You can use the following syntax to calculate a difference between two dates in a pandas DataFrame:

df['diff_days'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'D')

This particular example calculates the difference between the dates in the end_date and start_date columns in terms of days.

Note that we can replace the ‘D’ in the timedelta64() function with the following values to calculate the date difference in different units:

• W: Weeks
• M: Months
• Y: Years

The following examples show how to calculate a date difference in a pandas DataFrame in practice.

Example 1: Calculate Difference Between Two Dates with Datetime Columns

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'start_date': pd.date_range(start='1/5/2020', periods=6, freq='W'),
                   'end_date': pd.date_range(start='6/1/2020', periods=6, freq='M')})

#view DataFrame
print(df)

  start_date   end_date
0 2020-01-05 2020-06-30
1 2020-01-12 2020-07-31
2 2020-01-19 2020-08-31
3 2020-01-26 2020-09-30
4 2020-02-02 2020-10-31
5 2020-02-09 2020-11-30

#view dtype of each column in DataFrame
df.dtypes

start_date    datetime64[ns]
end_date      datetime64[ns]
dtype: object

Since both columns in the DataFrame already have a dtype of datetime64, we can use the following syntax to calculate the difference between the start and end dates:

import numpy as np

#create new columns that contains date differences
df['diff_days'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'D')
df['diff_weeks'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'W')
df['diff_months'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'M')
df['diff_years'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'Y')

#view updated DataFrame
print(df)

  start_date   end_date  diff_days  diff_weeks  diff_months  diff_years
0 2020-01-05 2020-06-30      177.0   25.285714     5.815314    0.484610
1 2020-01-12 2020-07-31      201.0   28.714286     6.603832    0.550319
2 2020-01-19 2020-08-31      225.0   32.142857     7.392349    0.616029
3 2020-01-26 2020-09-30      248.0   35.428571     8.148011    0.679001
4 2020-02-02 2020-10-31      272.0   38.857143     8.936528    0.744711
5 2020-02-09 2020-11-30      295.0   42.142857     9.692191    0.807683

The new columns contain the date differences between the start and end dates in terms of days, weeks, months, and years.

Example 2: Calculate Difference Between Two Dates with String Columns

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'start_date': ['2020-01-05', '2020-01-12', '2020-01-19'],
                   'end_date': ['2020-06-30', '2020-07-31', '2020-08-31']})

#view dtype of each columnprint(df.dtypes)

start_date    object
end_date      object
dtype: object

Since neither column in the DataFrame has a dtype of datetime64, we will receive an error if we attempt to calculate the difference between the dates:

import numpy as np

#attempt to calculate date difference
df['diff_days'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'D')

TypeError: unsupported operand type(s) for -: 'str' and 'str'

import numpy as np

#convert columns to datetime
df[['start_date','end_date']] = df[['start_date','end_date']].apply(pd.to_datetime)

#calculate difference between dates
df['diff_days'] = (df['end_date'] - df['start_date']) / np.timedelta64(1, 'D')

#view updated DataFrame
print(df)

  start_date   end_date  diff_days
0 2020-01-05 2020-06-30      177.0
1 2020-01-12 2020-07-31      201.0
2 2020-01-19 2020-08-31      225.0

Since we first converted each column to a datetime format, we were able to successfully calculate the difference between the dates without any errors.

Additional Resources

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