1D and 2D broadcasting in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is broadcasting in numpy?

Broadcasting is a way numpy allows operations between arrays of different shapes by automatically expanding the smaller array to match the larger one without copying data.

beginner

How does 1D broadcasting work when adding a 1D array to a 2D array?

The 1D array is stretched across the matching dimension of the 2D array so each row or column adds the 1D array element-wise.

intermediate

What shape rules must be met for broadcasting to work?

Starting from the trailing dimensions, the sizes must be equal or one of them must be 1 for each dimension.

intermediate

Example: What happens when you add a (3,1) array to a (1,4) array?

They broadcast to a (3,4) array by stretching the first array across columns and the second across rows, then adding element-wise.

beginner

Why is broadcasting useful in data science?

It allows fast, memory-efficient operations on arrays without explicit loops, making code simpler and faster.

What does numpy broadcasting allow you to do?

A Operate on arrays of different shapes without explicit loops

B Convert arrays to lists automatically

C Sort arrays in place

D Change array data types automatically

Broadcasting lets numpy perform operations on arrays of different shapes by expanding the smaller array automatically.

If you add a (3,) array to a (3,4) array, how does broadcasting work?

A The (3,4) array is reshaped to (3,)

B The (3,) array is broadcast across each row

C Broadcasting is not possible

D The (3,) array is broadcast across each column

Broadcasting works because (3,) is treated as (1,3) and is broadcast across each row of the (3,4) array.

Which shape pair can broadcast together?

A (2,3) and (3,)

B (2,3) and (2,2)

C (3,1) and (4,)

D (3,2) and (3,3)

Shapes (2,3) and (3,) broadcast because (3,) is treated as (1,3) and 1 can be stretched to 2.

What happens if broadcasting rules are not met?

A Operation returns None

B Numpy silently reshapes arrays

C Arrays are converted to lists

D Numpy raises a ValueError

If shapes are incompatible, numpy raises a ValueError explaining the mismatch.

Which numpy function can help check array shapes before broadcasting?

A numpy.shape

B numpy.reshape

C numpy.broadcast_shapes

D numpy.concatenate

numpy.broadcast_shapes returns the broadcasted shape or raises an error if shapes are incompatible.

Explain in your own words how 1D and 2D arrays can be added using broadcasting in numpy.

Think about how numpy matches dimensions from the end and stretches arrays.

Describe the rules numpy uses to decide if two arrays can be broadcast together.

Focus on how numpy compares each dimension starting from the right.