Treating Outliers in Python

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Z-score Method

Using Z-score method, we can find out how many standard deviations any particular value is away from the mean. The formula for Z-score is given as:

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If the Z-score of a data point is more than 3 (because it cover 99.7% of area), it indicates that the data value is quite different from the other values and so is considered as an outlier. Now let's use this method to detect outliers using Python.

  • First, let's import Numpy as np

    import numpy as <<your code goes here>>
  • Now let's define an array of datapoints as x as follows

    <<your code goes here>> = [5, 5, 5, -99, 5, 5, 5, 5, 5, 5, 88, 5, 5, 5]
  • Define a function calculate_zscore to find the outlier(s)

    def <<your code goes here>>(data):
        mean = np.mean(data) 
        std = np.std(data)
        threshold = 2
        outliers = []
        for i in data: 
            z = (i-mean)/std
            if abs(z) > threshold:
        print('outlier in dataset is', outliers)
  • Finally, let's call the function with our x set of datapoints to display the outliers

    calculate_zscore(<<your code goes here>>)
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