Best Python PDF Library
As Data Scientists, we may not stick to data format. PDFs, short for Portable Document Format files, are a good source of data. There are many organizations that release their data in PDFs only. As Artificial Intelligence is expanding, we require more data for prediction and classification. Thus, it could be a blunder if we ignore PDFs as data sources. Processing PDF is a bit complicated task; however, we can leverage the APIs discussed in this tutorial to make things easier. This tutorial will provide a brief knowledge of the different Python PDF libraries available for a Data Scientist for PDF processing using the Python programming language.
So, let's get begun.
Some PDF Libraries in Python
There are various PDF libraries available in the Python programming language. In this section, we will be discussing about some of the best PDF libraries that we can use to process PDF files in Python. These libraries are as follows:
The PDFMiner library
PDFMiner is a wonderful library for PDF processing in Python. It is easy to install and use. This tool is used to extract information from PDF documents. Unlike other PDF-associated utilities, it mainly centered on retrieving and analyzing text data. The PDFMiner library allows the programmers to extract the precise location of text in a page, along with other details like fonts or lines. It involves a PDF converter that can transform PDF files into other text formats (like HTML). It has an extensible PDF parser that can be utilized for purposes other than text analysis.
We can install the PDFMiner library using the following command with the pip installer:
The PyPDF4 library
PyPDF4 is a quite extensible PDF library in Python. It is a pure-python PDF library that is capable of splitting, combining together, cropping, and transforming the pages of PDF files. It can also insert custom data and viewing options along with PDF files' encryption and decryption features. We can use this library to get text and metadata from PDFs as well as combine whole files together.
We can install the PyPDF4 library using the following command with the pip installer:
The pdfrw library
Pdfrw is another Python PDF library with the same features as the two mentioned above. Apart from those similarities, the pdfrw library has its own USPs (Unique Selling Points). Actually, the need for Application Programming Interface relies on the use case.
We can install the pdfrw library using the following command with the pip installer:
The Slate library
Slate is another Python PDF library that helps in simplifying the procedure of text extraction from PDF files. This library acts as a wrapper implementation of the PDFMiner library. As we know, no API is perfect, and there were some deficiencies in PDFMiner; and however, Slate addresses them in a beautiful way.
Slate offers one class - PDF. PDF accepts a file-like object and will extract all text from the document, presenting every page as a text string.
The PDFQuery library
The PDFQuery library is considered among the fastest Python scraping library. It acts as a light wrapper around pdfminer, pyquery, and lxml. It is designed in order to extract data reliably from sets of PDFs with as little code as possible.
We can install the pdfquery library using the following command with the pip installer:
Why Python for PDF processing?
As we know, PDF processing lies within text analytics. There is a wide range of Text Analytics libraries or frameworks that are designed in the Programming language like Python only, and this provides leverage to text analytics. In addition to this, we can never process a PDF file directly using the 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, and we can easily achieve this with the help of any of the libraries mentioned earlier.
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