What is Python used for? Exploring 7 practical Python uses

According to Forbes, Python is a tool for anyone to write a script easily and fast. The language is called easy to learn, handy to read and write, and extremely flexible. Is this language really so cool and versatile? Let’s consider some examples of where is Python used and how it can help industries.

Python.org defines it as an interpreted, object-oriented, high-level programming language with dynamic semantics. It was created in 1991 by Guido Van Rossum, a Dutch programmer who wanted the language to be intuitively understandable.

Python is a general-purpose programming language that isn’t focused on specific areas but can be used to create various programs and applications. Python is a popular programming language for its versatility and its claimed beginner-friendliness. Unlike other popular languages like JavaScript, Python features a simple syntax similar to English that is easier to understand and learn.

What’s so special about Python that makes it so popular? We’ll mention some of the language’s peculiarities.

Being high-level means being easier for humans to read, write, and understand. Python allows developers to abstract away from hardware issues like memory management and focus on facing software challenges.

Python’s source code is publicly available for free and can be modified by the contributors. This results in continuous improvement of the language and its adoption by more developers.

One of Python’s main features is its simple and understandable syntax: Python code can even look like pseudo code at first glance. The clean syntax makes the code easier for both junior and senior developers to collaborate.

Python can be used for pretty much any industry and domain, to create a wide range of applications. Web development, software development, data analysis and engineering, machine learning are just several mentions of what is Python good for.

Python has an extensive set of additional tools for developing various applications. For instance, Django and Flask are widely used for web development, Pandas and NumPy are applied for data analysis, TensorFlow and Scikit-learn help in machine learning projects.

Python is known for its active and friendly community of contributors: there’s documentation and plenty of tutorials available. In addition, developers can find answers on forums. Knowledge sharing contributes to better software solutions within projects.

Python’s philosophy emphasizes “readability” as a crucial factor. It strives to minimize blocks of source code text and prefers white space, resulting in a clearer and less cluttered appearance. Unlike many other programming languages that use braces {} or keywords to delimit blocks of code, Python uses indentation to indicate blocks. This way Python simplified its syntax making the overall code structure and view more readable and neat.

What can you do with Python? Let’s consider some examples of practical uses for Python.

What is Python mainly used for within web development? It’s creating the back end: a part of a website or web app that handles sending data to and from servers. Python offers various frameworks for web development, with popular choices including Django and Flask. These frameworks come with specialized built-in modules for data sharing, information processing, database access, URL routing, content management, and website security.

Django: A high-level framework that encourages rapid development and clean, pragmatic design. It’s known for its robustness and the ability to handle complex applications.

Flask: A micro-framework that offers simplicity and flexibility. It’s lightweight and ideal for small to medium-sized applications.

Pyramid: Designed for both small and large applications, providing much flexibility.

Bottle: Another micro-framework that’s very simple and perfect for building small web applications.

Google, Facebook, Instagram, YouTube, Dropbox, and Reddit use Python in their products.

Let’s consider Python uses throughout all stages of the software development process, from build control and automated continuous compilation to prototyping, bug tracking, testing, and software maintenance. Its flexibility and power make it suitable for creating both simple and highly advanced applications, making it a versatile choice for various development needs.

PyQt: a set of Python bindings for the Qt framework. PyQt includes classes for building GUI applications.

Tkinter: a standard Python interface that allows building GUI applications without the need for third-party dependencies.

Kivy: a library for developing applications featuring cutting-edge UI, including multi-touch applications.

Blender: An open-source 3D modeling and animation software that uses Python for scripting and automation.

Dropbox: Initially used Python for its desktop client.

GIMP: Python along with other languages is used for creating plugins and scripts to extend GIMP’s functionality.

Python is a wonderful tool for automating repetitive tasks, also known as scripting. Such automation helps pass on mundane tasks to a program instead of doing everything manually. For instance, Python is great at file and folder management. A script written in Python can create or rename files, convert them into another format, check them for errors, modify and update file content, detect duplicates, split or merge files, and more. This can be helpful for business applications that increase productivity and mitigate problems of manual data processing.

Selenium: allows developers to write scripts that can interact with web pages as a human user would: click buttons, fill out forms, and navigate through pages.

BeautifulSoup: a library for pulling data out of HTML and XML files.

Requests: a lib for making HTTP requests, predominantly designed for web applications.

Web scraping scripts: automate data extraction from websites.

Task automation scripts: Automate repetitive tasks in system administration.

Python is used for both low-level and high-level network access. Python provides a built-in module called socket which allows for low-level network communication. This is useful for creating both clients and servers that communicate over TCP/IP or UDP. The high-level access allows for the implementation of HTTP, FTP, and other protocols. Flask and Django are popular web frameworks that simplify creating RESTful APIs for network applications.

Twisted: an event-driven networking engine suitable for building high-performance servers and clients.

asyncio: supports asynchronous network operations, which is useful for handling many connections simultaneously.

BitTorrent: Uses Python for peer-to-peer file-sharing protocols.

Paramiko: A library for making SSH2 connections (client and server).

Python can be used to create interactive learning platforms, automated grading systems, learning management systems, tutoring systems, and chatbots. Let’s consider some examples of how is Python used for educational purposes.

Pygame: a cross-platform set of Python modules used to create educational video games that make learning fun and interactive.

Django, Flask: used to build comprehensive learning management systems.

NLTK, spaCy: Python libraries used to generate educational content, quizzes, and assessments automatically.

Project Jupyter is aimed at developing open-source software, open standards, and services for interactive computing across multiple programming languages. Jupyter Notebook is a type of Integrated Development Environment that is widely used in education for creating interactive and executable documents that combine code, text, and visualizations.
Platforms like Coursera and edX use Jupyter Notebooks for their coding assignments.

AutoGrader: a Python-based tool that evaluates students’ code against predefined test cases.

Moss (Measure of Software Similarity): a tool used to detect plagiarism in coding assignments.

In today’s industries, data holds immense value, and businesses are keen on collecting, processing, and analyzing it to derive crucial insights. Here Python comes into play due to its extensive standard library and a rich array of specialized modules tailored for analytical tasks. For instance, it helps data analysts to make sense of complicated data sets and make them easier to understand. Data visualization is another essential component of data analysis as it allows for presenting information more engagingly and insightfully.

Pandas: used for analyzing, cleaning, exploring, and manipulating data.

NumPy: used for working with arrays.

Matplotlib: used for creating high-quality visualizations and graphs.

Seaborn: used for making statistical graphics.

Performance dashboards: Visualize student progress and identify areas where they need help.

Predictive analytics: Predict student outcomes and identify at-risk students.

Automation plays a significant role in SEO, and Python offers excellent support in this area. Whether it’s making changes across multiple pages or organizing keywords, Python proves to be a valuable asset. Python is brilliant at natural language processing technology so it can help understand how people form their search inquiries and how search engines return results.

Pandas, NumPy: used to analyze and visualize ranking data from search engines.

NLTK, SpaCy: natural language processing libs to analyze and optimize website content for better relevance and keyword usage.

Let’s briefly review ways in which Python helps various industries.

We often hear questions like why use Python? However, it’s not quite correct: it’s not about some languages being better than others, so we must use them. It’s about when to use Python, based on what can Python do for your project. The technology should be chosen only after the project goal is clearly defined. But let’s abstract away a bit and consider Python’s strengths and why developers love to use it.

Developers love Python because it’s open-source, has an accessible learning curve, and is highly readable. Some additional benefits of using Python include:

Python uses indentation to define code blocks, which enforces a uniform code structure. In contrast, commonly used programming languages like C++, Java, and C# use braces {} to define code blocks and require semicolons ; to terminate statements, making the code appear more cluttered.

Python is often considered one of the easiest programming languages to learn, especially for beginners, due to its straightforward syntax and comprehensive standard library. On the other hand, languages like C++ and Java have steeper learning curves due to more complex syntax and concepts.

Python’s interpreted nature and dynamic typing generally lead to slower execution compared to compiled languages like C++. However, performance can be improved with optimization techniques and libraries like NumPy for numerical computations. In contrast, languages like C++ are known for high performance and are often used in system-level programming, game development, and applications requiring intensive computation.

Python offers a vast and comprehensive standard library supporting common programming tasks, along with thousands of third-party packages on the Python Package Index (PyPI). While languages like Java also have extensive libraries and a robust ecosystem, the range and ease of using Python’s libraries are often seen as more user-friendly and diverse.

Python is widely used for web development (Django, Flask), data science tasks (Pandas, NumPy, Scikit-learn), machine learning (TensorFlow, PyTorch), automation, and scripting. Java is preferred for enterprise applications, Android app development, and large systems requiring robustness and scalability, while C++ is used in system and software development, game development, and applications requiring high performance. JavaScript is dominant in web development for both front-end (with frameworks like React and Angular) and back-end (Node.js) development.

Python enables rapid development and prototyping due to its simplicity and readability, making it ideal for startups and projects with tight deadlines. Development in languages like C++ and Java can be slower due to more complex syntax and longer compilation times.