#0058

Min-Max Scale an Array

EasyNumPy15 min15 XP

Problem

Given a NumPy array, return a scaled array where the minimum becomes 0 and the maximum becomes 1. If all values are equal, return an array of zeros with the same shape.

Why This Matters

Feature scaling is a small operation with large model impact. Implementing it once makes sklearn preprocessing feel less magical.

Function Signature

def minmax_scale(values):

Examples

Example 1

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

Outputarray([0.0, 0.5, 1.0])

10 is the minimum, 30 is the maximum, and 20 sits halfway.

Constraints

Return floats.
Preserve the input shape.
Handle constant arrays safely.

CodePython

import numpy as np

def minmax_scale(values):
    """
    Input example: values = np.array([10, 20, 30])
    Return: array([0.0, 0.5, 1.0])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return values

import numpy as np

def minmax_scale(values):
    """
    Input example: values = np.array([10, 20, 30])
    Return: array([0.0, 0.5, 1.0])
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return values

Visible browser tests run here when available.

Testcases2 visible / 3 hidden categories

Simple increasing vector

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

Expectednp.array([0.0, 0.5, 1.0])

Values are placed proportionally between the min and max.

Constant values

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

Expectednp.array([0.0, 0.0, 0.0])

A zero denominator would be invalid, so constant data maps to zeros.

Hidden Test Categories

2D inputnegative valuesfloat input