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 DataFrame
print(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.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
How to Convert Columns to DateTime in Pandas
How to Convert Datetime to Date in Pandas
How to Extract Month from Date in Pandas
