Shuffling arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this numpy shuffle code?

Consider the following code that shuffles a numpy array. What will be the output?

NumPy

import numpy as np
np.random.seed(0)
arr = np.array([1, 2, 3, 4, 5])
np.random.shuffle(arr)
print(arr.tolist())

A [2, 5, 1, 4, 3]

B [4, 1, 3, 5, 2]

C [5, 4, 1, 3, 2]

D [3, 5, 2, 4, 1]

💡 Hint

Remember that np.random.shuffle shuffles the array in place and the seed fixes the random order.

With np.random.seed(0), the shuffle order is fixed. The array [1, 2, 3, 4, 5] becomes [4, 1, 3, 5, 2] after shuffling.

❓ data_output

intermediate

How many elements remain in the array after shuffling?

Given this code, how many elements does the array arr have after shuffling?

NumPy

import numpy as np
arr = np.arange(10)
np.random.shuffle(arr)
print(len(arr))

A 0

B 9

C 10

D 1

💡 Hint

Shuffling changes order but not size.

Shuffling an array does not remove or add elements. The length remains the same.

🔧 Debug

advanced

What error does this code raise?

What error will this code raise when trying to shuffle a tuple?

NumPy

import numpy as np
t = (1, 2, 3, 4)
np.random.shuffle(t)

A TypeError: 'tuple' object does not support item assignment

B ValueError: cannot shuffle immutable sequence

C AttributeError: 'tuple' has no attribute 'shuffle'

D No error, it shuffles the tuple

💡 Hint

Think about whether tuples can be changed in place.

Tuples are immutable, so trying to shuffle them (which changes order in place) raises a TypeError about item assignment.

🚀 Application

advanced

Which code correctly shuffles rows of a 2D numpy array?

You have a 2D numpy array representing data rows. Which code correctly shuffles the rows but keeps columns intact?

NumPy

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

A np.random.shuffle(arr)

B arr = np.random.shuffle(arr.T)

C np.random.permutation(arr)

D arr = np.random.shuffle(arr)

💡 Hint

Remember np.random.shuffle shuffles in place and returns None.

np.random.shuffle(arr) shuffles rows in place. Option A assigns None to arr, losing data. Option A returns a shuffled copy but does not assign it back. Option A tries to shuffle the transpose, which is not the intended behavior.

🧠 Conceptual

expert

Why does np.random.shuffle behave differently on multi-dimensional arrays compared to np.random.permutation?

Choose the best explanation for the difference in behavior between np.random.shuffle and np.random.permutation when applied to multi-dimensional numpy arrays.

A <code>np.random.shuffle</code> only works on 1D arrays, while <code>np.random.permutation</code> works on any dimension.

B <code>np.random.shuffle</code> shuffles all axes randomly, while <code>np.random.permutation</code> only shuffles the first axis.

C <code>np.random.shuffle</code> returns a new shuffled array, while <code>np.random.permutation</code> shuffles in place.

D <code>np.random.shuffle</code> shuffles only the first axis in place, while <code>np.random.permutation</code> returns a new shuffled copy of the entire array.

💡 Hint

Think about in-place vs returning new arrays and which axis is shuffled.

np.random.shuffle shuffles the array along the first axis in place, meaning rows get reordered but columns stay intact. np.random.permutation returns a new shuffled copy of the entire array along the first axis without modifying the original.