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How to Calculate Quantiles by Group in Pandas?

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  • Last Updated : 19 Dec, 2021
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In this article, how to calculate quantiles by group in Pandas using Python.

There are many methods to calculate the quantile, but pandas provide groupby.quantile() function to find it in a simple few lines of code. This is the Method to use when the desired quantile falls between two points.

Syntax: 

DataFrameGroupBy.quantile(self, q=0.5, interpolation=’linear’)

Parameters:  

  • q : float or array-like, default 0.5 (50% quantile) Values are given between 0 and 1 providing the quantiles to compute.
  • Interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

In this method, the values and interpolation are passed as parameters. By default, the q value will be 0.5 and interpolation will be Linear. This returns the series or Dataframe determined by the GroupBy object.

Dataframe in use:

dataframe

Example 1: Calculate quantiles by group

Python3




# Importing libraries
import pandas as pd
  
# Storing data in dictionary
game = {'Player': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
                        'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',
                        'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
        'wins': [2, 4, 4, 5, 6, 9, 13, 13, 15, 15, 14, 13,
                 11, 9, 9, 8, 8, 16, 19, 21, 14, 20, 19, 18]
        }
# Creating data frame
df = pd.DataFrame(game)
  
# calculating quantile
df.groupby('Player').quantile(0.5)


Output:

output

Example 2: Calculate quantiles by group

Python3




# Importing libraries
import pandas as pd
  
# Storing data in dictionary
game = {'Player': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',
                        'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',
                        'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C'],
        'wins': [2, 4, 4, 5, 6, 9, 13, 13, 15, 15, 14, 13,
                 11, 9, 9, 8, 8, 16, 19, 21, 14, 20, 19, 18]
        }
# Creating data frame
df = pd.DataFrame(game)
  
# calculating quantile
df.groupby('Player').quantile(0.9)


Output:

output


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