Deleting Rows Based on Groupby Conditions

As we analyze the given data, we can see that there are customers who have been inactive for a certain period and then reactivated themselves. We need to delete all rows with Status = 1 (churn) for these customers in the observed period but only if their status changes from 2 to 1.

Problem Statement

We have a DataFrame df with columns “ID”, “Month”, and “Status”. The status is categorized as “Churn” = 1 and “Not Churn” = 2. We need to delete all rows with Status = 1 for the customers who had Status = 2 in the observed period but then reactivated themselves.

Solution

To solve this problem, we can use two different approaches: GroupBy.transform for case 1 and Series.where with GroupBy.bfill for case 2.

Case 1: Using GroupBy.transform

case1 = df['Status'].eq(1).groupby(df['ID']).transform('all')

In this approach, we first create a boolean mask where the value is True if the status is equal to 1. Then, we group by the “ID” column and apply the transform function to get all values in the group.

However, this approach will not work correctly because it will return all rows with Status = 1 for each group of IDs, regardless of whether the customer was inactive or active before reactivating themselves. We need a different approach to solve this problem.

Case 2: Using Series.where and GroupBy.bfill

case2 = (df['Status'].where(df['Status'].ne(1))
            .groupby(df['ID'])
            .bfill()
            .eq(2)
            .mul(df['Status'].eq(1)))

In this approach, we first create a new column new_status where the value is 2 if the original status is not equal to 1. Then, we group by the “ID” column and apply the bfill function to forward-fill the missing values. Finally, we multiply the result by the boolean mask where the value is True if the original status is equal to 1.

This approach will correctly identify the customers who had Status = 2 in the observed period but then reactivated themselves.

Filtering the DataFrame

Now that we have case1 and case2, we can use them to filter the DataFrame. We can do this by creating a boolean mask where the value is True if either of case1 or case2 is False, using the bitwise OR operator |.

df_filtered = df.loc[~(case1 | case2)]

This will return all rows in the original DataFrame where neither case1 nor case2 is True.

Conclusion

In this article, we have discussed how to delete rows from a DataFrame based on groupby conditions. We have presented two different approaches using GroupBy.transform and Series.where with GroupBy.bfill. Both approaches can be used to solve the problem, but they require careful consideration of the data and its structure.

We hope that this article has provided you with a better understanding of how to handle groupby conditions in Pandas DataFrames. If you have any questions or need further clarification, feel free to ask!

Last modified on 2025-01-17