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Discover a simple method to append the date portion of filenames to column headers when reading multiple CSV files using Python's Pandas library.
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How to Append Filename to Column Header When Reading Multiple CSV Files in Python

When working with data in CSV files, we often encounter scenarios where it’s essential to keep track of the source of each data column. A common challenge arises when reading multiple CSV files: how can you effectively append the date portion of each filename to the corresponding column header in your final DataFrame? In this post, we’ll walk through a straightforward solution to this problem using Python’s Pandas library.

Understanding the Problem

Suppose you have several CSV files with filenames formatted as follows:

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

Your goal is to read these files into a DataFrame and append only the date part (yyyy-mm-dd) of the filename to the column header for each corresponding column of data. This way, you can effortlessly track which data corresponds to which date.

The Initial Setup

First, let’s look at how you might typically read multiple CSV files into a list of DataFrames:

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

After reading your files, you concatenate them, but you find that the resulting DataFrame (frame) has only numbered column headers:

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

The Challenge

At this stage, you lose all the meaningful filename information. You need a way to extract the date from each filename and use it as a header for the respective column in your final DataFrame.

The Efficient Solution

Instead of going through a complex process of transposing the DataFrame and adjusting rows, we can achieve our goals in a more efficient manner. Here’s a step-by-step guide to appending the date portion to column headers:

1. Use Regular Expressions

Regular expressions (regex) provide a powerful way to extract specific patterns from strings, including our required dates from filenames.

2. Implement the Solution

Here’s an optimized version of the code that accomplishes our goal:

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

Breakdown of the Code

Import Required Libraries: We need pandas, glob, os, and re to handle data operations and file manipulations.

Specify the Path: Set your CSV file directory as path.

Collect Filenames: Use glob to gather all CSV files in the specified directory.

Extract Date and Read Data: For each file:

Extract the filename.

Use regex to extract just the date (yyyy-mm-dd).

Read the CSV file while squeezing the DataFrame to convert it to a Series. Rename the Series with the extracted date.

Concatenate Data: Finally, combine all Series into a single DataFrame that retains the newly named headers.

Conclusion

By following these steps, you will now have a DataFrame where each column header corresponds to the date from its respective CSV filename, making your data analysis more organized and insightful. Say goodbye to generic column names and hello to a clearer understanding of your data!

Feel free to implement and customize this solution based on your specific requirements. Happy coding!