# Python | Pandas Series.quantile ()

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`Series.quantile() ` Pandas `Series.quantile() ` returns the value in the given quantile for the underlying data in this 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 () ` to return the desired quantile of the underlying data in a given Series object.

 ` # import pandas as pd ` ` import ` ` pandas as pd ` ` # Create series ` ` sr ` ` = ` ` pd.Series ([` ` 10 ` `, ` ` 25 ` `, ` ` 3 ` `, ` ` 11 ` `, ` ` 24 ` `, ` ` 6 ` `]) ` ` ` ` # Create index ` ` index_ ` ` = ` ` [` `’ Coca Cola’ ` `, ` ` ’Sprite’ ` `, ` ` ’Coke’ ` `, ` ` ’Fanta’ ` `, ` `’ Dew’ ` `, ` ` ’ThumbsUp’ ` `] ` ` # set index ` ` sr.index ` ` = ` ` index_ ` ` # Print series ` ` print ` ` (sr) `

Output: We will now use ` Series.quantile () ` to find the quantile 40% of the base data in this series object.

 ` # return 40% quantile ` ` result ` ` = ` sr.quantile (q ` = ` ` 0.4 ` ` ) ` ` # Print result ` ` print ` ` (result) `

Output: As we can see in the output, ` Series.quantile () ` successfully returned the desired quantitative value of the underlying data of the given Series object.

Example # 2: Use ` Series.quantile () ` to return the desired quantile of the underlying data in the given Series object.

` `

` # import pandas as pd import pan das as pd # Create a series sr = pd.Series ([ 11 , 21 , 8 , 18 , 65 , 84 , 32 , 10 , 5 , 24 , 32 ]) # Print series print (sr) `

Output: Now we will use ` Series.quantile () ` to find the 90% quantile of the underlying data in a given series object.

 ` # return 90% quantile value ` ` result ` ` = ` ` sr.quantile (q ` ` = ` ` 0.9 ` `) ` ` # Print result ` ` print ` ` (result) `

Output: As we can see in the output, ` Series.quantile () ` successfully returned the desired quantitative value for the underlying data of this Series object.

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