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Discover how to handle `null values` in your DataFrames by redistributing weightages across KPIs using Python and Pandas.
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Transforming DataFrame Weightages in Python

When working with data in Python, especially using the Pandas library, you may encounter tables that contain null values. One common requirement is to calculate group-wise average weights while ensuring that any missing values are equitably distributed among the available KPIs within the same group. In this guide, we will walk through a simple solution to this problem.

The Problem

You work with a table that consists of various groups (Groups), corresponding Key Performance Indicators (KPIs), their associated weightages, and their performance values (value_KPI). Sometimes, the value for a specific KPI might be missing (null), and you need to adjust the weightages of the remaining KPIs in that group. The goal is to redistribute the weightages so that the total remains 100% across the group.

Here's an example for clarity:

Sample Input Table

GroupsKPIsWeightagesvalue_KPIG1KP130%45G1KP230%G1KP340%G2KP430%34G2KP530%G2KP620%90G2KP720%45Desired Output Table

GroupsKPIsWeightagesvalue_KPIG1KP1100%45G1KP2G1KP3G2KP440%34G2KP5G2KP630%90G2KP730%45In this case, weightages for KPIs in the same group that have null value should be updated to reflect the distribution of the weightages among the other KPIs.

The Solution: Step-by-Step Breakdown

1. Define a Helper Function

The first step in our solution is to create a helper function that will handle the distribution of weightages. Here’s how it looks:

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

2. Apply the Function to Each Group

Now that we have defined our function, we will apply it to each group in the DataFrame. This is achieved using the groupby method provided by Pandas:

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

3. Output the Result

After executing the above code, you will get the desired output as shown previously.

Final Output

The final DataFrame will have adjusted weights reflecting the absence of value_KPIs, and it will look something like this:

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

Conclusion

Handling null values in your DataFrames is a common challenge in data analysis. By using Python and the Pandas library, you can easily redistribute weightages among KPIs in the same group without losing the integrity of your data. We hope this guide helped you understand how to achieve this transformation efficiently. Happy coding!