Python | Pandas Series.dt.year

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Series.dt can be used to access series values ‚Äã‚Äãas datetimelike and return multiple properties. Series.dt.year Pandas Series.dt.year returns an empty array containing the year of the date and time in base data of this series object.

Syntax: Series.dt.year

Parameter: None

Returns: numpy array

Example # 1: Use the Series.dt.year attribute to return the year of the date and time in the underlying data of this Series object.

# import pandas as pd

import pandas as pd


# Create series

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’ ])


# Create index

idx = [ ’Day 1’ , ’ Day 2’ , ’Day 3’ , ’Day 4’ , ’ Day 5’ ]


# set index

sr.index = idx


# Convert base data to date and time

sr = pd.to_datetime (sr)


# Print series

print (sr)

Output:

We will now use the Series attribute. dt.year to return the year of the date and time in the underlying data of this Series object.

# return year

result = sr.dt.year


# print result

print (result)

Exit :

As we can see from the output, the Series.dt.year successfully accessed and returned the datetime year in the underlying data of this series object.

Example # 2: Use the Series.dt.year attribute to return the year of the date and time in the underlying data of this Series object.

# import pandas as pd

import pandas as pd


# Create series

sr = pd.Series (pd.date_range ( ’2012-12-12 12: 12’ ,

periods = 5 , freq = ’H’ ))


# Create index

idx = [ ’Day 1’ , ’ Day 2’ , ’Day 3’ , ’Day 4’ , ’Day 5’ ]


# set index

sr.index = idx


# Print series

print (sr)

Output:

We will now use the Series.dt.year attribute to return the year of the date and time in the underlying data of this Series object.

# return year

result = sr.dt.year


# print result

print (result)

Output:

Like us we can see from the output, the Series.dt.year attribute successfully accessed and returned the year of the datetime in the underlying data of the given series object.

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