{
 "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": 1,
   "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": 2,
   "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": 17,
   "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": 18,
   "id": "5bcba3f8",
   "metadata": {
    "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": 5,
   "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": 5,
     "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": 6,
   "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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/images/2_Edit_Cells.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a391731",
   "metadata": {},
   "source": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/images/4_GND.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c711dfef",
   "metadata": {},
   "source": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/images/15_revalues.JPG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca271e82",
   "metadata": {},
   "source": [
    "![Transform](https://raw.githubusercontent.com/deutsche-nationalbibliothek/dnblab/main/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": 7,
   "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": 7,
     "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": 8,
   "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",
      "Storm, Theodor                                               4\n",
      "Hoffmann, E. T. A.                                           4\n",
      "Schiller, Friedrich                                          4\n",
      "Stifter, Adalbert                                            3\n",
      "Gotthelf, Jeremias                                           3\n",
      "Mörike, Eduard                                               3\n",
      "Wassermann, Jakob                                            3\n",
      "Hofmannsthal, Hugo ˜vonœ                                     2\n",
      "Tieck, Ludwig                                                2\n",
      "Schnitzler, Arthur                                           2\n",
      "Grabbe, Christian Dietrich                                   2\n",
      "Ebner-Eschenbach, Marie ˜vonœ                                2\n",
      "Droste-Hülshoff, Annette ˜vonœ                               2\n",
      "Lessing, Gotthold Ephraim                                    2\n",
      "Hebbel, Friedrich                                            2\n",
      "Raabe, Wilhelm                                               2\n",
      "Anzengruber, Ludwig                                          2\n",
      "Spee, Friedrich ˜vonœ                                        1\n",
      "Ludwig, Otto                                                 1\n",
      "May, Karl                                                    1\n",
      "Horváth, Ödön ˜vonœ                                          1\n",
      "Rilke, Rainer Maria                                          1\n",
      "Hoffmann, Heinrich                                           1\n",
      "Spyri, Johanna                                               1\n",
      "Hauff, Wilhelm                                               1\n",
      "Fouqué, Friedrich de La Motte-                               1\n",
      "Wieland, Christoph Martin                                    1\n",
      "Reinheimer, Sophie                                           1\n",
      "Grimmelshausen, Hans Jakob Christoffel ˜vonœ                 1\n",
      "Chamisso, Adelbert ˜vonœ                                     1\n",
      "Uhland, Ludwig                                               1\n",
      "Hölderlin, Friedrich                                         1\n",
      "Heine, Heinrich                                              1\n",
      "Bassewitz, Gerdt ˜vonœ                                       1\n",
      "Schlegel, Friedrich ˜vonœ                                    1\n",
      "Raimund, Ferdinand                                           1\n",
      "Büchner, Georg                                               1\n",
      "Wedekind, Frank                                              1\n",
      "Marlitt, E.                                                  1\n",
      "Meyrink, Gustav                                              1\n",
      "Trakl, Georg                                                 1\n",
      "Heyse, Paul                                                  1\n",
      "Postl, Karl                                                  1\n",
      "Grillparzer, Franz                                           1\n",
      "Heym, Georg                                                  1\n",
      "Kafka, Franz                                                 1\n",
      "Jean Paul                                                    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": 13,
   "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": 16,
   "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}