Foundations Updated 2026-09 View as Markdown

Numpy Arrays

Numpy arrays are great alternatives to Python Lists.

Getting started#

Numpy arrays are great alternatives to Python Lists. Some of the key advantages of Numpy arrays are that they are fast, easy to work with, and give users the opportunity to perform calculations across entire arrays.

In the following example, you will first create two Python lists. Then, you will import the numpy package and create numpy arrays out of the newly created lists.

# Create 2 new lists height and weight
height = [1.87,  1.87, 1.82, 1.91, 1.90, 1.85]
weight = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45]

# Import the numpy package as np
import numpy as np

# Create 2 numpy arrays from height and weight
np_height = np.array(height)
np_weight = np.array(weight)

Print out the type of np_height

print(type(np_height))

Element-wise calculations#

Now we can perform element-wise calculations on height and weight. For example, you could take all 6 of the height and weight observations above, and calculate the BMI for each observation with a single equation. These operations are very fast and computationally efficient. They are particularly helpful when you have 1000s of observations in your data.