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You can use the following basic syntax to calculate quantiles by group in Pandas:

df.groupby('grouping_variable').quantile(.5)

The following examples show how to use this syntax in practice.

**Example 1: Calculate Quantile by Group**

Suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], 'score': [3, 4, 4, 5, 5, 8, 1, 2, 2, 3, 3, 5]}) #view first five rows df.head() team score 0 1 3 1 1 4 2 1 4 3 1 5 4 1 5

The following code shows how to calculate the 90th percentile of values in the â€˜pointsâ€™ column, grouped by the â€˜teamâ€™ column:

df.groupby('team').quantile(.90) score team 1 6.5 2 4.0

Hereâ€™s how to interpret the output:

- The 90th percentile of â€˜pointsâ€™ for team 1 is
**6.5**. - The 90th percentile of â€˜pointsâ€™ for team 2 is
**4.0**.

**Example 2: Calculate Several Quantiles by Group**

The following code shows how to calculate several quantiles at once by group:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2], 'score': [3, 4, 4, 5, 5, 8, 1, 2, 2, 3, 3, 5]}) #create functions to calculate 1st and 3rd quartiles def q1(x): return x.quantile(0.25) def q3(x): return x.quantile(0.75) #calculate 1st and 3rd quartiles by group vals = {'score': [q1, q3]} df.groupby('team').agg(vals) score q1 q3 team 1 4.0 5.0 2 2.0 3.0

Hereâ€™s how to interpret the output:

- The first and third quartile of scores for team 1 is
**4.0**and**5.0**, respectively. - The first and third quartile of scores for team 2 is
**2.0**and**3.0**, respectively.

**Additional Resources**

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

How to Find the Max Value by Group in Pandas

How to Count Observations by Group in Pandas

How to Calculate the Mean of Columns in Pandas