Extracting text from PDFs using a Python library

Python
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Data scientists extract text from PDFs for several reasons:

  1. Data collection: PDFs are often used to store and share data, such as reports, research papers, and government documents. Extracting text from these PDFs allows data scientists to collect and analyze the data contained within them.

  2. Text analysis: PDFs often contain unstructured text data, which can be difficult to analyze. By extracting the text, data scientists can use natural language processing techniques to analyze and understand the content of the PDFs.

  3. Data cleaning: PDFs can contain formatting, images, and other elements that are not useful for analysis. Extracting text allows data scientists to clean and preprocess the data, making it easier to work with.

There are several libraries available in Python for extracting text from PDFs, such as PyPDF2, pdfminer, and pdfplumber.

Here’s an example of how to extract text from a PDF using the PyPDF2 library:

import PyPDF2

# Open the PDF file
with open('sample.pdf', 'rb') as file:

    # Create a PDF reader object
    pdf_reader = PyPDF2.PdfFileReader(file)

    # Get the number of pages in the PDF
    num_pages = pdf_reader.numPages

    # Iterate over each page
    for i in range(num_pages):

        # Get the page object
        page = pdf_reader.getPage(i)

        # Extract the text from the page
        text = page.extractText()

        # Print the text
        print(text)
  • This example uses the open() function to open the PDF file in binary mode, then creates a PdfFileReader object to read the file.
  • The example then uses the numPages attribute of the PdfFileReader object to get the number of pages in the PDF and uses a for loop to iterate over each page.
  • The getPage() method is used to get the page object for each iteration and the extractText() method is used to extract the text from the page which is then printed.