txt  \n",
       "28                  Monnet      1009.txt  \n",
       "29             Beck Kadima      1009.txt  \n",
       "...                    ...           ...  \n",
       "73163      Cotting-Schalch      9883.txt  \n",
       "73164             Cattaneo      9883.txt  \n",
       "73165        Schenker Senn       989.txt  \n",
       "73166            Freihofer       989.txt  \n",
       "73167                Huber       989.txt  \n",
       "73168              Schürch        99.txt  \n",
       "73169                 Gysi        99.txt  \n",
       "73170            Freihofer       990.txt  \n",
       "73171                 Gysi       990.txt  \n",
       "73172                Theis       991.txt  \n",
       "73173             Cattaneo       991.txt  \n",
       "73174                Huber       992.txt  \n",
       "73175                 Gysi       992.txt  \n",
       "73176                Weber       992.txt  \n",
       "73177   de Coulon Scuntaro       993.txt  \n",
       "73178               Angeli       993.txt  \n",
       "73179            Freihofer       994.txt  \n",
       "73180              Haefeli       994.txt  \n",
       "73181             Galliker       995.txt  \n",
       "73182              Haefeli       995.txt  \n",
       "73183             Scherrer       996.txt  \n",
       "73184               Angeli       996.txt  \n",
       "73185                 Lang       997.txt  \n",
       "73186                Huber       997.txt  \n",
       "73187                 Gysi       997.txt  \n",
       "73188               Badoud       998.txt  \n",
       "73189      Cotting-Schalch       998.txt  \n",
       "73190               Monnet       999.txt  \n",
       "73191               Bovier       999.txt  \n",
       "73192                 Gysi       999.txt  \n",
       "\n",
       "[73193 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_judges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "jugdes_count = df_judges['judge'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "judges_count = pd.DataFrame(jugdes_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "judges_count.to_csv('jugdes_full_count.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the data. But we can't start analysing it just yet. We need to harmonise the various data points, i.e. dates "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First the dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting all the rows with no date. If there was a date, then the document wasn't a case file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_judges = df_judges[df_judges.date != '[]']\n",
    "df_cases = df_cases[df_cases.date != '[]']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a function, to harmonise all the dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def date_harm(date):\n",
    "    #German\n",
    "    date = date.replace('. Januar ', '.1.')\n",
    "    date = date.replace('. Februar ', '.2.')\n",
    "    date = date.replace('. März ', '.3.')\n",
    "    date = date.replace('. April ', '.4.')\n",
    "    date = date.replace(' April ', '.4.')\n",
    "    date = date.replace('. Mai ', '.5.')\n",
    "    date = date.replace('. Juni ', '.6.')\n",
    "    date = date.replace('. Juli ', '.7.')\n",
    "    date = date.replace('. August ', '.8.')\n",
    "    date = date.replace('. September ', '.9.')\n",
    "    date = date.replace('. Oktober ', '.10.')\n",
    "    date = date.replace('. November ', '.11.')\n",
    "    date = date.replace('. Dezember ', '.12.')\n",
    "    #French\n",
    "    date = date.replace(' janvier ', '.1.')\n",
    "    date = date.replace(' février ', '.2.')\n",
    "    date = date.replace(' mars ', '.3.')\n",
    "    date = date.replace(' avril ', '.4.')\n",
    "    date = date.replace(' mai ', '.5.')\n",
    "    date = date.replace(' juin ', '.6.')\n",
    "    date = date.replace(' juillet ', '.7.')\n",
    "    date = date.replace(' août ', '.8.')\n",
    "    date = date.replace(' septembre ', '.9.')\n",
    "    date = date.replace(' octobre ', '.10.')\n",
    "    date = date.replace(' novembre ', '.11.')\n",
    "    date = date.replace(' décembre ', '.12.')\n",
    "    #Italian\n",
    "    date = date.replace(' gennaio ', '.1.')\n",
    "    date = date.replace(' febbraio ', '.2.')\n",
    "    date = date.replace(' marzo ', '.3.')\n",
    "    date = date.replace(' aprile ', '.4.')\n",
    "    date = date.replace(' maggio ', '.5.')\n",
    "    date = date.replace(' giugno ', '.6.')\n",
    "    date = date.replace(' luglio ', '.7.')\n",
    "    date = date.replace(' agosto ', '.8.')\n",
    "    date = date.replace(' settembre ', '.9.')\n",
    "    date = date.replace(' ottobre ', '.10.')\n",
    "    date = date.replace(' novembre ', '.11.')\n",
    "    date = date.replace(' dicembre ', '.12.')\n",
    "    \n",
    "    return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "df_judges['date_new'] = df_judges['date'].apply(date_harm)\n",
    "df_cases['date_new'] = df_cases['date'].apply(date_harm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Making the dates the index to map them out, only in the cases file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse_date(str_date):\n",
    "    try:\n",
    "        return dateutil.parser.parse(str_date)\n",
    "    except:\n",
    "        None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "df_cases['date_new'] = df_cases['date_new'].apply(parse_date)\n",
    "df_judges['date_new'] = df_judges['date_new'].apply(parse_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_cases.index = df_cases['date_new']\n",
    "df_judges.index = df_judges['date_new']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two dates were entered wrongly. Correcting them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/usr/local/lib/python3.5/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2885: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "#Correcting wrongly posted dates.\n",
    "df_cases['date_new']['2001-09-30'] = '2011-09-30'\n",
    "df_judges['date_new']['2001-09-30'] = '2011-09-30'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_cases = df_cases.drop_duplicates(keep='first')\n",
    "df_judges = df_judges.drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ff03240>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AbffR3DZBEDB79mxMmDABvr6+iI6OxsqVK+XUDSNHjsQPP/yA/v37w9fXFyEh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f93d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize =(10,5), facecolor='White')\n",
    "df_cases.resample('M')['aktennummer'].count().plot(ax=ax)\n",
    "ax.set_title(\"Urteile Bundesverwaltungsgericht 2007 - September 2016\", fontname='DIN Condensed', fontsize=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Harmonising all the decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def decision_harm_auto(x):\n",
    "    try: \n",
    "        gutgeheissen = re.search(r'utgeheissen', x)\n",
    "        gutgeheissen2 = re.search(r'utheissen', x)\n",
    "        gutgeheissen3 = re.search(r'gutzuheissen', x)\n",
    "        admis = re.search(r'admis', x)\n",
    "        accolto = re.search(r'ccolto', x)\n",
    "        accolta = re.search(r'ccolta', x)\n",
    "        joint = re.search(r'Les causes D-3901/2008, D-3902/2008, D-3903/2008, D-3904/2008 et D-3905/2008 sont jointes', x)\n",
    "        annulée = re.search(r'annulée', x)\n",
    "        aufgehoben = re.search('aufgehoben', x)\n",
    "        \n",
    "        nicht_eingetreten = re.search('nicht eingetreten', x)\n",
    "        abgeschrieben = re.search('abgeschrieben', x)\n",
    "        gegenstandslos_geworden = re.search('gegenstandslos geworden', x)\n",
    "        \n",
    "        abgewiesen = re.search(r'bgewiesen', x)\n",
    "        abgewiesen2 = re.search(r'abge-wiesen', x)\n",
    "        abgewiesen3 = re.search(r'abgwiesen', x)\n",
    "        rejeté = re.search(r'ejet', x)\n",
    "        respinto = re.search(r'espint', x)\n",
    "        \n",
    "        irrecevable = re.search(r'irrecevable', x)   \n",
    "         \n",
    "        #angenommen\n",
    "        if gutgeheissen != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif gutgeheissen2 != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif gutgeheissen3 != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif admis != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif accolto != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif accolta != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif aufgehoben != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif joint != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "        elif annulée != None:\n",
    "            x = 'Gutgeheissen'\n",
    "            return x\n",
    "             \n",
    "        #abgewiesen\n",
    "        elif abgewiesen != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif rejeté != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif respinto != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif irrecevable != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif nicht_eingetreten != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif abgewiesen2 != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif abgewiesen3 != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif abgeschrieben != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "        elif gegenstandslos_geworden != None:\n",
    "            x = 'Abgewiesen'\n",
    "            return x\n",
    "    \n",
    "        else:\n",
    "            return x\n",
    "    except:\n",
    "        None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_cases['decision_harm_auto'] = df_cases['decision'].apply(decision_harm_auto)\n",
    "df_judges['decision_harm_auto'] = df_judges['decision'].apply(decision_harm_auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of cases that weren't considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3999999999999999"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cases_non_harm_count = df_cases[df_cases['decision_harm_auto'] != 'Abgewiesen']\n",
    "df_cases_non_harm_count = df_cases_non_harm_count[df_cases_non_harm_count['decision_harm_auto'] != 'Gutgeheissen']\n",
    "Weitergezogen_oder_vereinigt = df_cases_non_harm_count['aktennummer'].count()\n",
    "Prozent_weitergezogen_etc = round((Weitergezogen_oder_vereinigt / df_cases['aktennummer'].count()) * 100, 1)\n",
    "Prozent_weitergezogen_etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Judge Decisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Creating new dfs with decision counts\n",
    "df_gutgeheissen = pd.DataFrame(df_judges[df_judges['decision_harm_auto'] == 'Gutgeheissen']['judge'].value_counts())\n",
    "df_gutgeheissen = df_gutgeheissen.reset_index()\n",
    "df_abgewiesen = pd.DataFrame(df_judges[df_judges['decision_harm_auto'] == 'Abgewiesen']['judge'].value_counts())\n",
    "df_abgewiesen = df_abgewiesen.reset_index()\n",
    "df_judge_quota = df_gutgeheissen.merge(df_abgewiesen, left_on='index', right_on='index')\n",
    "df_judge_quota.columns = [['judge', 'gutgeheissen', 'abgewiesen']]\n",
    "#del df_judge_quota['index']\n",
    "df_judge_quota['quota'] = round(df_judge_quota['gutgeheissen'] / (df_judge_quota['gutgeheissen'] + df_judge_quota['abgewiesen']) * 100, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thoughest jugdes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bringing in the parties of the jugdges. This was scraped from the [BVGer site](http://www.bvger.ch/gericht/richter/index.html?lang=de). And gathered from [documentation from Swiss Parliament](https://www.parlament.ch/de/ratsbetrieb/amtliches-bulletin/amtliches-bulletin-erkl%C3%A4rt). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_judge_partei = pd.read_csv('data/richter_partei.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_judge_quota = df_judge_quota.merge(df_judge_partei, left_on='judge', right_on='Nachname')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>judge</th>\n",
       "      <th>Partei</th>\n",
       "      <th>gutgeheissen</th>\n",
       "      <th>abgewiesen</th>\n",
       "      <th>quota</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Wenger</td>\n",
       "      <td>SVP</td>\n",
       "      <td>33</td>\n",
       "      <td>447</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Haefeli</td>\n",
       "      <td>SVP</td>\n",
       "      <td>280</td>\n",
       "      <td>2553</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Balmelli</td>\n",
       "      <td>GLP</td>\n",
       "      <td>14</td>\n",
       "      <td>126</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Brüschweiler</td>\n",
       "      <td>BDP</td>\n",
       "      <td>30</td>\n",
       "      <td>246</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Willisegger</td>\n",
       "      <td>SVP</td>\n",
       "      <td>153</td>\n",
       "      <td>1175</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           judge Partei  gutgeheissen  abgewiesen  quota\n",
       "40        Wenger    SVP            33         447    6.9\n",
       "23       Haefeli    SVP           280        2553    9.9\n",
       "42      Balmelli    GLP            14         126   10.0\n",
       "41  Brüschweiler    BDP            30         246   10.9\n",
       "31   Willisegger    SVP           153        1175   11.5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_judge_quota[['judge', 'Partei', 'gutgeheissen', 'abgewiesen', 'quota']].sort_values(by='quota').head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softest jugdes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>judge</th>\n",
       "      <th>Partei</th>\n",
       "      <th>gutgeheissen</th>\n",
       "      <th>abgewiesen</th>\n",
       "      <th>quota</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Theis</td>\n",
       "      <td>Grüne</td>\n",
       "      <td>295</td>\n",
       "      <td>746</td>\n",
       "      <td>28.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Kojic</td>\n",
       "      <td>parteilos</td>\n",
       "      <td>103</td>\n",
       "      <td>264</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Weber</td>\n",
       "      <td>FDP</td>\n",
       "      <td>85</td>\n",
       "      <td>235</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Luterbacher</td>\n",
       "      <td>SP</td>\n",
       "      <td>536</td>\n",
       "      <td>1556</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Dubey</td>\n",
       "      <td>parteilos</td>\n",
       "      <td>51</td>\n",
       "      <td>168</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          judge     Partei  gutgeheissen  abgewiesen  quota\n",
       "19        Theis      Grüne           295         746   28.3\n",
       "34        Kojic  parteilos           103         264   28.1\n",
       "36        Weber        FDP            85         235   26.6\n",
       "0   Luterbacher         SP           536        1556   25.6\n",
       "38        Dubey  parteilos            51         168   23.3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_judge_quota[['judge', 'Partei', 'gutgeheissen', 'abgewiesen', 'quota']].sort_values(by='quota', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging with Richter Partei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_partei_vergleich = df_judges.merge(df_judge_partei, left_on='judge', right_on='Nachname')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parteien-Vergleich"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making sure all cells are stripped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def strip_partei(x):\n",
    "    x = x.strip()\n",
    "    return x\n",
    "df_partei_vergleich['Partei'] = df_partei_vergleich['Partei'].apply(strip_partei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SP           15104\n",
       "SVP          14653\n",
       "parteilos    14410\n",
       "FDP          10713\n",
       "CVP           9516\n",
       "Grüne         5183\n",
       "GLP           3170\n",
       "BDP            280\n",
       "Name: Partei, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_partei_vergleich['Partei'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating new dfs with decision counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_P_gutgeheissen = pd.DataFrame(df_partei_vergleich[df_partei_vergleich['decision_harm_auto'] == 'Gutgeheissen']['Partei'].value_counts())\n",
    "df_P_gutgeheissen = df_P_gutgeheissen.reset_index()\n",
    "df_P_abgewiesen = pd.DataFrame(df_partei_vergleich[df_partei_vergleich['decision_harm_auto'] == 'Abgewiesen']['Partei'].value_counts())\n",
    "df_P_abgewiesen = df_P_abgewiesen.reset_index()\n",
    "df_P_quota = df_P_gutgeheissen.merge(df_P_abgewiesen, left_on='index', right_on='index')\n",
    "df_P_quota.columns = [['judge', 'gutgeheissen', 'abgewiesen']]\n",
    "df_P_quota['quota in %'] = round(df_P_quota['gutgeheissen'] / (df_P_quota['gutgeheissen'] + df_P_quota['abgewiesen']) * 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>judge</th>\n",
       "      <th>gutgeheissen</th>\n",
       "      <th>abgewiesen</th>\n",
       "      <th>quota in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BDP</td>\n",
       "      <td>30</td>\n",
       "      <td>246</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVP</td>\n",
       "      <td>1850</td>\n",
       "      <td>12305</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDP</td>\n",
       "      <td>1596</td>\n",
       "      <td>8742</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CVP</td>\n",
       "      <td>1470</td>\n",
       "      <td>7752</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GLP</td>\n",
       "      <td>496</td>\n",
       "      <td>2563</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>parteilos</td>\n",
       "      <td>2295</td>\n",
       "      <td>11566</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP</td>\n",
       "      <td>3036</td>\n",
       "      <td>11515</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grüne</td>\n",
       "      <td>1049</td>\n",
       "      <td>3943</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       judge  gutgeheissen  abgewiesen  quota in %\n",
       "7        BDP            30         246        10.9\n",
       "2        SVP          1850       12305        13.1\n",
       "3        FDP          1596        8742        15.4\n",
       "4        CVP          1470        7752        15.9\n",
       "6        GLP           496        2563        16.2\n",
       "1  parteilos          2295       11566        16.6\n",
       "0         SP          3036       11515        20.9\n",
       "5      Grüne          1049        3943        21.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_P_quota.sort_values(by='quota in %', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5Hz9+PIWHh1PPnj3p73//O5WXl1u0tX79eho+fDip1WpKTU2l+fPnW1QWyMzM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\n9BP61J2DZTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1117a22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize =(10,5), facecolor='White')\n",
    "\n",
    "df_cases[df_cases['decision_harm_auto'] == 'Abgewiesen'].resample('M')['aktennummer'].count().plot(ax=ax)\n",
    "ax.set_title(\"Urteile Bundesverwaltungsgericht 2007 - , abgewiesene Klagen\", fontname='DIN Condensed', fontsize=24)\n",
    "\n",
    "df_cases[df_cases['decision_harm_auto'] == 'Gutgeheissen'].resample('M')['aktennummer'].count().plot(ax=ax)\n",
    "ax.set_title(\"Gutgeheissene vs Abgewiesene Urteile 2007 - 2016\", fontname='DIN Condensed', fontsize=24)\n",
    "\n",
    "plt.savefig('Gutgeheissene vs Abgewiesene Urteile 2007 - 2016.png', transparent=True, bbox_inches='tight')\n",
    "plt.savefig('Gutgeheissene vs Abgewiesene Urteile 2007 - 2016.pdf', transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geschlechtervergleich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.034852546916888"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_w = df_partei_vergleich[df_partei_vergleich['Geschlecht'] == 'w']\n",
    "w_gutgeheissen = df_w[df_w['decision_harm_auto'] == 'Gutgeheissen']['aktennummer'].count()\n",
    "w_abgewiesen = df_w[df_w['decision_harm_auto'] == 'Abgewiesen']['aktennummer'].count()\n",
    "Prozent_w_gutgeheissen = w_gutgeheissen / (w_gutgeheissen + w_abgewiesen) * 100\n",
    "Prozent_w_gutgeheissen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.759842196104215"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m = df_partei_vergleich[df_partei_vergleich['Geschlecht'] == 'm']\n",
    "m_gutgeheissen = df_m[df_m['decision_harm_auto'] == 'Gutgeheissen']['aktennummer'].count()\n",
    "m_abgewiesen = df_m[df_m['decision_harm_auto'] == 'Abgewiesen']['aktennummer'].count()\n",
    "Prozent_m_gutgeheissen = m_gutgeheissen / (m_gutgeheissen + m_abgewiesen) * 100\n",
    "Prozent_m_gutgeheissen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verlauf der einzelne Richter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n4j94N5maEViTvFJKhWnseKirhbf+k5HiNckrpVSIjOdhTpyRsWmINckrpVTI\nzBHHweoVuA8/SHvZmuSVUipkpqgIM/FkXAamIdYkr5RSOcB8fgrutRdx66vSWm5hZztYa0uAx4Hi\n+M9iEbnYWjsAWACMAlYAVkS2pDU6pZTqIUxpGeaYSbiHF2NO/2bayu20JS8i9cBxInIwcBBwvLX2\naGA2sExE9gOWAxelLSqllOqBzAkn456pxFVvTVuZSXXXiEhtfLEkfswmYBowP759PjA9bVEppVQP\nZPoPwow7EvfoP9JWZlJJ3lrrWWtfAtYClSLyOjBURKoARGQtsFvaolJKqR7KTJ6Bq7wfV1+XlvKS\nbcn78e6aEcAEa2050Pb1rMy8rqWUUj2IGTYC9hmNe2pZWsrr9MFrIhHZaq29HzgUqLLWDhWRKmvt\nMODj9o6JXxDKE8ogFot1P2Kl8lRxcbGe+yopTTPPpPaan9NnqsUUFLRut9bOTditUkQqOysrmdE1\ng4FGEdlire0NTAIuA5YAZwPzgLOAxe0dHw8iMZA51dXVnVWrVOTEYjH03FdJGbYn/oBBbH30Abzx\nxwLB+SMic7taVDLdNbsDj8b75J8BlojIIwTJfZK19i1gInBlVytXSinVPm/yTFwaJi4zmZr5bBfc\nmjVrsl2nUqHTlrzqCucc/mXfw5t1DubAcQwfPhzAdLUcfeNVKaVykDEGM3lmyhOXaZJXSqkcZQ6b\nAB+vwa14p9tlaJJXSqkcZQoLMZOm4T+4sNtlaJJXSqkcZo45Ed56tdvHa5JXSqkcZnr1xvvaBd0+\nXpO8UkrlOHPguG4fq0leKaUiTJO8UkpFmCZ5pZSKME3ySikVYZrklVIqwjTJK6VUhGmSV0qpCNMk\nr5RSEaZJXimlIkyTvFJKRZgmeaWUijBN8kopFWGa5JVSKsIKO9vBWjsCuBkYCvjA9SJyjbV2ALAA\nGAWsAKyIbMlgrEoppboomZZ8E3CBiBwAHAl8x1r7WWA2sExE9gOWAxdlLkyllFLd0WmSF5G1IvJy\nfHkb8AYwApgGzI/vNh+YnqkglVJKdU+X+uSttXsBY4FngKEiUgXBhQDYLe3RKaWUSkmnffItrLV9\ngLuA80Vkm7XWtdml7XrLceVAecu6iBCLxboeqVJ5rri4WM99lRJr7dyE1UoRqezsGONcu7m5bcGF\nwH3AAyJydXzbG0C5iFRZa4cBj4rI6CTidGvWrEliN6WiJRaLUV1dHXYYKk8NHz4cwHT1uGS7a/4G\nvN6S4OP3kLzGAAAQs0lEQVSWAGfHl88CFne1cqWUUpnVaUveWns08DjwH4IuGQdcDDwHCLAnsJJg\nCOXmJOrUlrzqkbQlr1LR3ZZ8Ut01aaZJXvVImuRVKjLdXaOUUioPaZJXSqkI0ySvlFIRpkleKaUi\nTJO8UkpFmCZ5pZSKME3ySikVYZrklVIqwjTJK6VUhGmSV0qpCNMkr5RSEaZJXimlIkyTvFJKRZgm\neaWUijBN8kopFWGa5JVSKsI0ySulVIRpkldKqQjTJK+UUhFW2NkO1tq/AlOBKhE5KL5tALAAGAWs\nIPiI95YMxqmUUqobkmnJ3whMbrNtNrBMRPYDlgMXpTswpZRSqes0yYvIk8CmNpunAfPjy/OB6WmO\nSymlVBp0t09+NxGpAhCRtcBu6QtJKaVUunTaJ58k19EvrLXlQHnLuogQi8XSVK1S+aO4uFjPfZUS\na+3chNVKEans7JjuJvkqa+1QEamy1g4DPu5ox3gQiYHMqa6u7ma1SuWvWCyGnvuqu2KxGCIyt6vH\nJdtdY+I/LZYAZ8eXzwIWd7VipZRSmWec67CnBQBr7W0E3S2DgCpgDnAPcCewJ7CSYAjl5iTrdGvW\nrOluvErlLW3Jq1QMHz4cdm5sJ6XTJJ8BmuRVj6RJXqWiu0le33hVSqkI0ySvlFIRpkleKaUiLF3j\n5JVSEbK90WfVlnpWbq5n1ebgvw3NjlH9S3b6iZUUhB2q6oQmeaV6sGbf8VF1Ays3xRN6PLFv3N7E\niL7Frcl83PAyigoMqzY3sHJzPY+t2MqqzfX0LvIY2b+EvRIS/4i+xZQUaidBrtDRNUplSZija5xz\nrK9tYuXmhNb5lno+2trAoNLCT7TQd+9TTIG364EczjnW1ewoc2W8zP9WNzC4tIhRbZL/0D5FnZap\nOqZDKJXKcdlK8tsamndKvC0Jvcgzn0jmI/uVpL3V3djsWFPdsHPy31zPlromRvQr2Sn5j+xfwoBe\nBRijyb8z3U3y2l2jVJ5qbPb5cMuOZLpqSz0rNtdT0+Azst+OrpajR8YY1b+Efr2y88+9qGDHxSRR\nbWNza3fPyi31PPfRNlZurgdo3X+v+IVnZP9iSou0vz8dtCWfhGbfsbmuifW1TfQq9BhSVqgnoOqy\n7rbkfeeo2tb4iZbxxzWNDOtTxMiElvle/UsYUlaElyctY+ccm+pa7jzqWBm/CKzeUk+/Xp/sRtqj\nbzGFPbTLR7trUlDb2My6mibW1TSyrqaR9bWJy41s3N5MrNhjUGkRdU0+62oaKfQMg8uKGFJayJCy\nIgaXFjG4LFgeUlrEwNLCHnsyqvYlk+Q31zV9Ipl/uKWBWLHX2r3Rksz36FtMUUE0H3A2+4612xpb\nR/asiP93fW0ju8fidyn9diT/IWWFke/y0STfgSbfsaF2R+JeX9PEutrGnZZ95xhcWhQk6LJChpQW\nBQk8vjyotHCnf0zOObY1BMl+XW1Qzvp4meviZW6pa6Jfr+D4IWWFreUPLmvZVkSfYi/yJ6baITHJ\n1zX5rQks8aFls+9a+8pbuy/6l1BWrHeOAPVNPqu37uiiakn+9U1+659ZVId49sgk75yjusFnfdsW\neG2QbNfXNLKlvon+vVqS7I5W95CEVndZBpJtk+/YWNsUvwg0sq62qTXOluXmhIvL4PgdQeLy4DYX\nF5VfGpp9NiTcFW5sMLzzcfUnhigmDkEc2Dv6LdJM2FrfvNNFc0X8gXPvIq814ffL84R/3qTPQdSS\nfNt/JDuS+I6EWVhgdmotJ3ahDCkrYmDvwpwdtlXb2BzcTbS5MLUsb9zeRKzYY3A7F6aWrqF+JToy\nIQy+c2ypa27t0mu5g1ufcDdX0+AzuLQw/vdXyMiBfRjWO3jIuHus8yGKKjVth3hua2gOO6SUXDx1\nLORTkvedY2tdc2vXyY5/JDu6PrY1+AwqLWRI6z+UHV0oLV0fUX4AmvjAd12bu4CWi972Rr+1Cyix\ni6nlTmBwWRG99MWULtve6Leeh61dfS3JvKaRDbVNlBZ5O/2Z7/z3UET/XgU7PQDVWShVKvKqu+aU\nPz/B+pomehd5O/dXt+my6N8rd1vhuaK+yW+9OO50RxBvVa6vbaKk0Gu9UPYpLqCsyKO02KO0yKO0\nqGW9gNIir3W5rMijuMBE8i6h2XdsqmtqbVy03D3teG7TSH2za/fuKfFc7er4ck3yKhV5leSfe/MD\nhpQW6avPWeCcY0t9c2uLtKahmZoGn9rGZmoafWo7WN7e6NPsu9aEX5qQ/Hu3XAyKCigt9igril8g\nihO3Bcu9CrN/oahpaKbtKKmWO6BgtFQTsZLCnbr1Bpfu/Lymbwa6wTTJq1TkVZLPtSGUqn2NzT61\njcHPzheG5mBbfLkmvk97y02+a71jKI1fLFovBkUeZcWJ23e+m2jZ3rvIa+32aGx2bNy+426ltTWe\nsOzgE916iV1YbUdLZYsmeZUKfeNVpV1RgUe/Ao9+vbpfRpPvWpN+cGFoprYhfoGIL2+tb2bttob4\nhSR+MUlYbmh29Cr0KCow1DQ007/Xzl0oew0o4bA9+gRdf2VFlBXp0FSlWqSU5K21U4DfE8xL/1cR\nmZeWqFRkFHqGviUF9E1h+Fqz79je6NPgO/qVFOhzGqW6oNv3rNZaD/gDMBk4ADjdWvvZdAWmVIsC\nz9CnpCCnh8MqlatS6Zg8HHhHRFaKSCNwBzAtPWEppZRKh1SS/B7Ahwnrq+PblFJK5Qgdw6iUUhGW\nyoPXj4CRCesj4tt2Yq0tB8pb1kWkZSiQUj1OLBYLOwSVx6y1cxNWK0WkstODnHPd+qmoqCioqKh4\nt6KiYlRFRUVxRUXFyxUVFaOTOK6yu3Xmwk9FRcXcsGPoqfHnc+zx+CvDjqGH//n3yPi73V0jIs3A\necBDwGvAHSLyRhKHruhunTmiMuwAUlQZdgApqAw7gBStCDuAFFWGHUCKKsMOIEWV3Tko62+8Wmvn\nisjcrFaqVA7Qc1+FIYwHr5Uh1KlULqgMOwDV84Qxd41SSqks0blrOmCt3QO4FtifYFKg+4AfA8cC\nVwJFQAPwYxF5NKw4O7KL+McC18V384BfisiCUILchQ7i/5GINMV/P5LgWdAcEfldaIFGVD6f/3ru\n70zHyXfsbuBuEdkX2BfoA/wCWAdMFZExwNnALaFFuGtt448RxP8f4BAROZhgSoprrbW5+OWV9uL/\nZcLvfwvcH0ZgPUQ+n/967ifIWHdNR1cjoC9wF3AYcKOIfC8jAaTAWns8cKmIlCdsiwEfACNEpC5h\n+3pg9/jUDjkh2fittXsDy0Tk06EE2oHO4gemAEcCNcC2XGzJ6/kfDj33PymTLfmOrkbbgUuAH2aw\n7lQdALyQuEFEqoGVQOtJYa2dBbyYKyd4gl3Gb6093Fr7KvAqcEEI8XVmV/HvQ5AsL6Mbc2tnkZ7/\n4dBzv42MJPn41Wi7iNwcD9IBPwDOBZyIPA3UZ6LuDDMEfZFYaw8ArgC+GWpEXWOAIhF5TkQOBMYB\nV1tr+4YcV7IMcDVwlYjUJmzLKXr+56Qee+5nqiWfVEsgh70OHJq4IX4yjADesdaOIGipnSkiK7If\nXqd2GX/LNhF5C3gP+ExWo+tce/HHgD2B/sCvrLXvA98HLrLW/k/2Q9wlPf/Do+d+G9l+8NraEshl\nIvII0NtaewZA/OHMb4DbCEYk3QdcKCLPhBdlx3YR/+3A4JaHTdbaUQS3gO90VFYYOoj/t8CtInKI\niHxKRD5F8MGaX4rIH0MMtyv0/M8wPfc/KVNJPqmraY6bAVRYa98G1hM8MPsRwVQOnwYutda+ZK19\n0Vo7OMQ4O9Je/P8LTABesda+CAjwTRHZGl6YHerozz8f6PkfLj33E2RydM1zwDUi8vf41ehPQK2I\nfD/++7OAQ0XkuxkJII2stUcA1wM2yfl5corGn316/ueGfI4d0hN/podQ/hEYDQwBlhL04TVaaz8g\nGG1QDGwGThSRNzMSiFIh0PNf5YqsTGuQ71dTpVKh578Kk85do5RSEabTGiilVIRpkldKqQhLeRbK\n+IsRNwNDAR+4XkSusdYOABYAowi+iGNFZIu1diAdzN1hrX0U2J3g1W9H8EBqfaoxKpUJaT73i4A/\nEHwPuRn4iYgsyuL/joqodLTkm4ALROQAgolzvmOt/Swwm2ACoP2A5cBF8f3r2PXcHaeLyMEiMk4T\nvMpx6Tz3fwJUich+IrI/8FjGo1c9QspJXkTWisjL8eVtwBsEL31MA+bHd5sPTI/vU9vJ3B3ahaTy\nQprP/XMJ5oJpKXtjBkNXPUhaE6q1di+CifmfAYaKSBUE/xiA3ZIs5qb4W3SXpDM2pTIplXPfWtsv\nvni5tfYFa+0Ca+2QTMareo60JXlrbR+C/sbz462atmMzkxmr+WUR+RzB68cTWuZvUCqXpeHcLyS4\nA3hSRA4huFD8Nu2Bqh4pLUneWltIcJLfIiKL45urrLVD478fBnzcWTki8t/4f2sIJkM6PB3xKZUp\n6Tj3RWQDUJPwoPVO4OAMhax6mHS15P8GvC4iVydsW0LweTCAs4DFbQ8iYT5ka22BtXZQfLkImEow\nsb9SuSzlcz/uXmvtcfHlEwgmOVMqZSm/8WqtPRp4nOD7iS7+czHwHMFMb3sSzKNtRWRz/JhPzN0B\nrIqXUwgUAMsIRi7oK7kqJ6Xr3BeRN23wceZbgH4E31E9R0RWZ/f/SEWRTmuglFIRpsMVlVIqwjTJ\nK6VUhGmSV0qpCNMkr5RSEaZJXimlIkyTvFJKRZgmeaWUijBN8iqvWGtvtNb+LOw4lMoXmuRVJFlr\nH7XWnht2HEqFTZO8UkpFmE5roHKatfZg4AZgH+ABgvlh3gF+RzDXy3iCuY6eBr4lImustZcTfJ2p\ngeDrTTeJyPfiX226BjiEYGbIS0Xkzk7qvxGoAfYCPg+8RjAl9gfx37dbZnx++ZdEZEB8v+uBU0Sk\nZXbKm4HnReSalP+QlNoFbcmrnBWfjXQRwdeVBhJMwXtq/NeGYAbIPYGRQC1wLYCIXAI8AZwnIn3j\nCb4UeAj4OzAYOA24Np6kO/MlYA7QH3gP+EU8vvbK/KO19rMisgLYEr9IQfCNhGpr7X7x9WPRT/yp\nLEj5Q95KZdARQGFCa3ehtfZfACKyieACAFBvrb0CeGQXZU0FPhCRm+Prr1hr7wYqgJ93EsciEXkB\nwFp7Kzs+6NFemQsTynwcONZauyb++7vi6/VATERe6aRepVKmSV7lsuHAR222rQSw1vYGfg9MJmhh\nG6CPtdZ0MD31KOAIa23Lt1MNQTfPLUnEsTZhuRbo00mZLUn/MeCU+P/DY0Al8FWCb7w+kUS9SqVM\nk7zKZf8F9mizbSTwLvBD4DPAYSKyzlo7BniRING2zO2e6EOgUkQmpzG+zsp8DPh1fL/HgKeAvwB1\naFeNyhJN8iqX/RNostZ+F/gTQav4cGA5wYc3tgNbrbUDgbltjq0CPpWwfh9wRfy7wXcQXAzGANtE\n5M1uxrfLMkXkXWvtduAM4AoRqbbWVgEziT8/UCrT9MGrylki0kiQEM8BNhD0dS+M//oqoBRYTzCy\n5v42h18NVFhrN1hrfx//wPaJBA9H18R/riT4QtOudDj8LMkyHwPWi8hHCesQ3HUolXE6hFIppSJM\nW/JKKRVh2ievejxr7asED3RbtDy8/ZaI3B5OVEqlh3bXKKVUhGl3jVJKRZgmeaWUijBN8kopFWGa\n5JVSKsI0ySulVIT9f8J5SAtct8VyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b954cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#resample documentations: \n",
    "#http://stackoverflow.com/questions/17001389/pandas-resample-documentation\n",
    "df_Wenger = df_judges[df_judges['judge'] == 'Wenger']\n",
    "df_Wenger[df_Wenger['decision_harm_auto'] == 'Abgewiesen'].resample('Q')['aktennummer'].count().plot()\n",
    "df_Wenger[df_Wenger['decision_harm_auto'] == 'Gutgeheissen'].resample('Q')['aktennummer'].count().plot()\n",
    "plt.savefig('wenger.png', transparent=True, bbox_inches='tight')\n",
    "plt.savefig('wenger.pdf', transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ir9AqUnFgT3AX+Cr0cTJJspvV2sUaoYGvWBC5rXB12OKiAaKSFz4pDLw6XGdt\nXgHLB9nbg5oEOTeUUlYlpwh8iVMBcf7lkJaOueFuUpettr//M863CoB7PR7Hwz3jC7FsNeqdrQHb\nqd5uKyInQFSJyE+SjJKt4cXAj2HufGioi2j3u7JhF+soUcgLP+kNvBrot96Uck8KV4fDtk0MUaer\nA9Izww+LmyIIhwPHTZ+DjnZSl0fBwGdmIdauQ7349xMHjx6CwUEr934cEctWBeeH96QoCPi0kSwJ\nxyKIgR9BZGZbk6tIdr+7Om3zwUcjXcGkN/AcOQhlM8cUXRaVk8RN09KoQySDRJROx/HtX5C69LTo\n9L/uStTmZ0dr3Y6kJoiXe2aUiiroPI4KNLMLtjZpEqQMVr3doAI/rQRDxG4aO3axjhCFdAWT3sCr\nw3WIk2KUx5S0CxH3L35A/1MPW1WWooyVS0O7Z4JFFJVEzeCK8pkwe95oZSlr92p83TNgpZIVp6wM\nOIu3YuCDCK9zFoCrE2WaNimMA60RxsB7E2lQRqTl+rwpngbtzbbubp70Bp6GephdMfZYpVXSLtQ3\nSrU0wr6dDL3+T8y7bkMdPWSbTJ9o/3tC4bjgStQLT1qfe1+vlesnEVi2GvVOADdN+wR54L0QaWlW\n/HdPt03i4kCwTytBEMnTvhoehv5eyIn8SQIsVyFpGdZNwyYi2uhkGEY+cC+wFDCBW4C9wEPAXKAe\nMKSUUcu8pA7X4Tgpy58oKIbU9JB3iartryFWnEHup79G15N/xvzh1xEXXYO4/H1jXEC20dIEVcmT\nLGzSs3w1PPhrzL/+wXLPJEj6CHHKStT9v0ANDflNsKbamhFzq4LrMK/A2s3qDK1YSqJgSwz8CDPn\nQktjeLvfuzshx2nv38lILLxNTwWRKvsp8JSUcjFwKrAHuA3YKKVcCDwP3B7hGH5RpjkmRHIMYfjW\n1I7XEaeeYS3qrbsSx7//GHVgD+Z3v4w6aGMFmJHxQqzkpIkuwpGCOP8K2P5aQrhnRhC5eTBjDuzb\n6b9RMGkKRpjsoZKtkS+wjiBS007sfg8Vm3axjqGo1Hoas4mwDbxhGHnAuVLK3wFIKYc9M/XrgA2e\nZhuA6yNW6Y/WJqsIco5z3CkRom9N9bisD9mrxqconobjC99EXPYezJ9+C/ORDajBAVukA9YMXi+y\nJhTinEsQp58L88PLNR8txNJVE7tpQnBbiLzJvZvVqlpl38Qo7N3vkdZi9aXF5kiaSGbwlUCrYRi/\nMwzjLcO4C6ZJAAAgAElEQVQwfmUYRjZQJqVsApBSNgLRS7RyuA5m+Zi9E/qHpt7eCguXI9LHpv0U\nQuBYewGOO++G1ibMb30RtX93RLIBq2qRqwMKSyLuS2MfwpmH4/99NWHcMyOIZatR7/qOh1eDA9aa\nQbCzyQI9gx9DRXgLrcrVibArgmYEm0v3ReJYTgVOAz4rpdxqGMaPsdwzJ69s+lzpNAxjHbBu5LWU\nEqdz/Ex8IvqajsD8hWT5uE4tXUHnz79HbnZ2UDVLe959k9S155PhdJKenj5ei9MJ//ptBl9/ib5f\nfJ/cH/0eRwR3b/fRBnqKSskr8P8H4lNHHEgUHZA4WmKtQy1ZTldfL9k9XaSUzxyjI2egl56SUvLy\ng/t77J9Wjupo8/m9iYRYvSedx1vInTsPh5+xQtXhXrqCnsf/FLL2/sF+VPE0v+9jOO/H4Kw5DB7Y\nTW6I1xmGcafXy01Syk0QmYE/DDRIKUemFY9gGfgmwzDKpJRNhmGUAz6fNzwCNnkdusPlcoUkwH2g\nBseZFzDs77qCIlx7d57Y5eoHNTSI+c6buD/4CQZdLpxOJ361LF4Jp52F64Ff47jhkyHpHTPmwQOY\nxdP8jwMT64ghiaIDEkdLXHScchrdr72E48ITmS+dTic9B+swC0uC1mNmZkFLs//vTZjE4j1Rvd2o\nYTfdSiD8jBWqDpWTj9ntoutIQ0hpMMyWRshx+n0fw3k/VLYTs+lYSNc5nU6klHf6Ohf2c6jHDdNg\nGMZILNlFwE7gMeBmz7GPAo+GO0ZADtf7ddGAJy9NMG6aPe/AzLlB70gT192A2roZdST8MErVEnod\nVs3UxtrV+ta446q9GRFCitlJ7YNva4ESm2LgPYS9+72r03YffMIssnr4AnC/YRjbsaJovgfcBVxi\nGEYNltH/QYRj+ET1dkO3a+JFysoFQX1oI+GRwSJy8xBXGZjy3vA3JbQ26Rh4TWgsWWHlzDl5oT+Y\nJGPeTOYomrYm22LgvQlnc2RUfPC5TnC7Ub09tnQXUXC3lHIHcLqPUxdH0m9QNNTDzDkTLoaJigWY\nm5+dsBtlmqgdr+P46vdCGl6cfwXqxadhx+sQws1hdNzWRkSc85xoJhciOxfmzIOad2HZqhMn2ppD\ny5E/ifPRqNZm+2LgvRCV1ZgvPRPaRXbuYh3RIYSVVbK9GbIndi0HQ2KFCoSAlaIgwBswuxKajqAG\nJghtPHgAsrIRIVZ1EampOIxbMeVvrIiYUGlpROg0wZoQEUvHZ5cMqlSfNzm50N8X3t9tvLEhyZhP\nwtn9bmcmSW9sjKSZtAaeBv8hkiOItHSYPgca/G9iCNU9M6b/pafB9NmoEIv3WmmCdQy8JnTEstNQ\n77451hC1N4dU5k04HJbv2DX5ZvHWDN7+iZG1+z3N+l4Go0Mp6/3Ltd/A2xkLP2kNvDpcH3gGj7Xh\naaIdrWpH+AYewLH+FtTTj4SWY3v3DnCk2JINTzPFmFkBQ0PQdATw5EPpCmM/RX7h5KzN2tYUnRk8\nQCjFgvp6ISUNkZERuG2oTPUZvHK74dgha4txICqqwc/iiWpptPxoESSVEuUzEWddjPrrH4Jqb77y\nPOa9P8LxyX+LfypazaRDCGFF03h2tZrtrZBXEHqupPxC6GyPgsIoY2OisZMJafd7tNwzYGtlp0lp\n4Gk8AgUlQSUHEhMU1lXbX0MsPz3iXYviKsN6bJ4gX41SCvOxP6EeewDHv37X3rqimimFd5Un1doY\nVBbJcX1MwuLb6lgDCAf4SE1iByFF0kRhgXVUR3Fp4Pz/QTIpDbw6XDc+RbA/ymda+a97xm8cUDte\nj8g9M4LIzkFcdyPmg7/2uUijhodQv/sJ6u03cNz+Q8SMORGPqZnCLF4OtXtR/X2YrU3B5YE/mbyC\nSeeiMeVvEVe+P3pPvhVV0FBreQgC4bI/D80oxaVTfAbfUBdwd+oIwpECc6vGuWlOJBezZyYtzr4I\nBgdQb/zzpHG6MX9yJ6qvF8dXv4fIL7RlPM3URWRmw7xq2PM2ZkuYceH5hVbK4EmCemcrNB9DeO3i\ntRuRnQuFxZb7N5Cerk5bi7+PIb8QertHq4tFwqQ08KohiBBJL6yyXGPdNP6Si4WLcKTg+MAnUI/8\nfjQsU7U2Yd71NcSsChyfvg2RkWnLWBqNWHoa6p03re3yYczgRd7kKb6thocx5W9wfOBWK71vFAl6\n93sUMkmOanA4rEVzGxZaJ6WB53Cd7xzwfrCqtoz1j1vhkWtslSWqT0HMW4R65hFU3T7LuJ9/OY4P\nfsJ6ktBobGIku6TZ2oQIwwdPfsGk2eykNj1pPaUss7/g+jiCzSzpsrEWqy+K7UlZMOkMvOo6DsPD\noYWFeT60Ef+4GhqE3TsQy31two0M8f6bUc8/iXn3t3Dc+CkcF11j+xgaDeWzQDgY3rszPBdN3uRI\nV6Bcnagn/4zjAx+PSdRZoLBqsJ4oVPPR6EXR4ImFb43cwEehDl2UaaiH2ZWhfdhFnptBe6v1OBti\ncrFQEMWlOD7yGSgpC76EmkYTIla45GrUpqdC2uQ0iqdsn1IqocN11d/uR5xxPmL67NgM6LX7/eQY\nd+XqRL30jPWel85AVC+Nno4ie2LhJ52BVw21IfnfwZPfobIa6vdC8bSIdq8GNV4ClXvTJC9i2Sp4\n65WwNtuIzCwQKdaGneycKKiLHNVQh9r2Ko5v/yJmY47Z/V61xNJxuA618XHUtlcRK8/E8YU7QrZB\nIVNcCnt2RNzNpDPwNNSHlljJg6ioshZPVp5pJRf71+/aLk2jiSlLVpL9uW/QH+71I5E0CWjglVKY\nD92LuPZDiJzY7vgWlQtQB2rA1YX53OPQdASx7koc3/llVJ76fWoonoZpQ6jkpDPw6nAdjstCL/Mq\nKqoxn34EMZJczKsqjkYzGRGpqaQtX01/uEU28j2x8OWz7BVmB2+9Ct1diHMvi/3YFdWoDT9DVVQh\nLroGseqsqEfvjMOmdAWTysCroUFoabQeoUKlcgEc3G89ZkXRPaPRTBZGCn8kmgdeDQ1i/vm3OD76\n+aDKbdqNWHMuYu68oPfaRIXCYug8jnK7I3oPJlcUzdFDUDodkRb63VTkOMGZj3rxacSp9oZHajST\nkgQt/KH+8TeYMy9u6TxEWnp8jTtYTwzOfOhoi6ifSWXgQ93gdDKishpSUqxdgBrNVMcTSZNIqI42\n1MZHcay/Jd5S4k9x5EnHJpWLJlAN1oAsXGaV29ObjjQaawa/71i8VYxB/eU+xLmX6XrFeJKOtbVE\n5EKbVAZeNdTiWB7+bjbHeXFYsNFoEhSRX4iZQDN4VVuD2r0Dx7d/Hm8piYENSccmjYFXSkU+g9do\nNCdIkN2sSil482XMB3+NMG61kqlpLAMfbAESP0waA097C6RlRC+Dm0Yz1cgviLuBVx3tmPf/EpqO\n4Pj07Yj5i+KqJ5EQxdMw33w5oj4mj4FvCCEHvEajCYyzAHpcKNMd83UppRTq5Y2Wz/28yxD/76th\nRcclNTbEwk8aA69CyAGv0WgCI1I8dYFdXdaCa4xQLY2Yf/hf6O3B8aX/jP62/8lK0TQ43ooyzbCr\nzk2KMEmlFGrrZsTi5fGWotEkFzGMhVemG3PjY5jf+wrilJVWdTNt3P0iMjIhNw9am8LuY3LM4N99\nExwpsDj0HDQajWYC8mJT2Un1uDB/9m1IScHxtf/SqUKCpaIKVb8PUTo9rMsnxQzefPoviMvfm9Bp\nTTWayYjIL0DFoDareutVyM3D8ZXvauMeAqKyely50VBIeAOvamugrRmx+px4S9Foko8YzeDZtR2x\n8sywfclTFV/lRkMh4d9t85m/Ii65Pi5JhzSapCcGPnhlulF7diDCSPM95ZlbBQ11KLc7rMsj9sEb\nhuEAtgKHpZTXGoZRCDwEzAXqAUNK2RlO36rxCOzbibjlXyKVqdFofJFXEFwN0kg4VAvOAkRhcXTH\nSUJEdo5VnvTooZDqUI9gxwz+i8Aur9e3ARullAuB54Hbw+1YPfs3xPlXWKvJGo3GdkR+IaqzPapj\nqF3b9ew9AkTlAlSYN+GIDLxhGLOAK4F7vQ5fB2zw/LwBCL06B6A6j6O2voy48KpIJGo0monIL7KK\nfkQRtWs74pSVUR0jqalYEHbKgkhn8D8Gvgoor2NlUsomACllIxBGyXdQzz+BWHNezEpkaTRTkvzo\npgxWA/1Qvx+iWaA6yRGV1Va50TAI2wdvGMZVQJOUcrthGOsmaKp8HfRcM3qdlBKn02ld0NdL1z+f\nIffbPyfFcyyWpKenj2qJJ1rHeBJFS7LoULm5dDlSyO7rIaU0shS9vrQM7d/JwPyF5JZMi6jvSHXE\nA7t0qMXL6Gw+Sm56ml93tWEYd3q93CSl3AQglPJpfwNiGMb3gA8Dw0AW4AT+CqwG1kkpmwzDKAde\nkFIuDub3OHr0KADms49CbQ2OT/5bWNoixel04gq3zqXWEVUSRUsy6TAfuhfS0nC896O2azEfuteK\nf7/KiKjvSHXEAzt1uL/7FRwfuBVRtWTcuRkzZgC+08aH7aKRUn5dSjlHSjkP+CDwvJTyI8DjwM2e\nZh8FHg2lXzU8hHr2UcTl7w1XmkajCQGx7krU5o1WzWObUTu3af+7DYiKBWG5aaIRB/8D4BLDMGqA\nizyvg0a9/k8on4mYWxUFaRqN5mRE2QyYPQ/1xmZb+1XH28DVAXPm2drvlKRyQVjhrLbkopFSvgi8\n6Pm5Hbg4nH6UUqhn/oLDuNUOWRqNJkgcF16F+aSEsy60rU+1azti0am6RKYNiMpqzCceCvm6xNrJ\nOpJUTMfMajSxZdkq6OoIO1rDJ7u26e+yXZTNhO4uVHdXSJcllIHXScU0mvggHCmIdVegXnjSlv6U\naaJ26/QEdiEcDpgzP+R4+IQy8LQ1I1adHW8VGs2URJx9CWr7ayhXaLNEnxyug+xcRHFY22A0Pggn\nHj6hDLy45DpE6uRIUa/RJBvCmYdYuRa1+dmI+9LpCezHyiw5mQ38OZfEW4JGM6URF1yJevHvKDO8\n7IUjaAMfBTyRNKHsXUosA6+Timk0cUVULLAyTL69New+1MAA1O6FhcvsE6axsko6HNAefCHuhDLw\nGo0m/ogLrsJ84anwO9i3E2ZXIrKy7ROlsYJPQkw8pg28RqMZg1h9NjTUWvUYwkDt3o44RbtnooG1\nozX4DU/awGs0mjGItHTEOZegNoU3i1c7tyEWawMfDUKNpNEGXqPRjEOcfwVqyyZUf19I16mOdjje\nZrkSNPZTUQWHDgS9CK4NvEajGYcongYLTkG99mJI16ndO2DRMl1DOUqIHKe1CH4sOPeZNvAajcYn\njguuRL3wZEhheezajliis0dGE1FRjaoPzg+vDbxGo/HN4lPBPWxFxQSBUspaYNXx79ElhMyS2sBr\nNBqfCCEQ665CBRkyaTbUQXoGYlpklaE0E2PtaN0fVFtt4DUajV/EmRegdm1HdbQFbDv09lY9e48F\nc+bBsUNBFWjRBl6j0fhFZOcg1pyLkr+1CnhMwPA7W7X/PQaI9AwonwWHagO21QZeo9FMiLj2BsjN\nw7zzc5i/+iHqwJ5xbdTQIMM178IinZ4gFlgLrYHj4XXqRo1GMyHCmY+44ZOo629Evfwc5r0/Amc+\n4qJrEKvOQqSmwf7dpMyeB9m58ZY7Naiogr3vBmymDbxGowkKkZ2LuOQ61EVXw9tvYG58HPXw7xDn\nXwHtLaQtX8VwvEVOEURlNeYzfw3YTht4jUYTEsKRAivWkrJiLepwHeq5J1CvvUjaN3+sDXysmDEb\nOtpRvd0TNtM+eI1GEzZiViWOj34ex90PkrpgSbzlTBmEIwXmVEKAcElt4DUaTcToSmyxx0o8NvGG\nJ23gNRqNZjISRCSNNvAajUYzCREVVRAgdbA28BqNRjMZKSmzcgVNgDbwGo1GMwkRQkBl9YRttIHX\naDSaSYoIUFhFG3iNRqOZpIhT10x4PuzYJsMwZgH3AWWACfxaSnm3YRiFwEPAXKAeMKSUneGOo9Fo\nNBrfiLnzJzwfyQx+GPiylPIU4Ezgs4ZhLAJuAzZKKRcCzwO3RzCGRqPRaMIkbAMvpWyUUm73/NwN\n7AZmAdcBGzzNNgDXRypSo9FoNKFjiw/eMIwKYAWwBSiTUjaBdRMASu0YQ6PRaDShEbGBNwwjF3gY\n+KJnJn9yhd4QKvZqNBqNxi4iSiBhGEYqlnH/g5TyUc/hJsMwyqSUTYZhlAPNfq5dB6wbeS2lxOl0\nRiLHNtLT0xNCi9YxnkTRonWMJ1G0TEUdhmHc6fVyk5RyE0SeLvi3wC4p5U+9jj0G3AzcBXwUeNTH\ndXgEbPI6dIfL5YpQjj04nU4SQYvWMZ5E0aJ1jCdRtEw1HU6nEynlnb7ORRImeTZwI/COYRjbsFwx\nX8cy7NIwjFuAg4AR7hgajUajCZ+wDbyU8mUgxc/pi8PtV6PRaDT2oJM4JygtPUMMpQyRFm8hGo1m\n0qJTFSQYblPx6O52Pvt4Lfe8fDDecjQazSRGG/gE4lDHALf94yCvH3bx3Uvm8EZDF10D7njL0mg0\nkxTtokkAhtyKR3a18WTNcW48tYRLqwpwCMHaOfm8WNfJNYuK4i1Ro9FMQvQMPgTcpuKVQ108+E4r\nzd1DtvS5r62Pr/y9nn2tffz4ygouX1CIQwgArlg8jY0HOlFK7xXTaDSho2fwQeAacPPsgQ6eqjlO\ncXYalYUZfPnvdSwty+GaRYUsmZZlJd8PgYFhkwfebuWFuk5uPa2U8yryxvWxYoaTvmGTA+0DVBVn\n2vkraTSaKYA28BNwqHOAJ/YcZ/OhLk6fkcvXzpvJguIsAG5aOY0Xaru4Z8sxMlMdXLOoiHPnOklL\n8f1QpJSiuWeIva391LT18frhbqqLM/nZVZXkZ/r+GBxCcNG8fDYe6KCquDxk/Xtb+3i7qZf3n1Ic\n8rUajWbyow38SZhKseVgB3LbEeo7BrhsQQH3XD2Poqyxb1V2WgpXLSzkiuoC3jrawxM1x9mwrZnL\nFhRwxYJCMlIF+9v6Rw363tY+ABaWZFFdnMWXz5rBomlZAfVcOC+fLz1Vx8dOKyUjNXiPmlKKe99s\npu54PwuKMzm1PCe0N0Kj0Ux6tIH3wm0qbn/2ECaCKxfk8+8TzMhHcAjB6pm5rJ6ZS0PnAE/UHOez\nj9cybCoqCzOpLsnk/Io8PrGqjGk5qSG7cqblpDG/OIstDS7Or8wP+rq3jvbQO+Tma+fO5J4tjdx9\nVSVZaXrJRaOZSmgD78Xmg10A/PJ9S+ju7g75+tn5GXx6TTkfO62UFCFISwnNmPvjkvn5PLOvI2gD\nr5Ti/rdb+dDyElbPzOXlQ13ct72ZT54euptHo9FMXvSUzoPbVDz4Tis3nloS8iz7ZDJTHbYZd4Az\nZuVS3zFAU/dgUO1fO9yNqRRnzrYy2d16WhlbGrrZ2dQb8tjP13byvg3b+PXWJo65ghtfo9EkBtrA\ne3ihrpOirFSWl2XHW8o40lIcnFeRx8YDgUvbmkrxwNut3LC8ZDTcMjcjhU+tKeNnrx1jYNgMetzX\nDrvYsK2Zb1w8n4wUwb89c5DvbGpg+7EeHbqp0UwCtIHH2mj00Dtt3HDqtIhn79Hi4vn5PF/biduc\n2LC+cshFeorg9Jm5Y46fMcvJguIs/rijJajx3m3q5X+3NPLv62Zx2sw8blpZyr3Xz2fNLCe/ebOJ\nLzxZxzP7OkK6YWg0mtiiDTyw8UAHM/LSOaU08WbvI1QWZpKfmcqOxh6/bdym4k+e2buvG9UnVpfx\nz/oudrdM7Kqpbe/nv/55hK+cM2M0LBQgI9XBpVUF3H1VJR9fXcYbR7r5+N8OsGFbM539w+H/chqN\nJipMeQM/6Db58842blxeEm8pAbl4fv6EbpqX6rvIy0hh5XTfIZF5GSl84vQyfralkUG375n30a5B\n/nPTYT69ptxvaKUQglPLc/j3dbP4r8vm0jXg5j+ea6Bb583RaBKKKW/gn9nXwbzCDKpLAsekx5vz\nKvLYfqzHZwKyYc8i8Q0BFonPnpPH3IIM/vR267hzbb1D3PF8AzcsL+HMOcGVGpvuTOdzZ5SzvDyb\nb286nPAum5rWPrY0uGjvi94Tx7Cp2Hywi5Yee9JZRMLu5l5e9mjR6yZTjykdJjkwbPLIrna+uW5W\nvKUERW56Cqtm5vpMQPZCbSelOWksKwu8oemTp5fxxSfrOGuOc9QF4xpwc8fzDVyxoIBLqwpC0iWE\n4JbTSvnpq8e4659HuP28WbZGEdnFoY4BvrPpMPOLMvnZlmNkpTqoLsmiuiSThcVZzCvKDGkzmS9q\n2/u5e8sxUh2CY65BTi3P4ZqFhSwKI51FJHQPuvn9W828dayHeYWZ/N/WJhxCUF2caW22K8mkqihL\n741Icqa0gX9y73EWlVhf7MnCJfPz+c2bzVy9sHDUYAy5TR56p5WvnDMjqD4KMlO5dVUZd796jP+5\nooJhE/7zhQZWz8jlvWGmNXAIwefXTucHLx3h7leP8aWzp49G8SQCvUNufvDPI3zstFIunJePUoqj\nriH2tvZR09rHS/UuGjoHmJ2fwZJpWVxaVcCcgoyg+x90mzz4disbD3Ry82mlXFCZR9+wyXMHOvnp\nlmNkp6Vw9cLCCdNZ2MVrh1383+tNnD4rl3uuriQ7LWU0VUZNaz972/r4w/YW6o8PMN2ZzvyiTDJS\nJ/6sctNTPLuwM8nzk1pDk3hM2U+qd8jN33a3852L5sRbSkgsLcsel4Ds2QOdzCnIYPG04BeJz53r\nZPPBLh54u5Xa9n7mFGTw0ZXTItKW6hB89ZwZ3Pl8A/dubeITq8sSIipJKcU9Wxo5pTSLC+dZm8WE\nEMzMS2dmXjoXeI4NDJvUtvezrbGHbz53iNkFGVy7sIhVM3MmvFntau7lZ1saqSjM4KdXVVLoSWuR\nnZbCNYuKuGphIW8d7eHxPe3c50lncfmCwtF2dtHZP8yvtjaxv62fL509fczTnBCCstx0ynLTOa8i\nD7AmBnXHB6g7PsBwgOis9r5hHt3Tzv62fvIyUqguyWJhifU0UFGQmZBPbBoQCeSXU0ePHo3ZYPLd\nVho6B/nK2eNnvYlelf2hd1o53jfMp9aUMzBs8unHarn9/JljIl6Cob1vmM89UcvS0my+du5MUhy+\nv6Shvh89g26+sfEQZ8zK5UPLI7tpRKoF4PE97bxQ18kPLp1LepCz5yG3yeaDLh6vOU7PoJurFxZy\n0fx8stNSRnU0tXfwx+0tvNLQzSdXlwW1bjEmgd3MXK5ZWBRRplCn00lXVxcv1Xfx27eaWVeZzw3L\nSyJ2NfnDbSqOdA2yt8168tnb2s8x1yCVhZl85PRZLC3yV6Y5dkT7+9s14Ea+20pVkZWGxN8kJlZ2\nZMaMGQA+RUxJA9896OZTj9Vy16VzmZmXPu58ohv4lp4hvvRUHb95TxVP7+tgZ3MvXz8/vHWEpu5B\nirJSJ3QbhPN+dPQNc9uzB7l6YSFXL7SvYEmoWna39PL9l47ww8vmUpY7/rMOhFKKPa19PL7nODsa\ne1hXmc/VCwvpdKfyo021LCvL4ZbTSsnNCM2wuQbcPLu/g6f2HqckJ41rFhaydrbT703WH30inf9+\n/gAtvcN8fm15yDd5O+gbMnm7qYffbWtlQVEGH19V6jdDaiyI1vdXKcXLh1zcu7WJM2Y72d3Sx7Ts\nVD61ppxpOeOrJ2sDP5aYGfj7d7TQ2jvMF8+c7vN8oht4gDufb+CsOU4e2NHCnRfOpqIweusI4b4f\nTd2D3P7sIW5aMY11PvLotPcNs7fVyrRZ09ZPfkYKt64qpTjbf6nxULR09A/z5b/X8+nTyzl9Vm7g\nCwLQ0jPE3/ce59kDnWSlpfDpNWV+Q1KDxW0qthx28cSe4zT3DHFFdSGXVhWQ5+eG0TvkHs1Suret\nj92t/VxVXcD7lhTH3U2SlpnN/71cx4v1Xdy6qoxz5zrj4qKLxve3vW+YX77eyFHXIJ9fO52FJVkM\nuRV/2dXGEydVYotEx5DbpPb4gOd70U9TzyCfO2P6hOtB2sB70TXg5jOPHeBHV1T4ndFNBgO/+WAX\nP3nlGGtm5fJv586Mm45AHOwY4D+eO8Sn15RTkJHC3rZ+z6N9H/3DphXFUmxFddS09vHU3g4+smIa\nl8zP92kcgtXiNhV3vtBAdXEWH1lhr5toyG3idDrp7/W/6SwcDrT380RNO68d7ubsOU6uqrYW0kfe\nr72t/TR2W+6QhSWZVJdkcXrlNDLMAVt1hMvIZ1PT2sc9W45R7kznU6eXTXjDjqYOO1BK8VxtJ/dt\na+GyBQUYS4vHPe0e6hjgZ1uOkZ4i+OwZ05nh8QoE0qGUoql7aMx34mDHADPy0kcXtPuHrXKe379k\njl97pQ28Fxu2NdMzaPKZM/xnVpwMBn7IbfKvTx/kX8+Zwez84KM97NYRDHta+vj+S4cpzk6zDFNx\nFgtLspjuTBtnxOuP93PPa41kpjr47BnlTHeO/aMOVsv9O1rY3dLHty6cHbLbIxii+TfS0TfM0/s7\neGZfB5mpwnMDtG6CJy9oJsrfKozVMuRWPLyzNeANO9o6vDnmGuTJmuNsquukKCuNas9NcmFJFrPy\n0sf9nTR1D/K/rzXSPWjy+bXlVE7wlOw2FU/UHOfPO9t435Iirl1UREF+3hgdPYNu9rVZT14jN+wU\nhxi9WS8szmJ+cSaZJ62fPFHTzpM1x/n+JXMp8LEwP2kM/JEjR8L+IzCVorVnGPcEv0/fkMl/PHeI\nn1xZ6dNnNkKifGmmqg63qXi8pp2Hd7az/pRirl5YOPrlC0bL1iPd/Py1Rv7nigqfXwg7mKqfzUT4\n0uJ9w/7UmjJm5YU/GTGVoqPfTWFmyoR2wluHUoodjb08UdNOTWs/F8/P57KqAlyDbsvN1drH3rY+\njve5WVCcOXojbe4eQr7bxnuWFHHdoqKgJwmNLuum0Dds8vG1czjY0jW6IN3SM8S8wszRCKTqkixK\nglxfvW8AAAyESURBVHy6+dPbLbx+uJvvXDyHnPSx7rtJY+Av/dmLnjfYegMm2ojR1T984tGmrZ99\nbX1kpDhID+CHvGh+PsbSidMSJMqXZqrrOOYa5J4txxhwKz6/djpzCzICamnqHuSrzxzktnNnsiSK\nuYWm+mfjC39aRma3j+xsI9UhTmwuK8miaoLNZR39w6Mz3Zq2Pva39ZMiAM+GrZHZ94LiTHK9jJ7T\n6aT1eCeb6rp4oqYdgGsWFXF+RZ7fsboG3OzzGPua1n4cAm5dVeYzCCMQSik2HujkH7VdzHKmjj6x\nzinIIDXMp0mlFL/a2sTBjgHuuGD2mN9j0hj4bXvrvUrc9VN/vJ/pznTrUao4i0G38hj0Pjr73VQV\njzzuWx92gU0r94nypdE6rFnbP/Z3cP+OVi5fUMBpc4rp6/OfLO3+Ha2cW+Hk+sXRrUOrP5vxBOtz\nHpmU1bT2ccjL57ygOJO+IXP0fPfAiVn1yPn8zFTaei2/9V5Pu/1t/ZRkp47aiY4hwVO7W1hYksU1\niwpZXpadFIu9plL8+OVj9LtNbvMKa540Bv5kH/yQW1HfYf0h7GvtJz1VjNY0nenDZ2YXifKl0TpO\n0No7xP07WnANwfCw/6RmFZ4NW9H+QifCe5JIOiA8LYOezVYjxjo7zTGaTmFGXnpQu6HdpqKhc2B0\nl25BTiYXV+SMW7+JNdH4bIbciu+9eJiCrBQ+v9baLR4XA28YxuXAT7ASmv1GSnlXgEtiutFpIhLl\nS6N1jCdRtGgd40kULcmuY2DY5JvPNVBdksktp5Uyc+ZM8GPgo7LdzTAMB3APcBlwCvAhwzAWRWMs\njUajmUpkpDr4j3Wz2HGsl4d3tk3YNlpZj9YA+6SUB6WUQ8CDwHVRGkuj0WimFLkZKdxx4SyeDVDG\nM1oGfibQ4PX6sOeYRqPRaGygODuNb104e8I2Ohm0RqPRTFICLSRHKyPQEcA7D+8sz7FRDMNYB6wb\neS2lHFkNTgiczuAqGkUbrWM8iaJF6xhPomiZajoMw7jT6+UmKeUmwIpNtfvf+vXrU9avX79//fr1\nc9evX5++fv367evXr18c4JpNQfR7ZyTnQ2gzoRY7xrFDR6zeEzt0xOo9SRQd+rNJXB1T6bOJiotG\nSukGPgf8A9gJPCil3B3gsvogut4U4flg29THYBw7dNg1TqA2dugIpk0wfdRPEh12jROojR06gmkT\nTB/1k0SHXeMEamOHjmDa+D8f6O4Rq3/B3MmmmhatI3G1aB2Jq0XrOPEvkRZZN8VbgBeb4i3Aw6Z4\nC/CwKd4CvNgUbwEeNsVbgIdN8RbgxaZ4C/CwKd4CPGyKt4BESlWg0Wg0GhtJpBm8RqPRaGxEG3iN\nRqNJUqJWGdcwjFnAfUAZYAK/llLebRhGIfAQMBdrldmQUnZ6rrkduAUYBr4opfyHYRi5wD8BhZVQ\nZxbwBynll2OtxXP8Y8CXATdwFPiwlLI9Djo+AHwd6yb9hJTy9mi9H4ZhFAEPA6cDv5NSfsGrr9OA\n3wOZwFNSyn8JVkcUtHwHuAkokFLmxUOHYRhZwJ+B+Vif2eNSyq/H6f34O1AOpAFbgE9JKYfjocWr\nz8eACinl8ji9Jy8A04E+LJtyqZSyNQ460rDyda3DsiXfkFL+Ndj3JFiiOYMfBr4spTwFOBP4rCfh\n2G3ARinlQuB54HYAwzCWAAawGLgC+LlhGEJK2S2lXCmlPE1KuRI4CDwSDy2eD+W/gfOklCuAd7DC\nQWOtowj4L+ACKeUyoNwwjAuipQPoB/4d+IqPvn4B3CqlrAaqDcO4LAQddmt5DOvLFA526vihlHIx\nsBI4J8T3xE4d6z3fnaVAAfCBEHTYrQXDMN4DdIWowXYdwIe8bEpQxj0KOr4BNEkpF0oplwAvhqAj\naKJm4KWUjVLK7Z6fu4HdWLPv64ANnmYbgOs9P1+LFS8/LKWsB/ZhJS0bxTCMamCalPLlOGkZBtoB\np2EYAsjDmsXHWsc8YK/Xk8NzwPuipUNK2SulfAUYU93ZMIxywCmlfMNz6D4v7THV4jn3upSyKZTx\n7dYhpeyTUr7o+XkYeMvTT0x1eF0/MltMByZOPRhFLYZh5ABfAr4Tiga7dXgIy+7ZrOMW4PtefQfl\nBQiVmPjgDcOoAFZgPSaWjXwJpZSNQKmn2ckJyo4wPkHZB7AeheKiRUqpgC8C72IlUFsM/CbWOoD9\nwELDMOYYhpGK9Qc1cdahyHT4YybW+zBCREnlItRiG3bpMAyjALgG6wYcFx2GYTwNNAJ9Usqnw9Fh\nk5ZvYz399oWrwSYdAL83DOMtwzD+PR46DMPI9/z4HcMw3jQM4yHDMKaFq2Uiom7gPT70h7H8x91Y\nfi9vQonT/CDwp3hpMQzDCfwMWC6lnInlognav2qXDillB/BpQGI92tVh+fFiqsNOEkWLXToMw0gB\nHgB+4nn6iosOKeXlWD7nDMMwbgpVhx1aDMM4FZgvpXwMax0trHJbNr0nN3jcmucC5xqG8eE46EjF\nmvlvllKuwrpJ/ChUHcEQVQPvmV0+jLUo+qjncJNhGGWe8+VAs+f4EcbOQsckKDMMYzmQIqXcFkct\ni4Fary+sxPLFxVoHUsonpZRrpZRnA3s9/6Klwx8TfmYx1hIxNuv4FVAjpfxZnHUgpRzEWrcKeX3C\nJi1nAqsMw6jFCpioNgzj+TjoQEp5zPN/D9YNeM3EV9ivQ0rZBvTIE4uqf8Zar7GdaM/gfwvsklL+\n1OvYY8DNnp8/CjzqdfyDhmGkG4ZRCVQBr3td9yEimL3bpKUWWGQYxkhF50uw/HCx1sHII51nBf8z\nwL1R1OHN6OzL8zjaaRjGGs+axE1+rom6liCPx0SHYUXz5EkpvxQvHYZh5HiMzYhRugrYHg8tUspf\nSilnSSnnAedg3fgujLWO/9/e/YTGVUVxHP+GdmOpisWVYCqi6K5IUQuC3YgFKS6E/gRRsLroxj8L\noaiI/6BEEbQWq4hipfUfR2MXFt2INgV14T8K/gMrIdVGS1utRltjC7o49+GzTTMTfJMwL78PDMzL\nzLtzeUnO3Ln3zjmSFlT/u2VdYjU53Tqr/Sjeqm2MuAr4aob96Eova7JeAewipzH+Lrd7yQAV5Mhv\njNxSdLiccw9wK3CM2pbA8tge4JqImNFItem+SLoJWE9OiYwBN0fEL3PQj1eAZaWNhyLi9R5fj1Hg\ndHKx7jC5vewbScv57zbJO7vtRw/68ihwAzklMQ48HxEPz2Y/gAly3eRr4K/SzlMR8cIs9+NnYEf5\n2QCZ+G99WUfqSpO/m1qbS8mtozPZJtnUNdlb2lkILADeJXfFdDvt1uTf6iCwDTgTOACsjYgfaJhT\nFZiZtZS/yWpm1lIO8GZmLeUAb2bWUg7wZmYt5QBvZtZSDvBmZi3lAG9m1lIO8NYKkrZI6upLTWbz\nhQO8zSuS3pd0y1z3w2w2OMCbmbWUUxVYX5J0CZlg7QLgHTIvyLfA42SOj8vJfCMfAusiYrwkAbub\nzBFzHHgxIu4oVXk2AcvJTID3d8rtI2kL8AdwHnAl8CWZina0PD5lmyWP+OcRcVZ53nPAtRFRZSPc\nCnwSEZv+90Wyec8jeOs7JRPgdrJ6zhIy3WpV0WqAzPh3LjAIHAE2A0TEfWS62tsi4owS3BeRibhe\nAs4maw5sLgG6k+uBB8hyeN8BG0r/pmrzaUkXl1TTv5Y3KMi85BOSLirHK+lR+Tabf3pWdNush1YA\nC2uj3GFJHwOUzJ5Vnu1JSUNMX1FpNTAaEVvL8W5JbwJryCpE09keEZ8CSHqZf4s2TNXmcK3NXcBK\nSVW5xzfK8SRZAnF3h9c164oDvPWjczi5sMgYgKTTgI3AKnJkPQAsVhZwn2o+cimwQlJVE3OAnNrZ\n1kU/fqrdPwIs7tBmFfBHyHq7+8r9nWQu/UnyE4ZZIxzgrR/9yMm1XwfJWrV3ARcCl0bEgVIu7jMy\nyFY5vOu+B3ZGxKoG+9epzRHgsfK8EeAD4FngTzw9Yw1ygLd+9BFwXNLtwDPkaPgy4D2yuMJR4DdJ\nS4AHTzh3P3B+7XgHMFRqc75GvhEsA36vF6qYoWnbjIg9ko4CNwJDETEhaT9wHWW9wKwJXmS1vhMR\nx8hguBY4RM5tD5eHnwAWAQfJHTRvn3D6k8AaSYckbSxFk68mF0LHy+0RsgLPdE65/azLNkeAgxGx\nr3YM+WnDrBHeJmlm1lIewZuZtZTn4M1OQdIX5OJtpVqoXRcRr85Nr8y65ykaM7OW8hSNmVlLOcCb\nmbWUA7yZWUs5wJuZtZQDvJlZS/0DNgaakGMs9CcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110970a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_haefeli = df_judges[df_judges['judge'] == 'Haefeli']\n",
    "df_haefeli[df_haefeli['decision_harm_auto'] == 'Abgewiesen'].resample('Q')['aktennummer'].count().plot()\n",
    "df_haefeli[df_haefeli['decision_harm_auto'] == 'Gutgeheissen'].resample('Q')['aktennummer'].count().plot()\n",
    "plt.savefig('haefeli.png', transparent=True, bbox_inches='tight')\n",
    "plt.savefig('haefeli.pdf', transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WIaVEnjiCGBXUHIu4aCvExSN3/H3y/QYHzcGsM/3XHU3YIhAXrFDWuMInyMrT/vOHAyIy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bf8860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_theis = df_judges[df_judges['judge'] == 'Theis']\n",
    "df_theis[df_theis['decision_harm_auto'] == 'Abgewiesen'].resample('Q')['aktennummer'].count().plot()\n",
    "df_theis[df_theis['decision_harm_auto'] == 'Gutgeheissen'].resample('Q')['aktennummer'].count().plot()\n",
    "plt.savefig('theis.png', transparent=True, bbox_inches='tight')\n",
    "plt.savefig('theis.pdf', transparent=True, bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}