How to scrape PDFs with Python3 and Tika library?

PDF is an exceptional format: easily readable, almost unalterable and durable over time. In this superb document in PDF format (of course) from the PDF association (yes it does exist), Google has publicly indexed more than 2.2 billion PDF files.

Moreover, more than 85% of the documents indexed on the net are recognized as PDFs. PDF reigns supreme.

In this tutorial, we will first retrieve the data from the registration statements of 4 French scraping companies, from the excellent business listing site Pappers. First download PDFs manually, then using Python 3 and the Tika library, we will parse the information present in the PDFs, and save them in a .csv file.

The code is available on GitHub here in full: https://gist.github.com/lobstrio/5fc088d44bba8383bf3f91acb11ebd3b

Prerequisites

In order to complete this tutorial from start to finish, be sure to have the following items installed on your computer.

1. python3
2. SublimeText

You can click on the links below, which will take you either to an installation tutorial or to the site in question.

To clarify the purpose of each of the above: python3 is the computer language with which we will scrape the pdf, and SublimeText is a text editor. Sublime.

Let's play!

Installation

Before starting our adventure, we will simply install the tika library. You just have to type the following commands in the console:

$ pip3 install tika

NB: be careful, if you work on Mac OS, don't forget to install the Java compiler first

$ ​​brew install --cask adoptopenjdk8

You can find more detailed information here: https://stackoverflow.com/questions/24342886/how-to-install-java-8-on-mac/28635465

And there you have it, we are set up and ready!

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Step-by-Step Guide

1. Download

First, we are going to download the registration statements of our 4 French companies specialized in scraping, from the excellent company listing site Pappers.

Just go to the site, type in the name of the company, and retrieve what is called the "Pappers extract":

Here is the link to the PDFs of the 4 companies:

1. Lobstr
2. ScrapingBee
3. Captain Data
4. PhantomBuster

With PDFs that all have a similar format, as follows:

Then, remember to save them in the same folder as your Python file.

Now it's time to recover the data!

2. Conversion

In this second part, once the PDFs are recovered, we will convert these PDFs documents into a text document. This will then allow us to easily recover the data.

Simple and readable lines of code, using the tika library:

from tika import parser
raw = parser.from_file(filename)
print(raw)

The result is a properly structured dictionary - and this is what makes this library particularly powerful! - with all the data of the PDF in text format, but also rich metadata: conversion status, publication date, last modification date, PDF version etc.

And here is the code in raw format:

sashabouloudnine@mbp-de-sasha dev % python3
Python 3.10.6 (main, Aug 11 2022, 13:37:17) [Clang 13.0.0 (clang-1300.0.29.30)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from tika import parser
>>> raw = parser.from_file(/Users/sashabouloudnine/Desktop/LOBSTR - Extrait d'immatriculation.pdf)
KeyboardInterrupt
>>> raw = parser.from_file("/Users/sashabouloudnine/Desktop/LOBSTR - Extrait d'immatriculation.pdf")
>>> print(raw)
{'metadata': {'Content-Type': 'application/pdf', 'Creation-Date': '2022-09-02T17:16:09Z', 'Last-Modified': '2022-09-02T17:16:09Z', 'Last-Save-Date': '2022-09-02T17:16:09Z', 'X-Parsed-By': ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'], 'X-TIKA:content_handler': 'ToTextContentHandler', 'X-TIKA:embedded_depth': '0', 'X-TIKA:parse_time_millis': '89', 'access_permission:assemble_document': 'true', 'access_permission:can_modify': 'true', 'access_permission:can_print': 'true', 'access_permission:can_print_degraded': 'true', 'access_permission:extract_content': 'true', 'access_permission:extract_for_accessibility': 'true', 'access_permission:fill_in_form': 'true', 'access_permission:modify_annotations': 'true', 'created': '2022-09-02T17:16:09Z', 'date': '2022-09-02T17:16:09Z', 'dc:format': 'application/pdf; version=1.4', 'dcterms:created': '2022-09-02T17:16:09Z', 'dcterms:modified': '2022-09-02T17:16:09Z', 'meta:creation-date': '2022-09-02T17:16:09Z', 'meta:save-date': '2022-09-02T17:16:09Z', 'modified': '2022-09-02T17:16:09Z', 'pdf:PDFVersion': '1.4', 'pdf:charsPerPage': '1603', 'pdf:docinfo:created': '2022-09-02T17:16:09Z', 'pdf:docinfo:creator_tool': 'Chromium', 'pdf:docinfo:modified': '2022-09-02T17:16:09Z', 'pdf:docinfo:producer': 'Skia/PDF m92', 'pdf:encrypted': 'false', 'pdf:hasMarkedContent': 'false', 'pdf:hasXFA': 'false', 'pdf:hasXMP': 'false', 'pdf:unmappedUnicodeCharsPerPage': '0', 'producer': 'Skia/PDF m92', 'resourceName': "bLOBSTR - Extrait d'immatriculation.pdf", 'xmp:CreatorTool': 'Chromium', 'xmpTPg:NPages': '1'}, 'content': "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLOBSTR Extrait Pappers\n\nCe document reproduit les informations présentes sur le site pappers.fr et est fourni à titre informatif. 1/1\n\nN° de gestion 2019B05393\n\nExtrait Pappers du registre national du commerce et des sociétés\nà jour au 02 septembre 2022\n\nIDENTITÉ DE LA PERSONNE MORALE\n\n \n \n\n \n\n \n\n \n \n\n \n\n \n\n \n \n\n \n\n \n\nDIRIGEANTS OU ASSOCIÉS\n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\n \n\nRENSEIGNEMENTS SUR L'ACTIVITÉ ET L'ÉTABLISSEMENT PRINCIPAL\n\n \n\n \n \n\n \n\n \n\nImmatriculation au RCS, numéro 841 
840 499 R.C.S. Creteil\n\nDate d'immatriculation 26/08/2019\n\nTransfert du R.C.S. en date du 05/08/2019\n\nDate d'immatriculation d'origine 21/08/2018\n\nDénomination ou raison sociale LOBSTR\n\nForme juridique Société par actions simpli'ée\n\nCapital social 2,00 Euros\n\nAdresse du siège 5 Avenue du Général de Gaulle 94160 Saint-Mandé\n\nActivités principales Collecte et analyse de données à haute fréquence sur internet\n\nDurée de la personne morale Jusqu'au 26/08/2118\n\nDate de clôture de l'exercice social 31 Décembre\n\nDate de clôture du 1er exercice social 31/12/2019\n\nPrésident\n\nNom, prénoms BOULOUDNINE Sasha\n\nDate et lieu de naissance Le 04/10/1993 à Marseille (13)\n\nNationalité Française\n\nDomicile personnel 6 Avenue de Corinthe 13006 Marseille 6e Arrondissement\n\nDirecteur général\n\nNom, prénoms ROCHWERG Simon\n\nDate et lieu de naissance Le 06/12/1990 à Vincennes (94)\n\nNationalité Française\n\nDomicile personnel la Garenne Colombes 42 Rue de Plaisance 92250 La Garenne-\nColombes\n\nAdresse de l'établissement 5 Avenue du Général de Gaulle 94160 Saint-Mandé\n\nActivité(s) exercée(s) Collecte et analyse de données à haute fréquence sur internet\n\nDate de commencement d'activité 24/07/2018\n\nOrigine du fonds ou de l'activité Création\n\nMode d'exploitation Exploitation directe\n\n\n", 'status': 200}

Now that these data are present in text format, we have one last step: retrieving the data, and saving it in a .csv file.

3. Extraction

We have the data in text format. This is much more convenient than PDF, which is a rigid document that is difficult to alter. Now we will retrieve the data.

First, we convert the dictionary into text format:

content = str(raw)

Then, thanks to the native library re, we recover the information present in the text, as follows:

numero_gestion = "".join(re.findall(r'(?<=N° de gestion )[^\\]+', content))
a_jour_au = "".join(re.findall(r'(?<=à jour au )[^\\]+', content))
numero_rcs = "".join(re.findall(r'(?<=Immatriculation au RCS, numéro )[^\\]+', content))
date_immatriculation = "".join(re.findall(r'(?<=Date d\'immatriculation )[^\\]+', content))
raison_sociale = "".join(re.findall(r'(?<=Dénomination ou raison sociale )[^\\]+', content))
forme_juridique = "".join(re.findall(r'(?<=Forme juridique )[^\\]+', content))
capital_social = "".join(re.findall(r'(?<=Capital social )[^\\]+', content))
adresse_siege = "".join(re.findall(r'(?<=Adresse du siège )[^\\]+', content))
activites_principales = "".join(re.findall(r'(?<=Activités principales )[^\\]+', content))

NB: to use the re library properly, we strongly advise you to follow this very good w3school tutorial, which mixes theoretical lessons and practical applications, all directly online, and to use the regex101 tool, which allows you to test your regex directly from your browser

And that's it!

We have retrieved 9 attributes in each PDF:

1. numero_gestion
2. a_jour_au
3. numero_rcs
4. date_immatriculation
5. raison_sociale
6. forme_juridique
7. capital_social
8. adresse_siege
9. activites_principales

All we have to do is save the data in a .csv file, and that's it.

First we import the csv library:

import csv

Then we create a function that saves all the data in a .csv file:

def write_csv(rows): 

    # write csv
    assert rows
    with open('parsed_pdf.csv', 'w') as f: 

        writer = csv.DictWriter(f, fieldnames=HEADERS)
        writer.writeheader()
        for row in rows: 
            writer.writerow(row)

And there you have it!

Code

You can find the full code right here, directly available from our github: https://gist.github.com/lobstrio/5fc088d44bba8383bf3f91acb11ebd3b.

To execute the code:

1. download the .py code
2. download the 4 pdfs mentioned in the tutorial
3. put the code and the pdfs in the same folder
4. and launch the script via the command line

And this is what will appear directly on your terminal:

$ python3 pappers_pdf_parser_with_python_and_tika.py
1 LOBSTR
2 VostokInc
3 Captain Data
4 PHANTOMBUSTER
~~ success
 _       _         _            
| |     | |       | |           
| | ___ | |__  ___| |_ __ __  
| |/ _ \| '_ \/ __| __/| '__|
| | (_) | |_) \__ \ |_ | |   
|_|\___/|_.__/|___/\__||_|

✨

You will find, in the same folder as the script, a CSV file, parsed-pdf.csv, with all the extracted data. The data is cleanly structured, and directly usable:

Wonderful!

Limitations

This code will allow you to quickly scrape the data present on a PDF file, transforming it into text using Python3 and the tika library.

In particular, you will be able to transform PDFs of company extracts from the Pappers company listing site into readable, structured and usable data.

Beware, however, that you have to download the files by hand, and place them in the same folder as the Python scraper. Moreover, the parser only retrieves 9 attributes - not bad, but it could be better.

Finally, this scraper only works with the Pappers company extract, in a very specific format. We can however imagine other scrapers, with other PDF formats: INPI extract, financial data, sales catalog etc.

Conclusion

And that's the end of the tutorial!

In this tutorial, we have seen how to transform a PDF into text with Python and the tika library, retrieve the data present using regex, and insert all this data into a cleanly structured and usable .csv file.

If you have any questions, or if you need a custom, robust and scalable data collection service, contact us here.

Happy scraping!

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