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

df.groupby(df.your_date_column.dt.day)['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.day() function extracts the day from a date column in pandas.

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

Example: How to Group by Day 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='8h', 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 2020-01-01 00:00:00      6        0
1 2020-01-01 08:00:00      8        3
2 2020-01-01 16:00:00      9        2
3 2020-01-02 00:00:00     11        2
4 2020-01-02 08:00:00     13        1
5 2020-01-02 16:00:00      8        3
6 2020-01-03 00:00:00      8        2
7 2020-01-03 08:00:00     15        4
8 2020-01-03 16:00:00     22        1
9 2020-01-04 00:00:00      9        5

Related:

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

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

date
1    23
2    32
3    45
4     9
Name: sales, dtype: int64

Here’s how to interpret the output:

• The total sales made on January 1st was 23.
• The total sales made on January 2nd was 32.
• The total sales made on January 3rd was 45.
• The total sales made on January 4th was 9.

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

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

date
1     9
2    13
3    22
4     9
Name: sales, dtype: int64

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

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

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