Detecting Outliers using Standard Deviation

Data Wrangling
Prob. and Stats

Difficulty: 2 | Problem written by ankita
Problem reported in interviews at


Outliers are certain points in the dataset which deviate from the general trends in the dataset.

There are several ways to detect outliers. In this problem, we will use the standard deviation in conjunction with the mean.

According to this method, a point is called an outlier if it deviates from the mean of the dataset by threshold*(standard_deviation of the dataset). Here, we assign a threshold of 3.

You are given as input:

X: X is a list of features.


Arrays that are free of outliers from the input lists.

Sample Input:
<class 'list'>
X: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 2000]

Expected Output:
<class 'numpy.ndarray'>
[ 1 2 3 4 5 6 7 8 9 10 100]

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numpy has been already imported as np (import numpy as np)