Python Programming For Beginners – FREE Python Tutorials

Python Tutorial Series for beginners with hands-on Video Tutorials:

We live in an era full of awesome and powerful programs. As such, there are hundreds of programming languages which if we had to study and master all of them, would take us our lifetime just to scratch the surface.

What exactly do programming languages do? The answer to this is that they allow us to give instructions to a computer in a language that the computer understands. Each programming language has its features, purpose, benefits, and drawbacks. However, many have some commonalities between them.

The Python programming language is just one of the hundreds of programming languages out there. Learn Python from scratch with this informative hands-on free Python Training course.

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Python Programming Tutorials

Python Tutorial Series

Tutorial #1: Python Introduction and Installation
Tutorial #2: Python Variables
Tutorial #3: Python Data Types

Tutorial #4: Python Operator
Tutorial #5: Python Conditional Statements: if_else, elif, nested if statements
Tutorial #6: Looping in Python

Tutorial #7: Python Control Statements
Tutorial #8: Python Functions
Tutorial #9: Input-Output and Files in Python

Tutorial #10: Python OOPs concept
Tutorial #11: Python DateTime
Tutorial #12: Python String Functions

Tutorial #13: Python File Handling
Tutorial #14:
Python Main Function
Tutorial #15:
Python Interview Questions and Answers

Tutorial #16: Working With Python Dictionary
Tutorial #17: Python Try Except – Python Handling Exception With Examples
Tutorial #18: Python Advanced List Tutorial (List Sort, Reverse, Index, Copy, Join, Sum)

Tutorial #19: Python String Split Tutorial
Tutorial #20: Python Tuple Tutorial With Hands-On Examples
Tutorial #21: 12 Best Python IDEs And Code Editors In 2020

Tutorial #22: Top 6 BEST Python Testing Frameworks
Tutorial #23: 10 BEST Python Books For Beginners
Tutorial #24: Python List Functions – Tutorial With Examples

Tutorial #25: Common Python List Methods With Syntax And Examples
Tutorial #26: Python List – Create, Access, Slice, Add Or Delete Elements
Tutorial #27: Python List Comprehension Tutorial With Examples

Tutorial #28: How To Use Python Lambda Function With Examples
Tutorial #29: Python Docstring: Documenting And Introspecting Functions
Tutorial #30: Complete Guide To Python Print() Function With Examples

Tutorial #31: Pytest Tutorial – How To Use Pytest For Python Testing
Tutorial #32: Python Dictionary Methods To Create, Access, Delete And More
Tutorial #33: Top Python Certification Guide: PCAP, PCPP, PCEP
Tutorial #34: What Are Data Structures In Python [Complete Guide]


What Is Python

Python is an open-sourced, interpreted, object-oriented, high-level programming language with dynamic syntax. It is highly attractive for Rapid Application Development and scripting.

Most importantly, it is readable, simple, easy to learn & use which indeed increases productivity and reduces the cost of maintenance.

It was initially formulated by Guido van Rossum in the late 1980s at Centrum Wiskunde & Informatica(CWI) in Netherland as a successor to the ABC language. The name Python was named after a BBC’s TV Show called ‘Monty Python’s Flying Circus‘ which he was a fan of.

The name was perfect at that time since he wanted a short, unique, and slightly mysterious name for his invention.

It may be interesting to know how the different Python versions evolved and what features they introduced. In the table below, we can see Python’s first two major versions(1.0, 2.0), when they were released and what features they introduced before version 3 was designed to rectify the fundamental flaw of the language.

Table on Python versions 1.0 and 2.0 features and released date.

Python VersionFeaturesReleased Date
1.0Exception Handling, lambda, filter, reduce, mapJanuary 1994
2.0List comprehensions, garbage collection systemsOctober 16, 2020

Python versions 2.x and 3.x are the most used Python versions. As of this writing, the latest stable version of Python is 3.9.0, released on October 5, 2020.

Since the first release in 1994, Python has had regular updates with new features and supports. The table below shows all Python releases as of this writing.

Table on Python versions and release dates.

Python VersionReleased Date
Python 1.0January 1994
Python 1.5December 31, 1997
Python 1.6September 5, 2000
Python 2.0October 16, 2000
Python 2.1April 17, 2001
Python 2.2December 21, 2001
Python 2.3July 29, 2003
Python 2.4November 30, 2004
Python 2.5September 19, 2006
Python 2.6October 1, 2008
Python 2.7July 3, 2010
Python 3.0December 3, 2008
Python 3.1June 27, 2009
Python 3.2February 20, 2011
Python 3.3September 29, 2012
Python 3.4March 16, 2014
Python 3.5September 13, 2015
Python 3.6December 23, 2016
Python 3.7June 27, 2018
Python 3.8October 14, 2019
Python 3.9October 5, 2020

Why Python

The question should be: “Why not Python?“. Python is one of the fastest-growing programming languages in the world and it is used by top companies like Google, Facebook, YouTube, Spotify, Instagram, Netflix, etc.

In this section, we shall look at where Python is being used, some benefits/drawbacks, and lastly how it is compared to other popular programming languages.

What Is Python Used For

As of now, Python has many libraries and frameworks ranging from Numpy, SQLALchemy, Pytorch, Pandas, Keras, Tensorflow, Django, Flask, etc. and it is still growing rapidly. These have made Python a top choice for many developers and companies.

Python is popularly used for Development, Scripting, and software testing which indeed has made it suitable for various domains.

Table on Domain where Python is used with description.

DomainDescription
Desktop and Web ApplicationsA Desktop application is one that runs stand-alone in a desktop or laptop computer for example BitTorrent, Blender, Juice while a Web application requires a Web browser to run, for example Mailman, Plone, MoinMoin.
Data ScienceIt is a field that uses scientific methods such as data collection; algorithms and machine learning techniques to extract, analyze and process insights from raw data.
Machine LearningIt is an application of artificial intelligence (AI) that gives systems the ability to automatically learn and improve from experience and data without being explicitly programmed
RoboticsIt is a branch of engineering that deals with the conception, design, manufacture, and operation of robots.
Artificial IntelligenceIt is a broad field that deals with enabling machines to demonstrate intelligence similar to human's intelligence such as decision-making, facial recognition, etc.
Artificial intelligence incorporates other fields like Machine Learning, Robotics, Natural Language Processing(NLP), etc.
Internet of Things (IoT)It is a field that describes the network of things that are embedded with software, and other technologies for the purpose of connecting and exchanging data with other devices over the internet.
GamingIt is the art of designing and programming games for entertainment, educational, or experimental purposes and that runs on computers and mobile devices.
Mobile ApplicationsIt is a computer program or app designed to run on a mobile device such as a phone, table, or watch.
Natural Language processingIt is a field that analyses speech in both audible speech, as well as text of a language.

Benefits And Drawbacks Of Python

The various attractive features of Python make it popular and preferred in many fields.

Some of the top features of Python include:

  • Free and Open-Sourced
  • Dynamically typed
  • Portable
  • Numerous libraries and applications
  • Large supportive community
  • Flexibility
  • Easy to use and learn
  • Extensible
  • Embeddable
  • Shorter line of code than most languages

Though Python is popular, it is not effective in some domains. Knowing these drawbacks will help us to limit Python to where it is effective, thereby building robust applications.

Some Drawbacks of Python are:

  • Slow speed
  • Memory inefficient
  • Ineffective in mobile computing.
  • Undeveloped database layers.
  • Run time error prompt due to its dynamism.

Python Vs Other Languages

Python is not the only outstanding and popular language out there. We have other interpreted languages like Java, JavaScript, C++, and much more that are often compared to Python.

In this section, we will briefly compare Python to other languages at the language level and not constraints such as cost, community size, emotional attachment, etc.

Differences between Python and other programming languages.

Other LanguageDifferences with Python
JavaPython programs are slower than Java programs
Python programs take much less time to develop.
Python programs are 3-5 types shorter.
Python is dynamically typed while Java is statically typed.
JavaScriptPython's "object-based" subset is roughly equivalent to JavaScript
Python is weaker in the mobile development world
JavaScript is better in frontend development and it has the best frameworks for building morden interfaces
JavaScript works better in I/O intensive situations while Python works best in CPU-intensive situations.
C++Python programs are 5-10 times shorter than C++ programs
Python programs are slower.
Python programs take much less time to develop.
Python is dynamically typed while Java is statically typed.

How To Learn Python

After having fallen in love with a programming language like Python, the next tricky question is “How to effectively learn Python”? The mistake most newbies make is to avoid this question and delve straight into learning the language.

A programming language like Python is rapidly growing and is used in many domains. Unless we want to be “Jack of all trades, master of none“, we need to address this question thoroughly.

Given below are the various steps that we can take to effectively learn Python from zero to hero.

#1) Explore The Usage Of Python

As we saw in one of the tables above, Python is used in so many interesting and promising domains. Learning Python without a clear idea of which domain we will like to focus on or build our next big project is like a boat without sails. Efforts and courage are not enough without purpose and direction.

Once we have explored the various domains and decided on which domain(s) to focus on, we can move on to the next step.

#2) Choose A Learning Environment

Before getting started with writing any code, it’s important to find out which IDEs and code editors are tailored to make Python editing easy and comfortable.

Choosing the right IDE or text editor will enable us to focus more on being productive.

Recommended Reading => 12 BEST Python IDEs And Code Editors You Must Know

#3) Learn The Basic Syntax

The most essential requirement to master a programming language is its syntax, at least to a basic level. It is just like the English language. We first learn the different verb tenses, then use them to construct sentences.

Python particularly is easy to learn due to its simple syntax and dynamism. Most Pythonistas compare its syntax to the English language. As we saw earlier, Python has two stable versions i.e. 2x and 3x. It is recommended to learn Python 3x, and not Python 2x as the industry no longer uses it.

However, it is important to know that Python 2x comes shipped with some operating systems like Linux, macOS.

While learning these syntaxes, it is important and recommended to make notes that can be referenced later. Also, the online documentation should be our first port of call for definitive information.

#4) Practice Writing Code

This step consists of getting our hands dirty with code. As the saying goes: “Practice makes perfect“. The mistake many newbies make is to think that reading the concepts alone is sufficient.

But, keeping a daily routine and being consistent will help us develop our muscle memory to master the art of coding faster than expected.

We have an awful lot of problem exercises and interview questions on all Python concepts. It will take us no time to search the internet for such questions and attempt to solve them. Applying all that we have learned while solving these problem exercises is the key to help us get familiar quickly with the syntax and concepts.

#5) Discus Experience With Others

While coding helps us to get familiar with the syntax, discussing our worries, findings and failures will help us get familiar with the concepts and terminologies. We may as well learn things from others that would rather take us days and even months to come across or understand.

The big news is that Python has a large and active community. Therefore, if you come across a problem that seems difficult to solve, then there is a chance that somewhere, someone has already solved this problem.

#6) Do Mini Projects

This step will expose us to some more advanced concepts of programming. However, with a strong foundation on the basics, it will be easy to wrap our hands around quickly.

The aim of this step is not to work on complex projects, but to work on projects that will require us to work on the domain we are interested in and also use everything that we have learned so far. This step helps us to use what we know in order to produce something meaningful.

The internet is full of some mini-projects for beginners which we can choose from. Some examples can be seen below. Make sure to search the internet for more details.

Table on Python Mini Project Ideas For Beginners

DomainMini Project Ideas
GamingRock, Paper, Scissors
Hangman
Guessing Game
Web ApplicationsURL Shortener
Single-page portfolio website with Flask or Django.
Desktop ApplicationsPassword generator
Address Book

#7) Explore Libraries And Frameworks In Domain Of Interest

We have come to a very important step prior to working on our first big project or contributing to open-sourced projects. Python has a lot of libraries and frameworks that are used in all domains as we saw in one of the above tables.

Using libraries and frameworks will make our life painless while working on large, complex projects. So, it is essential to explore the various libraries/frameworks and decide on which to use before delving into any big project.

Some of the commonly used Python libraries/frameworks are:

Library/FrameworkDescriptionDomain Commonly Used
Django, FlaskOpen source frameworks that allow us to develop web applications.Web application
Tensorflow, KerasOpen source libraries which allows us to create large scale AI based projects.Artificial Intelligence
Numpy, PandaLibraries that is mostly used to perform scientific computations.Machine Learning, Data Science,
PyQT5, Tkinter, wxPythonGraphical User Interface(GUI) framework for Python.Desktop Applications
Pygame, PyKyraAn open source library and framework respectfully, that is highly used to build multimedia applications like games.Game development
MatplotlibIt is a multi-platform plotting library built on NumPy arrays, that helps in the visualization of data.Data Science, Machine Learning.
Scikit-learnIt is an open sourced library designed on top of SciPy that incorporates various machine learning algorithms like classification, regression, clustering, etc. Machine Learning

#8) Mentor And Share Knowledge

This step is highly overlooked, but it is a very important aspect of learning that will help you to be at the top of new concepts, terminologies, and technologies. It is often said, teaching is the best way to learn, and to know if you understand something, is to teach others to understand what you know.

Sharing knowledge can be done in many ways such as writing articles, giving presentations, etc. Not only do these push us to do more research, but it also takes us from a proficient level to a master level.

Frequently Asked Questions

Q #1) Is Python good for gaming?

Answer: Python and its gaming frameworks like Pygame are good for rapid game prototyping. It is also good for simple games but not good enough for performance-intensive games.

Q #2) Should I learn C++ or Python?

Answer: This will depend on what you want to do. Python is good for beginners as it has a simple syntax and it is easy to learn.

Also, compared to C++, Python has good frameworks for the web and also dominates in the domain of data science, machine learning, AI, etc.

Q #3) What is the main use of Python?

Answer: Python is known to be a general-purpose programming language i.e. it can be used for various types of software development like front-end as well as backend.

Python is highly exploited in the domain of Machine Learning, Artificial intelligence, Data Science, Robotics, etc.

Q #4) Can I learn Python on my own?

Answer: Python is one of the simplest programming languages with simple and easy-to-learn syntax. But just like all  the other programming languages, in order to effectively learn Python, we recommended following the below steps:

  • Explore the usage of Python
  • Choose a coding environment
  • Learn the Basic Syntax
  • Practice Writing code
  • Discus experience with others
  • Do mini projects
  • Explore libraries and frameworks in the domain of interest.
  • Mentor and share knowledge

Conclusion

In this tutorial, we looked at Python programming where we described what Python is and where it is being used.

We also looked at why and how we should learn Python, where we discussed some steps that could be adopted to take us from zero to hero in Python.

We hope that the list of Python training tutorials mentioned above would be a perfect guide for any beginner.

=> Start with the First Tutorial from here