Скачать или смотреть How to Efficiently Shift Rows in a Python Pandas DataFrame 50 Times

Learn how to manipulate rows in a Pandas DataFrame using Python. This guide covers creating new rows by shifting existing data and adding custom values, enabling efficient data processing.
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This video is based on the question https://stackoverflow.com/q/68115846/ asked by the user 'Yaser' ( https://stackoverflow.com/u/16292010/ ) and on the answer https://stackoverflow.com/a/68116219/ provided by the user 'Mustafa Aydın' ( https://stackoverflow.com/u/9332187/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Efficiently Shift Rows in a Python Pandas DataFrame 50 Times

If you're working with data manipulation in Python using the Pandas library, you might have a scenario where you need to copy a set of rows multiple times—say, 50 times! But you also need to drop the first row of the original data and add a new row with a specific value.

In this post, we will break down how to achieve this efficiently using Python and Pandas.

The Problem

Imagine you have the following DataFrame:

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

Your requirement is to repeat this block of rows 50 times while also maintaining a new row with the DFM value set to 100.00% at the end of each repetition, and dropping the first row in each iteration after the first.

The Solution

To solve this problem, we can utilize the shift functionality in Pandas along with some clever indexing. Let’s break down the steps:

Step 1: Setup Your DataFrame

First, ensure you have your DataFrame set up correctly. You can set the Cal and Group columns as your index to facilitate row shifting:

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

Step 2: Create the Shifted Rows with Concatenation

Next, we will create a new DataFrame by shifting the rows and concatenating them together. We repeat this operation multiple times (in this case, 50 times).

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

Step 3: Resetting the Index

After concatenating, reset the index so that the Cal and Group columns are back to being regular columns:

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

Example Output

If you repeat the above steps for illustration (perhaps only 3 times instead of 50 for visualization), your DataFrame will look something like this:

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

Conclusion

By following the above method, you can efficiently duplicate your DataFrame rows while manipulating the data as needed. This approach makes it simple to perform large-scale data transformations using Pandas, saving both time and resources.



Now, you are ready to apply this technique in your own projects whenever you need to manipulate rows in a DataFrame efficiently. Happy coding!