Скачать или смотреть How to Fill DataFrame Columns Based on Other Column Values in Pandas Efficiently

Learn how to fill a DataFrame column with winning team names based on match scores in Pandas without losing previous values.
---
This video is based on the question https://stackoverflow.com/q/74863359/ asked by the user 'hpelnaggar' ( https://stackoverflow.com/u/20823206/ ) and on the answer https://stackoverflow.com/a/74864192/ provided by the user 'Paul' ( https://stackoverflow.com/u/7194474/ ) 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: Try to fill dataframe column based on other column values

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.
---
Introduction

Analyzing sports data is a great way to practice your data analysis and programming skills, particularly if you're using Python and the Pandas library. In this guide, we'll address a common problem that arises when trying to derive new columns based on conditions from existing columns in a DataFrame. Specifically, we'll focus on how to properly fill a column with the winning team name in a football dataset without losing previous values.

The Problem: Filling the winner_id Column

Imagine working with a DataFrame that tracks football match results. You have two important columns: home_score and away_score. Your goal is to create a new column named winner_id that captures the name of the winning team based on the scores. The initial approach might look something like this:

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

This piece of code correctly fills in the winner_id with the home_team name where the home team score is higher than the away team score. However, when you try to implement a second condition like this:

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

You notice that it overwrites the previous entries in winner_id, resulting in a loss of data. This can be frustrating, especially if you've structured your code to handle multiple conditions.

The Solution: Using np.select

Instead of overwriting the winner_id column, we can utilize the np.select function from the NumPy library, which is designed for efficiently selecting values based on multiple conditions without losing previously stored values.

Step-by-Step Implementation

Import Required Libraries:
Ensure you have both Pandas and NumPy libraries imported.

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

Prepare Your DataFrame:
Assume you have a DataFrame named df that includes the scores and teams.

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

Use np.select:
Here’s how to use np.select to fill in the winner_id column.

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

Explanation of the Code:

Conditions: The list contains the conditions we're checking — whether the home_score is greater than or less than the away_score.

Choices: The corresponding team names are indicated as choices if the conditions are true.

Default Value: We've added a default value of 'draw', which will be placed in the winner_id if neither condition is met (i.e., when scores are equal).

Conclusion

By using np.select, you can efficiently create a new column in your DataFrame that conditions on multiple criteria without overwriting previous values. This approach not only ensures that you maintain all relevant information but also keeps your code clean and clear.

By understanding and applying these techniques, you can enhance your data manipulation skills and avoid common pitfalls in data analysis with Pandas. Happy coding!