{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a235c0fa",
   "metadata": {},
   "source": [
    "# DNBLab Tutorial: Daten bereinigen und zusammenführen mittels OpenRefine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efab486",
   "metadata": {},
   "source": [
    "## Part 1: Datenbezug mittels SRU-Schnittstelle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a835ec16",
   "metadata": {},
   "source": [
    "Als Datenbasis dienen die Metadaten des Digitalisierungsprojektes \"100 Bände Klassik\". Es enthält namhafte klassische Werke u.a. von Theodor Fontane, J.W. von Goethe und Rainer Maria Rilke und eignet sich daher besonders für einen ersten Einstieg in die Datenanreicherung, da die AutorInnen bereits umfassende Einträge in der GND haben. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd4b171",
   "metadata": {},
   "source": [
    "Die Daten werden mittels SRU-Schnittstelle bezogen und zur weiteren Verarbeitung in einer csv-Datei gespeichert. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c1bf1f",
   "metadata": {},
   "source": [
    "## Einrichten der Arbeitsumgebung"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d66639",
   "metadata": {},
   "source": [
    "Um die Arbeitsumgebung für die folgenden Schritte passend einzurichten, sollten zunächst die benötigten Python-Bibliotheken importiert werden. Für Anfragen über die SRU-Schnittstelle wird `Requests` https://docs.python-requests.org/en/latest/ und zur Verarbeitung der XML-Daten `etree` https://docs.python.org/3/library/xml.etree.elementtree.html verwendet. Mit `Pandas` https://pandas.pydata.org/ können Elemente aus dem MARC21-Format ausgelesen werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17e5d771",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup \n",
    "import unicodedata\n",
    "from lxml import etree\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681af64f",
   "metadata": {},
   "source": [
    "## SRU-Abfrage mit Ausgabe in MARC21-xml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780d8207",
   "metadata": {},
   "source": [
    "Die Funktion `dnb_sru` nimmt den Paramter \"query\" der SRU-Abfrage entgegen und liefert alle Ergebnisse als eine Liste von Records aus. Bei mehr als 100 Records werden weitere Datensätze mit \"&startRecord=101\" abgerufen (mögliche Werte 1 bis 99.000). Weitere Informationen und Funktionen der SRU- Schnittstelle werden unter https://www.dnb.de/sru beschrieben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "201fc688",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def dnb_sru(query):\n",
    "    base_url = \"https://services.dnb.de/sru/dnb\"\n",
    "    params = {\n",
    "        'recordSchema': 'MARC21-xml',\n",
    "        'operation': 'searchRetrieve',\n",
    "        'version': '1.1',\n",
    "        'maximumRecords': '100',\n",
    "        'query': query\n",
    "    }\n",
    "\n",
    "    r = requests.get(base_url, params=params)\n",
    "    # Verwende den XML-Parser\n",
    "    xml = BeautifulSoup(r.content, features=\"xml\")\n",
    "    records = xml.find_all('record', {'type': 'Bibliographic'})\n",
    "\n",
    "    if len(records) < 100:\n",
    "        return records\n",
    "    else:\n",
    "        num_records_fetched = 100  # Anzahl der abgerufenen Datensätze\n",
    "        start_record = 101  # Startindex für die nächste Abfrage\n",
    "        while num_records_fetched == 100:\n",
    "            params.update({'startRecord': start_record})\n",
    "            r = requests.get(base_url, params=params)\n",
    "            # Verwende den XML-Parser \n",
    "            xml = BeautifulSoup(r.content, features=\"xml\")\n",
    "            new_records = xml.find_all('record', {'type': 'Bibliographic'})\n",
    "            records += new_records\n",
    "            start_record += 100\n",
    "            num_records_fetched = len(new_records)\n",
    "\n",
    "        return records"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b0ba64",
   "metadata": {},
   "source": [
    "# Durchsuchen eines MARC-Feldes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008c6382",
   "metadata": {},
   "source": [
    "Die Funktion `parse_records` nimmt als Parameter jeweils ein Record entgegen und sucht über xpath die gewünschte Informationen heraus und liefert diese als Dictionary zurück. Die Schlüssel-Werte-Paare können beliebig angepasst und erweitert werden. \n",
    "\n",
    "Tipp: Schauen Sie sich gern unsere Feldübersicht für MARC21 an. \n",
    "https://www.dnb.de/SharedDocs/Downloads/DE/Professionell/Services/efa2023HandoutInhalteInMarc.pdf?__blob=publicationFile&v=2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e5a5d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def parse_record(record):\n",
    "    ns = {\"marc\": \"http://www.loc.gov/MARC21/slim\"}\n",
    "    xml = etree.fromstring(unicodedata.normalize(\"NFC\", str(record)))\n",
    "\n",
    "    def safe_xpath(xpath_expr):\n",
    "        elements = xml.xpath(xpath_expr, namespaces=ns)\n",
    "        return elements[0].text if elements else \"unknown\"\n",
    "\n",
    "    # IDN\n",
    "    idn = safe_xpath(\"marc:controlfield[@tag = '001']\")\n",
    "\n",
    "    # Titel\n",
    "    titel = safe_xpath(\"marc:datafield[@tag = '245']/marc:subfield[@code = 'a']\")\n",
    "\n",
    "    # Erscheinungsjahr\n",
    "    jahr = safe_xpath(\"marc:datafield[@tag = '264']/marc:subfield[@code = 'c']\")\n",
    "\n",
    "    # Verfasserangabe\n",
    "    verfasser = safe_xpath(\"marc:datafield[@tag = '100']/marc:subfield[@code = 'a']\")\n",
    "\n",
    "    # GND-ID\n",
    "    gnd_id = safe_xpath(\"marc:datafield[@tag = '100']/marc:subfield[@code = '0']\")\n",
    "\n",
    "    # URN\n",
    "    urn = safe_xpath(\"marc:datafield[@tag = '856']/marc:subfield[@code = 'u']\")\n",
    "\n",
    "    # Verlag\n",
    "    verlag = safe_xpath(\"marc:datafield[@tag = '264']/marc:subfield[@code = 'b']\")\n",
    "\n",
    "    # Verlagsort\n",
    "    verlagsort = safe_xpath(\"marc:datafield[@tag = '264']/marc:subfield[@code = 'a']\")\n",
    "\n",
    "    meta_dict = {\n",
    "        \"idn\": idn,\n",
    "        \"titel\": titel,\n",
    "        \"jahr\": jahr,\n",
    "        \"verfasser\": verfasser,\n",
    "        \"gnd_id\": gnd_id,\n",
    "        \"urn\": urn,\n",
    "        \"verlag\": verlag,\n",
    "        \"verlagsort\": verlagsort\n",
    "    }\n",
    "\n",
    "    return meta_dict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74578c3",
   "metadata": {},
   "source": [
    "Das Digitalisierungsprojekt \"100 Bände Klassik\" kann mt dem Projektcode \"d002\" als Datenset abgefragt und die Ergebnismenge ausgegeben werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5bcba3f8",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108 Ergebnisse\n"
     ]
    }
   ],
   "source": [
    "records = dnb_sru('cod=d002')\n",
    "print(len(records), 'Ergebnisse')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da801857",
   "metadata": {},
   "source": [
    "## CSV Download"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6c3f37",
   "metadata": {},
   "source": [
    "Für die Datenbereinigung und Datenanreicherung wird die Arbeit im csv-Format empfohlen. Hierfür werden die Suchergebnisse im Folgenden in einem Dataframe (Tabelle) ausgegeben und anschließend für die weitere Bearbeitung heruntergeladen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "684a40c6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idn</th>\n",
       "      <th>titel</th>\n",
       "      <th>jahr</th>\n",
       "      <th>verfasser</th>\n",
       "      <th>gnd_id</th>\n",
       "      <th>urn</th>\n",
       "      <th>verlag</th>\n",
       "      <th>verlagsort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003104487</td>\n",
       "      <td>Egmont</td>\n",
       "      <td>[1946]</td>\n",
       "      <td>Goethe, Johann Wolfgang ˜vonœ</td>\n",
       "      <td>(DE-588)118540238</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Schöningh</td>\n",
       "      <td>Paderborn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>999490184</td>\n",
       "      <td>˜Dasœ Amulett</td>\n",
       "      <td>[1939]</td>\n",
       "      <td>Meyer, Conrad Ferdinand</td>\n",
       "      <td>(DE-588)118581775</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Verl. Dt. Volksbücher</td>\n",
       "      <td>Wiesbaden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000047377</td>\n",
       "      <td>˜Derœ Struwwelpeter oder lustige Geschichten u...</td>\n",
       "      <td>[1939]</td>\n",
       "      <td>Hoffmann, Heinrich</td>\n",
       "      <td>(DE-588)11855249X</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>[Loewe]</td>\n",
       "      <td>[Stuttgart]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000290328</td>\n",
       "      <td>˜Derœ Zweikampf</td>\n",
       "      <td>1939</td>\n",
       "      <td>Kleist, Heinrich ˜vonœ</td>\n",
       "      <td>(DE-588)118563076</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Kohlhammer</td>\n",
       "      <td>Stuttgart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99962461X</td>\n",
       "      <td>Heidi</td>\n",
       "      <td>1939</td>\n",
       "      <td>Spyri, Johanna</td>\n",
       "      <td>(DE-588)118616455</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Rascher</td>\n",
       "      <td>Zürich</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1000746348</td>\n",
       "      <td>Leyer und Schwerdt</td>\n",
       "      <td>1913</td>\n",
       "      <td>Körner, Theodor</td>\n",
       "      <td>(DE-588)118713507</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Morawe &amp; Scheffelt</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>100003917X</td>\n",
       "      <td>Schillers Wallenstein</td>\n",
       "      <td>[1913]</td>\n",
       "      <td>Schiller, Friedrich</td>\n",
       "      <td>(DE-588)118607626</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Dt. Bibliothek</td>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1000062104</td>\n",
       "      <td>Vor dem Sturm</td>\n",
       "      <td>1913</td>\n",
       "      <td>Fontane, Theodor</td>\n",
       "      <td>(DE-588)118534262</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Cotta</td>\n",
       "      <td>Stuttgart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1000775615</td>\n",
       "      <td>˜Derœ Tod des Tizian</td>\n",
       "      <td>[1912]</td>\n",
       "      <td>Hofmannsthal, Hugo ˜vonœ</td>\n",
       "      <td>(DE-588)118552759</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Insel-Verl.</td>\n",
       "      <td>Leipzig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1000778517</td>\n",
       "      <td>˜Dieœ drei gerechten Kammacher</td>\n",
       "      <td>[1903]</td>\n",
       "      <td>Keller, Gottfried</td>\n",
       "      <td>(DE-588)11856109X</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Cotta</td>\n",
       "      <td>Stuttgart</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            idn                                              titel    jahr  \\\n",
       "0    1003104487                                             Egmont  [1946]   \n",
       "1     999490184                                      ˜Dasœ Amulett  [1939]   \n",
       "2    1000047377  ˜Derœ Struwwelpeter oder lustige Geschichten u...  [1939]   \n",
       "3    1000290328                                    ˜Derœ Zweikampf    1939   \n",
       "4     99962461X                                              Heidi    1939   \n",
       "..          ...                                                ...     ...   \n",
       "103  1000746348                                 Leyer und Schwerdt    1913   \n",
       "104  100003917X                              Schillers Wallenstein  [1913]   \n",
       "105  1000062104                                      Vor dem Sturm    1913   \n",
       "106  1000775615                               ˜Derœ Tod des Tizian  [1912]   \n",
       "107  1000778517                     ˜Dieœ drei gerechten Kammacher  [1903]   \n",
       "\n",
       "                         verfasser             gnd_id  \\\n",
       "0    Goethe, Johann Wolfgang ˜vonœ  (DE-588)118540238   \n",
       "1          Meyer, Conrad Ferdinand  (DE-588)118581775   \n",
       "2               Hoffmann, Heinrich  (DE-588)11855249X   \n",
       "3           Kleist, Heinrich ˜vonœ  (DE-588)118563076   \n",
       "4                   Spyri, Johanna  (DE-588)118616455   \n",
       "..                             ...                ...   \n",
       "103                Körner, Theodor  (DE-588)118713507   \n",
       "104            Schiller, Friedrich  (DE-588)118607626   \n",
       "105               Fontane, Theodor  (DE-588)118534262   \n",
       "106       Hofmannsthal, Hugo ˜vonœ  (DE-588)118552759   \n",
       "107              Keller, Gottfried  (DE-588)11856109X   \n",
       "\n",
       "                                                   urn                 verlag  \\\n",
       "0    https://nbn-resolving.org/urn:nbn:de:101:2-201...              Schöningh   \n",
       "1    https://nbn-resolving.org/urn:nbn:de:101:2-201...  Verl. Dt. Volksbücher   \n",
       "2    https://nbn-resolving.org/urn:nbn:de:101:2-201...                [Loewe]   \n",
       "3    https://nbn-resolving.org/urn:nbn:de:101:2-201...             Kohlhammer   \n",
       "4    https://nbn-resolving.org/urn:nbn:de:101:2-201...                Rascher   \n",
       "..                                                 ...                    ...   \n",
       "103  https://nbn-resolving.org/urn:nbn:de:101:2-201...     Morawe & Scheffelt   \n",
       "104  https://nbn-resolving.org/urn:nbn:de:101:2-201...         Dt. Bibliothek   \n",
       "105  https://nbn-resolving.org/urn:nbn:de:101:2-201...                  Cotta   \n",
       "106  https://nbn-resolving.org/urn:nbn:de:101:2-201...            Insel-Verl.   \n",
       "107  https://nbn-resolving.org/urn:nbn:de:101:2-201...                  Cotta   \n",
       "\n",
       "      verlagsort  \n",
       "0      Paderborn  \n",
       "1      Wiesbaden  \n",
       "2    [Stuttgart]  \n",
       "3      Stuttgart  \n",
       "4         Zürich  \n",
       "..           ...  \n",
       "103       Berlin  \n",
       "104       Berlin  \n",
       "105    Stuttgart  \n",
       "106      Leipzig  \n",
       "107    Stuttgart  \n",
       "\n",
       "[108 rows x 8 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = [parse_record(record) for record in records]\n",
    "df = pd.DataFrame(output)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "acd0d64e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# DataFrame als CSV speichern\n",
    "df.to_csv('Klassik.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b6dc4d",
   "metadata": {},
   "source": [
    "# Part 2: Datenbereinigung "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e2a943-801d-4db9-9a03-de2f184ad55c",
   "metadata": {},
   "source": [
    "In unserer Ausgabe fällt auf, dass viele Titel mit eckigen Klammern ausgegeben werden oder zusätzliche Informationen enthalten, die nicht für die Analysen relevant sind. Grundlage für jede Datenanalyse sind normierte und bereinigte Ausgangsdaten. Diese können wir mittels OpenRefine bereinigen. OpenRefine ist ein Tool zur Datenbereinigung und Datenaufbereitung. Es kann mit sehr großen Datenmengen und mit diversen Formaten umgehen, zb.: .csv, .xml, .json etc. und eignet sich somit gut für die Analyse unserer Metadaten. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7aea664-bc42-4891-829b-47bd1f746e28",
   "metadata": {},
   "source": [
    "Für die Datenbereinigung wird die Open-Source-Software OpenRefine eingesetzt. Eine lokale Installation (https://openrefine.org/download) wird empfohlen. Alternativ kann OpenRefine in der Webumgebung Binder gestartet werden: https://mybinder.org/v2/gh/innovationOUtside/nb_serverproxy_openrefine/main?urlpath=openrefine \n",
    "\n",
    "Bitte beachten Sie, dass die Ladezeiten der Online-Instanz stark variieren können."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9cea7dd",
   "metadata": {},
   "source": [
    "![Datenimport Bild](images/1_Datenimport.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a872a45",
   "metadata": {},
   "source": [
    "Folgende Felder sollen für eine exemplarische Analyse bereinigt werden. Dafür nutzen wir GREL-Funktionen, um die Werte zu extrahieren. GREL-Funktionen arbeiten mit regulären Ausdrücken. Die genauen Bezeichnungen kann man im Handbuch von OpenRefine nachlesen. \"GREL\" steht für \"General Refine Expression Language\" und bezieht sich auf die Ausdruckssprache, die in OpenRefine verwendet wird. Siehe: https://openrefine.org/docs/manual/grelfunctions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac8e27a",
   "metadata": {},
   "source": [
    "**Erscheinungsjahr** \n",
    "- `value.find(/(\\b\\d{4}\\b)/)[0]`\n",
    "- Diese GREL-Funktion extrahiert das erste vierstellige Jahr, das in einer Zeichenkette gefunden wird. Dabei wird nach einer vierstelligen Zahl (z.B. \"2024\") gesucht und diese als Ergebnis zurückgegeben. \n",
    "- Hierfür wählen wir im Drop-Down Menü der Spalte den Befehl EDIT CELLS - Transform. Nun können wir unsere GREL-Expression eingeben. \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4162e165",
   "metadata": {},
   "source": [
    "![Transform](images/2_Edit_Cells.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a391731",
   "metadata": {},
   "source": [
    "![Year](images/3_Year.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52c777d",
   "metadata": {},
   "source": [
    "**GND ID** \n",
    "- `value.replace(/\\(DE-588\\)/, \"\").trim()`\n",
    "- Diese GREL-Funktion entfernt den Text \"(DE-588)\" aus einer Zeichenkette und entfernt anschließend überflüssige Leerzeichen am Anfang oder Ende der Zeichenkette. Das Ergebnis ist die bereinigte GND-ID ohne den Präfix \"(DE-588)\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92b2909",
   "metadata": {},
   "source": [
    "![GND](images/4_GND.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c711dfef",
   "metadata": {},
   "source": [
    "![GND](images/5_GNDT.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73f285c4",
   "metadata": {},
   "source": [
    "**Verlagsort**\n",
    "\n",
    "- Um die Verlagsorte zu bereinigen, nutzen wir eine andere Möglichkeit der Bearbeitung von Open Refine. Zuerst werden wir die Verlagsorte vorselektieren. Dieser Weg eignet sich besonders bei kleinen Datenmengen. \n",
    "- Wir wählen im Drop-Down-Menü das Feld `Facetten - Custom Facet`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2031344",
   "metadata": {},
   "source": [
    "![Verlag](images/6_Verlag.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e809c21",
   "metadata": {},
   "source": [
    "- In einem Popup sehen wir nun eine Liste aller Verlagsorte. Diese bestätigen wir mit OK. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98798dff",
   "metadata": {},
   "source": [
    "![Facet](images/7_customfacet.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4897632a",
   "metadata": {},
   "source": [
    "- Daraufhin erscheint am linken Bildrand eine Liste. Wir suchen nun nach abweichenden Verlagsnamen und fügen sie mittels `include` zur Liste hinzu.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5d8f23",
   "metadata": {},
   "source": [
    "![Facet](images/8_Include.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9069daaa",
   "metadata": {},
   "source": [
    "- Die dadurch gebildete Facette sollte nun angezeigt werden.\n",
    "- Mittels Mouse-Over und `Edit` gelangen wir zu dem einzelnen Value. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1e483a",
   "metadata": {},
   "source": [
    "![Facet](images/9_edit.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704c7d29",
   "metadata": {},
   "source": [
    "- Dieser wird nun in den bereinigten Verlagsort, zb.: Berlin umbenannt und mittels `Apply to all identical cells` wird jeder gleichnamige Value dieser Orte zu Berlin normiert. Diese Variante eignet sich bei überschaubaren Datenmengen.\n",
    "- Zur Entfernung der Filter einfach `reset` in der rechten Spalte wählen "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7c3a9f",
   "metadata": {},
   "source": [
    "![Facet](images/10_apply.JPG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecbb2f85",
   "metadata": {},
   "source": [
    "# Part 3: Datenanreicherung mittels Open Refine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7254a4e1",
   "metadata": {},
   "source": [
    "In der Gemeinsamen Normdatei (GND) finden wir viele Informationen zu Personen, Werken und Orten. Dort sind unter anderem Informationen zu Berufen, Beziehungen, Sterbe- und Geburtsdaten sowie Wirkungsdaten und Orte verzeichnet. Für unsere exemplarische Analyse reichern wir die Spalte `verfasser`mit Informationen aus der GND an. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e611c44",
   "metadata": {},
   "source": [
    "Folgende Felder sollen angereichert werden: \n",
    "- Geburtsdatum\n",
    "- Geburtsort\n",
    "- Liste der Berufe der Person\n",
    "\n",
    "Weitere Felder. Informieren Sie sich dafür im GND Explorer und fügen Sie ein Feld Ihrer Wahl hinzu:  https://explore.gnd.network/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322abb43",
   "metadata": {},
   "source": [
    "- Für die Datenanreicherung wählen wir die Spalte `gnd_id` \n",
    "- Unter dem Punkt `Reconcile` finden wir den Punkt `Start Reconciling`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d238b42",
   "metadata": {},
   "source": [
    "![Reconcile](images/11_reconcile.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f8cf978",
   "metadata": {},
   "source": [
    "- Anschließend den Punkt `Add Standard Service` wählen\n",
    "- Zum Abgleich nutzen wir `https://lobid.org/gnd/reconcile/`\n",
    "- Mit `Add Service` bestätigen\n",
    "- Fenster mit \"Cancel\" schließen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca37783",
   "metadata": {},
   "source": [
    "![Reconcile](images/13_lobid.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14484132",
   "metadata": {},
   "source": [
    "- Nach dem Einrichten des Standard Service können wir nun über `Reconcile` `Use Values as Identifiers` die Verknüpfung mit der GND herstellen\n",
    "- Wir wählen unseren Service `GND reconcililation  for OpenRefine`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01be506",
   "metadata": {},
   "source": [
    "![Reconcile](images/14_values.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f97fd2c",
   "metadata": {},
   "source": [
    "- Die Daten erscheinen nun als Links. Beim Mouse-Over werden Informationen über den Autor angezeigt.\n",
    "- Um zusätzliche Spalten zu erstellen, wählen wir nun unsere Spalte `gnd_id` `Edit column` `Add column from reconciled values...` und können anschließend unsere gewünschten Werte wählen\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91a6bfc",
   "metadata": {},
   "source": [
    "![Reconcile](images/15_revalues.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca271e82",
   "metadata": {},
   "source": [
    "![Reconcile](images/16_new.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd647574",
   "metadata": {},
   "source": [
    "- `OK` anwählen und einen Moment warten, während Open Refine die neuen Spalten erstellt.\n",
    "- Im Drop-Down Menü unter Exportieren kann die Datei als .csv Datei exportiert werden. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39aba0d",
   "metadata": {},
   "source": [
    "## Part 4: Datenanalyse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11cc5bce-b222-40c3-82e7-b7b721aafdfa",
   "metadata": {},
   "source": [
    "## Daten einlesen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea78a0e-1fc0-4d7e-8f1f-8b1124e0e59a",
   "metadata": {},
   "source": [
    "Nachdem die Daten bereinigt und mit Daten aus der GND angereichert wurden, führen wir eine exemplarische Analyse auf unseren angereicherten Daten durch. Zuerst wird die csv-Datei importiert und als Dataframe (Tabelle) ausgegeben. Der Dataframe bildet die Basis für die weiteren Bearbeitungen. \n",
    "\n",
    "Da die Zeilen in Open Refine untereinander angezeigt werden, findet sich bei leeren Werten die Bezeichnung \"NaN\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "641be6c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idn</th>\n",
       "      <th>titel</th>\n",
       "      <th>jahr</th>\n",
       "      <th>verfasser</th>\n",
       "      <th>gnd_id</th>\n",
       "      <th>Geburtsdatum</th>\n",
       "      <th>Geburtsort</th>\n",
       "      <th>Beruf oder Beschäftigung</th>\n",
       "      <th>Sterbeort</th>\n",
       "      <th>urn</th>\n",
       "      <th>verlag</th>\n",
       "      <th>verlagsort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1003104487</td>\n",
       "      <td>Egmont</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>Goethe, Johann Wolfgang ˜vonœ</td>\n",
       "      <td>118540238</td>\n",
       "      <td>1749-08-28</td>\n",
       "      <td>Frankfurt am Main</td>\n",
       "      <td>Schriftsteller</td>\n",
       "      <td>Weimar</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Schöningh</td>\n",
       "      <td>Paderborn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Publizist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Politiker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jurist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Naturwissenschaftler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>1000778517</td>\n",
       "      <td>˜Dieœ drei gerechten Kammacher</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>Keller, Gottfried</td>\n",
       "      <td>11856109X</td>\n",
       "      <td>1819-07-19</td>\n",
       "      <td>Zürich</td>\n",
       "      <td>Schriftsteller</td>\n",
       "      <td>Zürich</td>\n",
       "      <td>https://nbn-resolving.org/urn:nbn:de:101:2-201...</td>\n",
       "      <td>Cotta</td>\n",
       "      <td>Stuttgart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Librettist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lyriker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lyriker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>623 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            idn                           titel    jahr  \\\n",
       "0    1003104487                          Egmont  1946.0   \n",
       "1           NaN                             NaN     NaN   \n",
       "2           NaN                             NaN     NaN   \n",
       "3           NaN                             NaN     NaN   \n",
       "4           NaN                             NaN     NaN   \n",
       "..          ...                             ...     ...   \n",
       "618  1000778517  ˜Dieœ drei gerechten Kammacher  1903.0   \n",
       "619         NaN                             NaN     NaN   \n",
       "620         NaN                             NaN     NaN   \n",
       "621         NaN                             NaN     NaN   \n",
       "622         NaN                             NaN     NaN   \n",
       "\n",
       "                         verfasser     gnd_id Geburtsdatum         Geburtsort  \\\n",
       "0    Goethe, Johann Wolfgang ˜vonœ  118540238   1749-08-28  Frankfurt am Main   \n",
       "1                              NaN        NaN          NaN                NaN   \n",
       "2                              NaN        NaN          NaN                NaN   \n",
       "3                              NaN        NaN          NaN                NaN   \n",
       "4                              NaN        NaN          NaN                NaN   \n",
       "..                             ...        ...          ...                ...   \n",
       "618              Keller, Gottfried  11856109X   1819-07-19             Zürich   \n",
       "619                            NaN        NaN          NaN                NaN   \n",
       "620                            NaN        NaN          NaN                NaN   \n",
       "621                            NaN        NaN          NaN                NaN   \n",
       "622                            NaN        NaN          NaN                NaN   \n",
       "\n",
       "    Beruf oder Beschäftigung Sterbeort  \\\n",
       "0             Schriftsteller    Weimar   \n",
       "1                  Publizist       NaN   \n",
       "2                  Politiker       NaN   \n",
       "3                     Jurist       NaN   \n",
       "4       Naturwissenschaftler       NaN   \n",
       "..                       ...       ...   \n",
       "618           Schriftsteller    Zürich   \n",
       "619               Librettist       NaN   \n",
       "620                    Maler       NaN   \n",
       "621                  Lyriker       NaN   \n",
       "622                  Lyriker       NaN   \n",
       "\n",
       "                                                   urn     verlag verlagsort  \n",
       "0    https://nbn-resolving.org/urn:nbn:de:101:2-201...  Schöningh  Paderborn  \n",
       "1                                                  NaN        NaN        NaN  \n",
       "2                                                  NaN        NaN        NaN  \n",
       "3                                                  NaN        NaN        NaN  \n",
       "4                                                  NaN        NaN        NaN  \n",
       "..                                                 ...        ...        ...  \n",
       "618  https://nbn-resolving.org/urn:nbn:de:101:2-201...      Cotta  Stuttgart  \n",
       "619                                                NaN        NaN        NaN  \n",
       "620                                                NaN        NaN        NaN  \n",
       "621                                                NaN        NaN        NaN  \n",
       "622                                                NaN        NaN        NaN  \n",
       "\n",
       "[623 rows x 12 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passe den Dateinamen entsprechend an\n",
    "filename = 'Klassik_angereichert.csv'\n",
    "\n",
    "# Daten aus der CSV-Datei in DataFrame laden\n",
    "df = pd.read_csv(filename)\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c0e99e-65bd-46a0-b495-2828f3f55965",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Vertretene AutorInnen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a32249-854b-4c66-a42a-7ba42783618a",
   "metadata": {},
   "source": [
    "Zuerst soll herausgefunden werden, welche AutorInnen mit wie vielen Werken in dem Datenset vorhanden sind. Dazu zählen wir die values in dem gegebenen Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "95841108",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl der Werke pro AutorIn:\n",
      "                                              Anzahl der Werke\n",
      "verfasser                                                     \n",
      "Kleist, Heinrich ˜vonœ                                       7\n",
      "Goethe, Johann Wolfgang ˜vonœ                                6\n",
      "Fontane, Theodor                                             6\n",
      "Meyer, Conrad Ferdinand                                      5\n",
      "Keller, Gottfried                                            5\n",
      "Eichendorff, Joseph ˜vonœ                                    4\n",
      "Schiller, Friedrich                                          4\n",
      "Hoffmann, E. T. A.                                           4\n",
      "Storm, Theodor                                               4\n",
      "Wassermann, Jakob                                            3\n",
      "Stifter, Adalbert                                            3\n",
      "Mörike, Eduard                                               3\n",
      "Gotthelf, Jeremias                                           3\n",
      "Tieck, Ludwig                                                2\n",
      "Anzengruber, Ludwig                                          2\n",
      "Schnitzler, Arthur                                           2\n",
      "Grabbe, Christian Dietrich                                   2\n",
      "Droste-Hülshoff, Annette ˜vonœ                               2\n",
      "Hofmannsthal, Hugo ˜vonœ                                     2\n",
      "Ebner-Eschenbach, Marie ˜vonœ                                2\n",
      "Lessing, Gotthold Ephraim                                    2\n",
      "Hebbel, Friedrich                                            2\n",
      "Raabe, Wilhelm                                               2\n",
      "Hauff, Wilhelm                                               1\n",
      "Meyrink, Gustav                                              1\n",
      "Wedekind, Frank                                              1\n",
      "Büchner, Georg                                               1\n",
      "Rilke, Rainer Maria                                          1\n",
      "Marlitt, E.                                                  1\n",
      "Heyse, Paul                                                  1\n",
      "Trakl, Georg                                                 1\n",
      "Schlegel, Friedrich ˜vonœ                                    1\n",
      "Postl, Karl                                                  1\n",
      "Grillparzer, Franz                                           1\n",
      "Heym, Georg                                                  1\n",
      "Kafka, Franz                                                 1\n",
      "Jean Paul                                                    1\n",
      "Raimund, Ferdinand                                           1\n",
      "Bassewitz, Gerdt ˜vonœ                                       1\n",
      "Heine, Heinrich                                              1\n",
      "Chamisso, Adelbert ˜vonœ                                     1\n",
      "Horváth, Ödön ˜vonœ                                          1\n",
      "Grimmelshausen, Hans Jakob Christoffel ˜vonœ                 1\n",
      "Uhland, Ludwig                                               1\n",
      "Reinheimer, Sophie                                           1\n",
      "Fouqué, Friedrich de La Motte-                               1\n",
      "Spyri, Johanna                                               1\n",
      "May, Karl                                                    1\n",
      "Hoffmann, Heinrich                                           1\n",
      "Wieland, Christoph Martin                                    1\n",
      "Ludwig, Otto                                                 1\n",
      "Hölderlin, Friedrich                                         1\n",
      "Spee, Friedrich ˜vonœ                                        1\n",
      "Körner, Theodor                                              1\n"
     ]
    }
   ],
   "source": [
    "# Anzahl der Werke pro AutorIn zählen und in ein DataFrame konvertieren\n",
    "anzahl_werke_pro_autor_df = pd.DataFrame(df['verfasser'].value_counts())\n",
    "\n",
    "# Umbenennen der Spalte in 'Anzahl der Werke'\n",
    "anzahl_werke_pro_autor_df.columns = ['Anzahl der Werke']\n",
    "\n",
    "# Anzeige des DataFrames\n",
    "print(\"Anzahl der Werke pro AutorIn:\")\n",
    "print(anzahl_werke_pro_autor_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d1077b-5da9-4689-a84b-ade1c6387951",
   "metadata": {},
   "source": [
    "## Erscheinungsjahre visualisieren "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b913b70-846d-4ef7-8d96-bfb88e6fd229",
   "metadata": {},
   "source": [
    "Die Erscheinungsjahre der Publikationen sollen grafisch dargestellt werden.\n",
    "Testen Sie, was passiert, wenn Sie die Werte unter plt.figure(figsize=(15, 8)) & plt.hist(years, bins=50, ändern. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "71513965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Erscheinungsjahre aus der Spalte 'jahr' im DataFrame df extrahieren\n",
    "years = df['jahr']\n",
    "\n",
    "# Histogramm erstellen\n",
    "plt.figure(figsize=(15, 8)) # definiert die Größe des Diagramms \n",
    "plt.hist(years, bins=50, color='skyblue', edgecolor='black')  # Anzahl der Bins anpassen, um die gewünschte Granularität zu erreichen\n",
    "plt.title('Verteilung der Erscheinungsjahre')\n",
    "plt.xlabel('Erscheinungsjahr')\n",
    "plt.ylabel('Anzahl der Werke')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17eb8800-34c3-4d5f-b875-810d77b56fe6",
   "metadata": {},
   "source": [
    "# Berufe visualisieren "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad2f3c9-2679-4908-ba3b-db48e042521f",
   "metadata": {},
   "source": [
    "Was sind die 10 häufigsten Berufe, die unsere enthaltenen AutorInnen ausüben? Dazu können wir mit der matplotlib.pyplot ein Kreisdiagramm erstellen. Aufgrund der Diversität der Berufe wird der Datenbestand auf die Top-10-Berufe beschränkt. \n",
    "Im Codefeld beruf_beschaeftigung_counts = df['Beruf oder Beschäftigung'].value_counts().head(10) lässt sich der Wert 10 beliebig anpassen. Hier sollte man jedoch die Auswirkungen auf die Übersichtlichkeit beachten. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61833127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beruf oder Beschäftigung\n",
    "beruf_beschaeftigung_counts = df['Beruf oder Beschäftigung'].value_counts().head(10)  # Nur die 10 häufigsten Werte\n",
    "\n",
    "# Kreisdiagramm erstellen\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(beruf_beschaeftigung_counts, labels=beruf_beschaeftigung_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Verteilung der 10 häufigsten Berufe oder Beschäftigungen')\n",
    "plt.axis('equal')  \n",
    "plt.show()\n"
   ]
  }
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