explains why Python continues to grow The GitHub Blog (2023)

What programming language has been around for over three decades and is growing in popularity every year?

If you guessed Python, you got it.In the October 2022 reportWe note that Python is still the second most used programming language on GitHub. Interestingly, Python usage has grown by over 22% year-over-year, with over four million developers on GitHub using Python at some point in 2022.

In this article, we dive into a brief history of Python, its benefits and use cases, and attempt to answer why a programming language conceived in the 1980s continues to dominate development. And since this is GitHub, we also offer some useful tips and tricks for developers who are new to Python and have experience with it.

So what is Python? 🤔

Python is a high-level interpreted programming language with a simple syntax that is easy to read and extremely easy to use for beginners. Originally built to pleaseGuido Van Rossum's desire for a programming languageThat was easy to use and beautiful to look at, Python was first released to the world in 1991.

Fun Fact:python warnamed after the BBC television program, "Monty Python's Flying Circus."

Since its development, it has become widespread applicability for developers, data scientists, researchers, and more. But, you might ask, how can a programming language be simple and beautiful to look at? Here are some tests:

Python

print("Hello Well.")

contra

Java

public class HelloWorld { public static void main (String[]args) { System.out.println.("Hallo Welt"); }}

Because Python is a general-purpose language, it can be used in a wide variety of applications, and its simple nature makes it an excellent language for automating tasks, creating websites or software, and analyzing data.

Python also has other features that make it popular among developers and engineers. These include:

• It's easy to read.Python code uses English keywords instead of punctuation, and its line breaks help define blocks of code. In practice, this means you can see what the code is designed to do just by looking at it.

• It's open source.He canDownload the source code, change it and use it as you like.

• It's wearable.Some languages ​​require you to change the code to run on different platforms, but Python is a cross-platform language, which means you can run them.The same thingCode on any operating system with a Python interpreter.

• It's expandable.Python code can be written in other languages ​​(eg C++) and users can add low-level modules to the Python interpreter to customize and optimize their tools.

• It has an extensive pattern library.Helibraryit's accessible to everyone and means users don't have to code for each individual feature – they can access built-in modules to help with day-to-day scheduling issues and much more.

What is Python commonly used for? 💻

Python can be used for just about anything, from web and software development to machine learning and artificial intelligence (AI). Let's take a look at some of the more common use cases.

import antigravitydef main(): antigravity.fly()if __name__ == '__main__': main()

Run this command to see an inside joke among Python developers.

Using Python for Web and Software Development

Python is a popular language for web and software development because it allows you to create complex multiprotocol applications while maintaining a concise and readable syntax. In fact, some of the most popular apps are built using Python. In addition, the open source community of Python developers offers a rich set of reusable code, frameworks, and support. case study:DjangoIt is one of the most used Python frameworks, created by experienced developers to help other people to reduce application creation time and avoid problems that can hinder their progress.

Using Python for Task Automation

One of the main advantages of Python is its ability to automate manual and repetitive tasks. With Python, you can learn to automate almost anything, using built-in modules or pre-built code from its robust library. Or you can write your own custom scripts to perform specific actions. For example, you can easily automate emailsModule "smtplib".or copy files with theModule "shutil". Python also has a robust set of test frameworks, making it an excellent language for test automation. pictures likePytest,Behavior, YrobotAllow developers to write simple yet effective tests to ensure the quality of their builds.

Using Python for Machine Learning and Data Science

Here's a fun fact: Python is the language of choice for data science and research. Because its syntax is easy to understand and customizable, people with little or no development experience can easily learn Python and use it to manipulate data for research, reporting, predictive or regression analysis, and much more. Collecting and analyzing data can be a time-consuming task for data scientists. Python is also one of the best languages ​​for training machine learning (ML) models. Through specific algorithms, these models can analyze and identify patterns in the data to make predictions or make decisions based on this data. They are also constantly evolving based on results from previous datasets to handle new variables. Data scientists and developers who train ML models often use libraries likeNumPy,pandas, Ymatplotlibto automate functions such as cleaning, data transformation, and visualization.

Using Python for financial analysis

Just as Python can help data scientists deal with massive amounts of data, Python is widely used in the finance industry to quickly perform complex calculations. Stock markets generate vast amounts of data and Python can be used to import stock price data and algorithmically generate strategies to identify trading opportunities. The language can also be used for portfolio optimization, risk management, financial modeling and visualization, cryptocurrency analysis, and even fraud detection.

Using Python for Artificial Intelligence

Python is also found in some of the most complex artificial intelligence (AI) technologies and is actually one of the languages ​​of choice for AI. Python's concise, readable code allows developers to build consistent and reliable systems, and its extensive library provides a variety of frameworks such asPyBrainGenericName, which provides developers with powerful algorithms for machine learning tasks. Additionally, Python's visualization capabilities can help turn these large datasets into comprehensible AI or ML charts or reports. Interestingly, OpenAI, the artificial intelligence research lab, uses the Python framework,Pytorch, as its default framework for deep learning that trains its AI systems.

Why is Python so popular? 🙌

Besides being relatively easy to learn, there are a few other reasons why Python is growing in popularity. These include:

• It's more productive.Compared to more complex programming languages ​​like C++, Python's syntax allows users to do more with less, saving time and effort writing the same lines of code.

• It has an extensive community of supportive users.Even the best developers have problems, and this is where user communities can become an invaluable resource. Python has a large community of documentation, tutorials, tips and tricks for mastering the language. HePython Community on GitHubfor example, it offers everything from information about the latest language version to bug reports and update notes.

• It's academic.Python has become the language of choice in science, with some students encountering Python even in elementary school. (Believe it or not, there isIllustrated Children's Books Dedicated to Python.) While Python is often taught to computer science students, its use extends beyond computer science into other areas of STEM and academic research. For example, Python can be used to solve differential equations, perform statistical analysis, simulate and track particle diffusion, and much more.

• It has high commercial demand.Due to its wide applicability in data analysis and development work, learning and knowing Python is often considered one of the top skills among job seekers.According to the statistics, Python was the third most requested language by recruiters worldwide in 2022.

the end result

Python is everywhere and has been used to build a significant number of technologies, websites and even systems that most people encounter on a daily basis. It supports everything from your favorite video streaming service to ML algorithms that can help you with your next cryptocurrency trade. And for an even broader example (pun absolutely intended),NASA uses Python to analyze data with its sophisticated James Webb Space Telescope, making it one of the few programming languages ​​that is literally out of this world. 🚀

Introduction to Python 📓

A quick Google search will reveal hundreds of resources to start your Python journey, and it can get a little overwhelming quickly. To keep things simple, here are some useful GitHub repositories to get you started with Python:

• Discover pre-built Python algorithms: From network flows to physics and neural networks, this repository is a great guide to creating algorithms in Python.
• Learn Python in 30 days- This step-by-step guide will take you through the basics of Python in 30 days.
• Get some tips from this cheat sheet- Check out this collection of Python scripts with code examples and explanations to learn the language.
• Improve your Python skills: Get some tips from this tutorial for beginners and experts alike, created by a Python superfan!

To beginDownload the latest version of Python.

Start creating on GitHub today

GitHub offers two easier ways to work with Python: GitHub Codespaces and GitHub Copilot.

You can start building for free todayGitHub-Codespaces, which gives every developer on GitHub 60 hours of free airtime per month to accelerate a cloud development environment from any device at the speed of light. Have a look at Djangoquick start templateto start coding right in your browser!

You can also use GitHubco-pilot, GitHub AI programmer, to write his first lines of Python. That's how:

1. Install the GitHub Copilot extension in your code editor.
2. Describe the purpose of your project in a comment.
3. Write a comment describing which libraries you might need.
4. Start tabulating and let GitHub Copilot suggest lines of code to help you learn a new technique or method.

From machine learning to data analysis, Python's versatility allows it to continue its explosive growth with developers and non-developers alike. To be a part of this growth, try Python via GitHub or on your local machine and get started today!