Groupby Columns and Apply Function in Python

In this article, we will explore how to group by multiple columns and apply a function to each group in a Pandas DataFrame using the groupby method.

Introduction

The groupby method in Pandas is used to partition the values of a DataFrame into groups based on one or more columns. This allows you to perform operations on each group separately, such as applying a custom function, calculating aggregates, and more.

In this article, we will focus on grouping by multiple columns and applying a function to each group. We will use the transform method instead of apply because it is more efficient and suitable for our use case.

Grouping by Multiple Columns

To group by multiple columns, you simply need to pass them as a tuple or list to the groupby method.

df.groupby(['Conference', 'Division', 'Year'])

This will create a grouped DataFrame where each row is a group based on the specified columns.

Applying a Function to Each Group

To apply a function to each group, you can use the transform method. The transform method applies a function element-wise along an axis of the input DataFrame.

df.groupby(['Conference', 'Division', 'Year'])['W-L%'].transform(lambda x: (x.sum() - x) / (len(x) - 1))

This will create a new column in the original DataFrame with the calculated values.

Understanding the Formula

The formula used in the transform method is:

(x.sum() - x) / (len(x) - 1)

Here’s how it works:

x.sum() calculates the sum of all values in the group.
x refers to the current row being processed.
len(x) - 1 subtracts one from the length of the group because we want to exclude the current row itself.

By applying this formula to each group, we get the average value for the division W-L% except for the team we are calculating it on.

Example Use Case

Let’s use an example DataFrame to demonstrate how to group by multiple columns and apply a function to each group:

import pandas as pd

# Create a sample DataFrame
data = {
    'Tm': ['Bills', 'Dolphins', 'Patriots', 'Jets', 'Cowboys', 'Giants', 'Eagles', 'Commanders'],
    'Conference': ['AFC', 'AFC', 'AFC', 'AFC', 'NFC', 'NFC', 'NFC', 'NFC'],
    'Division': ['East', 'East', 'East', 'East', 'East', 'East', 'East', 'East'],
    'W-L%': [0.813, 0.529, 0.471, 0.412, 0.706, 0.559, 0.824, 0.500],
    'Year': [2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022]
}

df = pd.DataFrame(data)

# Group by multiple columns and apply a function to each group
df['Division W-L%'] = (df.groupby(['Conference', 'Division', 'Year'])['W-L%']
                      .transform(lambda x: (x.sum() - x) / (len(x) - 1)))

print(df)

Output:

           Tm Conference Division  W-L%.  Year  Division W-L%
0       Bills        AFC     East  0.813  2022       0.470667
1    Dolphins        AFC     East  0.529  2022       0.565333
2    Patriots        AFC     East  0.471  2022       0.584667
3        Jets        AFC     East  0.412  2022       0.604333
4     Cowboys        NFC     East  0.706  2022       0.627667
5      Giants        NFC     East  0.559  2022       0.676667
6      Eagles        NFC     East  0.824  2022       0.588333
7  Commanders        NFC     East  0.500  2022       0.696333

In this example, we first create a sample DataFrame with multiple columns. We then group by the Conference, Division, and Year columns using the groupby method. Finally, we apply the formula to each group using the transform method to calculate the average division W-L% except for the team we are calculating it on.

Conclusion

In this article, we demonstrated how to group by multiple columns and apply a function to each group in a Pandas DataFrame using the groupby and transform methods. We used an example use case to illustrate the concept and provided a sample code snippet to demonstrate the steps involved.

Last modified on 2024-09-26