#0134

Replace NaN With Column Mean

MediumNumPy25 min20 XP

Problem

Given a 2D NumPy array, return a copy where NaN values are replaced by their column mean ignoring NaNs.

Why This Matters

Mean imputation is not always best, but implementing it teaches masks, indexes, and column-wise statistics.

Function Signature

def fill_nan_column_mean(matrix):

Examples

Example 1

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

Output[[1, 5], [3, 5]]

The second column mean ignoring NaN is 5.

Constraints

Return the exact requested structure.
Handle normal edge cases cleanly.
Prefer readable code over clever shortcuts.

CodePython

import numpy as np

def fill_nan_column_mean(matrix):
    """
    Input example: [[1, nan], [3, 5]]
    Return: [[1, 5], [3, 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 fill_nan_column_mean(matrix):
    """
    Input example: [[1, nan], [3, 5]]
    Return: [[1, 5], [3, 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[[1, 5], [3, 5]]

The second column mean ignoring NaN is 5.

Hidden Test Categories

multiple NaNsno NaNsdifferent columns