Computing Quantile Value For A Pandas DataFrame

Overview:

Similar to the measures of central tendency the quantile is a measure of location.

The quantile() function of Pandas DataFrame class computes the value, below which a given portion of the data lies.

 

Example:

The Python example prints for the given distributions - the scores on Physics and Chemistry class tests, at what point or below 100%(1), 95%(.95), 50%(.5) of the scores are lying.

import pandas as pd

 

scores = {"Physics":(37,45,45,46,52,55,55,55,61,75),

          "Chemistry":(45,50,52,52,57,60,67,72,76,78)};

           

dataFrame = pd.DataFrame(data=scores);

print("Data Frame:");

print(dataFrame);

 

# Compute the 100th percentile

quantileValues = dataFrame.quantile(1);

print("100th percentile:");

print(quantileValues);

 

# Compute the 95th percentile

quantileValues = dataFrame.quantile(.95);

print("95th percentile:");

print(quantileValues);

 

# Compute the 50th percentile

quantileValues = dataFrame.quantile(.50);

print("50th percentile:");

print(quantileValues);

Output:

Python example for computing quantiles of a DataFrame using pandas


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