Open Refine Workshop.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a235c0fa",
   "metadata": {},
   "source": [
    "# DNBLab Workshop: Daten bereinigen und zusammenführen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efab486",
   "metadata": {},
   "source": [
    "# Datenbezug"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a835ec16",
   "metadata": {},
   "source": [
    "Als Datenbasis dient das Digitalisierungsprojekt \"100 Bände Klassik\". Es enthält namenhafte 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 OAI-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": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup as soup\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": {},
   "outputs": [],
   "source": [
    "def dnb_sru(query):\n",
    "    \n",
    "    base_url = \"https://services.dnb.de/sru/dnb\"\n",
    "    params = {'recordSchema' : 'MARC21-xml',\n",
    "          'operation': 'searchRetrieve',\n",
    "          'version': '1.1',\n",
    "          'maximumRecords': '100',\n",
    "          'query': query\n",
    "         }\n",
    "    r = requests.get(base_url, params=params)\n",
    "    xml = soup(r.content)\n",
    "    records = xml.find_all('record', {'type':'Bibliographic'})\n",
    "    \n",
    "    if len(records) < 100:\n",
    "        \n",
    "        return records\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        num_results = 100\n",
    "        i = 101\n",
    "        while num_results == 100:\n",
    "            \n",
    "            params.update({'startRecord': i})\n",
    "            r = requests.get(base_url, params=params)\n",
    "            xml = soup(r.content)\n",
    "            new_records = xml.find_all('record', {'type':'Bibliographic'})\n",
    "            records+=new_records\n",
    "            i+=100\n",
    "            num_results = 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 agepasst und erweitert werden. In diesem Fall werden Elemente für IDN und Titel geliefert.\n",
    "\n",
    "Tipp! Die SRU Abfrage https://services.dnb.de/sru/dnb?version=1.1&operation=searchRetrieve&query=cod=d002&recordSchema=MARC21-xml&maximumRecords=100 kann dazu genutzt werden, um die Marc-Tags und Unterfelder für bestimmte, gesuchte Inhalte zu finden. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5a5d20a",
   "metadata": {},
   "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",
    "    # IDN\n",
    "    idn = xml.xpath(\"marc:controlfield[@tag = '001']\", namespaces=ns)\n",
    "    try:\n",
    "        idn = idn[0].text\n",
    "    except:\n",
    "        idn = 'fail'\n",
    "    \n",
    "    # Titel\n",
    "    titel = xml.xpath(\"marc:datafield[@tag = '245']/marc:subfield[@code = 'a']\", namespaces=ns)\n",
    "    try:\n",
    "        titel = titel[0].text\n",
    "    except:\n",
    "        titel = \"unknown\"\n",
    "    \n",
    "    # Erscheinungsjahr\n",
    "    jahr = xml.xpath(\"marc:datafield[@tag = '264']/marc:subfield[@code = 'c']\", namespaces=ns)\n",
    "    try:\n",
    "        jahr = jahr[0].text\n",
    "    except:\n",
    "        jahr = \"unknown\"\n",
    "    \n",
    "    # Verfasserangabe\n",
    "    verfasser = xml.xpath(\"marc:datafield[@tag = '100']/marc:subfield[@code = 'a']\", namespaces=ns)\n",
    "    try:\n",
    "        verfasser = verfasser[0].text\n",
    "    except:\n",
    "        verfasser = \"unknown\"\n",
    "    \n",
    "    # GND-ID\n",
    "    gnd_id = xml.xpath(\"marc:datafield[@tag = '100']/marc:subfield[@code = '0']\", namespaces=ns)\n",
    "    try:\n",
    "        gnd_id = gnd_id[0].text\n",
    "    except:\n",
    "        gnd_id = \"unknown\"\n",
    "    \n",
    "    # URN\n",
    "    urn = xml.xpath(\"marc:datafield[@tag = '856']/marc:subfield[@code = 'u']\", namespaces=ns)\n",
    "    try:\n",
    "        urn = urn[0].text\n",
    "    except:\n",
    "        urn = \"unknown\"\n",
    "    \n",
    "    # Verlag\n",
    "    verlag = xml.xpath(\"marc:datafield[@tag = '264']/marc:subfield[@code = 'b']\", namespaces=ns)\n",
    "    try:\n",
    "        verlag = verlag[0].text\n",
    "    except:\n",
    "        verlag = \"unknown\"\n",
    "    \n",
    "    # Verlagsort\n",
    "    verlagsort = xml.xpath(\"marc:datafield[@tag = '264']/marc:subfield[@code = 'a']\", namespaces=ns)\n",
    "    try:\n",
    "        verlagsort = verlagsort[0].text\n",
    "    except:\n",
    "        verlagsort = \"unknown\"\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": "code",
   "execution_count": 4,
   "id": "0c2d1400",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fuchsf\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\bs4\\builder\\__init__.py:545: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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, weswegen die Suchergebnisse im folgenden in einem Dataframe ausgegeben und anschließend für die weitere Bearbeitung heruntergeladen werden. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "684a40c6",
   "metadata": {},
   "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 von</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>Das 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>Der 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>Der Zweikampf</td>\n",
       "      <td>1939</td>\n",
       "      <td>Kleist, Heinrich von</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>Der Tod des Tizian</td>\n",
       "      <td>[1912]</td>\n",
       "      <td>Hofmannsthal, Hugo von</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>Die 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                                      Das Amulett  [1939]   \n",
       "2    1000047377  Der Struwwelpeter oder lustige Geschichten u...  [1939]   \n",
       "3    1000290328                                    Der 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                               Der Tod des Tizian  [1912]   \n",
       "107  1000778517                     Die drei gerechten Kammacher  [1903]   \n",
       "\n",
       "                         verfasser             gnd_id  \\\n",
       "0    Goethe, Johann Wolfgang von  (DE-588)118540238   \n",
       "1          Meyer, Conrad Ferdinand  (DE-588)118581775   \n",
       "2               Hoffmann, Heinrich  (DE-588)11855249X   \n",
       "3           Kleist, Heinrich von  (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 von  (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": {},
   "outputs": [],
   "source": [
    "# DataFrame als CSV speichern\n",
    "df.to_csv('Klassik.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38e7737",
   "metadata": {},
   "source": [
    "# Datenbereinigung mittels Open Refine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fd443c",
   "metadata": {},
   "source": [
    "Um die Daten zu bereinigen, wird das Tool Open Refine genutzt, welches ebenfalls in Binder gestartet werden kann: https://mybinder.org/v2/gh/betatim/openrefineder/master "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92eda349",
   "metadata": {},
   "source": [
    "In der oberen Liste fällt bereits auf, dass einige Werte mit eckigen Klammern [] angegeben sind oder zusätzliche Informationen enthalten, die nicht für die Analysen relevant sind. Diese müssen mittels Open Refine bereinigt werden.  \n",
    "\n",
    "Weiterhin werden die Daten auf Basis Ihrer GND-ID mit folgenden Daten angereichert: Lebensdaten, Geburtsort, Beruf (1-4), Wirkungsort (1-4). Dafür nutzen wir die Schnittstelle: https://lobid.org/gnd/reconcile "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a1a99c",
   "metadata": {},
   "source": [
    "### Vorgehensweise "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d57b954",
   "metadata": {},
   "source": [
    "Folgende Felder sollen bereinigt werden: \n",
    "- Jahr\n",
    "- GND_ID\n",
    "- Verlagsort \n",
    "\n",
    "Alternativ kann man auch die Vornamen von den Nachnamen der VerfasserInnen trennen. \n",
    "\n",
    "Folgende Felder sollen angereichert werden: \n",
    "- GND_ID mit...\n",
    "- Geburtsdatum\n",
    "- Geburtsort\n",
    "- Liste der Berufe der Person\n",
    "- ein Feld Ihrer Wahl. Informieren Sie sich dafür gern im GND Explorer: https://explore.gnd.network/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c9d2b8",
   "metadata": {},
   "source": [
    "## Daten einlesen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038d4473",
   "metadata": {},
   "source": [
    "Nachdem die Daten bereinigt und mit Daten aus der GND angereichert wurden, führen wir eine exemplarische Analyse auf unsere angereicherten Daten durch. Zuerst wird die .csv Datei importiert und als Dataframe ausgegeben. Der Dataframe bildet die Basis der weiteren Bearbeitungen. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a9b0433",
   "metadata": {},
   "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 1</th>\n",
       "      <th>verfasser 2</th>\n",
       "      <th>gnd_id</th>\n",
       "      <th>Geburtsdatum</th>\n",
       "      <th>Geburtsort</th>\n",
       "      <th>Beruf oder Beschäftigung</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</td>\n",
       "      <td>Johann Wolfgang von</td>\n",
       "      <td>118540238</td>\n",
       "      <td>1749-08-28</td>\n",
       "      <td>Frankfurt am Main</td>\n",
       "      <td>Schriftsteller</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>NaN</td>\n",
       "      <td>Publizist</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>NaN</td>\n",
       "      <td>Politiker</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>NaN</td>\n",
       "      <td>Jurist</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>NaN</td>\n",
       "      <td>Naturwissenschaftler</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>517</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>NaN</td>\n",
       "      <td>Philologe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>1000778517</td>\n",
       "      <td>Die drei gerechten Kammacher</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>Keller</td>\n",
       "      <td>Gottfried</td>\n",
       "      <td>11856109X</td>\n",
       "      <td>1819-07-19</td>\n",
       "      <td>Zürich</td>\n",
       "      <td>Schriftsteller</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>519</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>NaN</td>\n",
       "      <td>Librettist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</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>NaN</td>\n",
       "      <td>Maler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</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>NaN</td>\n",
       "      <td>Lyriker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>522 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            idn                           titel    jahr verfasser 1  \\\n",
       "0    1003104487                          Egmont  1946.0      Goethe   \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",
       "517         NaN                             NaN     NaN         NaN   \n",
       "518  1000778517  Die drei gerechten Kammacher  1903.0      Keller   \n",
       "519         NaN                             NaN     NaN         NaN   \n",
       "520         NaN                             NaN     NaN         NaN   \n",
       "521         NaN                             NaN     NaN         NaN   \n",
       "\n",
       "                verfasser 2     gnd_id Geburtsdatum         Geburtsort  \\\n",
       "0     Johann Wolfgang von  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",
       "517                     NaN        NaN          NaN                NaN   \n",
       "518               Gottfried  11856109X   1819-07-19             Zürich   \n",
       "519                     NaN        NaN          NaN                NaN   \n",
       "520                     NaN        NaN          NaN                NaN   \n",
       "521                     NaN        NaN          NaN                NaN   \n",
       "\n",
       "    Beruf oder Beschäftigung  \\\n",
       "0             Schriftsteller   \n",
       "1                  Publizist   \n",
       "2                  Politiker   \n",
       "3                     Jurist   \n",
       "4       Naturwissenschaftler   \n",
       "..                       ...   \n",
       "517                Philologe   \n",
       "518           Schriftsteller   \n",
       "519               Librettist   \n",
       "520                    Maler   \n",
       "521                  Lyriker   \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",
       "517                                                NaN        NaN        NaN  \n",
       "518  https://nbn-resolving.org/urn:nbn:de:101:2-201...      Cotta  Stuttgart  \n",
       "519                                                NaN        NaN        NaN  \n",
       "520                                                NaN        NaN        NaN  \n",
       "521                                                NaN        NaN        NaN  \n",
       "\n",
       "[522 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Annahme: Die Datei \"deine_datei.csv\" befindet sich im selben Verzeichnis wie dein Notebook\n",
    "# Passe den Dateinamen entsprechend an\n",
    "filename = 'Klassik-csv-csv-csv (2).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": "849efb9f",
   "metadata": {},
   "source": [
    "## Vertretene AutorInnen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a89530",
   "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": 24,
   "id": "34ef6beb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anzahl der Werke pro AutorIn:\n",
      "                  Anzahl der Werke\n",
      "Kleist                           7\n",
      "Goethe                           6\n",
      "Fontane                          6\n",
      "Hoffmann                         5\n",
      "Meyer                            5\n",
      "Keller                           5\n",
      "Eichendorff                      4\n",
      "Storm                            4\n",
      "Schiller                         4\n",
      "Stifter                          3\n",
      "Wassermann                       3\n",
      "Mörike                           3\n",
      "Gotthelf                         3\n",
      "Tieck                            2\n",
      "Anzengruber                      2\n",
      "Grabbe                           2\n",
      "Schnitzler                       2\n",
      "Droste-Hülshoff                  2\n",
      "Lessing                          2\n",
      "Ebner-Eschenbach                 2\n",
      "Hebbel                           2\n",
      "Hofmannsthal                     2\n",
      "Raabe                            2\n",
      "Trakl                            1\n",
      "Marlitt                          1\n",
      "Wedekind                         1\n",
      "Jean Paul                        1\n",
      "Büchner                          1\n",
      "Kafka                            1\n",
      "Heym                             1\n",
      "Schlegel                         1\n",
      "Grillparzer                      1\n",
      "Postl                            1\n",
      "Meyrink                          1\n",
      "Heyse                            1\n",
      "Raimund                          1\n",
      "Wieland                          1\n",
      "Heine                            1\n",
      "Bassewitz                        1\n",
      "Chamisso                         1\n",
      "Grimmelshausen                   1\n",
      "Uhland                           1\n",
      "Reinheimer                       1\n",
      "Fouqué                           1\n",
      "Hölderlin                        1\n",
      "Hauff                            1\n",
      "Spee                             1\n",
      "Ludwig                           1\n",
      "May                              1\n",
      "Horváth                          1\n",
      "Rilke                            1\n",
      "Spyri                            1\n",
      "Körner                           1\n"
     ]
    }
   ],
   "source": [
    "# Anzahl der Werke pro AutorIn zählen und in ein DataFrame konvertieren\n",
    "anzahl_werke_pro_autor_df = pd.DataFrame(df['verfasser 1'].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 DataFrame\n",
    "print(\"Anzahl der Werke pro AutorIn:\")\n",
    "print(anzahl_werke_pro_autor_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec248d1e",
   "metadata": {},
   "source": [
    "## Erscheinungsjahre visualisieren "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d8dd0d5",
   "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": 22,
   "id": "4f8fc89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": "2cc8c83f",
   "metadata": {},
   "source": [
    "# Berufe visualisieren "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a08bfc1",
   "metadata": {},
   "source": [
    "Was sind die 10 häufigsten Berufe, die unsere enthaltenen AutorInnen ausleben? 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 Übersichtlichkeit beachten. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be16fb0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6dd7041",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}