Quantile Rank

Algorithm :

Import pandas and numpy modules.
Create a dataframe.
Use pandas.qcut() function, the Score column is passed, on which the quantile discretization is calculated. And q is set to 4 so the values are assigned from 0-3
Print the dataframe with the quantile rank.

# importing the modules 
import pandas as pd 
import numpy as np 
    
# creating a DataFrame 
df = {'Name' : ['Amit', 'Darren', 'Cody', 'Drew', 
                'Ravi', 'Donald', 'Amy'], 
      'Score' : [50, 71, 87, 95, 63, 32, 80]} 
df = pd.DataFrame(df, columns = ['Name', 'Score']) 
  
# adding Quantile_rank column to the DataFrame 
df['Quantile_rank'] = pd.qcut(df['Score'], 4, 
                               labels = False) 
  
# printing the DataFrame 
print(df) 

Output :

Decile Rank

Algorithm :

Import pandas and numpy modules.
Create a dataframe.
Use pandas.qcut() function, the Score column is passed, on which the quantile discretization is calculated. And q is set to 10 so the values are assigned from 0-9
Print the dataframe with the decile rank.

# importing the modules 
import pandas as pd 
import numpy as np 
    
# creating a DataFrame 
df = {'Name' : ['Amit', 'Darren', 'Cody', 'Drew', 
                'Ravi', 'Donald', 'Amy'], 
      'Score' : [50, 71, 87, 95, 63, 32, 80]} 
df = pd.DataFrame(df, columns = ['Name', 'Score']) 
  
# adding Decile_rank column to the DataFrame 
df['Decile_rank'] = pd.qcut(df['Score'], 10, 
                            labels = False) 
  
# printing the DataFrame 
print(df) 

Output :

Quantile and Decile rank of a column in Pandas-Python

Let’s see how to find the Quantile and Decile ranks of a column in Pandas. We will be using the qcut() function of the pandas module.

Tags:

#Python pandas-dataFrame #Python-pandas #Python #python

pandas.qcut()

Quantile Rank

Decile Rank

Quantile and Decile rank of a column in Pandas-Python

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