Array shapes and dimensions in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the shape of the resulting array?

Consider the following code that creates a NumPy array. What is the shape of arr after running this code?

Data Analysis Python

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

A (3,)

B (3, 2)

C (2, 3)

D (6,)

💡 Hint

Think about how many rows and columns the array has.

The array has 2 rows and 3 columns, so its shape is (2, 3).

❓ data_output

intermediate

What is the number of dimensions of this array?

Given the code below, what is the value of arr.ndim?

Data Analysis Python

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

A 3

B 2

C 1

D 4

💡 Hint

Count how many levels of nested lists are inside the array.

The array has three levels of nested lists, so it is 3-dimensional.

🔧 Debug

advanced

Why does this reshape operation fail?

The code below tries to reshape an array but raises an error. What is the reason?

Data Analysis Python

import numpy as np
arr = np.arange(10)
arr_reshaped = arr.reshape(3, 4)

A The array must be 2-dimensional before reshaping.

B The reshape method requires a tuple, not separate arguments.

C The reshape method only works on square arrays.

D The total number of elements does not match the new shape.

💡 Hint

Check how many elements are in the original array and the new shape.

The original array has 10 elements, but the new shape requires 12 elements (3*4), so reshape fails.

🚀 Application

advanced

How to flatten a 3D array into 1D?

Given a 3D NumPy array, which code correctly converts it into a 1D array?

Data Analysis Python

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

A arr.flatten()

B arr.reshape(2, 4)

C arr.reshape(1, 8)

D arr.reshape(-1, 1)

💡 Hint

Flatten means to make all elements in one dimension.

The flatten() method returns a copy of the array collapsed into one dimension.

🧠 Conceptual

expert

What is the shape of the array after this operation?

Consider this code snippet. What is the shape of arr_new after execution?

Data Analysis Python

import numpy as np
arr = np.arange(24).reshape(2, 3, 4)
arr_new = arr.swapaxes(1, 2)

A (3, 2, 4)

B (2, 4, 3)

C (4, 3, 2)

D (2, 3, 4)

💡 Hint

swapaxes swaps two axes positions in the shape tuple.

Original shape is (2, 3, 4). Swapping axes 1 and 2 changes shape to (2, 4, 3).