How to Efficiently Edit CSV Files with gnu parallel and sed

Описание: Discover how to modify CSV headers and content using `gnu parallel` and `sed` to streamline your data processing tasks.
---
This video is based on the question https://stackoverflow.com/q/69791651/ asked by the user 'paulochf' ( https://stackoverflow.com/u/597349/ ) and on the answer https://stackoverflow.com/a/69800486/ provided by the user 'potong' ( https://stackoverflow.com/u/967492/ ) 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: gnu parallel + sed to edit both csv header and contents

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

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Efficiently Edit CSV Files with gnu parallel and sed

When working with multiple CSV files, especially in a large dataset that spans across different directories and years, it can become overwhelming to manage and edit them manually. This guide addresses a common problem: how to add a new column to your CSV files that includes the filename, and then compress those files for easier storage. The goal is to automate this process using command-line tools like gnu parallel and sed.

The Challenge

Imagine you have a directory containing CSV files organized by year, with filenames like csv_filename_1.csv, csv_filename_2.csv, and so on. You want to append a new column called filename to each CSV file that contains the full path of the file, such as ./year_1/csv_filename_1.csv. Additionally, after modifying the files, you want to compress them using gzip. Given the sheer volume of data – with almost 100 folders and over 100,000 files – accomplishing this manually is impractical. This is where gnu parallel comes into play, enabling you to process multiple files simultaneously.

The Solution

To achieve this efficiently, you can use a combination of find, gnu parallel, and sed. Here’s how to do it step by step:

Step 1: Finding CSV Files

Use the find command to locate all CSV files within your dataset directory. This command helps you filter out only the files that meet your criteria.

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

Step 2: Utilizing gnu parallel for Concurrent Processing

You’ll want to pipe the results from the find command into gnu parallel, which will allow handling multiple files simultaneously. The command structure looks like this:

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

Don’t forget to include the -0 option in case you plan to handle filenames with spaces or unusual characters, and adjust your command accordingly.

Step 3: Using sed to Modify Each CSV

Now, inside parallel, you can run a sed command to perform the required modifications. The sed command will serve two primary functions:

Add a Header: Append ,filename to the first line of each file.

Add the Filename: Append the respective filename to each subsequent line.

Here is the command you can use:

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

Breaking Down the sed Command

1s/$/,filename/: This command appends ,filename to the end of the first line (the header) of each CSV file.

1!s-$-,{}-: For all subsequent lines, this command appends the full filename (denoted by {}) at the end of each line. The use of - as a delimiter avoids issues with the slash / in file paths.

Step 4: Compressing the Files

After the files have been modified, you can use gzip to compress them. You can add this step in the same parallel command to compress each file after editing. Here’s a way to include it:

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

By running this command, you ensure all modifications are made to the files before they are compressed, ensuring no data is lost.

Conclusion

Automation is key when handling large datasets, and tools like gnu parallel and sed can save you a considerable amount of time. By following the steps outlined in this guide, you can efficiently add headers and filenames to your CSV files while preparing them for storage. Whether you're a data analyst, researcher, or simply managing files, these command-line tools can make your life significantly easier.

If you have any questions or further clarifications, feel free to ask. Happy coding!