Why Python Keeps Growing? Explained

Which programming language, with a history spanning over three decades, exhibits a consistent surge in popularity each passing year? If your answer is Python, you’re spot on. According to the 2022 Octoverse report, Python maintains its stronghold as the second most widely utilized programming language on GitHub. Remarkably, Python experienced a growth of over 22 percent year over year, boasting a user base of more than four million developers on GitHub in 2022.

This article delves into a concise history of Python, explores its advantages, examines its diverse use cases, and endeavors to unravel the mystery of why a programming language conceived in the 1980s continues to dominate the development landscape. As we navigate through the GitHub realm, we’ll also share valuable tips and tricks for developers both new to, and seasoned in, Python, adding depth to your programming expertise.

Python, introduced to the world in 1991, is a high-level, interpreted programming language renowned for its straightforward syntax, ensuring readability and user-friendliness, especially for beginners. Guido Van Rossum’s vision for a programming language that is both easy to use and aesthetically pleasing drove the development of Python. Its simplicity and elegance were key objectives during its creation, reflecting in a language that has stood the test of time. Python has become a staple in the programming community, gaining popularity for its versatility and approachability. Beginners and seasoned developers alike appreciate Python’s elegant design and accessible nature.

Ever since its inception, this programming language has experienced remarkable expansion, finding utility across a broad spectrum, catering to developers, data scientists, researchers, and various other professionals. One might inquire, how can a programming language possess both simplicity and aesthetic appeal? Let’s delve into some evidence to address this query:

This coding language, conceived with multifaceted versatility in mind, has evolved to become an indispensable tool for an array of specialists. Its reach spans developers, data scientists, researchers, and beyond. A pertinent question arises: how can a coding language achieve the dual feat of being both elegant and straightforward? Allow me to present compelling evidence that sheds light on this intriguing intersection.

Python

print("Hello world.")

vs.

Java

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

Given that Python is a versatile programming language with broad applicability, it finds utility in diverse realms. Its inherent simplicity renders it particularly well-suited for tasks such as task automation, web and software development, and data analysis.

Python boasts a range of features that contribute to its widespread adoption among developers and engineers. Notably:

1. Readability: Python code utilizes English keywords, eschewing excessive punctuation, and employs line breaks to demarcate code blocks. This facilitates a straightforward understanding of the code’s intended functionality through visual inspection alone.

2. Open Source: The source code for Python is freely accessible, allowing users to download, modify, and deploy it according to their requirements without any constraints.

3. Portability: Python stands out as a cross-platform language, eliminating the need for code modification to run on different operating systems. Code written in Python can seamlessly execute on any platform equipped with a Python interpreter.

4. Extensibility: Python’s versatility extends to its capacity to incorporate code written in other languages, such as C++. Users can enhance the language’s functionality by adding low-level modules to the Python interpreter, tailoring and optimizing their tools as needed.

5. Broad Standard Library: Python’s extensive standard library is a valuable resource accessible to all users. This obviates the necessity to craft code for every function, as built-in modules address common programming challenges and more complex issues encountered in day-to-day coding.

These attributes collectively contribute to Python’s standing as a language of choice for a myriad of applications, emphasizing its readability, adaptability, and extensive built-in capabilities.

What is Python commonly used for? 

Python is an incredibly versatile programming language with applications spanning a wide array of domains. Its utility extends across diverse fields, encompassing everything from web and software development to the realms of machine learning and artificial intelligence (AI). This adaptability has contributed to Python’s widespread popularity and adoption in the tech industry. Now, let’s delve into a comprehensive exploration of some of the prevalent and noteworthy applications that Python serves in various industries, shedding light on its multifaceted nature and highlighting its pivotal role in shaping contemporary technological landscapes.

import antigravity

def main():
    antigravity.fly()

if __name__ == '__main__':
    main()

Execute this command to explore a humorous reference shared among Python developers.

Using Python for web and software development

Python stands out as a favored language in the realms of web and software development due to its capability to craft intricate, multi-protocol applications, all the while upholding a succinct and easily readable syntax. A multitude of widely embraced applications is a testament to Python’s effectiveness. Moreover, the open-source community surrounding Python generously equips developers with a vast repository of reusable code, frameworks, and comprehensive support. A shining example is Django, a highly utilized Python framework meticulously crafted by seasoned developers to expedite application development, steering clear of potential hindrances that could impede progress.

Using Python for task automation

Python offers a significant advantage in automating manual, repetitive tasks. Whether utilizing its built-in modules, leveraging pre-existing code from its extensive library, or crafting custom scripts, Python empowers users to automate a wide array of processes. For instance, streamlining email automation becomes seamless with the “smtplib” module, and file copying is efficiently achieved using the “shutil” module. Python further distinguishes itself with a robust suite of testing frameworks, making it an ideal language for test automation. Popular frameworks like Pytest, Behave, and Robot enable developers to create straightforward yet powerful tests, ensuring the quality of their software builds.

Using Python for machine learning and data science

Here’s an interesting tidbit: Python stands out as the preferred language for data science and research due to its easily comprehensible and adaptable syntax. Even individuals with minimal or no coding experience can swiftly grasp Python, leveraging it to manipulate data for diverse purposes such as research, reporting, predictive or regression analyses, and more. The process of collecting and parsing data can be quite time-consuming for data scientists. Moreover, Python ranks among the leading languages for training machine learning (ML) models. Through specialized algorithms, these models can scrutinize and detect patterns in data, enabling them to generate predictions or make decisions based on the available information. Furthermore, these models continually evolve by learning from the outputs of previous datasets, allowing them to adapt to new variables. When training ML models, data scientists and developers often employ libraries like NumPy, Pandas, and Matplotlib to automate tasks such as data cleaning, transformation, and visualization.

Using Python for financial analysis

Similar to its support for data scientists in handling large datasets, Python finds extensive use in the financial sector for efficiently conducting intricate computations. Given the substantial data volumes produced by stock markets, Python proves valuable for importing stock price data and employing algorithms to identify trading opportunities. Its versatility extends to portfolio optimization, risk management, financial modeling, visualization, cryptocurrency analysis, and even the detection of fraudulent activities.

Using Python for and artificial intelligence

Python stands out as a favored language for artificial intelligence (AI) due to its presence in some of the most intricate AI technologies. Its code, characterized by conciseness and readability, empowers developers to build dependable systems consistently. Python’s extensive library enriches the AI landscape with frameworks like PyBrain, equipping developers with robust algorithms for machine learning endeavors. Furthermore, Python’s visualization capabilities play a crucial role in transforming vast datasets for AI or machine learning into intelligible graphs or reports. Notably, OpenAI, the prominent artificial intelligence research lab, employs the Python framework PyTorch as its standard for deep learning, facilitating the training of their AI systems.

Why is Python so popular? 

In addition to its ease of learning, Python’s consistent rise in popularity can be attributed to several factors:

1. Enhanced productivity: Python’s syntax, in contrast to more intricate programming languages like C++, enables users to achieve more with less code, reducing the time and effort required for coding tasks.

2. Robust user community: Python boasts a vast and supportive community, serving as a valuable resource for developers encountering challenges. Platforms such as GitHub host a large Python community, offering documentation, tutorials, tips, and tricks to help users master the language and address issues like bug reports and updates.

3. Academic prominence: Python has emerged as the preferred language in academia, with students often encountering it as early as elementary school. Beyond computer science, Python’s versatility extends to various STEM disciplines and academic research, enabling applications such as solving differential equations, conducting statistical analyses, simulating particle diffusion, and more.

4. Corporate demand: Python’s broad applicability in development and data analysis positions it as a sought-after skill among job seekers. According to Statista, Python ranked as the third most demanded language by recruiters worldwide in 2022.

The Bottom Line

Python is ubiquitous, contributing significantly to the development of numerous technologies, websites, and systems that people interact with daily. It drives diverse applications, from popular video streaming services to machine learning algorithms facilitating cryptocurrency transactions. As a testament to its versatility, NASA employs Python to conduct data analysis for the cutting-edge James Webb Space Telescope, establishing it as one of the few programming languages that extends beyond our planet’s boundaries.

How To Get Started With Python 

Performing a brief Google search will unveil numerous resources to kickstart your Python journey, potentially leading to a sense of overwhelm. To streamline the process, here are several beneficial GitHub repositories that can assist you in initiating your Python exploration:

1. Delve into pre-constructed Python algorithms: Covering a spectrum from networking flows to physics and neural networks, this repository serves as an excellent roadmap for constructing algorithms in Python.

2. Master Python in a month: This systematic guide will guide you through the fundamentals of Python over a 30-day period.

3. Extract insights from this cheatsheet: Take a look at this compilation of Python scripts featuring code examples and explanations, offering a valuable resource for learning the language.

4. Enhance your Python proficiency: Acquire valuable tips from this comprehensive study guide designed for both novice and experienced users, crafted by a dedicated Python enthusiast!

To commence, download the most recent version of Python.

Start Building On GitHub Today

Explore Python development on GitHub effortlessly with GitHub Codespaces and GitHub Copilot.

Get started with GitHub Codespaces for free, as every GitHub developer receives 60 complimentary hours per month. This allows you to set up a cloud-based development environment from any device quickly. Dive into coding right in your browser with the Django quick start template!

Alternatively, leverage GitHub Copilot, the AI pair programmer, to kickstart your Python journey:

1. Install the GitHub Copilot extension in your code editor.
2. Define your project’s purpose in a comment.
3. Specify the libraries you might require in another comment.
4. Begin typing, and let GitHub Copilot propose code lines, aiding you in mastering new techniques and methods.