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Quantile and Decile rank of a column in Pandas-Python

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  • Last Updated : 10 Jul, 2020
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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.

pandas.qcut()

Pandas library’s function qcut() is a Quantile-based discretization function. This means that it discretize the variables into equal-sized buckets based on rank or based on sample quantiles.

Syntax : pandas.qcut(x, q, labels=None, retbins: bool = False, precision: int = 3, duplicates: str = ‘raise’)

Parameters :

  • x : 1d ndarray or Series.
  • q : Number of quantiles. For example, 10 refers to deciles and 4 refers to quantiles.
  • labels : Used as labels for the resulting bins. If it is set as False, it returns only integer indicators of the bins. If True, then it raises an error. By default, it is set to None.
  • retbins : (Optional) It is a boolean which returns the (bins, labels) when set to True.
  • precision : (Optional) The precision at which to store and display the bins labels.
  • duplicates : (Optional) If bin edges are not unique, raise ValueError or drop non-uniques.

Quantile Rank

Algorithm :

  1. Import pandas and numpy modules.
  2. Create a dataframe.
  3. 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
  4. 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 :

  1. Import pandas and numpy modules.
  2. Create a dataframe.
  3. 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
  4. 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 :


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