Python Numpy for Multi-Dimensional Arrays

Problem

Python is enriched with various libraries with built-in functions for various scientific computations. NumPy is one of the popular and widely used libraries. Do you know how to install NumPy and use its functions with multi-dimensional arrays? If not, let’s check out NumPy together in this NumPy tutorial.

Solution

NumPy, aka Numerical Python, works well with multi-dimensional arrays and matrices enabling users to perform quick and efficient numerical computations. A few key features of NumPy are:

• Is an open source module.
• Supports homogenous multi-dimensional arrays.
• Supports data science machine learning computations for data analysis manipulations.
• Integrates with Python libraries such as SciPy and Pandas.
• Requires users to perform complex tasks using its built-in functions with few lines of code. Its mathematic functions include linear algebra, Fourier transforms, matrix operations (sum, multiplications), etc.
• Is memory efficient for handling large datasets. It supports indexing and slicing techniques to work with the relevant data.

To install NumPy in Python, we can run the pip command – pip install numpy, as shown below.

Usually, it is best practice to import the NumPy module with the popular shortcut np for code readability:

import numpy as np

Now, we can use the Numpy modules with np alias as np.<function\object>. You do not need to import it by calling every module. You can import it once and use it subsequently in the same Python file\session.

Let’s explore the NumPy module functions with various examples.

Exploring Single and Two-dimensional NumPy Array

A Python NumPy array can store multiple homogenous values. A NumPy array can be n-directional, such as a one-dimensional or two-dimensional array.

NumPy Array Examples

Description	Sample Syntax and Output
One-dimensional array Function: np.array()	my1darray = np.array([15.9,16.9,17.9]) print(my1darray)
Two-dimensional array	my2darray = np.array([[101, 202, 303], [404, 505, 606], [707, 808, 909]]) print(my2darray)
Create a NumPy array	num1 = [100,989,6589] array1= np.array(num1) print(array1)
Three-dimensional array	arr3d = np.array([[[1.1, 2.2, 3.3, 4.4], [5.5, 6.6, 7.7, 8.8], [9.9, 10.10, 11.11, 12.12]], [[13.13, 14.14, 15.15, 16.16], [17.17, 18.18, 19.19, 20.20], [21.21, 22.22, 23.23, 24.24]]]) print(arr3d)
Create a 1D array and initialize it with zeros. Function: np.zeros()	ar1 = np.zeros(4) print(ar1) [0. 0. 0. 0.]
Create a 2D array and initialize it with zeros Function: np.zeros()	ar2= np.zeros((3,4)) print(ar2) [[0. 0. 0. 0.]  [0. 0. 0. 0.]  [0. 0. 0. 0.]]
Create a 1D array and initialize it with ones Function: np.ones()	ar3= np.ones(5) print(ar3) [1. 1. 1. 1. 1.]
Initialize the array within a specified number interval Function: np.arange()	array1 = np.arange(10) print(“Using np.arange(10):”, array1) [0 1 2 3 4 5 6 7 8 9]
Use the starting, step and end of interval value in the array Function: np.arrange()	#start value 2 #step value 2 #End value 20 array1 = np.arange(2,20,2) print(array1) [ 2 4 6 8 10 12 14 16 18]
Using the negative starting value in the np.arrange()	print(np.arange(-3, 3, 0.5)) [-3. -2.5 -2. -1.5 -1. -0.5 0. 0.5 1. 1.5 2. 2.5]
Create an array of evenly numbered sample values Function: np.linspace()	print( np.linspace(0,2,9) ) [0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]
Create an array with random numbers. Function: np.random.rand() Default Data Type: Float	print( np.random.rand(7)) [0.93142177 0.96102912 0.45886488 0.54519262 0.19622125 0.54321376 0.02658373]
Create an integer array 1D with random numbers Function: np.random.randint()	#total number of elements 5 #Lowest value 1 #Highest value 10 print( np.random.randint(1, 10, size=5)) [5 1 4 7 8]
Create an integer array 2D with random numbers Function: np.random.randint()	#array dimensions 4 *4 #Lowest value 0 #Highest value 50 random2darray = np.random.randint(0, 50, size=(4, 4)) print(random2darray) [[21 0 10 42]  [22 42 40 38]  [34 18 1 25]  [27 13 34 41]]
Create an empty array Function: np.empty() #does not initialize the array	earray = np.empty(5) print(earray _array) Returns garbage value stored in the memory area
Create an array and initialize it with a constant value. Function np.full()	# array dimension 2*2 #Constant value 7 a = np.full((2,2),7) print(a) [[7 7]  [7 7]]
Creates a 2D array with ones on the diagonal and zeros Function: np.eye()	f = np.eye(5) print(f) [[1. 0. 0. 0. 0.]  [0. 1. 0. 0. 0.]  [0. 0. 1. 0. 0.]  [0. 0. 0. 1. 0.]  [0. 0. 0. 0. 1.]]

NumPY Q&A

Using NumPy to Perform Various Mathematical Operations

As discussed earlier, we can perform mathematical operations using built-in mathematical functions. These functions can be categorize into the following categories:

• Arithmetic
• Trigonometry
• Exponential
• Statistical

Let’s explore these functions with various examples.

Next Steps

• Stay tuned for Python tutorials in the upcoming tips.
• Explore more Python programming tips on MSSQLTips:
  • Introduction to Creating Data Visualizations with Python matplotlib
  • Introduction to Creating Interactive Data Visualizations with Python matplotlib in VS Code