np.any() and np.all() in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.any() on a 2D array

What is the output of this code snippet?

NumPy

import numpy as np
arr = np.array([[False, False], [False, True]])
result = np.any(arr)
print(result)

A True

B False

C [False False False True]

D [False False False False]

💡 Hint

np.any() returns True if any element in the array is True.

The array contains one True value. np.any() checks if any element is True and returns a single boolean.

❓ Predict Output

intermediate

Using np.all() with axis parameter

What is the output of this code?

NumPy

import numpy as np
arr = np.array([[True, False, True], [True, True, True]])
result = np.all(arr, axis=1)
print(result)

A [True True]

B [True False]

C [False True]

D [False False]

💡 Hint

np.all() with axis=1 checks if all elements in each row are True.

First row has a False, so result is False. Second row all True, so result is True.

❓ data_output

advanced

Result shape after np.any() with axis

Given this array, what is the shape of the result after applying np.any(arr, axis=0)?

NumPy

import numpy as np
arr = np.array([[True, False, True], [False, False, True], [True, True, False]])
result = np.any(arr, axis=0)
print(result.shape)

A (3, 1)

B (1, 3)

C (2,)

D (3,)

💡 Hint

np.any with axis=0 reduces rows, keeping columns dimension.

Original shape is (3,3). Applying np.any with axis=0 returns one boolean per column, shape (3,).

🔧 Debug

advanced

Identify the error in np.all() usage

What error will this code raise?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.all(arr, axis=1)
print(result)

A AxisError: axis 1 is out of bounds for array of dimension 1

B TypeError: unsupported operand type(s) for all()

C No error, prints True

D ValueError: invalid axis value

💡 Hint

Check the dimension of the array and the axis parameter.

The array is 1D, so axis=1 is invalid and causes AxisError.

🚀 Application

expert

Filter rows where all values are positive

Given this 2D array, which code snippet correctly filters rows where all values are positive?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [-1, 5, 6], [4, 0, 7], [8, 9, 10]])

A filtered = arr[np.any(arr > 0, axis=1)]

B filtered = arr[np.all(arr > 0, axis=1)]

C filtered = arr[np.all(arr >= 0, axis=0)]

D filtered = arr[np.any(arr >= 0, axis=0)]

💡 Hint

Use np.all() to check all elements in each row are positive.

Option B correctly selects rows where every element is greater than 0.

Option B selects rows with any positive value, which is incorrect.

Options C and D use axis=0, filtering columns, not rows.