How to sum negative and positive values using GroupBy in Pandas?

How to sum negative and positive values using GroupBy in Pandas?

In Pandas, you can use the groupby method in combination with sum to calculate the sum of negative and positive values separately. First, you'll want to create a group for positive and negative values, which can be achieved by using a simple function or a lambda to check the sign of the values in the column you're interested in.

Here's an example that assumes you have a DataFrame with a 'Values' column and a 'Categories' column you want to group by:

import pandas as pd # Sample data data = { 'Categories': ['A', 'A', 'B', 'B', 'A', 'B'], 'Values': [10, -20, 30, -40, 50, -60] } # Create the DataFrame df = pd.DataFrame(data) # Define a function to determine the sign group def sign_group(value): return 'Positive' if value > 0 else 'Negative' # Group by Categories and sign of Values, then sum grouped_sum = df.groupby(['Categories', df['Values'].apply(sign_group)])['Values'].sum().reset_index() print(grouped_sum)

This will give you a DataFrame with the sum of positive and negative values for each category:

 Categories Values Values 0 A Negative -20 1 A Positive 60 2 B Negative -100 3 B Positive 30

You can achieve the same result using a lambda function, which might be more concise:

grouped_sum = df.groupby(['Categories', df['Values'].apply(lambda x: 'Positive' if x > 0 else 'Negative')])['Values'].sum().reset_index() print(grouped_sum)

This code snippet does the same as the first example, grouping by category and the sign of the values before summing.

Remember that groupby without reset_index() creates a MultiIndex DataFrame, which may be exactly what you want for further processing. If you want a flat DataFrame as a result, use reset_index() as shown in the examples.

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