NumPy

As an example, we take a closer look at NumPy (numerical Python), which provides an efficient way to store and manipulate multi-dimensional dense arrays in Python.

The important features of NumPy are:

• It allows efficient storage and manipulation of vectors, matrices, and higher-dimensional datasets.

• It provides a readable and efficient syntax for operating on this data, from simple element-wise arithmetic to more complicated linear algebraic operations.

In the simplest case, NumPy arrays look a lot like Python lists. For example, here is an array containing the range of numbers 1 to 9 (compare this with Python’s built-in range()):

import numpy as np
x = np.arange(1, 10)
x

array([1, 2, 3, 4, 5, 6, 7, 8, 9])

NumPy’s arrays offer both efficient storage of data, as well as efficient element-wise operations on the data. For example, to square each element of the array, we can apply the “**” operator to the array directly:

x = np.arange(1, 10)
x ** 2

array([ 1,  4,  9, 16, 25, 36, 49, 64, 81])

Compare this with the much more verbose Python-style list comprehension for the same result:

x = range(1, 10)
[val ** 2 for val in x]

[1, 4, 9, 16, 25, 36, 49, 64, 81]

Unlike Python lists (which are limited to one dimension), NumPy arrays can be multi-dimensional. A 2-dimensional array of size 2 x 3, composed of 4-byte integer elements:

x = np.array([[1, 2, 3], [4, 5, 6]], np.int32)

print("Type:", type(x), "\n" "Shape:", x.shape, "\n" "Data Type:", x.dtype)

#x.shape

#x.dtype

Type: <class 'numpy.ndarray'> 
Shape: (2, 3) 
Data Type: int32