Python | Pandas Series.dt.hour

NumPy | Python Methods and Functions

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.





Get Solution for free from DataCamp guru