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.quantile() function return value at the given quantile for the underlying data in the given Series object.
Syntax: Series.quantile(q=0.5, interpolation=’linear’)
Parameter :
q : float or array-like, default 0.5 (50% quantile)
interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}Returns : quantile : float or Series
Example #1: Use Series.quantile() function to return the desired quantile of the underlying data in the given Series object.
Python3
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 10 , 25 , 3 , 11 , 24 , 6 ]) # Create the Index index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ] # set the index sr.index = index_ # Print the series print (sr) |
Output : Now we will use Series.quantile() function to find the 40% quantile of the underlying data in the given series object.
Python3
# return the value of 40 % quantile result = sr.quantile(q = 0.4 ) # Print the result print (result) |
Output : As we can see in the output, the Series.quantile() function has successfully returned the desired quantile value of the underlying data of the given Series object. Example #2: Use Series.quantile() function to return the desired quantile of the underlying data in the given Series object.
Python3
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([ 11 , 21 , 8 , 18 , 65 , 84 , 32 , 10 , 5 , 24 , 32 ]) # Print the series print (sr) |
Output : Now we will use Series.quantile() function to find the 90% quantile of the underlying data in the given series object.
Python3
# return the value of 90 % quantile result = sr.quantile(q = 0.9 ) # Print the result print (result) |
Output : As we can see in the output, the Series.quantile() function has successfully returned the desired quantile value of the underlying data of the given Series object.