Python | Series of Pandas. Day



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

Syntax: Series.dt.day

Parameter: None

Returns: numpy array

Example # 1: Use the Series.dt.day attribute to return the day of the datetime in the underlying data of the given 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:

Now we will use the Series.dt.day attribute to return the day datetime in the underlying data of a given Series object.

# return day

result = sr.dt.day

 
# print the result

print (result)

Output:

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

Example # 2: Use the Series.dt.day attribute to return the datetime day 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.day attribute to return the datetime day in the underlying data of this Series object.

# return day

result = sr.dt.day

  
# print the result

print (result)

Exit:

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