*266*

You can use the following syntax to group data by hour and perform some aggregation in pandas:

df.groupby([df['time'].dt.hour]).sales.sum()

This particular example groups the values by hour in a column called **time** and then calculates the sum of values in the **sales** column for each hour.

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

**Example: Group Data by Hour in Pandas**

Suppose we have the following pandas DataFrame that shows the number of sales made at various times throughout the day for some store:

import pandas as pd #create DataFrame df = pd.DataFrame({'time': ['2022-01-01 01:14:00', '2022-01-01 01:24:15', '2022-01-01 02:52:19', '2022-01-01 02:54:00', '2022-01-01 04:05:10', '2022-01-01 05:35:09'], 'sales': [18, 20, 15, 14, 10, 9]}) #convert date column to datetime df['time'] = pd.to_datetime(df['time']) #view DataFrame print(df) time sales 0 2022-01-01 01:14:00 18 1 2022-01-01 01:24:15 20 2 2022-01-01 02:52:19 15 3 2022-01-01 02:54:00 14 4 2022-01-01 04:05:10 10 5 2022-01-01 05:35:09 9

We can use the following syntax to group the **time** column by hours and calculate the sum of **sales** for each hour:

#group by hours in time column and calculate sum of sales df.groupby([df['time'].dt.hour]).sales.sum() time 1 38 2 29 4 10 5 9 Name: sales, dtype: int64

From the output we can see:

- A total of
**38**sales were made during the first hour. - A total of
**29**sales were made during the second hour. - A total of
**10**sales were made during the fourth hour. - A total of
**9**sales were made during the fifth hour.

Note that we can also perform some other aggregation.

For example, we could calculate the **mean** number of sales per hour:

#group by hours in time column and calculate mean of sales df.groupby([df['time'].dt.hour]).sales.mean() time 1 19.0 2 14.5 4 10.0 5 9.0 Name: sales, dtype: float64

We can also group by hours and minutes if weâ€™d like.

For example, the following code shows how to calculate the sum of sales, grouped by hours and minutes:

#group by hours and minutes in time column and calculate mean of sales df.groupby([df['time'].dt.hour, df['time'].dt.minute]).sales.mean() time time 1 14 18 24 20 2 52 15 54 14 4 5 10 5 35 9 Name: sales, dtype: int64

From the output we can see:

- The mean number of sales during 1:14 was
**18**. - The mean number of sales during 1:23 was
**20**. - The mean number of sales during 2:52 was
**15**.

And so on.

**Additional Resources**

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

How to Create a Date Range in Pandas

How to Extract Month from Date in Pandas

How to Convert Timestamp to Datetime in Pandas