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Python | Pandas Series.between_time()

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  • Last Updated : 17 Feb, 2019
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Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.between_time() function select values between particular times of the day (e.g., 9:00-9:30 AM). By setting start_time to be later than end_time, you can get the times that are not between the two times.

Syntax: Series.between_time(start_time, end_time, include_start=True, include_end=True, axis=None)

Parameter :
start_time : datetime.time or string
end_time : datetime.time or string
include_start : boolean, default True
include_end : boolean, default True
axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Returns : values_between_time : same type as caller

Example #1: Use Series.between_time() function to return the values lying in the given time duration.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])
  
# Create the Index
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='H')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)


Output :

Now we will use Series.between_time() function to return the values lying in the given time duration.




# return values between the passed time duration
result = sr.between_time(start_time = '10:45', end_time = '15:45')
  
# Print the result
print(result)


Output :

As we can see in the output, the Series.between_time() function has successfully returned the values lying in the given time duration.
 
Example #2 : Use Series.between_time() function to return the values lying in the given time duration. Skip the values corresponding to the start and end time.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])
  
# Create the Index
index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='H')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)


Output :

Now we will use Series.between_time() function to return the values lying in the given time duration. Skip the values corresponding to the start and end time.




# return values between the passed time duration
# skip the start and end time
result = sr.between_time(start_time = '10:45', end_time = '15:45',
                       include_start = False, include_end = False)
  
# Print the result
print(result)


Output :

As we can see in the output, the Series.between_time() function has successfully returned the values lying in the given time duration. Notice the values corresponding to the start and end time has not been included.


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