Data scientists extract text from PDFs for several reasons:
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.
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.
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
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
PdfFileReaderobject to read the file.
- The example then uses the
numPagesattribute of the
PdfFileReaderobject to get the number of pages in the PDF and uses a
forloop to iterate over each page.
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.