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.