#0170

Filter Rows With a Boolean Mask

EasyNumPy15 min10 XP

Problem

Given a 2D array X, return rows where the first column is greater than threshold.

Why This Matters

Boolean masks are the foundation of vectorized filtering in NumPy, Pandas, and ML preprocessing.

Function Signature

def filter_first_col(X, threshold):

Examples

Example 1

InputX = np.array([[1,10],[5,20],[3,30]]), threshold = 2

Outputnp.array([[5,20],[3,30]])

Rows with first-column values 5 and 3 are kept.

Constraints

Return the exact requested output.
Handle ordinary edge cases.
Prefer simple, interview-readable code.

CodePython

import numpy as np

def filter_first_col(X, threshold):
    """
    Input example: X = np.array([[1,10],[5,20],[3,30]]), threshold = 2
    Return: np.array([[5,20],[3,30]])
    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 filter_first_col(X, threshold):
    """
    Input example: X = np.array([[1,10],[5,20],[3,30]]), threshold = 2
    Return: np.array([[5,20],[3,30]])
    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

InputX = np.array([[1,10],[5,20],[3,30]]), threshold = 2

Expectednp.array([[5,20],[3,30]])

Rows with first-column values 5 and 3 are kept.

Hidden Test Categories

empty or minimal inputduplicates or tieslarger input