Python | Pandas Series.quantile ()

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.