How to Extract Tables from Multiple Docx Files Using Python

Learn how to efficiently loop through multiple Word documents and extract tables into CSV format using Python. Perfect for data analysis and reporting!
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How to Extract Tables from Multiple Docx Files Using Python

In today’s digital world, data extraction from various formats is a common task. If you have a collection of Word files that contain tables with consistent structures, extracting these tables for analysis or reporting could save you hours of manual work. In this post, we will walk you through a Python solution to extract tables from multiple .docx files and save them into CSV format using the python-docx library.

The Challenge

Your task is to work with several Word documents that each contain one or more tables with the same structure. You want to extract these tables and save each one into separate CSV files for easier access and utilization. The initial concern is that the basic script only extracts the first table from a document and that the extraction process doesn’t handle multiple files in a folder properly.

The Solution

Let's break down the solution into several well-defined steps:

1. Setting Up Your Environment

You’ll need to have the following libraries installed:

python-docx: For reading .docx files.

pandas: For manipulating and exporting data as CSV.

You can install these libraries using pip:

[[See Video to Reveal this Text or Code Snippet]]

2. Code Walkthrough

Here’s the revised code that will iterate through all .docx files in a specified folder, extract each table, and save them as CSV files.

[[See Video to Reveal this Text or Code Snippet]]

3. Explanation of the Code

Importing Libraries: We start by importing the necessary libraries.

Defining Folder Path: Set the path to the folder containing your .docx files.

File Handling: The script uses glob to find all .docx files within the specified directory.

Document Processing:

Each document is processed one by one.

For each document, the script loops through all tables.

Each cell’s content is checked; non-empty text is stored in a DataFrame.

Saving Data: Finally, each DataFrame is saved as a CSV file named uniquely based on its index.

Conclusion

By using this Python solution, you can efficiently extract tables from multiple .docx files without manually sifting through each document. This method not only saves time but also ensures accurate data handling for further analysis.

If you encounter any challenges while implementing this, feel free to reach out for help or clarification!