Pandas Groupby Multiple Aggregations with Exclusion of the Focal Group

In this article, we will explore how to perform multiple aggregations on a pandas DataFrame using groupby while excluding one or more groups from the calculation. We’ll use an example dataset and walk through each step of the process.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its most useful features is the groupby method, which allows us to perform aggregations on a DataFrame based on one or more columns. However, sometimes we want to exclude certain groups from our calculation. In this article, we will show how to achieve this using pandas.

Stacking the DataFrame

To start, we need to reshape our DataFrame to flatten the features. We can do this by setting the Building and Department columns as the index of the DataFrame and then stacking the remaining columns.

# stack the dataframe
s = df.set_index(['Building', 'Department']).stack()

Grouping and Aggregating

Next, we group the stacked DataFrame by Building and Department, and then aggregate using sum and count.

# groupby and aggregate 
m1 = s.groupby(level=[0, 1]).agg(['sum', 'count'])
m2 = s.groupby(level=0).agg(['sum', 'count']) - m1

Calculating Averages

We then calculate the averages for m1 (Score1) and m2 (Score2) by dividing column sum with count.

# compute mean=sum/count and concatenate along axis=1
out = pd.concat([m1['sum'] / m1['count'], m2['sum'] / m2['count']],
                   axis=1, keys=['Score1', 'Score2']).reset_index()

The Complete Code

Here is the complete code for our example:

# stack to reshape the dataframe
s = df.set_index(['Building', 'Department']).stack()

# groupby and aggregate 
m1 = s.groupby(level=[0, 1]).agg(['sum', 'count'])
m2 = s.groupby(level=0).agg(['sum', 'count']) - m1

# compute mean=sum/count and concatenate along axis=1
out = pd.concat([m1['sum'] / m1['count'], m2['sum'] / m2['count']],
                   axis=1, keys=['Score1', 'Score2']).reset_index()

Example Use Case

Let’s say we have a DataFrame df with the following structure:

We can use the code above to calculate Score1 and Score2 for each building and department.

Conclusion

In this article, we have shown how to perform multiple aggregations on a pandas DataFrame using groupby while excluding one or more groups from the calculation. We have also provided an example of how to stack the DataFrame, group and aggregate, calculate averages, and concatenate the results along axis=1.

Last modified on 2024-11-06