DNB_Volltextsuche_Tutorial.ipynb

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    "nbpresent": {
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   },
   "source": [
    "# DNBLab Jupyter Notebook Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "558898c3-e162-4e46-92d5-b4c914325790"
    }
   },
   "source": [
    "## Digitalisierte Inhaltsverzeichnisse: Datenabfrage, Auslieferung und Volltextanalyse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ee78241c-36e7-45e6-b015-0ef46f1e777d"
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   "source": [
    "Dieses DNBLab-Tutorial beschreibt eine Beispielabfrage zu digitalisierten Inhaltsverzeichnissen über die SRU-Schnittstelle und umfasst das temporäre Speichern der Inhaltsverzeichnisse als Textdateien sowie die Analyse der Volltexte nach \n",
    "\n",
    "* [1. Häufigkeit eines beliebigen Suchwortes](#Teil1)\n",
    "* [2. Vorkommen des Suchwortes in den einzelnen Textdateien](#Teil2)\n",
    "* [3. Top 20 häufigste Wörter in den Textdateien](#Teil3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "3c6f6024-cb7c-4b65-8646-a753069cb5a4"
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   "source": [
    "## Einrichten der Arbeitsumgebung\n",
    "\n",
    "Um die Arbeitsumgebung für die folgenden Schritte passend einzurichten, sollten zunächst die benötigten Python-Bibliotheken importiert werden. Für Anfragen über die SRU-Schnittstelle wird BeautifulSoup https://www.crummy.com/software/BeautifulSoup/ 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. In der Jupyter Notebook Umgebung kann der dokumentierte Code direkt ausgeführt und angepasst werden."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SRU-Abfrage mit Ausgabe in MARC21-xml\n",
    "\n",
    "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."
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
   "metadata": {},
   "source": [
    "## Durchsuchen eines MARC-Feldes\n",
    "\n",
    "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 nur die Permalinks zu den digitalisierten Inhaltsverzeichnissen als \"link\" ausgegeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_record(record):\n",
    "    \n",
    "    ns = {\"marc\":\"http://www.loc.gov/MARC21/slim\"}\n",
    "    xml = etree.fromstring(unicodedata.normalize(\"NFC\", str(record)))\n",
    "    \n",
    "    #link\n",
    "    link = xml.xpath(\"marc:datafield[@tag = '856']/marc:subfield[@code = 'u']\", namespaces=ns)\n",
    "    \n",
    "    try:\n",
    "        link = link[0].text\n",
    "    except:\n",
    "        link = \"unkown\"\n",
    "        \n",
    "    meta_dict = {\"link\":link + '/text'}\n",
    "    \n",
    "    return meta_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Über verschiedenen Indices https://services.dnb.de/sru/dnb?operation=explain&version=1.1 kann die SRU-Abfrage \"dnb_sru\" mittels CQL https://www.dnb.de/DE/Service/Hilfe/Katalog/kataloghilfeExpertensuche.html eingeschränkt werden. Im Folgenden Code wird die Abfrage über das Stichwort \"Sandwespe\" im Volltextindex der digitalisierten Inhaltsverzeichnisse eingeschränkt. Durch Anpassen der SRU-Abfrage kann die Trefferliste beliebig geändert werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 Ergebnisse\n"
     ]
    }
   ],
   "source": [
    "records = dnb_sru('inh=Sandwespe')\n",
    "print(len(records), 'Ergebnisse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beispielanzeige zur weiteren Bearbeitung\n",
    "\n",
    "Mit der Bibliothek \"pandas\" für Python wird das Ergebnis (Dictionary-Element \"link\") als Dataframe ausgegeben."
   ]
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   "source": [
    "output = [parse_record(record) for record in records]\n",
    "df = pd.DataFrame(output)\n",
    "df\n",
    "\n",
    "#Die Ausgabe der ermittelten Links kann je nach Bedarf über verschiedene Funktionen erfolgen:\n",
    "#print(df.to_string(index=False))\n",
    "#HTML(df.to_html(index=False))\n",
    "#document = df.to_dict(orient='list')\n",
    "#print(document)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download der Textdateien \n",
    "\n",
    "Mit der folgenden Funktion df.to_csv() werden die Ergebnisse als \"links.csv\" in das Jupyter-Verzeichnins der Einstiegsseite abgelegt und können dort heruntergeladen werden. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"links.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit wget werden alle in der CSV-Datei gespeicherten Links heruntergeladen und als Textdateien (text, text.1, text.2, usw.) im temporären Jupyter-Verzeichnis gespeichert (Downloadlimit 1MB). "
   ]
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      "--2021-10-25 08:11:54--  http://link/\n",
      "Resolving link (link)... failed: No address associated with hostname.\n",
      "wget: unable to resolve host address ‘link’\n",
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      "Resolving d-nb.info (d-nb.info)... 193.175.100.223\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Reusing existing connection to d-nb.info:443.\n",
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      "Length: 10595 (10K) [text/plain]\n",
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      "\n",
      "--2021-10-25 08:11:57--  https://d-nb.info/987655590/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
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      "Length: 347 [text/plain]\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:57--  https://d-nb.info/921186746/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5705 (5.6K) [text/plain]\n",
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      "\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:57--  https://d-nb.info/890058644/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
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      "Length: 10886 (11K) [text/plain]\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:57--  https://d-nb.info/880976144/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2540 (2.5K) [text/plain]\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:57--  https://d-nb.info/206780095/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
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      "Length: 1627 (1.6K) [text/plain]\n",
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      "\n",
      "--2021-10-25 08:11:58--  https://d-nb.info/1026175356/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:58--  https://d-nb.info/205921620/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1628 (1.6K) [text/plain]\n",
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      "\n",
      "2021-10-25 08:11:58 (113 MB/s) - ‘text.53’ saved [1628/1628]\n",
      "\n",
      "--2021-10-25 08:11:58--  https://d-nb.info/861003659/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 8792 (8.6K) [text/plain]\n",
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      "\n",
      "2021-10-25 08:11:58 (38.6 MB/s) - ‘text.54’ saved [8792/8792]\n",
      "\n",
      "--2021-10-25 08:11:58--  https://d-nb.info/860266699/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1992 (1.9K) [text/plain]\n",
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      "\n",
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      "\n",
      "2021-10-25 08:11:58 (48.6 MB/s) - ‘text.55’ saved [1992/1992]\n",
      "\n",
      "--2021-10-25 08:11:58--  https://d-nb.info/850284678/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 579 [text/plain]\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:58--  https://d-nb.info/840766017/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 7406 (7.2K) [text/plain]\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/458194603/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 8161 (8.0K) [text/plain]\n",
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      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/451709578/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 29718 (29K) [text/plain]\n",
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      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/368150585/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4910 (4.8K) [text/plain]\n",
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      "\n",
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      "\n",
      "2021-10-25 08:11:59 (109 MB/s) - ‘text.60’ saved [4910/4910]\n",
      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/367009765/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 12115 (12K) [text/plain]\n",
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      "\n",
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      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/577420860/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4255 (4.2K) [text/plain]\n",
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      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/576065226/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4763 (4.7K) [text/plain]\n",
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      "\n",
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      "\n",
      "2021-10-25 08:11:59 (97.8 MB/s) - ‘text.63’ saved [4763/4763]\n",
      "\n",
      "--2021-10-25 08:11:59--  https://d-nb.info/365358088/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 59944 (59K) [text/plain]\n",
      "Saving to: ‘text.64’\n",
      "\n",
      "text.64             100%[===================>]  58.54K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2021-10-25 08:12:00 (3.54 MB/s) - ‘text.64’ saved [59944/59944]\n",
      "\n",
      "--2021-10-25 08:12:00--  https://d-nb.info/366644602/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23523 (23K) [text/plain]\n",
      "Saving to: ‘text.65’\n",
      "\n",
      "text.65             100%[===================>]  22.97K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2021-10-25 08:12:00 (36.7 MB/s) - ‘text.65’ saved [23523/23523]\n",
      "\n",
      "--2021-10-25 08:12:00--  https://d-nb.info/361169019/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1160 (1.1K) [text/plain]\n",
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      "\n",
      "text.66             100%[===================>]   1.13K  --.-KB/s    in 0s      \n",
      "\n",
      "2021-10-25 08:12:00 (74.5 MB/s) - ‘text.66’ saved [1160/1160]\n",
      "\n",
      "--2021-10-25 08:12:00--  https://d-nb.info/367009935/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 13188 (13K) [text/plain]\n",
      "Saving to: ‘text.67’\n",
      "\n",
      "text.67             100%[===================>]  12.88K  47.3KB/s    in 0.3s    \n",
      "\n",
      "2021-10-25 08:12:00 (47.3 KB/s) - ‘text.67’ saved [13188/13188]\n",
      "\n",
      "--2021-10-25 08:12:00--  https://d-nb.info/366374125/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 29798 (29K) [text/plain]\n",
      "Saving to: ‘text.68’\n",
      "\n",
      "text.68             100%[===================>]  29.10K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2021-10-25 08:12:00 (1.80 MB/s) - ‘text.68’ saved [29798/29798]\n",
      "\n",
      "--2021-10-25 08:12:00--  https://d-nb.info/367009900/04/text\n",
      "Reusing existing connection to d-nb.info:443.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 13250 (13K) [text/plain]\n",
      "Saving to: ‘text.69’\n",
      "\n",
      "text.69             100%[===================>]  12.94K  --.-KB/s    in 0s      \n",
      "\n",
      "2021-10-25 08:12:00 (84.7 MB/s) - ‘text.69’ saved [13250/13250]\n",
      "\n",
      "FINISHED --2021-10-25 08:12:00--\n",
      "Total wall clock time: 6.2s\n",
      "Downloaded: 35 files, 298K in 0.3s (926 KB/s)\n"
     ]
    }
   ],
   "source": [
    "!wget -Q1m -i links.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volltextanalyse \n",
    "\n",
    "### 1. Häufigkeit eines beliebigen Suchwortes<a class=\"anchor\" id=\"Teil1\"></a>\n",
    "\n",
    "Die heruntergeladenen Textdateien können nach einem Suchwort, z.B. search = \"biene\" durchsucht werden. Hierbei wird die Groß- und Kleinschreibung beachtet.\n",
    "Als Antwort werden die durchsuchten Textdateien genannt und die Häufigkeit des gesuchten Wortes in den genannten Dateien ausgegeben. Dabei entspricht die Dateibenennung den im Verzeichnis heruntergeladen Textdateien (text, text1, text2 usw.).\n",
    "Das Suchwort kann beliebig geändert und die Suche durch Ausführen des Codes angepasst werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2d5e4413a57418880532fb603048fdc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='biene', description='Suchwort: ')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f0db86d31cb4da79387f3d42ca8d0dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Übernehmen!', layout=Layout(width='200px'), style=ButtonStyle(button_color='lightgreen'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eingabe des Suchwortes mit Button zur Übernahme der eingegebenen Werte \n",
    "\n",
    "from IPython.display import display \n",
    "import ipywidgets as widgets \n",
    "from ipywidgets import interact, Layout \n",
    "\n",
    "tb1 = widgets.Text(value = 'biene', description ='Suchwort: ');display(tb1);\n",
    "\n",
    "\n",
    "button = widgets.Button(description='Übernehmen!', layout=Layout(width='200px')); \n",
    "button.style.button_color='lightgreen';display(button); \n",
    "def on_button_clicked(sender): \n",
    "    a = tb1.value; \n",
    "    print('Folgendes Suchwort wurde übernommen: ' + str(tb1.value))     \n",
    "button.on_click(on_button_clicked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nach Übernahme des Suchworts werden erstmal alle Treffer in den Dateien gezählt und ausgegeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Automatische Verarbeitung der Dateinamen\n",
    "\n",
    "def adding_name(counter):\n",
    "\n",
    "    i = 1\n",
    "    names = []\n",
    "\n",
    "    for i in range (1, counter): \n",
    "        number = str(i)\n",
    "        name = \"text.\" + number\n",
    "        names.append(name)\n",
    "        i+=1\n",
    "    \n",
    "    return names \n",
    "    \n",
    "\n",
    "\n",
    "counter = len(records)\n",
    "\n",
    "var = adding_name(counter)\n",
    "print(\"Folgende Textdateien wurden durchsucht: \\n text\", *var, sep = \"\\n\") \n",
    "\n",
    "\n",
    "# Suche und Zählen der Zeilen mit Treffern\n",
    "\n",
    "search = str(tb1.value)\n",
    "anzahl = 0 \n",
    "anzahl2 = 0\n",
    "\n",
    "filename = 'text'\n",
    "with open(filename) as f:\n",
    "    for num, line in enumerate(f, 1):\n",
    "        line = line.lower() #Umwandeln in Kleinbuchstaben\n",
    "        if search in line:\n",
    "            anzahl += 1\n",
    "            \n",
    "for name in var: \n",
    "    with open(name) as f:\n",
    "        for num, line in enumerate(f, 1):\n",
    "            line = line.lower() #Umwandeln in Kleinbuchstaben\n",
    "            if search in line:\n",
    "                anzahl2 += 1\n",
    "\n",
    "print('Insgesamt wurde das Suchwort', anzahl+anzahl2, \"Mal in den zu durchsuchenden Textdateien gefunden.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Vorkommen des Suchwortes in den einzelnen Textdateien<a class=\"anchor\" id=\"Teil2\"></a>\n",
    "\n",
    "Alle Treffer werden mit Angabe der Zeile und Datei ausgegeben. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 11 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 13 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 14 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 15 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 16 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 17 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 18 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 31 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 33 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 43 in der Datei  text\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 43 in der Datei  text.1\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 29 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 30 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 31 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 32 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 33 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 34 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 35 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 36 in der Datei  text.2\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 9 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 11 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 12 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 13 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 14 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 20 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 23 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 30 in der Datei  text.3\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 32 in der Datei  text.5\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 33 in der Datei  text.5\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 9 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 13 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 14 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 23 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 24 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 26 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 27 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 28 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 31 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 49 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 67 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 137 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 139 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 140 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 141 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 142 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 146 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 149 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 151 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 152 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 156 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 157 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 158 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 159 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 160 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 164 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 172 in der Datei  text.6\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 48 in der Datei  text.7\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 50 in der Datei  text.7\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 51 in der Datei  text.7\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 9 in der Datei  text.10\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 16 in der Datei  text.10\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 17 in der Datei  text.10\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 6 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 10 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 11 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 20 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 21 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 23 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 24 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 25 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 44 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 65 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 90 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 92 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 94 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 95 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 96 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 97 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 98 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 99 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 100 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 104 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 107 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 109 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 110 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 115 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 116 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 117 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 118 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 122 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 130 in der Datei  text.11\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 59 in der Datei  text.13\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 78 in der Datei  text.15\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 18 in der Datei  text.16\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 51 in der Datei  text.16\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 41 in der Datei  text.17\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 18 in der Datei  text.18\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 51 in der Datei  text.18\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 46 in der Datei  text.20\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 137 in der Datei  text.23\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 101 in der Datei  text.25\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 9 in der Datei  text.27\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 39 in der Datei  text.27\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 95 in der Datei  text.28\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1190 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1666 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1667 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1669 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1670 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1671 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1672 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1673 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1674 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1675 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1676 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1678 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1679 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1683 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1684 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1686 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1687 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1689 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1697 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1698 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1700 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 1715 in der Datei  text.29\n",
      "Das Suchwort \"biene\" wurde gefunden in Zeile: 340 in der Datei  text.33\n"
     ]
    }
   ],
   "source": [
    "search = str(tb1.value)\n",
    "new_file = open('Trefferliste.txt', 'w')\n",
    "\n",
    "filename = 'text'\n",
    "with open(filename) as f:\n",
    "    for num, line in enumerate(f, 1):\n",
    "        line = line.lower() #Umwandeln in Kleinbuchstaben\n",
    "        if search in line:\n",
    "            print('Das Suchwort \"%s\" wurde gefunden in Zeile:' % search, num , 'in der Datei ',filename)\n",
    "            text = ('Das Suchwort \"%s\" wurde gefunden in Zeile:' % search, num , 'in der Datei ',filename)\n",
    "            new_file.write(str(text) + \"\\n\")\n",
    "            #summary.append(text)\n",
    "            \n",
    "            \n",
    "#Einbau der Schleife erst hier wegen des leicht anderen Formats der Dateinamen... :             \n",
    "for name in var: \n",
    "    with open(name) as f:\n",
    "        for num, line in enumerate(f, 1):\n",
    "            line = line.lower() #Umwandeln in Kleinbuchstaben\n",
    "            if search in line:\n",
    "                print('Das Suchwort \"%s\" wurde gefunden in Zeile:' % search, num , 'in der Datei ',name) \n",
    "                text2 = ('Das Suchwort \"%s\" wurde gefunden in Zeile:' % search, num , 'in der Datei ',name)\n",
    "                new_file.write(str(text2) + \"\\n\")\n",
    "                \n",
    "new_file.close()\n",
    "\n",
    "with open('Trefferliste.txt') as f:\n",
    "    #lines = f.readlines()\n",
    "    result = f.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Top 20 häufigste Wörter in den Textdateien<a class=\"anchor\" id=\"Teil3\"></a>\n",
    "\n",
    "Das Vorkommen der Wörter in den Textdateien wird ausgenommen vordefinierter Stopwörter gezählt und die 20 häufigsten Wörter inklusive Treffermenge ausgegeben. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    }
   ],
   "source": [
    "content = []\n",
    "\n",
    "filename = 'text'\n",
    "with open(filename) as f:\n",
    "    file1 = f.read()\n",
    "    content.append(file1.lower())\n",
    "            \n",
    "#Einbau der Schleife erst hier wegen anderen Formats der Dateinamen:             \n",
    "for name in var: \n",
    "    with open(name) as f:\n",
    "        morefiles = f.read()\n",
    "        content.append(morefiles.lower())\n",
    "        \n",
    "import string\n",
    "\n",
    "listofthings = list()\n",
    "\n",
    "for entry in content:      \n",
    "# Entfernen der Interpunktion:\n",
    "    for c in string.punctuation:\n",
    "        entry= entry.replace(c,\" \")\n",
    "    test = str.split(entry)\n",
    "    listofthings.extend(test)\n",
    "    \n",
    "#Enternen der Seitenzahlen:\n",
    "words = []\n",
    "        \n",
    "for item in listofthings: \n",
    "    if item.isdecimal() is True: \n",
    "        continue\n",
    "    else: \n",
    "        words.append(item)\n",
    "        \n",
    "counts = Counter(words)\n",
    "\n",
    "#Importieren der zu entfernenden Stopwörter (wie bpsw. \"und\", \"der\", etc.):\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "nltk.download('stopwords')\n",
    "\n",
    "stopger = stopwords.words('german')\n",
    "\n",
    "newStopwords = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',\n",
    "              'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'vgl', '\\x97', '•', '■', 'v',\n",
    "               'beim', 'inhaltsverzeichnis', 'seite']\n",
    "\n",
    "stopger.extend(newStopwords)\n",
    "\n",
    "tokens_without_sw = [word for word in words if not word in stopger]\n",
    "counts = Counter(tokens_without_sw)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('familie', 203), ('arbeitsblatt', 142), ('materialgebundene', 119), ('aufgabe', 119), ('nr', 95), ('ordnung', 81), ('gemeine', 63), ('bienen', 62), ('insekten', 54), ('unterordnung', 49), ('sandwespe', 38), ('ii', 38), ('menschen', 37), ('unterfamilie', 37), ('hebel', 36), ('vögel', 35), ('leben', 34), ('wespen', 34), ('große', 34), ('uhland', 32)]\n"
     ]
    }
   ],
   "source": [
    "#Ausgabe der \"Top 20\", Zahl kann beliebig angepasst werden:\n",
    "print(counts.most_common(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das Ergebnis wird für die weitere Verarbeitung als Dataframe ausgegeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Wort</th>\n",
       "      <th>Häufigkeit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>familie</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arbeitsblatt</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>materialgebundene</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aufgabe</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nr</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ordnung</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gemeine</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bienen</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>insekten</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>unterordnung</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sandwespe</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ii</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>menschen</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>unterfamilie</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hebel</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vögel</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>leben</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>wespen</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>große</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>uhland</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Wort  Häufigkeit\n",
       "0             familie         203\n",
       "1        arbeitsblatt         142\n",
       "2   materialgebundene         119\n",
       "3             aufgabe         119\n",
       "4                  nr          95\n",
       "5             ordnung          81\n",
       "6             gemeine          63\n",
       "7              bienen          62\n",
       "8            insekten          54\n",
       "9        unterordnung          49\n",
       "10          sandwespe          38\n",
       "11                 ii          38\n",
       "12           menschen          37\n",
       "13       unterfamilie          37\n",
       "14              hebel          36\n",
       "15              vögel          35\n",
       "16              leben          34\n",
       "17             wespen          34\n",
       "18              große          34\n",
       "19             uhland          32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(counts.most_common(20), columns=['Wort', 'Häufigkeit'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit der Bibliothek \"matplotlib.pyplot\" wwerden die 20 häufigsten Wörter in einem Bar Diagram dargestellt. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Worthäufigkeiten'}, xlabel='Wort'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df.plot(kind='bar', x=0, y=1, title='Worthäufigkeiten', figsize=(10,7), color='#86bf91')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}