Dateoffsets are a standard kind of date increment used for a date range in Pandas. It works exactly like relativedelta in terms of the keyword args we pass in. DateOffsets work as follows, each offset specify a set of dates that conform to the DateOffset. For example, Bday defines this set to be the set of dates that are weekdays (M-F). DateOffsets can be created to move dates forward a given number of valid dates. For example, Bday(2) can be added to date to move it two business days forward. If the date does not start on a valid date, first it is moved to a valid date and then offset is created. Pandas tseries.offsets.DateOffset.normalize attribute returns boolean value. It returns True when the DateOffset value has been normalized else it returns False. Note : Normalizing means to round the result of a DateOffset addition down to the previous midnight.
Syntax: pandas.tseries.offsets.DateOffset.normalize Parameter : None Returns : boolean
Example #1: Use pandas.tseries.offsets.DateOffset.normalize attribute to check if the given DateOffset value has been normalized or not.
Python3
# importing pandas as pdimport pandas as pd# Creating Timestampts = pd.Timestamp('2019-10-10 07:15:11')# Create the DateOffsetdo = pd.tseries.offsets.DateOffset(n = 2)# Print the Timestampprint(ts)# Print the DateOffsetprint(do) |
Output : 
Now we will add the dateoffset to the given timestamp object to increment it. We will also check if the DateOffset has been normalized or not.
Python3
# Adding the dateoffset to the given timestampnew_timestamp = ts + do# Print the updated timestampprint(new_timestamp)# check if the DateOffset has been normalized or notprint(do.normalize) |
Output : 
As we can see in the output, the attribute has successfully returned the a boolean value indicating whether the given DateOffset has been normalized or not. Example #2: Use pandas.tseries.offsets.DateOffset.normalize attribute to check if the given DateOffset value has been normalized or not.
Python3
# importing pandas as pdimport pandas as pd# Creating Timestampts = pd.Timestamp('2019-10-10 07:15:11')# Create the DateOffset# Also normalize itdo = pd.tseries.offsets.DateOffset(days = 10, hours = 2, normalize = True)# Print the Timestampprint(ts)# Print the DateOffsetprint(do) |
Output : 
Now we will add the dateoffset to the given timestamp object to increment it. We will also check if the DateOffset has been normalized or not.
Python3
# Adding the dateoffset to the given timestampnew_timestamp = ts + do# Print the updated timestampprint(new_timestamp)# check if the DateOffset has been normalized or notprint(do.normalize) |
Output : 
As we can see in the output, the attribute has successfully returned the a boolean value indicating whether the given DateOffset has been normalized or not.
