Python: NumPy

Numpy is a Python library for numerical computing. In addition to pandas, it is one of the key pieces in the Python data science toolkit! NumPy can be used to perform a wide variety of mathematical operations on arrays and matrices. Since data science often involves performing operations on arrays and matrices*, NumPy is an integral component in a large majority of data science projects in Python.

Today's learning objectives

  • Understand how to learn and use NumPy.
  • Understand the dimensions of a NumPy array.
  • Perform basic operations using NumPy.

Please watch the following video to learn more about Numpy and understand how it is related to pandas.

NumPy arrays

An array is a data structure that defines the NumPy library1. Similar to a list, a NumPy array contains variables that can be indexed in various ways. However, unlike a list, the elements are all of the same type, referred to as the array dtype. To create a NumPy array, we first create a list (using []) and then convert the list into a NumPy array as shown below.

import numpy as np

#initialize a numpy array
a = np.array([1, 2, 3, 4, 5, 6])

Similar to Python lists, we can access elements of a NumPy array using indexes. To print the first element of a, we use the following array indexing which will return 1.

import numpy as np

#initialize a numpy array
a = np.array([1, 2, 3, 4, 5, 6])
print(a[0])

To initialize a matrix (or a multidimensional ndarray), we can use nested lists (using [[..],[..]]).

import numpy as np

#initialize a multidimensional numpy array
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print(a.shape)

To read more about NumPy arrays, please refer to the official documentation which you can find here.

Blended Learning - Code Academy - Introduction To Numpy (4 hours)

  • Please complete the INTRODUCTION TO NUMPY module in Code Academy which you can find here.

References

1 https://numpy.org/doc/stable/user/absolute_beginners.html
2 https://www.codecademy.com/learn/intro-statistics-numpy/modules/dspath-intro-numpy 

We will recap today's concepts and discuss the assignments at the Q&A, see you at 4pm!