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

df.groupby(df.your_date_column.dt.month)['values_column'].sum()

This particular formula groups the rows by date in your_date_column and calculates the sum of values for the values_column in the DataFrame.

Note that the dt.month() function extracts the month from a date column in pandas.

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

Example: How to Group by Month 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='1/1/2020', freq='W', periods=10),
                   'sales': [6, 8, 9, 11, 13, 8, 8, 15, 22, 9],
                   'returns': [0, 3, 2, 2, 1, 3, 2, 4, 1, 5]})

#view DataFrame
print(df)

        date  sales  returns
0 2020-01-05      6        0
1 2020-01-12      8        3
2 2020-01-19      9        2
3 2020-01-26     11        2
4 2020-02-02     13        1
5 2020-02-09      8        3
6 2020-02-16      8        2
7 2020-02-23     15        4
8 2020-03-01     22        1
9 2020-03-08      9        5

Related:

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

#calculate sum of sales grouped by month
df.groupby(df.date.dt.month)['sales'].sum()

date
1    34
2    44
3    31
Name: sales, dtype: int64

Here’s how to interpret the output:

• The total sales made during month 1 (January) was 34.
• The total sales made during month 2 (February) was 44.
• The total sales made during month 3 (March) was 31.

We can use similar syntax to calculate the max of the sales values grouped by month:

#calculate max of sales grouped by month
df.groupby(df.date.dt.month)['sales'].max()

date
1    11
2    15
3    22
Name: sales, dtype: int64

We can use similar syntax to calculate any value we’d like grouped by the month value of a date column.

Note: You can find the complete documentation for the GroupBy operation in pandas .

Additional Resources

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