Explore how computer systems store, manipulate, and convert various data types like text, images, and videos through integrated software and hardware. Gain insights into accessible data representation.

Computer systems do not understand human instructions, nor can they perceive real-life data in its raw form. Therefore, computer systems require a way to store and represent information in an accessible way. They use software and hardware components combined to help retrieve information, store it, manipulate it, and convert it back to a human-accessible format. If you want to learn how computer systems perform complex tasks such as storing and manipulating textual data, videos, music, images of your cat, and much more, this course is meant for you.

Information Representation in Computer Systems

# Introduction to NumPy

Sometimes, we need a data structure called arrays to represent a group of the same data type together. For this purpose, in Python, we can either use a list or use NumPy arrays. NumPy is a library for the Python programming language that adds support for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions to operate on these arrays. To use NumPy, you will need to import it, as shown in the code snippet below.

You can create an uninitialized array using the  `empty(shape, type)` function. Alternatively, you can create an array of zeros or ones by using the `zeros(shape, type)` or `ones(shape, type)` function, respectively.

For creating a  one-dimensional array, the argument `shape` has to be equal to the length of the array.

import numpy as np

#creating an uninitialised array
arr = np.empty((2), dtype = int)
print("Empty array:", arr)

#creating an array of zeros
arr1 = np.zeros((3), dtype = int)
print("Array of zeros:", arr1)

#creating an array of ones
arr2 = np.ones((7), dtype = int)
print("Array of ones:", arr2)

For creating a two-dimensional or an N-dimensional array, the argument `shape` has to encompass all the dimensions, as shown in the code snippet below. Here, we are creating a two-dimensional and three-dimensional array that is initialized by zeros. We can use the `shape` function later to retrieve the shape of the array, as shown in line 6 and line 11. 

In this lesson you will learn how to use NumPy to represent large, multi-dimensional arrays and matrices.

Computer basics

Hardware and Software Components of a Computer

Data Representation in Python

Image, Audio, and Video Representation in Python

Data Compression

Representing Data with NumPy

Introduction to NumPy

NumPy arrays