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Скачать или смотреть How to Get Values from Other Rows Based on Multiple Conditions in Pandas

  • vlogize
  • 2025-05-28
  • 0
How to Get Values from Other Rows Based on Multiple Conditions in Pandas
How to get values from other rows based on multiple conditions in Pandas?pythonpandas
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Описание к видео How to Get Values from Other Rows Based on Multiple Conditions in Pandas

Learn how to extract values from different rows in a Pandas DataFrame based on multiple conditions using the `merge()` function. Perfect for data analysis tasks!
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This video is based on the question https://stackoverflow.com/q/66962022/ asked by the user 'Lars' ( https://stackoverflow.com/u/13583660/ ) and on the answer https://stackoverflow.com/a/66962103/ provided by the user 'Anurag Dabas' ( https://stackoverflow.com/u/14289892/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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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.

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How to Get Values from Other Rows Based on Multiple Conditions in Pandas

If you're working with data in Python, specifically using the Pandas library, you might find yourself in a situation where you need to retrieve values from other rows, based on certain conditions. This is particularly common in sports analytics, where you may need to compare a team's performance to its opponent's in a structured format. In this post, we'll explore how to achieve this with a practical example using the merge() function in Pandas.

The Problem

Let's say you have a DataFrame (df) that looks like this:

GameIDTeamIDTeamOpponentIDOpponentLocationScore11Alabama2Jacksonville StateH1.09863312Jacksonville State1AlabamaV0.75656223UAB4Alcorn StateH1.27063824Alcorn State3UABV0.682791Your goal is to transform this DataFrame so that it includes an additional column, showing each team's opponent's score for the corresponding match. The desired DataFrame format should look like this:

GameIDTeamIDTeamOpponentIDOpponentLocationScoreOpponents Score11Alabama2Jacksonville StateH1.0986330.75656212Jacksonville State1AlabamaV0.7565621.09863323UAB4Alcorn StateH1.2706380.68279124Alcorn State3UABV0.6827911.270638The Solution

To accomplish this, we will use the merge() function from the Pandas library. Here’s a step-by-step guide on how you can pull off this transformation.

Step 1: Merge the DataFrames

We will merge the original DataFrame (df) with a modified version of itself. The following code snippet demonstrates this process:

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

This command merges the DataFrame where GameID and TeamID in the first DataFrame match with GameID and OpponentID in the second DataFrame, allowing us to pull in the opponents' scores.

Step 2: Drop Unnecessary Columns

After the merge, the DataFrame includes some extra columns that we do not need, specifically the duplicate OpponentID. Thus, we will drop this column:

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

Step 3: Rename the Columns

To make the DataFrame easier to read and understand, we should rename the columns appropriately. Here’s how you can do that:

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

Final Step: Print the Result

Once the DataFrame is reorganized, you can simply display the result to see your desired output:

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

Conclusion

By following these steps, you can efficiently retrieve and display data from different rows based on specific criteria in Pandas. This technique is extremely useful in various data analysis scenarios, especially in sports data analytics. Remember, the key functions in this method are merge(), drop(), and rename(), making them essential tools in your Pandas toolkit.

Now you can effectively tackle similar data manipulation challenges with ease in your future analysis!

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