#0008

Standardize a Numeric Column

EasyNumPy12 min10 XP

Problem

Given a one-dimensional NumPy array, return the standardized values: each value minus the mean, divided by the standard deviation.

Why This Matters

Standardization appears everywhere in ML because many algorithms behave better when features share a comparable scale.

Function Signature

def standardize(x):

Examples

Example 1

Inputx = np.array([10, 20, 30])

Outputarray([-1.2247, 0.0, 1.2247])

The mean is 20. Values below the mean become negative; values above become positive.

Constraints

Input is a one-dimensional numeric array.
If standard deviation is zero, return zeros to avoid division by zero.
Use population standard deviation, matching np.std default.

CodePython

import numpy as np

def standardize(x):
    """
    Input example: x = np.array([10, 20, 30])
    Return: array([-1.2247, 0.0, 1.2247])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return x

import numpy as np

def standardize(x):
    """
    Input example: x = np.array([10, 20, 30])
    Return: array([-1.2247, 0.0, 1.2247])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return x

Visible browser tests run here when available.

Testcases2 visible / 4 hidden categories

Three evenly spaced values

Inputnp.array([10, 20, 30])

Expectedapproximately [-1.2247, 0.0, 1.2247]

The middle value equals the mean, so its standardized value is zero.

Constant column

Inputnp.array([5, 5, 5])

Expected[0.0, 0.0, 0.0]

There is no spread, so dividing by standard deviation would be invalid.

Hidden Test Categories

Negative valuesFloating point valuesLarge arraysConstant arrays