Best Python PDF Library: Must know for Data Scientist

Python PDF Library

As a Data Scientist, You may not stick to data format. PDFs are a good source of data. Most organizations release their data in PDFs only. As AI is growing, We need more data for prediction and classification. Hence ignoring PDFs as data sources could be a blunder. Actually, PDF processing is a little difficult but we can leverage the below API for making it easier. This article [ Best Python PDF Library: Must know for Data Scientist ] will give a brief on PDF processing using Python.

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Best Python PDF Library-

1. PDFMiner-

Amazing Library for PDF processing in Python. Easy to install and use. Here is the link for the official Documentation for PDFMiner. A community is never great without their supporter. Here is the community link for PDFMiner. You can use a link to leverage community users. PDFMiner provides a command utility for Non Programmers and an API interface for programmers.



This Python PDF Library is quite extensible. You may extract text from pdf, crop, and merge PDF Document with Encryption and decryption feature. There are so many versions of PyPDF. Actually, before PyPDF4,  PyPDF2 was more trendy. It is still there but PyPDF4 is the latest version for this. Here is the official documentation of PyPDF4.

Examples are always best. Let’s see  How to Extract Text from PDF File Using Python with example.


3. pdfrw-

Quite similar to the above two mentions. Apart from that similarity, pdfrw has its own USPs (Unique Selling Points). Actually, the requirement of API depends on the use case. Get a full description of pdfrw.


4.Slate –

It is wrapper Implementation of PDFMiner. No API is perfect, There were few shortcomings in PDFMiner. Slate beautifully address them. Here is the complete code description for Slate.

python pdf library slate

5. pikepdf –

This pikepdf library is an emerging python library for PDF processing. It is Python + QPDF = “py” + “qpdf” = “pyqpdf”. If you look at the comparison between PyPDF2 and pdfrw, You will see, It provide some feature which is not available in both of them.



This PDFQuery is one of the fastest python scrapping library. Use the below command to install the PDFQuery package and use it.

pip install pdfquery
PDFQuery python library

7. Other Libraries –

I am always stuck in this place. Where I have to decide which is the best place holder for this rank. Actually, No library is perfect. This choice should be in the use case. Its requirement oriented. The choices for you at this position are –

  1. xpdf-python
  2. reportlab python
  3. PyMuPDF 
  4. PDFNetPython3
  5. Borb

Why Python for PDF processing –

As you know PDF processing comes under text analytics. Most of the Text Analytics libraries or frameworks are designed in Python only. This gives leverage to text analytics. One more thing you can never process a pdf directly in existing frameworks of Machine Learning or Natural Language Processing. Unless they are proving an explicit interface for this. We have to convert pdf to text first. We can easily achieve this using any of the above mention libraries.

How is java for PDF processing –

Truly! telling when it comes to PDF processing Java is awesome.  At Data Science Learner we have created a brief article on java pdf library. Actually, PDF Processing Involves so many processes. Like text, image extraction from pdf, merging document, pdf document metadata extraction, etc. Few java pdf libraries are all in one. I mean you can perform most of the PDF tasks using a single Library.

Conclusion –

How did you find this article? If you know any python library which should be mentioned with others. Please let us know. The above list is dynamic which may vary on future releases of the existing library or new arrival in this category.


Data Science Learner Team 

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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages, where he and his team share knowledge and help others learn more about data science.
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