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.

Practice

1. What does np.random.shuffle() do to a NumPy array?

easy

A. Randomly rearranges the elements of the array in-place

B. Sorts the array elements in ascending order

C. Creates a new shuffled copy of the array without changing the original

D. Reverses the order of elements in the array

Solution

1. Step 1: Understand the function purpose

   np.random.shuffle() is designed to mix elements randomly within the array.

2. Step 2: Check how it modifies the array

   This function changes the original array directly (in-place), not creating a new one.

3. Final Answer:

   Randomly rearranges the elements of the array in-place -> Option A

4. Quick Check:

   Shuffle = random in-place rearrangement [OK]

Hint: Shuffle mixes elements inside the same array [OK]

Common Mistakes:

• Thinking shuffle returns a new array
• Confusing shuffle with sorting
• Assuming shuffle reverses elements

2. Which of the following is the correct syntax to shuffle a 1D NumPy array named arr?

easy

A. np.random.shuffle(arr, axis=1)

B. np.shuffle(arr)

C. arr.shuffle()

D. np.random.shuffle(arr)

Solution

1. Step 1: Identify the correct function call

   The shuffle function is inside the np.random module, so it must be called as np.random.shuffle().

2. Step 2: Check the parameters

   It takes the array as the only argument. The axis parameter is not valid for 1D arrays.

3. Final Answer:

   np.random.shuffle(arr) -> Option D

4. Quick Check:

   Correct shuffle syntax = np.random.shuffle(array) [OK]

Hint: Use np.random.shuffle(array) to shuffle in-place [OK]

Common Mistakes:

• Using np.shuffle instead of np.random.shuffle
• Calling shuffle as a method on the array
• Passing axis parameter incorrectly

3. What will be the output of the following code?

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

medium

A. [1 2 3 4 5]

B. [5 4 3 2 1]

C. A randomly shuffled version of [1 2 3 4 5]

D. Error because shuffle returns a new array

Solution

1. Step 1: Understand shuffle effect on array

   np.random.shuffle(arr) rearranges elements randomly in-place, so arr changes order.

2. Step 2: Predict output of print

   Since shuffle is random, the printed array will be a shuffled version of the original, not sorted or reversed.

3. Final Answer:

   A randomly shuffled version of [1 2 3 4 5] -> Option C

4. Quick Check:

   Shuffle output = random order of original array [OK]

Hint: Shuffle changes order randomly, output varies each run [OK]

Common Mistakes:

• Expecting original order after shuffle
• Thinking shuffle returns a new array
• Assuming shuffle sorts or reverses

4. Identify the error in the following code snippet:

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

medium

A. shuffle does not accept an axis argument

B. arr must be 1D to shuffle

C. shuffle returns a new array, so assignment is needed

D. np.random.shuffle cannot shuffle 2D arrays

Solution

1. Step 1: Check shuffle function parameters

   np.random.shuffle() only accepts the array argument; it does not have an axis parameter.

2. Step 2: Understand shuffle behavior on 2D arrays

   Shuffle works in-place on the first axis of multi-dimensional arrays by default, no axis argument needed.

3. Final Answer:

   shuffle does not accept an axis argument -> Option A

4. Quick Check:

   shuffle axis param invalid = error [OK]

Hint: np.random.shuffle has no axis parameter [OK]

Common Mistakes:

• Passing axis argument to shuffle
• Thinking shuffle returns a new array
• Believing shuffle only works on 1D arrays

5. You have a 2D NumPy array representing 5 samples with 3 features each:

data = np.array([[10, 20, 30],
                 [40, 50, 60],
                 [70, 80, 90],
                 [15, 25, 35],
                 [45, 55, 65]])

You want to shuffle the samples (rows) but keep the feature order intact. Which code correctly does this?

hard

A. np.random.shuffle(data.T)

B. np.random.shuffle(data)

C. np.random.permutation(data)

D. np.random.shuffle(data, axis=1)

Solution

1. Step 1: Understand shuffle on 2D arrays

   np.random.shuffle() shuffles along the first axis (rows) in-place, which is what we want.

2. Step 2: Evaluate other options

   np.random.shuffle(data.T) shuffles columns (wrong axis), np.random.permutation(data) returns a new shuffled array (not in-place), np.random.shuffle(data, axis=1) is invalid (axis param not accepted).

3. Final Answer:

   np.random.shuffle(data) -> Option B

4. Quick Check:

   Shuffle rows in-place = np.random.shuffle(data) [OK]

Hint: Shuffle rows by calling np.random.shuffle on 2D array [OK]

Common Mistakes:

• Trying to shuffle columns by transposing
• Expecting shuffle to return a new array
• Passing axis parameter to shuffle