#0199

Standardize Columns

MediumNumPy25 min20 XP

Problem

Given a 2D NumPy array X, return column-wise z-scores using population standard deviation.

Why This Matters

Standardization is used before distance-based models, gradient methods, PCA, and many regularized models.

Function Signature

def standardize_columns(X):

Examples

Example 1

Input[[1, 10], [2, 20], [3, 30]]

Output[[-1.2247, -1.2247], [0.0, 0.0], [1.2247, 1.2247]]

Each column is transformed using its own mean and standard deviation.

Constraints

Return the exact requested result.
Handle common edge cases.
Keep the solution readable before optimizing.

CodePython

import numpy as np

def standardize_columns(X):
    """
    Input example: [[1, 10], [2, 20], [3, 30]]
    Return: [[-1.2247, -1.2247], [0.0, 0.0], [1.2247, 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_columns(X):
    """
    Input example: [[1, 10], [2, 20], [3, 30]]
    Return: [[-1.2247, -1.2247], [0.0, 0.0], [1.2247, 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.

Testcases1 visible / 3 hidden categories

Core example

Input[[1, 10], [2, 20], [3, 30]]

Expected[[-1.2247, -1.2247], [0.0, 0.0], [1.2247, 1.2247]]

Each column is transformed using its own mean and standard deviation.

Hidden Test Categories

negative valuesfloat inputsmore rows than columns