np.power() and np.square() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.power() do in NumPy?

np.power() raises each element of an array to the power of a given exponent. It works element-wise, meaning it applies the power operation to each number in the array separately.

beginner

How is np.square() different from np.power()?

np.square() is a shortcut to square each element of an array (raise to the power of 2). It is equivalent to np.power(array, 2) but simpler and faster for squaring.

beginner

Example: What is the result of np.power([2, 3, 4], 3)?

The result is [8, 27, 64] because each element is raised to the power 3: 2³=8, 3³=27, 4³=64.

intermediate

Why might you choose np.square() over np.power() when squaring numbers?

np.square() is more readable and can be faster because it is optimized specifically for squaring. It makes your code clearer when you only want to square values.

intermediate

Can np.power() handle fractional exponents? Give an example.

Yes, np.power() can handle fractional exponents. For example, np.power([4, 9, 16], 0.5) returns [2., 3., 4.], which are the square roots of the numbers.

What does np.square(array) do?

A Multiplies each element by 2

B Raises each element to the power of 3

C Calculates the square root of each element

D Raises each element to the power of 2

np.square() raises each element of the array to the power of 2.

Which function can raise array elements to any power, including fractional powers?

A np.square()

B np.power()

C np.sqrt()

D np.exp()

np.power() can raise elements to any power, including fractional ones.

What is the output of np.power([1, 2, 3], 0)?

A [1, 1, 1]

B [1, 2, 3]

C [0, 0, 0]

D [1, 4, 9]

Any number raised to the power 0 is 1, so the output is [1, 1, 1].

Which is faster and more readable for squaring an array: np.power(array, 2) or np.square(array)?

A np.power(array, 2)

B Both are equally fast

C np.square(array)

D Neither works for squaring

np.square() is optimized for squaring and is more readable.

What will np.power([8, 27, 64], 1/3) return?

A [2, 3, 4]

B [64, 27, 8]

C [512, 19683, 262144]

D [4, 9, 16]

Raising to the power 1/3 calculates the cube root: 8^(1/3)=2, 27^(1/3)=3, 64^(1/3)=4.

Explain how np.power() and np.square() work and when to use each.

Think about general vs specific use cases for exponentiation.

Give an example of using np.power() with a fractional exponent and explain the result.

Fractional powers relate to roots of numbers.