Shuffling arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does shuffling an array mean in data science?

Shuffling an array means rearranging its elements in a random order. It helps to mix data so that patterns or order do not bias analysis or models.

beginner

Which NumPy function is used to shuffle arrays in-place?

The function numpy.random.shuffle() shuffles the elements of an array in-place, meaning it changes the original array order randomly.

intermediate

How does numpy.random.shuffle() behave differently for 1D and 2D arrays?

For 1D arrays, it shuffles all elements randomly. For 2D arrays, it shuffles only the rows, keeping the order of elements within each row unchanged.

beginner

Why is shuffling data important before training machine learning models?

Shuffling prevents the model from learning the order of data, which could cause bias. It ensures the model sees a random mix, improving generalization.

intermediate

What is the difference between numpy.random.shuffle() and numpy.random.permutation()?

shuffle() changes the original array in-place. permutation() returns a new shuffled array, leaving the original unchanged.

What does numpy.random.shuffle() do to a 2D array?

A Shuffles the rows randomly

B Shuffles all elements randomly

C Shuffles the columns randomly

D Does not change the array

For 2D arrays, numpy.random.shuffle() shuffles the rows but keeps the order of elements within each row.

Which function returns a new shuffled array without changing the original?

A numpy.random.shuffle()

B numpy.random.permutation()

C numpy.shuffle()

D numpy.random.sample()

numpy.random.permutation() returns a new shuffled array and leaves the original array unchanged.

Why should you shuffle data before training a model?

A To speed up training

B To increase data accuracy

C To reduce data size

D To prevent bias from data order

Shuffling prevents the model from learning patterns based on data order, reducing bias.

What happens when you use numpy.random.shuffle() on a 1D array?

A The array elements are shuffled randomly

B The array is sorted

C The array is reversed

D Nothing changes

For 1D arrays, numpy.random.shuffle() rearranges elements in random order.

Is numpy.random.shuffle() a pure function (does it return a new array)?

A Yes, it returns a new shuffled array

B Yes, it returns a sorted array

C No, it shuffles the array in-place

D No, it only returns the indices

numpy.random.shuffle() modifies the original array directly and does not return a new array.

Explain how to shuffle a 1D NumPy array and why shuffling is useful in data science.

Think about randomizing data before training models.

Describe the difference between numpy.random.shuffle() and numpy.random.permutation() with examples.

One modifies the original, the other creates a new 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