How to Extract Table From PDF in Python

How to Extract Tables from PDF in Python - Full Tutorial

1. Create or open a Python project to begin.
2. Install the IronPDF library using pip.
3. Load the PDF file you want to extract data from.
4. Extract text from the PDF file.
5. Filter and extract tabular data from the extracted text.

Extracting tables from PDFs can be challenging due to the lack of a standardized structure in PDF documents. However, with the help of libraries like IronPDF, we can efficiently extract tables and data from PDFs. IronPDF is a powerful tool that allows developers to manipulate and read PDF files easily.

In this tutorial, we'll walk through the steps to extract tables from PDFs using IronPDF in Python. We'll cover the installation process, basic usage, and a practical example to extract tables from a sample PDF.

IronPDF for Python:

IronPDF is a robust and versatile Python library designed to handle a wide range of PDF manipulation tasks, including the creation, editing, and extraction of content. It boasts features such as converting HTML to PDF, merging multiple PDFs, adding watermarks, and extracting text and images with precision. One of its standout capabilities is the ability to maintain the integrity and formatting of complex PDF documents during manipulation. Ideal for generating reports, archiving web pages, and automating document workflows, IronPDF streamlines PDF-related processes, saving time and enhancing productivity. Its comprehensive API and ease of integration into Python projects make it a valuable tool for developers seeking to manage PDFs efficiently and effectively.

Step By Step Tutorial:

Let's begin a step-by-step tutorial for extracting tabular data from a PDF file.

Step # 1: Create or Open Python Project:

The first step is to create a Python Project or open an existing one in your favorite IDE. I am using Microsoft Visual Studio 2022. You may use any as per your preference. The process will remain the same for each IDE.

Step # 2: Install IronPDF Library:

Next, you need to install the IronPDF library. You can do this via pip. Note that IronPDF requires a .NET runtime to be installed on your machine.

pip install IronPDF

Step # 3: Extract Text from PDF File:

To extract tabular data from a PDF using IronPDF in Python, the first essential step is to extract all the text from the PDF. This extracted text can then be filtered to isolate and process the table data. In the following code snippet, we demonstrate how to load a PDF document, apply a license key, and extract all the text content using IronPDF. This foundational step is crucial for further processing to identify and Table Extraction from the document.

The following PDF File is used in this tutorial.

from ironpdf import *

# Apply your license key
License.LicenseKey = "IRONSUITE.ABC.TRIAL-G43TTA.TRIAL.EXPIRES.20.MAY.2025"

# Load a PDF File
pdf = PdfDocument("TabularData.pdf") # File Location

# Extract all text from the PDF
text = pdf.ExtractAllText();
print(text)

This Python code starts by importing the necessary components from the IronPDF library. It then sets the license key to enable full functionality. The PdfDocument class is used to load a PDF file named "TabularData.pdf". Finally, the ExtractAllText method is called to retrieve all the text content from the PDF, which is then printed to the console. This initial extraction is critical as it provides the raw text data from which PDF tables can be subsequently identified and processed.

Step # 4: PDF Table Extraction from Extracted Text Content

To further refine the extraction of tabular data from a PDF, we need to filter out irrelevant lines and focus on the ones that likely contain the table data. The following Python code demonstrates how to split the extracted text into individual lines and then filter these lines based on the presence of a period (.), which is typically not found in table data. This helps in isolating potential table rows from the complete text extraction. Once isolated, this data can be easily converted to an Excel file for better analysis and storage.

# Split the extracted text by newline characters
text_list = text.split("\n")

# Iterate through each item in the text list
for text_item in text_list:
    # If the item contains a period, skip it
    if '.' in text_item:
        continue
    else:
        # Print the item
        print(text_item)

This code starts by splitting the extracted text into separate lines using the newline character as a delimiter. It then iterates through each line, checking for the presence of a period. Lines containing periods are skipped, while the remaining lines are printed. This process effectively isolates lines that are more likely to contain table data, streamlining the task of identifying and extracting relevant information from the PDF.

Extracting Tabular Data from Scanned PDF Pages

Consider a scenario where you have multiple PDF pages containing scanned images of documents, and you need to extract tabular data from these pages and save it to an Excel file. In such cases, Optical Character Recognition (OCR) can be used to convert scanned images into text, and then the above method can be applied to isolate and extract table data.

Here’s an example to handle such a case:

Perform OCR on Scanned Images:

Use a library like IronOCR to perform OCR on scanned images within the PDF.

Extract Text and Filter Table Data:

Use the same technique as above to split the text and filter out the table data.

Save Data to an Excel File:

Use IronXL to save the filtered data to an Excel file.

Conclusion:

Extracting tables from PDFs in Python using IronPDF is a robust and efficient way to manage and manipulate PDF content. By following the steps outlined in this tutorial, you can easily extract text from PDFs, filter out irrelevant content, and isolate tabular data for further processing. This approach is particularly useful for digitizing data from documents, generating reports, and automating document workflows. Additionally, integrating OCR capabilities for scanned PDFs and saving the data to Excel files expands the range of applications for IronPDF, making it an invaluable tool for developers working with complex PDF documents.

IronPDF offers a free trial period, allowing you to evaluate its capabilities before making a purchase, for those who require extended features and support, purchasing a license is necessary. IronSuite, the comprehensive product suite from IronSoftware, includes IronPDF along with other powerful tools like IronOCR and IronXL. By purchasing the complete IronSuite, you can get a significant discount and access a wide range of functionalities for handling PDFs, OCR tasks, and Excel automation. This suite of products is designed to enhance productivity and streamline your document management workflows.