Python Simplified Video Navigator

Updated: Feb 20, 2023

Learn Python step by step by following my series of video tutorials (and soon - detailed articles). I'll keep updating this post with new, logically organized videos so please stay tunned! :)

Table of Content

Please refer to this table of content to navigate faster through this post:

Run Python for the first time
Basic data types and variables
Advanced data types
Operators
Control flow operations
Functions
Classes
Important Python Libraries
More Python Libraries

STEP 1: Run Python for the first time!

There are several ways in which we can access and run Python.

We can install Python on our computer and run it locally. One way we can do is is by installing an additional piece of software called Anaconda. It helps us manage both our working environments and the Python libraries we are using. If you'd like to find out more about it, please checkout this video of mine:

Anaconda Beginners Guide for Linux and Windows

Alternatively, we can run Python without even installing it! we can simply use a cloud service such as Google Colab, Jupyter Lab, Kaggle, Wayscript. Your code is accessible from any computer, and you are not limited by the processing powers of your local machine!

In the following tutorial I demonstrate how to work with Google Colab and I discuss it's similarities and differences with Jupyter Notebook, which runs locally.

Coding Software - Jupyter Notebook vs Google Colab

STEP 2: Basic Data Types and Variables

In Python, we have 4 basic built-in data types:

integers - represent whole numbers
floating point numbers - represent fractions or numbers with a decimal point
strings - represent text
boolean - can be either of two binary options: 0 or 1, True or False.

you can find out more about those basic data types in the videos bellow:

Python Data Types and Variables - PART 1

Naming Python Variables - PART 2

STEP 3: Advanced Data Types

Python also deals with more complex build-in data types, such as:

Tuples:


# those are tuples
width, height = (1920, 1080)
width, height = 1920, 1080

Lists:


# those are lists
groceries = ["bananas", "apples", "sugar"]
grades = [100, 50, 78, 95]

Dictionaries:


# this is a dictionary
exam_score = {"Mariya": 100, "Batman": 78, "Spongebob": 35}

Unfortunately, I do not have any dedicated tutorials for those data types at this point of time.

Will update this section of the post as soon as I film this tutorial.

Complex Python Data Types

TO BE FILMED... no Python Simplified tutorial yet!

STEP 4: Python Operators

Operators are special characters that symbolize mathematic and logical operations in Python. Operators are often used as a much shorter alternative to functions, for example:


addition_operator = 5 + 4
addition_function = add(5,4)

In both cases - the result is 9. However, with operators we only use a single character (+) while the function instance requires 6 of them.

You can find the full list of operators and their corresponding functions in the Python documentation:

https://docs.python.org/3/library/operator.html#mapping-operators-to-functions

Python Operators

TO BE FILMED... no Python Simplified tutorial yet!

STEP 5: Python Control Flow Operations

In most cases, we would like our program to perform operations in a sequence. We write one command below the other to create this sequence, and when it's time to execute our program - we do it one line of code at a time. However, in some cases, we would like to halt the sequential execution of you program by using conditional statements and loops, or in other words - control flow operations.

conditional statements / if statements: TO BE FILMED
while loops: TO BE FILMED
for loops: we use "for loops" for repetition as well as iteration over a collection of items. If you'd like to find out more about "for loops" please see my detailed tutorial below:

Python For Loops

STEP 6: Python Functions

functions help us avoid repetition and add modularity to our code. If you'd like to find our more about functions, please checkout my tutorial below:

Python Functions

STEP 7: Python Classes

classes help us combine data with functionality to create something called an "object".

Objects consist of 2 main components:

attributes - bits of data related to the object.
methods - actions or tasks that the object can perform.

for example, the attributes of a motorcycle object are: colour, speed and engine capacity.

while the methods would be: drive, speed up and stop.

To learn more about classes please checkout my details tutorial with lots of visual examples:

Python Classes and OOP

Please keep in mind that it may take you a bit of time to get comfortable with classes. They're a bit more complex than functions and require a bit of practice.

Luckily, I have a few very handy exercises related to classes and OOP which will help you with this task:

Python Classes Inheritance and Private Class Members

Python OOP Exercise with OpenCV

STEP 8: Important Python Libraries

Python offers an overwhelming number of libraries we can work with. However, some common libraries to start with are:

NumPy - a library that deals with numeric data structures and operations.
Pandas - a library that deals with organizing and manipulating tables of data.
Matplotlib - a library that deals with plotting professional graphs.

All those libraries are covered extensively on my channel, and here's a list of videos which will help you get up to speed.

STEP 8.1: NumPy

Ultimate Guide to NumPy Arrays - PART 1

Ultimate Guide to NumPy Operations - PART 2

Fun NumPy Exercise

STEP 8.2: Pandas

Basic Guide to Pandas

Much Better Web Scraping with Pandas

Plot Graphs with Pandas

STEP 8.3: Matplotlib

Plot Google Trends Graphs with Matplotlib and Pandas

STEP 9: More Python Libraries

if you reached this step - it means you already know the basics of Python and can move on with exploring it to the fullest!

Depending on your interests, I recommend you to checkout the following libraries:

Web Applications:

Flask
Django
Anvil
Brython

Web Scraping and Automation:

Selenium (my all times favourite!)
Beautiful Soup
Mechanical Soup
Scrapy

Image Processing:

Pillow (PIL)
OpenCV (cv2)
Turtle
Matplotlib (not just for graphs! can also process images!)

Artificial Intelligence and Machine Learning:

TensorFlow
Pytorch (my all times favourite!)
Scikit Learn

And many more...

You can find tutorials about most of those libraries on my channel. Here are a few examples

STEP 9.1: GUI and Desktop Applications

Advanced GUI App with Tkinter and SQLite

From GUI App to Real Software with Pyinstaller and Inno Setup

Simple Greeting App with Kivy

Mobile App with KivyMD - PART 1

Mobile App with KivyMD - PART 2

Convert Python App to Android .apk with WINDOWS

Simple Video Game with Pygame

Simple PDF Extract App with Tkinter

Advanced PDF Image Extracting App with Tkinter

Simple Trivia Game with PyQT5 - PART 1

Simple Trivia Game with PyQT5 - PART 2

Convert Trivia App from .PY to .EXE - PART 3

Simple GUI App with Dear PyGUI

STEP 9.2: Web Applications

Simple Greeting Web Application with Flask

Advanced Web Application with Flask and Sqlite

Simple Calculator Web App with Anvil

STEP 9.3: Web Scraping

A Full Guide to Selenium Bots - Automating Twitter

Web Scraping Instagram with Selenium

Web Scraping Facebook with Selenium

you can find this tutorial on Rumble if YouTube doesn't allow you to see it: https://rumble.com/v2919ni-web-scraping-facebook-with-selenium.html