<h2>Introduction to the course</h2><p>NumPy is a powerful library for the Python programming language that provides support for large, multi-dimensional arrays and matrices of numerical data, as well as a large collection of mathematical functions to operate on these arrays. It is one of the fundamental packages for scientific computing with Python.</p><h2>Who is this course for?</h2><p>NumPy is primarily designed for scientific computing and data science. It is a powerful tool for numerical operations and is widely used in these fields to perform a variety of tasks which we will be covering throughout the course.</p>

NumPy

<h2>What does this lesson cover?</h2><p>In order to access the shape of an array, developers can access the array’s shape attribute. The shape attribute is the focus of the lesson, particularly when referring to shaping and then we transition to reshaping.</p>

Shaping and Reshaping

<h2>What does this lesson cover?</h2><p>There is some confusion when it comes to Random Numbers. So our first mission in this lesson will be to uncover those myths. Then we will slowly introduce the concept of NumPy Random, Random Floats, and Random Arrays.</p><p>The lesson comes equipt with lesson challenges and code examples.</p>

NumPy Random

<h2>What does this lesson cover?</h2><p>To work with arrays, we will first and foremost need to create them.</p><p>In this section, we learn three different functions that can be used to create arrays with NumPy. To help developers better comprehend this important concept, the lesson comes equipt with quizzes and standard code examples.</p>

Creating Arrays

<h2>What does this lesson cover?</h2><p>The best way to define a concept or term is by looking at its benefits and where we&#x27;ll need it.</p><p>That&#x27;s what we will be focusing on in this section.</p><p>By covering the benefits, NumPy has when compared with regular Python lists, we will slowly define and understand its usage and characteristics.</p>

Introduction

<h2>What does this lesson cover?</h2><p>To join arrays, developers can use the numpy.concatenate() method. The method carries a list of arrays and an axis along which to concatenate them.</p><p>In this lesson we will be diving into the topic further with lesson quizzes and code examples all the way.</p>

Joining and Splitting 

<h2>What does this lesson cover?</h2><p>Let&#x27;s refer to data distribution in NumPy, specifically, Normal or Gaussian Distribution.</p><p>We will introduce this concept thoroughly with a seperate lesson for the most famous distribution in NumPy.</p>

Data Distribution

<h2>What does this lesson cover?</h2><p>Array indexing in NumPy refers to the process of accessing and manipulating specific elements or sub-arrays within a larger array. In this lesson, we will be learning all about the methods NumPy offers for array indexing, and to better help us learn the syntax and structure of the given methods, we will be using code examples and lesson quizzes.</p>

Array Indexing

<h2>What does this lesson cover?</h2><p>NumPy arrays offer several benefits over lists in Python. This lesson assesses those benefits we will then get into other concepts, such as the fixed data type when referring to arrays, n-dimensional arrays, and so on.</p>

Core Concepts

Lesson Challenge

<h2>What does this lesson cover?</h2><p>Developers have the ability to search for and sort values in a NumPy array. This section will discuss the NumPy functions that will help us achieve that.</p><p>With the help of standard code examples and quizzes, we will finalize the lesson and help you put your knowledge to practice.</p>

Searching and Sorting

<h2>What does this lesson cover?</h2><p>Array slicing in NumPy refers to the process of extracting a specific subset of elements from an array. In NumPy, arrays can be sliced using the colon (:) operator, not unlike slicing in Python.</p>

Array Slicing

<h2>What does this lesson cover?</h2><p>Although installing NumPy is pretty easy, there are a couple of methods developers can choose from. For example, you could install NumPy by using the package manager pip, but that&#x27;s not the only way and we will be uncovering the rest in this lesson.</p>

Installation

<p>NumPy arrays can be indexed and sliced in the same way as Python lists and other sequences. In this lesson, we will be referring to <strong>Array Indexing</strong>.</p>

<p><strong>Definition: Array Indexing</strong> refers to the process of accessing array elements with the help of the given array element’s index number.</p>

<p>Since NumPy array indexes begin at <strong><slot>0</slot></strong>, the first element has an index <strong><slot>0</slot></strong>, the second has an index <strong><slot>1</slot></strong>, and so on.</p>

<p>Let’s take a look at a few simple examples:</p>

<p>In the code snippet above, we have a 1-dimensional array with three elements, as seen in the second line of code. Now, we want to access the second element (<strong><samp>2</samp></strong>); the element&#x27;s index is <samp><slot><strong>1</strong> </slot></samp>since indexing begins from <strong><slot>0</slot></strong>. The last line of code depicts the syntax of NumPy array indexing by specifying the array name and the element index in square brackets ( <strong><samp>[]</samp></strong> ).</p>

Write Missing

<p>Get the first element of the array.</p>

Indexing with 2-dimensional arrays

<p>Now that we’ve covered the simple examples, let’s get into more complex NumPy array indexing.</p>

<p>Assume we have the given code above, which represents a 2-dimensional array with the (3,3) shape, it will look like this:</p>

<p>With the help of array indexing, let us now access an element located in the first row and second column. The index of this element is <strong><samp>[0,1]</samp></strong>.</p>

<p>The output will be<samp> <strong>2</strong></samp>.</p>

<p>We can even do simple addition with elements from the above-given array, like so:</p>

<p>The output will be <strong><samp>6</samp></strong>.</p>

<p>Add the array element located in the second row, first column to the element in the third row, third column.</p>

Negative indexing

<p>To access the last element of your array, developers could use the <strong>negative indexing </strong>method.</p>

<p>Let’s say you want to print the last element from the following array:</p>

<p>The output will be <strong><samp>9</samp></strong>.</p>

<h2>What does this section cover?</h2><p>In this section we will be introducing and defining the use and need of NumPy in applications. We will look over the core concepts that NumPy has to offer before finally getting into the installation process which is swift and fast as usual.</p>

Get Started

<h2>What does this section cover?</h2><p>A NumPy array is a multi-dimensional array of homogeneous data, but we&#x27;ve covered this definition more or less in previous lessons. The simpler concepts regarding arrays are crossed out, and in this section, we will be working towards enhancing our knowledge and upping the intensity.</p>

Arrays

<h2>What does this section cover?</h2><p>NumPy provides a wide range of mathematical and logical operations that can be performed on arrays. This section will cover shaping, reshaping, joining, splitting, searching, and sorting. The lessons will come equipt with standard code examples depicting relevant syntax and structure of new methods used alongside lesson challenges to better guide us.</p>

Array Operations

<h2>What does this section cover?</h2><p>The NumPy library provides the probability distributions we will discuss in this section. Alongside a quick introduction to distributions as a whole.</p><p>Finally, you will use your newly acquired knowledge to solve lesson quizzes and move forward.</p>

Overview

Array Indexing

Indexing with 2-dimensional arrays

Negative indexing