#0036

Select Positive Values

EasyNumPy10 min10 XP

Problem

Given a NumPy array, return a new array containing only values greater than zero, preserving their original order.

Why This Matters

Boolean indexing is one of the first NumPy ideas that feels different from normal Python loops. It lets you filter whole arrays at once.

Function Signature

def positive_values(x):

Examples

Example 1

Inputx = np.array([-2, 5, 0, 3, -1])

Outputarray([5, 3])

Only 5 and 3 are greater than zero. The zero is not positive.

Constraints

Input is a one-dimensional numeric NumPy array.
Return a NumPy array, not a Python list.
Do not sort the values.

CodePython

import numpy as np

def positive_values(x):
    """
    Input example: x = np.array([-2, 5, 0, 3, -1])
    Return: array([5, 3])
    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 positive_values(x):
    """
    Input example: x = np.array([-2, 5, 0, 3, -1])
    Return: array([5, 3])
    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

Mixed signs

Inputnp.array([-2, 5, 0, 3, -1])

Expectednp.array([5, 3])

The boolean mask is [False, True, False, True, False].

No positives

Inputnp.array([-4, 0, -1])

Expectedempty array

No value satisfies x > 0.

Hidden Test Categories

All values positiveFloating point valuesSingle-element arrayLarge array