DNB_Volltextsuche_Tutorial_Geburtstag.ipynb

{"cells":[{"metadata":{"nbpresent":{"id":"d0d72cd5-034d-4b94-a4e2-54f7753cb9f0"}},"cell_type":"markdown","source":"# DNBLab Jupyter Notebook Tutorial"},{"metadata":{"nbpresent":{"id":"558898c3-e162-4e46-92d5-b4c914325790"}},"cell_type":"markdown","source":"## Digitalisierte Inhaltsverzeichnisse: Datenabfrage, Auslieferung und Volltextanalyse"},{"metadata":{"nbpresent":{"id":"ee78241c-36e7-45e6-b015-0ef46f1e777d"}},"cell_type":"markdown","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"},{"metadata":{"nbpresent":{"id":"3c6f6024-cb7c-4b65-8646-a753069cb5a4"}},"cell_type":"markdown","source":"## Einrichten der Arbeitsumgebung\n\nUm 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."},{"metadata":{"trusted":true},"cell_type":"code","source":"import requests\nfrom bs4 import BeautifulSoup as soup\nimport unicodedata\nfrom lxml import etree\nimport pandas as pd\nfrom collections import Counter","execution_count":1,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## SRU-Abfrage mit Ausgabe in MARC21-xml\n\nDie 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."},{"metadata":{"trusted":true},"cell_type":"code","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","execution_count":2,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Durchsuchen eines MARC-Feldes\n\nDie 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."},{"metadata":{"trusted":true},"cell_type":"code","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    #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    \n    try:\n        titel = titel[0].text\n        #umfang = unicodedata.normalize(\"NFC\", titel)\n    except:\n        titel = \"unkown\"\n        \n         # Archivlink\n    link = xml.xpath(\"marc:datafield[@ind1 = ' ']/marc:subfield[@code = 'u']\", namespaces=ns)\n    \n    try:\n        link = link[0].text\n        #link = unicodedata.normalize(\"NFC\", urn)\n    except:\n        link = 'fail'\n        \n         # umfang\n    umfang = xml.xpath(\"marc:datafield[@tag = '300']/marc:subfield[@code = 'a']\", namespaces=ns)\n    try:\n        umfang = umfang[0].text\n        #umfang = unicodedata.normalize(\"NFC\", umfang)\n    except:\n        umfang = 'fail'\n        \n    meta_dict = {\"idn\":idn,\n                 \"titel\":titel,\n                 \"link\":link,\n                 \"umfang\":umfang\n                               }\n    \n    return meta_dict","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","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."},{"metadata":{"trusted":true},"cell_type":"code","source":"records = dnb_sru('tit=Faust and vlg=Ungar and cod=d0* and location=onlinefree')\nprint(len(records), 'Ergebnisse')","execution_count":9,"outputs":[{"output_type":"stream","text":"2 Ergebnisse\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Beispielanzeige zur weiteren Bearbeitung\n\nMit der Bibliothek Pandas für Python wird das Ergebnis (Dictionary-Element \"link\") als Dataframe ausgegeben."},{"metadata":{"trusted":true},"cell_type":"code","source":"output = [parse_record(record) for record in records]\ndf = pd.DataFrame(output)\ndf\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)","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"          idn  titel                             link  umfang\n0  1032408820  Faust  https://d-nb.info/1032408820/34  138 S.\n1  1032408839  Faust  https://d-nb.info/1032408839/34  221 S.","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>link</th>\n      <th>umfang</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1032408820</td>\n      <td>Faust</td>\n      <td>https://d-nb.info/1032408820/34</td>\n      <td>138 S.</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1032408839</td>\n      <td>Faust</td>\n      <td>https://d-nb.info/1032408839/34</td>\n      <td>221 S.</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Download der Textdateien \n\nMit der folgenden Funktion df.to_csv() werden die Ergebnisse als \"links.csv\" in das Jupyter-Verzeichnins der Einstiegsseite abgelegt und können dort heruntergeladen werden. "},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"!wget https://d-nb.info/1032408839/34","execution_count":15,"outputs":[{"output_type":"stream","text":"--2021-09-06 16:28:17--  https://d-nb.info/1032408839/34\nResolving d-nb.info (d-nb.info)... 193.175.100.223\nConnecting to d-nb.info (d-nb.info)|193.175.100.223|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: unspecified [application/zip]\nSaving to: ‘34’\n\n34                      [       <=>          ] 996.73M  13.1MB/s    in 68s     \n\n2021-09-06 16:29:25 (14.7 MB/s) - ‘34’ saved [1045152068]\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","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). "},{"metadata":{"trusted":true},"cell_type":"code","source":"!unzip 34","execution_count":16,"outputs":[{"output_type":"stream","text":"Archive:  34\n  inflating: 1032408839_00000220.tif  \n  inflating: 1032408839_00001470.tif  \n  inflating: 1032408839_00001760.tif  \n  inflating: 1032408839_00000880.tif  \n  inflating: 1032408839_00000360.tif  \n  inflating: 1032408839_00001900.tif  \n  inflating: 1032408839_00001100.tif  \n  inflating: 1032408839_00000170.tif  \n  inflating: 1032408839_00000910.tif  \n  inflating: 1032408839_00001600.tif  \n  inflating: 1032408839_00001530.tif  \n  inflating: 1032408839_00000100.tif  \n  inflating: 1032408839_00000180.tif  \n  inflating: 1032408839_00000770.tif  \n  inflating: 1032408839_00000230.tif  \n  inflating: 1032408839_00001930.tif  \n  inflating: 1032408839_00002250.tif  \n  inflating: 1032408839_00000890.tif  \n  inflating: 1032408839_00001560.tif  \n  inflating: 1032408839_00000060.tif  \n  inflating: 1032408839_00000050.tif  \n  inflating: 1032408839_00000740.tif  \n  inflating: 1032408839_00002320.tif  \n  inflating: 1032408839_00001110.tif  \n  inflating: 1032408839_00001780.tif  \n  inflating: 1032408839_00000570.tif  \n  inflating: 1032408839_00000440.tif  \n  inflating: 1032408839_00001330.tif  \n  inflating: 1032408839.xml          \n  inflating: 1032408839_00002240.tif  \n  inflating: 1032408839_00001940.tif  \n  inflating: 1032408839_00000920.tif  \n  inflating: 1032408839_00001300.tif  \n  inflating: 1032408839_00001090.tif  \n  inflating: 1032408839_00001480.tif  \n  inflating: 1032408839_00000830.tif  \n  inflating: 1032408839_00002140.tif  \n  inflating: 1032408839_00001490.tif  \n  inflating: 1032408839_00001450.tif  \n  inflating: 1032408839_OCR.xml      \n  inflating: 1032408839_00000390.tif  \n  inflating: 1032408839_00000380.tif  \n  inflating: 1032408839_00000350.tif  \n  inflating: 1032408839_00000750.tif  \n  inflating: 1032408839_00000480.tif  \n  inflating: 1032408839_00000450.tif  \n  inflating: 1032408839_00001220.tif  \n  inflating: 1032408839_00000070.tif  \n  inflating: 1032408839_00000090.tif  \n  inflating: 1032408839_00001800.tif  \n  inflating: 1032408839_00002230.tif  \n  inflating: 1032408839_00000330.tif  \n  inflating: 1032408839_00001830.tif  \n  inflating: 1032408839_00001250.tif  \n  inflating: 1032408839_00001170.tif  \n  inflating: 1032408839_00001910.tif  \n  inflating: 1032408839_00001210.tif  \n  inflating: 1032408839_00001640.tif  \n  inflating: 1032408839_00000500.tif  \n  inflating: 1032408839_00001260.tif  \n  inflating: 1032408839_00001360.tif  \n  inflating: 1032408839_00000210.tif  \n  inflating: 1032408839_00001860.tif  \n  inflating: 1032408839_00000550.tif  \n  inflating: 1032408839_OCR.txt      \n  inflating: 1032408839_00002220.tif  \n  inflating: 1032408839_00000650.tif  \n  inflating: 1032408839_00001670.tif  \n  inflating: 1032408839_00000130.tif  \n  inflating: 1032408839_00002150.tif  \n  inflating: 1032408839_00001140.tif  \n  inflating: 1032408839_00001970.tif  \n  inflating: 1032408839_00001850.tif  \n  inflating: 1032408839_00001150.tif  \n  inflating: 1032408839_00000260.tif  \n  inflating: 1032408839_00000850.tif  \n  inflating: 1032408839_00002260.tif  \n  inflating: 1032408839_00002100.tif  \n  inflating: 1032408839_00000680.tif  \n  inflating: 1032408839_00001500.tif  \n  inflating: 1032408839_00000010.tif  \n  inflating: 1032408839_00001060.tif  \n  inflating: 1032408839_00001180.tif  \n  inflating: 1032408839_00001230.tif  \n  inflating: 1032408839_00001320.tif  \n  inflating: 1032408839_00001280.tif  \n  inflating: 1032408839_00000020.tif  \n  inflating: 1032408839_00001610.tif  \n  inflating: 1032408839_00000420.tif  \n  inflating: 1032408839_00001870.tif  \n  inflating: 1032408839_00001840.tif  \n  inflating: 1032408839_00001240.tif  \n  inflating: 1032408839_00000840.tif  \n  inflating: 1032408839_00000720.tif  \n  inflating: 1032408839_00001390.tif  \n  inflating: 1032408839_00000930.tif  \n  inflating: 1032408839_00000900.tif  \n  inflating: 1032408839_00001810.tif  \n  inflating: 1032408839_00001200.tif  \n  inflating: 1032408839_00001270.tif  \n  inflating: 1032408839_00001290.tif  \n  inflating: 1032408839_00000240.tif  \n  inflating: 1032408839_00002200.tif  \n  inflating: 1032408839_00002280.tif  \n  inflating: 1032408839_00000940.tif  \n  inflating: 1032408839_00000820.tif  \n  inflating: 1032408839_00000110.tif  \n  inflating: 1032408839_00001340.tif  \n  inflating: 1032408839_00001880.tif  \n  inflating: 1032408839_00000800.tif  \n  inflating: 1032408839_00000030.tif  \n  inflating: 1032408839_00001370.tif  \n  inflating: 1032408839_00000690.tif  \n  inflating: 1032408839_00000630.tif  \n  inflating: 1032408839_00001120.tif  \n  inflating: 1032408839_00001950.tif  \n  inflating: 1032408839_00002040.tif  \n  inflating: 1032408839_00002110.tif  \n  inflating: 1032408839_00000290.tif  \n  inflating: 1032408839_00000810.tif  \n  inflating: 1032408839_00002310.tif  \n  inflating: 1032408839_00002180.tif  \n  inflating: 1032408839_00001190.tif  \n  inflating: 1032408839_00000860.tif  \n  inflating: 1032408839_00002190.tif  \n  inflating: 1032408839_00000780.tif  \n  inflating: 1032408839_00000320.tif  \n  inflating: 1032408839_00000250.tif  \n  inflating: 1032408839_00000980.tif  \n  inflating: 1032408839_00001700.tif  \n  inflating: 1032408839_00001710.tif  \n  inflating: 1032408839_00000400.tif  \n  inflating: 1032408839_00001960.tif  \n  inflating: 1032408839_00000660.tif  \n  inflating: 1032408839_00000540.tif  \n  inflating: 1032408839_00001310.tif  \n  inflating: 1032408839_00001160.tif  \n  inflating: 1032408839_00001720.tif  \n  inflating: 1032408839_00000640.tif  \n  inflating: 1032408839_00000370.tif  \n  inflating: 1032408839_00000490.tif  \n  inflating: 1032408839_00000580.tif  \n  inflating: 1032408839_00001130.tif  \n  inflating: 1032408839_00001920.tif  \n  inflating: 1032408839_00002060.tif  \n  inflating: 1032408839_00002270.tif  \n  inflating: 1032408839_00001000.tif  \n  inflating: 1032408839_00000670.tif  \n  inflating: 1032408839_00002120.tif  \n  inflating: 1032408839_00000160.tif  \n  inflating: 1032408839_00001980.tif  \n  inflating: 1032408839_00001350.tif  \n  inflating: 1032408839_00001520.tif  \n  inflating: 1032408839_00001070.tif  \n  inflating: 1032408839_00000270.tif  \n  inflating: 1032408839_00001460.tif  \n  inflating: 1032408839_00000610.tif  \n  inflating: 1032408839_00000340.tif  \n  inflating: 1032408839_00001380.tif  \n  inflating: 1032408839_00001680.tif  \n  inflating: 1032408839_00000760.tif  \n  inflating: 1032408839_00000520.tif  \n  inflating: 1032408839_00002010.tif  \n  inflating: 1032408839_00001750.tif  \n  inflating: 1032408839_00000700.tif  \n  inflating: 1032408839_00001820.tif  \n  inflating: 1032408839_00001990.tif  \n  inflating: 1032408839_00000140.tif  \n  inflating: 1032408839_00000560.tif  \n  inflating: 1032408839_00001790.tif  \n  inflating: 1032408839_00000280.tif  \n  inflating: 1032408839_00002210.tif  \n  inflating: 1032408839_00002070.tif  \n  inflating: 1032408839_00001550.tif  \n  inflating: 1032408839_00001510.tif  \n  inflating: 1032408839_00001400.tif  \n  inflating: 1032408839_00001030.tif  \n  inflating: 1032408839_00000080.tif  \n  inflating: 1032408839_00001570.tif  \n  inflating: 1032408839_00000960.tif  \n  inflating: 1032408839_00002030.tif  \n  inflating: 1032408839_00001740.tif  \n  inflating: 1032408839_00001040.tif  \n  inflating: 1032408839_00000300.tif  \n  inflating: 1032408839_00002130.tif  \n  inflating: 1032408839_00001620.tif  \n  inflating: 1032408839_00001410.tif  \n  inflating: 1032408839_00001010.tif  \n  inflating: 1032408839_00000590.tif  \n  inflating: 1032408839_00000620.tif  \n  inflating: 1032408839_00000040.tif  \n  inflating: 1032408839_00001890.tif  \n  inflating: 1032408839_00001430.tif  \n  inflating: 1032408839_00002330.tif  \n  inflating: 1032408839_00001440.tif  \n  inflating: 1032408839_00002020.tif  \n  inflating: 1032408839_00000970.tif  \n  inflating: 1032408839_00001080.tif  \n  inflating: 1032408839_00001630.tif  \n  inflating: 1032408839_00000310.tif  \n  inflating: 1032408839_00000530.tif  \n  inflating: 1032408839_00001730.tif  \n  inflating: 1032408839_00000200.tif  \n  inflating: 1032408839_00001540.tif  \n  inflating: 1032408839_00002300.tif  \n  inflating: 1032408839_00000190.tif  \n  inflating: 1032408839_00000730.tif  \n  inflating: 1032408839_00000870.tif  \n  inflating: 1032408839_00000460.tif  \n","name":"stdout"},{"output_type":"stream","text":"  inflating: 1032408839_00000790.tif  \n  inflating: 1032408839_00001650.tif  \n  inflating: 1032408839_00000120.tif  \n  inflating: 1032408839_00001580.tif  \n  inflating: 1032408839_00001020.tif  \n  inflating: 1032408839_00000510.tif  \n  inflating: 1032408839_00001770.tif  \n  inflating: 1032408839_00000150.tif  \n  inflating: 1032408839_00000430.tif  \n  inflating: 1032408839_00000600.tif  \n  inflating: 1032408839_00002080.tif  \n  inflating: 1032408839_00001420.tif  \n  inflating: 1032408839_00001050.tif  \n  inflating: 1032408839_00002000.tif  \n  inflating: 1032408839_00000710.tif  \n  inflating: 1032408839_00002290.tif  \n  inflating: 1032408839_00000410.tif  \n  inflating: 1032408839_00000470.tif  \n  inflating: 1032408839_00001660.tif  \n  inflating: 1032408839_00002160.tif  \n  inflating: 1032408839_00002090.tif  \n  inflating: 1032408839_00000950.tif  \n  inflating: 1032408839_00001590.tif  \n  inflating: 1032408839_00002050.tif  \n  inflating: 1032408839_00001690.tif  \n  inflating: 1032408839_00002170.tif  \n  inflating: 1032408839_00000990.tif  \n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Volltextanalyse \n\n### 1. Häufigkeit eines beliebigen Suchwortes<a class=\"anchor\" id=\"Teil1\"></a>\n\nDie heruntergeladenen Textdateien können nach einem Suchwort, z.B. search = \"biene\" durchsucht werden. Hierbei wird die Groß- und Kleinschreibung beachtet.\nAls 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.).\nDas Suchwort kann beliebig geändert und die Suche durch Ausführen des Codes angepasst werden."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Eingabe des Suchwortes mit Button zur Übernahme der eingegebenen Werte \n\nfrom IPython.display import display \nimport ipywidgets as widgets \nfrom ipywidgets import interact, Layout \n\ntb1 = widgets.Text(value = 'Tag', description ='Suchwort: ');display(tb1);\n\n\nbutton = widgets.Button(description='Übernehmen!', layout=Layout(width='200px')); \nbutton.style.button_color='lightgreen';display(button); \ndef on_button_clicked(sender): \n    a = tb1.value; \n    print('Folgendes Suchwort wurde übernommen: ' + str(tb1.value))     \nbutton.on_click(on_button_clicked)","execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"Text(value='Tag', description='Suchwort: ')","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4532e36f9abf498083aea1007fcad14b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Button(description='Übernehmen!', layout=Layout(width='200px'), style=ButtonStyle(button_color='lightgreen'))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3b22ba0ef89443d9bef4b4b280e6643"}},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"filename = '1032408839_OCR.txt'\nsearch = str(tb1.value)\nwith open(filename) as f:\n    for num, line in enumerate(f, 1):\n        if search in line:\n            print('%s - found at line:' % search, num)","execution_count":18,"outputs":[{"output_type":"stream","text":"Tag - found at line: 58\nTag - found at line: 72\nTag - found at line: 84\nTag - found at line: 100\nTag - found at line: 214\nTag - found at line: 263\nTag - found at line: 306\nTag - found at line: 307\nTag - found at line: 350\nTag - found at line: 500\nTag - found at line: 503\nTag - found at line: 625\nTag - found at line: 824\nTag - found at line: 840\nTag - found at line: 878\nTag - found at line: 959\nTag - found at line: 1414\nTag - found at line: 1468\nTag - found at line: 1515\nTag - found at line: 1612\nTag - found at line: 1636\nTag - found at line: 1777\nTag - found at line: 2082\nTag - found at line: 2355\nTag - found at line: 2512\nTag - found at line: 2604\nTag - found at line: 2628\nTag - found at line: 2794\nTag - found at line: 2992\nTag - found at line: 3172\nTag - found at line: 3188\nTag - found at line: 3718\nTag - found at line: 3877\nTag - found at line: 3968\nTag - found at line: 4211\nTag - found at line: 4218\nTag - found at line: 4298\nTag - found at line: 4912\nTag - found at line: 4960\nTag - found at line: 4993\nTag - found at line: 5163\nTag - found at line: 5524\nTag - found at line: 5677\nTag - found at line: 5800\nTag - found at line: 5802\nTag - found at line: 6067\nTag - found at line: 6109\nTag - found at line: 6113\nTag - found at line: 6134\nTag - found at line: 6200\nTag - found at line: 6244\nTag - found at line: 6300\nTag - found at line: 6312\nTag - found at line: 6629\nTag - found at line: 6917\nTag - found at line: 7199\nTag - found at line: 7232\nTag - found at line: 7361\nTag - found at line: 7463\nTag - found at line: 7540\nTag - found at line: 7543\nTag - found at line: 7796\nTag - found at line: 7876\nTag - found at line: 7936\nTag - found at line: 7964\nTag - found at line: 8043\nTag - found at line: 8128\nTag - found at line: 8190\nTag - found at line: 8243\nTag - found at line: 8352\nTag - found at line: 8651\n","name":"stdout"}]},{"metadata":{"trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"anaconda-cloud":{},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}