Pandas dt.quarter attribute returns the quarter of the date in the underlying DateTime based data in the given Series object.
Example
import pandas as pd
sr = pd.Series(['2012-10-21 09:30', '2019-7-18 12:30', '2008-02-2 10:30',
'2010-4-22 09:25', '2019-11-8 02:22'])
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
sr.index = idx
sr = pd.to_datetime(sr)
result = sr.dt.quarter
print(result)
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Output
Syntax
Syntax: Series.dt.quarter
Parameter : None
Returns: NumPy array containing quarter value
How to Get Quarter Value from DateTime Object in Pandas Series
To get the quarter value from the DateTime object in the Pandas series we use the dt.quarter attribute of the Pandas library in Python.
Let us understand it better with an example:
Example:
Use the dt.quarter attribute to return the quarter of the date in the underlying data of the given Series object.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series(pd.date_range('2012-12-12 12:12',
periods = 5, freq = 'M'))
# Creating the index idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
# set the index sr.index = idx
# Print the series print(sr)
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Output :
Now we will use the Series.dt.quarter attribute to return the quarter of the date in the DateTime based data in the given series object.
# return the quarter of the date result = sr.dt.quarter
# print the result print(result)
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Output :
As we can see in the output, the Pandas dt.quarter attribute has successfully accessed and returned the quarter of the date in the underlying data of the given series object.