Python | Pandas Series.dt.hour

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

Syntax: Series.dt.hour

Parameter: None

Returns: numpy array

Example # 1: Use the Series.dt.hour attribute to return the hour of 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

< p> sr.index = idx

 
# Convert base data to date and time

sr = pd.to_datetime (sr)

  
# Print series

print (sr)

Exit :

Now we will use the Series.dt.hour attribute to return the hour of the date and time in the underlying data of this Series object.

# return hour

resu lt = sr.dt.hour

  
# print the result

print (result)

Output:

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

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

# return hour

result = sr.dt.hour < / p>

 
# print the result

print (result)

Output:


Like us as seen from the output, the Series.dt.hour attribute successfully accessed and returned the datetime hour in the underlying data of the given series object.