You can use the following syntax to calculate the mean and standard deviation of a column after using the **groupby()** operation in pandas:

df.groupby(['team'], as_index=False).agg({'points':['mean','std']})

This particular example groups the rows of a pandas DataFrame by the value in the **team** column, then calculates the mean and standard deviation of values in the **points** column.

The following example shows how to use this syntax in practice.

**Example: Calculate Mean & Std of One Column in Pandas groupby**

Suppose we have the following pandas DataFrame that contains information about basketball players on various teams:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'], 'points': [12, 15, 17, 17, 19, 14, 15, 20, 24, 28], 'assists': [5, 5, 7, 9, 10, 14, 13, 8, 2, 7]}) #view DataFrame print(df) team points assists 0 A 12 5 1 A 15 5 2 A 17 7 3 A 17 9 4 B 19 10 5 B 14 14 6 B 15 13 7 C 20 8 8 C 24 2 9 C 28 7

We can use the following syntax to calculate the mean and standard deviation of values in the **points** column, grouped by the **team** column:

#calculate mean and standard deviation of points, grouped by team output = df.groupby(['team'], as_index=False).agg({'points':['mean','std']}) #view results print(output) team points mean std 0 A 15.25 2.362908 1 B 16.00 2.645751 2 C 24.00 4.000000

From the output we can see:

- The mean points value for team A is
**15.25**. - The standard deviation of points for team A is
**2.362908**.

And so on.

We can also rename the columns so that the output is easier to read:

#rename columns output.columns = ['team', 'points_mean', 'points_std'] #view updated results print(output) team points_mean points_std 0 A 15.25 2.362908 1 B 16.00 2.645751 2 C 24.00 4.000000

**Note**: You can find the complete documentation for the pandas **groupby()** operation here.

**Additional Resources**

The following tutorials explain how to perform other common operations in pandas:

How to Perform a GroupBy Sum in Pandas

How to Use Groupby and Plot in Pandas

How to Count Unique Values Using GroupBy in Pandas