Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Index.slice_locs() function compute slice locations for input labels. It takes start and end labels as the parameter and returns the integer value corresponding to those values.
Syntax: Index.slice_locs(start=None, end=None, step=None, kind=None)
Parameters :
start : If None, defaults to the beginning
end : If None, defaults to the end
step : If None, defaults to 1
kind : {‘ix’, ‘loc’, ‘getitem’} or NoneReturns : start, end : int
Example #1: Use Index.slice_locs() function to find the slice labels for the input values.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.Index(['Beagle', 'Pug', 'Labrador', 'Sephard', 'Mastiff', None, 'Husky']) # Print the index idx |
Output :
Now we will find the slice labels for ‘Pug’ and ‘Mastiff’
# finding the slice labels for the input value. idx.slice_locs(start ='Pug', end ='Mastiff') |
Output :
As we can see in the output, the function has returned the slice locations for the input labels.
Example #2: Use Index.slice_locs() function to find the slice labels in the date-time base index.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.date_range('1 / 1/2018', periods = 5, freq ='MS') # Print the index idx |
Output :
Now we would find the slice labels for the input labels.
# finding the slice labels idx.slice_locs(start ='2018-02-01', end ='2018-04-01') |
Output :
As we can see in the output, the function has returned the range values which contains the input slice label value.
