Pandas Series dt.month | Extract Month Part From DateTime Series
Last Updated :
06 Feb, 2024
The dt.month attribute returns a NumPy array containing the month of the DateTime in the underlying data of the given Series object.
Example
Python3
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)
|
Output:
Syntax
Syntax: Series.dt.monthÂ
Parameter : NoneÂ
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.
Python3
import pandas as pd
sr = pd.Series(pd.date_range( '2012-12-12 12:12' ,
periods = 5 , freq = 'H' ))
idx = [ 'Day 1' , 'Day 2' , 'Day 3' , 'Day 4' , 'Day 5' ]
sr.index = idx
print (sr)
|
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.
Python3
result = sr.dt.month
print (result)
|
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.
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