You can use the following basic syntax to group rows by quarter in a pandas DataFrame:

#convert date column to datetime
df['date'] = pd.to_datetime(df['date'])

#calculate sum of values, grouped by quarter
df.groupby(df['date'].dt.to_period('Q'))['values'].sum()

This particular formula groups the rows by quarter in the date column and calculates the sum for the values column in the DataFrame.

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

Example: How to Group by Quarter in Pandas

Suppose we have the following pandas DataFrame that shows the sales made by some company on various dates:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'date': pd.date_range(start='1/1/2022', freq='M', periods=12),
                   'sales': [6, 8, 10, 5, 4, 8, 8, 3, 5, 14, 8, 3]})

#view DataFrame
print(df)

         date  sales
0  2022-01-31      6
1  2022-02-28      8
2  2022-03-31     10
3  2022-04-30      5
4  2022-05-31      4
5  2022-06-30      8
6  2022-07-31      8
7  2022-08-31      3
8  2022-09-30      5
9  2022-10-31     14
10 2022-11-30      8
11 2022-12-31      3

Related:

We can use the following syntax to calculate the sum of sales grouped by quarter:

#convert date column to datetime and subtract one week
df['date'] = pd.to_datetime(df['date'])

#calculate sum of sales, grouped by quarter
df.groupby(df['date'].dt.to_period('Q'))['sales'].sum()

date
2022Q1    24
2022Q2    17
2022Q3    16
2022Q4    25
Freq: Q-DEC, Name: sales, dtype: int64

Here’s how to interpret the output:

• There were 24 total sales made during the first quarter.
• There were 17 total sales made during the second quarter.
• There were 16 total sales made during the third quarter.
• There were 25 total sales made during the fourth quarter.

We can use similar syntax to calculate some other metric, grouped by quarter.

For example, we could instead calculate the max value of sales, grouped by quarter:

#convert date column to datetime
df['date'] = pd.to_datetime(df['date'])

#calculate max of sales, grouped by quarter
df.groupby(df['date'].dt.to_period('Q'))['sales'].max()

date
2022Q1    10
2022Q2     8
2022Q3     8
2022Q4    14
Freq: Q-DEC, Name: sales, dtype: int64

Here’s how to interpret the output:

• The max sales on an individual month during the first quarter was 10.
• The max sales on an individual month during the second quarter was 8.
• The max sales on an individual month during the third quarter was 8.
• The max sales on an individual month during the fourth quarter was 14.

Additional Resources

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