Python | Pandas Series.quantile ()

Michael Zippo

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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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