#0098

Column Means Ignoring NaN

EasyNumPy15 min10 XP

Problem

Given a 2D NumPy array, return column means ignoring NaN values.

Why This Matters

NaN-aware aggregation appears in real datasets before imputation, reporting, and feature analysis.

Function Signature

def column_nanmeans(matrix):

Examples

Example 1

Input[[1, nan], [3, 5]]

Output[2, 5]

The NaN value is ignored for the second column mean.

Constraints

Return the exact structure requested.
Handle the edge cases mentioned in the prompt.
Prefer clear logic over clever one-liners.

CodePython

import numpy as np

def column_nanmeans(matrix):
    """
    Input example: [[1, nan], [3, 5]]
    Return: [2, 5]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return matrix

import numpy as np

def column_nanmeans(matrix):
    """
    Input example: [[1, nan], [3, 5]]
    Return: [2, 5]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return matrix

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Input[[1, nan], [3, 5]]

Expected[2, 5]

The NaN value is ignored for the second column mean.

Hidden Test Categories

no NaN valuesmultiple NaNs in one columnfloat input