You can use the following basic syntax to use a groupby with multiple aggregations in pandas:
df.groupby('team').agg( mean_points=('points', np.mean), sum_points=('points', np.sum), std_points=('points', np.std))
This particular formula groups the rows of the DataFrame by the variable called team and then calculates several summary statistics for the variable called points.
The following example shows how to use this syntax in practice.
Example: Using Groupby with Multiple Aggregations in Pandas
Suppose we have the following pandas DataFrame that contains information about various basketball players:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['Mavs', 'Mavs', 'Mavs', 'Heat', 'Heat', 'Heat'], 'points': [18, 22, 19, 14, 14, 11], 'assists': [5, 7, 7, 9, 12, 9]}) #view DataFrame print(df) team points assists 0 Mavs 18 5 1 Mavs 22 7 2 Mavs 19 7 3 Heat 14 9 4 Heat 14 12 5 Heat 11 9
We can use the following syntax to group the rows of the DataFrame by team and then calculate the mean, sum, and standard deviation of points for each team:
import numpy as np #group by team and calculate mean, sum, and standard deviation of points df.groupby('team').agg( mean_points=('points', np.mean), sum_points=('points', np.sum), std_points=('points', np.std)) mean_points sum_points std_points team Heat 13.000000 39 1.732051 Mavs 19.666667 59 2.081666
The output displays the mean, sum, and standard deviation of the points variable for each team.
You can use similar syntax to perform a groupby and calculate as many aggregations as you’d like.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
How to Count Unique Values Using Pandas GroupBy
How to Apply Function to Pandas Groupby
How to Create Bar Plot from Pandas GroupBy