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Learn how to correctly handle multiple CSV files in Python using for loops, ensuring your output files are complete and not truncated.
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How to Avoid Truncating Your CSV Output in a Python For Loop

If you're working with multiple CSV files in Python, you may have encountered a frustrating issue: your output files turn out to be truncated, showing only a fraction of the data you're expecting. This issue can arise due to errors in managing file reads and writes in a loop. In this guide, we will explore the problem and provide a clear solution to ensure your CSV outputs are complete.

The Problem

Imagine you have a folder filled with numerous CSV files, and you need to add a new column with fresh data to each file. Upon running your code, you discover that all the output files contain only a limited number of rows—specifically, the number of rows in the first CSV file processed. This prompts the question: What went wrong?

Why Does This Happen?

In the example code:

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

Here's what is happening:

The code reads the first file and creates a DataFrame but does not actually load the data into it before trying to add a new column.

As a result, when the code attempts to write back to each file, it’s only affecting the first file due to the lack of a proper read operation for subsequent files.

This oversight causes each output file to retain a truncated state based on the first read file.

The Solution

To rectify this issue, you need to ensure that you read the content of each CSV file properly within your loop before you manipulate and save it back. Below is the modified code that addresses the problem:

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

Code Breakdown

Import Required Libraries: You import pandas, glob, and os modules. pandas is crucial for reading and writing CSV files, while glob helps you gather all CSV files in the specified directory.

Get All CSV Files: Using glob.glob('path/*.csv'), you collect all the CSV files located in the provided path.

Loop Through Each File: Inside the loop, you must read each CSV file into a DataFrame using pd.read_csv(file).

Add a New Column: After reading the contents, we can safely add a new column named client with the value 'newContent'.

Write Back to CSV: Finally, when saving the updated DataFrame, ensure to set the parameter index=False to avoid unnecessary index columns in your output files.

Conclusion

By following this approach, you can effectively manage large numbers of CSV files without the risk of truncating your output. Always remember to read the content of the file before performing operations on it. This simple adjustment can save you time and ensure your data remains intact.

Implement these changes in your own script, and you can confidently iterate through your CSV files, enriching them with new data without losing any rows in the process.