Element-wise arithmetic in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does element-wise arithmetic mean in numpy?

It means performing arithmetic operations on each pair of elements from two arrays of the same shape, producing a new array with the results.

beginner

How do you add two numpy arrays element-wise?

You can use the + operator or the numpy.add() function to add two arrays element-wise.

intermediate

What happens if you try element-wise arithmetic on arrays of different shapes?

Numpy tries to broadcast smaller arrays to match the shape of larger ones if possible; otherwise, it raises an error.

beginner

Show how to multiply two numpy arrays element-wise.

Use the * operator or numpy.multiply() to multiply arrays element-wise.

beginner

Why is element-wise arithmetic useful in data science?

It allows fast and simple operations on datasets represented as arrays, like scaling, combining features, or applying formulas to each data point.

What operator performs element-wise addition of two numpy arrays?

A /

B +

C *

D -

The + operator adds corresponding elements of two arrays.

If array A has shape (3, 2) and array B has shape (3, 2), what is the shape of A * B?

A Error

B (2, 3)

C (3, 3)

D (3, 2)

Element-wise multiplication keeps the same shape when arrays have the same shape.

What does numpy do if you add arrays of shapes (3, 1) and (3, 2)?

A Raises an error

B Broadcasts (3, 2) to (3, 1) and adds element-wise

C Broadcasts (3, 1) to (3, 2) and adds element-wise

D Returns a scalar

Numpy broadcasts the smaller array to match the larger shape if compatible.

Which numpy function can be used for element-wise subtraction?

A numpy.subtract()

B numpy.add()

C numpy.multiply()

D numpy.divide()

numpy.subtract() subtracts elements of one array from another element-wise.

What is the result of element-wise division of two arrays?

A A new array with each element divided by the corresponding element

B A scalar value

C An error always

D Concatenation of arrays

Element-wise division divides each element of the first array by the corresponding element of the second.

Explain element-wise arithmetic in numpy and why it is useful.

Think about how operations apply to each element in arrays.

Describe what happens when you perform element-wise operations on arrays of different shapes.

Consider how numpy handles shape differences.