ndarray as the core data structure in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of ndarray shape and size

What is the output of the following code snippet?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape, arr.size)

A (3, 2) 6

B (2, 3) 6

C (2, 3) 5

D (3, 2) 5

💡 Hint

Shape gives rows and columns, size is total elements.

The array has 2 rows and 3 columns, so shape is (2, 3). Total elements are 6.

❓ data_output

intermediate

Result of ndarray slicing

What is the output of slicing the array below?

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])

A [20 30 40 50]

B [10 20 30]

C [30 40 50]

D [20 30 40]

💡 Hint

Slicing excludes the last index.

Slicing from index 1 to 4 includes elements at positions 1, 2, and 3.

🔧 Debug

advanced

Identify the error in ndarray reshaping

What error does this code raise?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
arr.reshape(2, 3)

A ValueError: cannot reshape array of size 5 into shape (2,3)

B TypeError: reshape argument must be tuple

C IndexError: index out of range

D No error, reshaping works

💡 Hint

Total elements must match new shape.

The array has 5 elements but reshape tries to make shape (2,3) which needs 6 elements.

❓ visualization

advanced

Visualize the effect of ndarray transpose

What is the output of transposing this array?

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
print(arr.T)

A

[[1 3 5]
 [2 4 6]]

B

[[1 2 3]
 [4 5 6]]

C

[[1 2]
 [3 4]
 [5 6]]

D

[[6 5 4]
 [3 2 1]]

💡 Hint

Transpose flips rows and columns.

Original shape is (3,2), transpose shape is (2,3) with rows and columns swapped.

🧠 Conceptual

expert

Understanding ndarray broadcasting rules

Given arrays a with shape (3,1) and b with shape (1,4), what is the shape of a + b?

A (3, 1)

B (1, 1)

C (3, 4)

D (1, 4)

💡 Hint

Broadcasting matches dimensions by expanding singleton dimensions.

Shapes (3,1) and (1,4) broadcast to (3,4) for element-wise addition.