Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Timestamp.is_quarter_end attribute return a boolean value. It return True if the date in the given Timestamp object is the quarter end else it return False.
Syntax : Timestamp.is_quarter_end
Parameters : None
Return : boolean
Example #1: Use Timestamp.is_quarter_end attribute to check if the date in the given Timestamp object is the quarter end or not.
# importing pandas as pdimport pandas as pd # Create the Timestamp objectts = pd.Timestamp(2016, 3, 31, 12) # Print the Timestamp objectprint(ts) |
Output :
Now we will use the Timestamp.is_quarter_end attribute to check if the date in the ts object is the quarter end or not.
# check if the date is quarter end or notts.is_quarter_end |
Output :
As we can see in the output, the Timestamp.is_quarter_end attribute has returned True indicating the date in the given Timestamp object is quarter end
Example #2: Use Timestamp.is_quarter_end attribute to check if the date in the given Timestamp object is the quarter end or not.
# importing pandas as pdimport pandas as pd # Create the Timestamp objectts = pd.Timestamp(year = 2009, month = 5, day = 31, hour = 4, tz = 'Europe/Berlin') # Print the Timestamp objectprint(ts) |
Output :
Now we will use the Timestamp.is_quarter_end attribute to check if the date in the ts object is the quarter end or not.
# check if the date is quarter end or notts.is_quarter_end |
Output :
As we can see in the output, the Timestamp.is_quarter_end attribute has returned False indicating the date in the given Timestamp object is not the quarter end.
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