The dt.month attribute returns a NumPy array containing the month of the DateTime in the underlying data of 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.month
print (result)
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Output:
Syntax
Syntax: Series.dt.month
Parameter : None
Returns: NumPy array with month values of DateTime object
How to Extract Month Value From DateTime Object in Pandas Series
To extract the month value from the DateTime object we use the Series.dt.month attribute of the Pandas library in Python
Let us understand it better with an example:
Example:
Use the Series.dt.month attribute of Pandas library to return the month of the DateTime 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 = 'H' ))
# 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.month attribute to return the month of the DateTime in the underlying data of the given Series object.
# return the month result = sr.dt.month
# print the result print (result)
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Output :
As we can see in the output, the Series.dt.month attribute has successfully accessed and returned the month of the DateTime in the underlying data of the given series object.