Shuffling arrays in NumPy - Mini Project: Build & Apply

Shuffling Arrays with NumPy

📖 Scenario: You are working with a list of student IDs for a class. You want to randomly shuffle the order of these IDs to assign presentation slots fairly.

🎯 Goal: Learn how to shuffle a NumPy array to randomize the order of elements.

📋 What You'll Learn

Create a NumPy array with specific student IDs

Set a random seed for reproducibility

Shuffle the array using NumPy

Print the shuffled array

💡 Why This Matters

🌍 Real World

Randomly shuffling data is useful in experiments, games, and fair assignments like presentations or seating.

💼 Career

Data scientists often shuffle data to prepare for training machine learning models or to randomize samples.

1

Create the NumPy array

Import NumPy as np and create a NumPy array called student_ids with these exact values: 101, 102, 103, 104, 105.

NumPy

# Import numpy as np and create the array student_ids
# Your code here

Hint

Use np.array() to create the array with the given numbers.

2

Set the random seed

Set the random seed to 42 using np.random.seed(42) to make the shuffle results reproducible.

NumPy

import numpy as np
student_ids = np.array([101, 102, 103, 104, 105])
# Set the random seed to 42
# Your code here

Hint

Use np.random.seed(42) before shuffling to get the same random order every time.

3

Shuffle the array

Use np.random.shuffle(student_ids) to shuffle the student_ids array in place.

NumPy

import numpy as np
student_ids = np.array([101, 102, 103, 104, 105])
np.random.seed(42)
# Shuffle the student_ids array
# Your code here

Hint

Call np.random.shuffle() with the array you want to shuffle.

4

Print the shuffled array

Print the student_ids array to see the shuffled order.

NumPy

import numpy as np
student_ids = np.array([101, 102, 103, 104, 105])
np.random.seed(42)
np.random.shuffle(student_ids)
# Print the shuffled student_ids array
# Your code here

Hint

Use print(student_ids) to display the shuffled array.

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