How to Efficiently Edit Portions of a CSV File Using Python's Pandas

Discover how to edit parts of your CSV file at a time with Python's Pandas without rewriting the entire file. Learn efficient strategies here!
---
This video is based on the question https://stackoverflow.com/q/66701092/ asked by the user 'Jiovanny Alejos' ( https://stackoverflow.com/u/14903568/ ) and on the answer https://stackoverflow.com/a/66701239/ provided by the user 'Tim Roberts' ( https://stackoverflow.com/u/1883316/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How do I edit parts of my csv file at a time?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Editing Parts of a CSV File with Python's Pandas

When working with CSV files in Python, particularly with the Pandas library, you may find yourself needing to update specific portions of a file repeatedly. However, the default workflow can be inefficient because it requires reading the whole file and rewriting it. In this post, we will explore a more efficient method for making targeted edits without the overhead of multiple file read and write operations.

The Problem

You're probably familiar with a scenario where you load a CSV file into a DataFrame using the pd.read_csv() function, make some edits via custom functions, and then write it back using df.to_csv(). However, when the file is repeatedly rewritten, it can lead to inefficiencies and potential loss of changes.

Example Situation

Consider this piece of code:

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

In this example, you first read the CSV into a DataFrame, make some changes, and finally save it. If your method setFacVal reads the file again, it effectively ignores any previous changes, resetting the DataFrame to its original state before last modifications.

The Solution: Efficient DataFrame Editing

The optimal solution is straightforward: instead of reading from the CSV every time you want to make a change, you should directly pass the existing DataFrame to your editing function. This way, you can maintain all prior modifications seamlessly within the same session. Below is how you can adjust your function:

Updated Function Example

Instead of re-reading the CSV file inside your setFacVal function, modify it to accept a DataFrame as a parameter:

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

Main Steps in the Workflow

Read the CSV File Once: Read the CSV file into a DataFrame using pd.read_csv(), at the beginning of your script or function:

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

Edit the DataFrame Directly: Call your setFacVal function and pass the DataFrame along with the necessary parameters:

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

Save Changes Once: After all changes have been made, write back to the CSV file in one go:

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

Benefits of This Approach

Efficiency: This method reduces the number of I/O operations by reading the file and writing it back only once.

Safety: By passing the DataFrame, you ensure that all modifications are preserved and not lost during repeated file accesses.

Clarity: This approach simplifies the code and makes it easier to maintain and understand.

Conclusion

Editing portions of a CSV file can be done efficiently using Python's Pandas by passing the DataFrame to functions instead of repeatedly reading and writing the file. This simple adjustment not only improves performance but also preserves changes effectively. By adopting such strategies, you can handle your data more adeptly and minimize unnecessary processes. Happy coding!

Видео How to Efficiently Edit Portions of a CSV File Using Python's Pandas канала vlogize