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How to Calculate Timedelta in Months in Pandas

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  • Last Updated : 05 Apr, 2022
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The difference between two dates or times is represented as a timedelta object. The duration describes the difference between two dates, datetime, or time occurrences, while the delta means an average of the difference. One may estimate the time in the future and past by using timedelta. This difference between two dates when calculated in terms of months, it’s called time delta in months. Let’s demonstrate a few ways to calculate the time delta in months in pandas.

Method 1: Calculate Timedelta Using pandas.Series.dt.to_period() function

pandas.Series.dt.to_period() function:

Syntax:

Series.dt.to_period(*args, **kwargs)

converts datetime array to period array.

parameters

freq= optional value, offset string or offset object

In this example, we read the time.csv and convert values in each column to DateTime. after converting columns to DateTime we use pandas.Series.dt.to_period() to calculate time delta in months. ‘M’ string in to_period() function symbolizes months. Month-end objects are returned. 

CSV Used:

Python3




# import packages and libraries
import pandas as pd
 
# reading the csv file
data = pd.read_csv('time.csv')
 
# converting columns to datetime
data['start_date'] = pd.to_datetime(data['start_date'])
data['end_date'] = pd.to_datetime(data['end_date'])
 
# calculating time delta in months
data['time_delta_months'] = data['end_date'].dt.to_period('M') - \
    data['start_date'].dt.to_period('M')
print(data)


Output:

Method 2: Calculate Timedelta using months in integer

In the previous method, Monthends object is returned. If we want it to be in integer we have to convert it using the astype() function or by using view(dtype=’int64′). 

Python3




# import packages and libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# reading the csv file
data = pd.read_csv('time.csv')
 
# converting columns to datetime
data['start_date'] = pd.to_datetime(data['start_date'])
data['end_date'] = pd.to_datetime(data['end_date'])
 
# calculating time delta in months
data['time_delta_months'] = data['end_date'].dt.to_period('M').astype(int) - \
    data['start_date'].dt.to_period('M').astype(int)
 
# print(data)
print(data)


Output:

Example 2: Using .view(dtype=’int64′) to convert into integers

Python3




# import packages and libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# reading the csv file
data = pd.read_csv('time.csv')
 
# converting columns to datetime
data['start_date'] = pd.to_datetime(data['start_date'])
data['end_date'] = pd.to_datetime(data['end_date'])
 
# calculating time delta in months
data['time_delta_months'] = data['end_date'].dt.to_period('M').view(dtype='int64') -\
    data['start_date'].dt.to_period('M').view(dtype='int64')
 
# print(data)
print(data)


Output:

Method 3:  Calculate Timedelta using a user-defined function

Instead of using built-in functions, we can use our own user-defined function pd.Timestamp() function converts DateTime-like, str, int, or float time object to timestamp. Then we extract year and month values from the timestamps. as each year has 12 months we multiply 12 with the year difference and add the month difference. 

Python3




# import packages and libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
# creating a dataframe
data = pd.DataFrame({'startdate': [pd.Timestamp('20181211'),
                                   pd.Timestamp('20180701')],
                     'enddate': [pd.Timestamp('20190612'),
                                 pd.Timestamp('20190712')]})
 
def time_delta_month(end, start):
    return 12 * (end.dt.year - start.dt.year) \
        + (end.dt.month - start.dt.month)
 
print(time_delta_month(data['enddate'], data['startdate']))


 
 

Output:

 

0     6
1    12
dtype: int64

 


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