You can use the following methods to convert a timedelta column to an integer column in a pandas DataFrame:

Method 1: Convert Timedelta to Integer (Days)

df['days'] = df['timedelta_column'].dt.days

Method 2: Convert Timedelta to Integer (Hours)

df['hours'] = df['timedelta_column'] / pd.Timedelta(hours=1)

Method 3: Convert Timedelta to Integer (Minutes)

df['minutes'] = df['timedelta_column'] / pd.Timedelta(minutes=1)

The following example shows how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'promotion': ['A', 'B', 'C', 'D'],
                   'start': ['2021-10-04 13:29:00', '2021-10-07 12:30:00',
                             '2021-10-15 04:20:00', '2021-10-18 15:45:03'],
                   'end':   ['2021-10-08 11:29:06', '2021-10-15 10:30:07',
                             '2021-10-29 05:50:15', '2021-10-22 15:40:03']})

#convert start date and end date columns to datetime
df['start'] = pd.to_datetime(df['start'])
df['end'] = pd.to_datetime(df['end'])

#create new column that contains timedelta between start and end
df['duration'] = df['end'] - df['start']

#view DataFrame
print(df)

  promotion               start                 end         duration
0         A 2021-10-04 13:29:00 2021-10-08 11:29:06  3 days 22:00:06
1         B 2021-10-07 12:30:00 2021-10-15 10:30:07  7 days 22:00:07
2         C 2021-10-15 04:20:00 2021-10-29 05:50:15 14 days 01:30:15
3         D 2021-10-18 15:45:03 2021-10-22 15:40:03  3 days 23:55:00

Example 1: Convert Timedelta to Integer (Days)

The following code shows how to create a new column called days that converts the timedelta in the duration column into an integer value that represents the number of days in the timedelta column.

#create new column that converts timedelta into integer number of days
df['days'] = df['duration'].dt.days

#view updated DataFrameprint(df)

  promotion               start                 end         duration  days
0         A 2021-10-04 13:29:00 2021-10-08 11:29:06  3 days 22:00:06     3
1         B 2021-10-07 12:30:00 2021-10-15 10:30:07  7 days 22:00:07     7
2         C 2021-10-15 04:20:00 2021-10-29 05:50:15 14 days 01:30:15    14
3         D 2021-10-18 15:45:03 2021-10-22 15:40:03  3 days 23:55:00     3

We can use dtype to check the data type of this new column:

#check data type
df.days.dtype

dtype('int64')

The new column is an integer.

Example 2: Convert Timedelta to Integer (Hours)

The following code shows how to create a new column called hours that converts the timedelta in the duration column into a numeric value that represents the total number of hours in the timedelta column.

#create new column that converts timedelta into total number of hours
df['hours'] = df['duration'] / pd.Timedelta(hours=1)

#view updated DataFrame
print(df)

  promotion               start                 end         duration      hours
0         A 2021-10-04 13:29:00 2021-10-08 11:29:06  3 days 22:00:06   94.001667  
1         B 2021-10-07 12:30:00 2021-10-15 10:30:07  7 days 22:00:07  190.001944
2         C 2021-10-15 04:20:00 2021-10-29 05:50:15 14 days 01:30:15  337.504167
3         D 2021-10-18 15:45:03 2021-10-22 15:40:03  3 days 23:55:00   95.916667

We can use dtype to check the data type of this new column:

#check data type
df.hours.dtype

dtype('float64')

The new column is a float.

Example 3: Convert Timedelta to Integer (Minutes)

The following code shows how to create a new column called minutes that converts the timedelta in the duration column into a numeric value that represents the total number of minutes in the timedelta column.

#create new column that converts timedelta into total number of minutes
df['minutes'] = df['duration'] / pd.Timedelta(minutes=1)

#view updated DataFrame
print(df)

  promotion               start                 end         duration        minutes
0         A 2021-10-04 13:29:00 2021-10-08 11:29:06  3 days 22:00:06    5640.100000  
1         B 2021-10-07 12:30:00 2021-10-15 10:30:07  7 days 22:00:07   11400.116667
2         C 2021-10-15 04:20:00 2021-10-29 05:50:15 14 days 01:30:15   20250.250000
3         D 2021-10-18 15:45:03 2021-10-22 15:40:03  3 days 23:55:00    5755.000000

We can use dtype to check the data type of this new column:

#check data type
df.minutes.dtype

dtype('float64')

The new column is a float.

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