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:** How to Create a Date Range in Pandas

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 here.

**Additional Resources**

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

How to Group by Week in Pandas

How to Group by Month in Pandas

How to Group by Quarter in Pandas