What is an array?

Some libraries/packages have their on specific version of a list. Pandas as a pd.series which we’ve seen before. Numerical computing libraries like numpy or jax have an array: np.array([1, 2]) , jnp.array([1, 2]) .

Arrays have additional functionality relative to lists which make them helpful. Let’s explore this further through a couple of examples.

Example One

With both a list and an array, we can use python’s built-in function max to compute the maximum value of a list like object.

x1s = [1, 2, 3, 4, 5]
x2s = np.array([1, 2, 3, 4, 5])
print(max(x1s))
print(max(x2s))

However, there is no built-in argmax function, so we have to rely on numpy’s.

x1s = [1, 2, 3, 4, 5]
x2s = np.array([1, 2, 3, 4, 5])
print(np.argmax(x1s))
print(np.argmax(x2s))

Example Two

We’ll sometimes want to compare each element in a list-like structure to a value. For example, let’s say we have a list of p-values corresponding to the coefficients from a regression. With a pure list, we cannot simply check whether each value is significant using a single comparison operator as below.

[0.00030985831158296417,2.9724745921917515e-14, 0.0,] <= 0.05

If we convert the list to an array though, we can use a single comparison operator!

np.array([0.00030985831158296417,2.9724745921917515e-14, 0.0]) <= 0.05