How to Use Python Programming: Top Python Use Cases and Applications in the Real World

Introduction

Python is a general-purpose, high-level, interpreted programming language that has not only maintained its popularity ever since its foundation in 1991 but also set records among all coding languages.

So, what’s the secret sauce of this programming language and how is Python used in the real world? In this article, we’ll explore it all — from the most common Python projects to benefits, pitfalls, must-have Python skills, the ways different companies use its strenghts, and top tips for the implementation of real projects.

The World of Python: Key Stats and Observations

Python confidently leads the ranking of the most popular programming languages, outperforming its closest competitors, C++ by 53.44% and Java by 58%, based on popularity metrics.

According to StackOverflow Developer Survey 2024, Python is one of the top 3 most widely-used programming languages and a top choice for those learning to code. Plus, it ranks high in the category of “admired and desired” programming languages, based on 67.6% of respondents reporting they love it.

Python Used in Real Life: Key Use Cases Explained

The language’s versatility and popularity made it a common choice for various projects built on Python. Let’s explore some of the most common ones in detail.

Python in Web Application Development

Python web projects often require rapid development, high scalability to handle high traffic, and secure coding practices with built-in protections against vulnerabilities. Python is a great, powerful choice used for web app development in all these scenarios.

The language empowers ease of coding through its simple syntax, ORM support for seamless database management, robust cross-platform support, and efficient scalability tools like caching and load balancing. Plus, thanks to extensive Python libraries and frameworks, such as Django and Flask, developers can build robust web applications even more efficiently.

To see its capabilities in action, let’s examine one of the most prominent Python-powered projects — Instagram. Python is employed here extensively, powering Instagram’s massive, rapidly evolving codebase that consists of millions of lines of code and hundreds of daily commits. To further maximize its efficiency, Instagram introduced strict modules that ensured side-effect-free imports and immutability. As a result, this approach enhances performance and minimizes accidental errors in large-scale development.

Apart from the above-mentioned project, common web development projects using Python are:

• Building dynamic web applications with robust content management and data visualization;
• Developing RESTful APIs for communication between client and server applications;
• E-commerce platform development featuring payment integration, user management, and secure web frameworks for data handling.

While robust, Python also presents certain challenges. To address potential issues, ensure utilizing a set of the following best practices, compiled with the help of Mobilunity experts:

> Choose frameworks wisely

For example, choose Django for complex business applications, Flask for lightweight projects, or FastAPI for high-performance APIs.

> Follow PEP 8 guidelines

Maintain clean, consistent, and readable code following Python’s official style guide.

> Leverage virtual environments

Benefit from tools like venv or pipenv to isolate project dependencies and avoid conflicts.

> Prioritize database query optimization

Utilize ORMs like Django ORM or SQLAlchemy efficiently and implement caching for frequently accessed data.

Applications of Python in Data Science and Data Analysis

Python programming language can be a good choice for data science and analysis. Its adaptability, ease of integration, and rich ecosystem of tools make it a cornerstone for data-driven projects.

For example, Pandas, NumPy, and SciPy support data science projects, while Scikit-learn, TensorFlow, and PyTorch simplify machine learning. Python also excels in visualization with Matplotlib, Seaborn, and Plotly and integrates with tools like Apache Spark and SQL databases.

When it comes to real-life Python projects in this domain, one notable example is Harvard Medical School and the Chan Zuckerberg Initiative. Python, through its Dask library, enables scalable analysis of high-resolution, 4D cellular imagery that generates large amounts of data. Dask streamlines the processing of these massive datasets with the help of scalable workflows and API, effectively overcoming the limitations of traditional methods.

Other popular Python projects within this domain include:

• Exploratory data analysis for data manipulation, visualization, and better understanding of data structures;
• Predictive modeling to analyze trends or forecast outcomes for data scientists;
• Big data processing for distributed computing across large datasets.

While any project may pose certain risks, they can be mitigated with a toolkit of the following tips gathered by Mobilunity experts:

> Maintain clean code

Adopt modular coding practices and adhere to PEP 8 guidelines – this way, you can ensure clarity and maintainability in data workflows.

> Optimize data handling

Apply tools like Dask for large-scale processing of data and implement efficient memory management techniques.

> Visualize data effectively

Leverage Matplotlib, Seaborn, or Plotly to create insightful and visually appealing dashboards that support decision-making.

How Machine Learning and Artificial Intelligence Projects Use Python

ML and AI projects can be complex and challenging, involving sophisticated algorithm implementation, time-consuming data preprocessing, integration with diverse tools, and scalability for large datasets, to name a few — all of which require robust technology to address these aspects effectively.

For ML and AI, Python addresses these demands with a rich toolkit for building and training models. This includes pre-built functionalities for a range of tasks: neural network creation, model evaluation, hyperparameter optimization, and more. Besides, Python supports diverse workflows, ranging from data preprocessing and feature engineering to model training and deployment — thus making it suitable for end-to-end AI pipelines. On top of that, its integration capabilities enable smooth interaction with APIs, cloud services, and big data platforms for efficient processing of large datasets.

When it comes to real-life Python use cases in AI/ML, companies like Netflix leverage Python extensively in their AI and machine learning workflows. In the Netflix case, Python is applied to power personalized recommendations, automate content tagging, and optimize video quality with tools like Metaflow, PyTorch, and VMAF. Python enables Netflix to build and deploy large-scale ML models for demand prediction, user behavior analysis, and streaming optimization. This way, Python’s rich ecosystem and scalability make it integral to Netflix’s AI innovation.

Apart from that, additional common application scenarios of Python for AI/ML include:

• Developing predictive algorithms with Scikit-learn for classification, regression, and clustering tasks;
• Deep learning model training using TensorFlow or PyTorch for image recognition or NLP tasks;
• Reinforcement learning simulations for autonomous systems and decision-making models.

To leverage Python-powered AI projects efficiently, follow these best practices collected by Mobilunity through years of expertise:

> Leverage pre-trained models

Utilize pre-trained models (BERT, ResNet, YOLO, etc.) to save time and resources, especially for tasks like image recognition and text processing.

> Optimize performance

Incorporate tools like NumPy, Numba, or Cython to optimize computations and manage resource-intensive tasks.

> Focus on explainability

Leverage SHAP or LIME to interpret and explain machine learning models efficiently.

How Python is Used for Automation and Scripting

Automation-focused projects commonly stem from the need to streamline repetitive work and enhance overall operational efficiency. The Python language is a standout option for such tasks.

With its ecosystem of tools, developers can streamline workflow development and leverage tailored solutions for web scraping, browser interactions, and GUI automation tasks. Its ecosystem of tools — Selenium, Beautiful Soup, and PyAutoGUI — simplifies automating repetitive processes across diverse domains and helps developers leverage comprehensive automated pipelines.

Many Python projects in this area have proven themselves successful. For example, Cisco utilizes Python scripts to automate internal user management tasks — retrieving user details, creating accounts, changing passwords, and integrating with Active Directory. This approach streamlines the process of querying users by ID, managing guest accounts, enforcing security policies, and others.

Other popular applications of Python programming in this domain are:

• Automated web scraping to collect data from dynamic websites;
• File management scripts for organizing, renaming, and processing large datasets;
• Automating repetitive tasks such as software testing and deployment pipelines.
  

To mitigate potential challenges or these projects, check out the best practices below:

> Modularize your code

Break down automation scripts into reusable modules and functions to improve maintainability and scalability.

> Optimize resource usage

Use tools like asyncio for asynchronous programming or subprocess to reduce CPU and memory strain.

> Leverage virtual environments

Isolate dependencies with tools like venv or conda to prevent conflicts and ensure smooth operation across multiple projects.

Use of Python Development for IoT and Robotics

IoT and robotics projects commonly hold high requirements: the ability to handle the complexities of hardware-software integration, real-time processing of data, resource constraints, interoperability, security, and maintenance. All of these challenges make development and deployment highly intricate, but Python tools handle them effectively.

With support for IoT-specific protocols like MQTT and CoAP, compatibility with microcontrollers such as Raspberry Pi and Arduino, and seamless hardware integration through GPIO Zero or PySerial, Python simplifies complex IoT and robotics workflows — all while enabling rapid IoT development and integration.

A practical example highlighting Python’s role in robotics is RobotIO — a Python library for robot manipulation experiments. Python was instrumental in creating a standardized interface for controlling a variety of robotic hardware. This library enabled researchers to simplify complex tasks by providing a unified API for interacting with different robot platforms, reducing the effort needed to switch between hardware systems.

Based on Mobilunity expertise, other common use cases for Python include:

• Sensor data collection and processing using PySerial and PyBluez for seamless communication with sensors;
• Controlling robotics hardware with Python frameworks like ROS (Robot Operating System);
• Home automation systems using Python with Raspberry Pi to manage IoT devices.
  

To leverage these and other Python-powered IoT and Robotics projects efficiently, apply the following best practices:

> Apply efficient communication libraries

Streamline IoT communication with libraries like MQTT and WebSocket.

> Optimize data handling

Utilize lightweight Python libraries like MicroPython or pyModbus to handle sensor data efficiently on resource-constrained devices.

> Implement asynchronous programming

Utilize asyncio or other libraries to manage real-time tasks and improve the level of responsiveness in IoT and robotic systems.

> Apply event-driven programming

Design systems with event-driven architectures for devices that interact in dynamic IoT ecosystems.

Python Programming Language in Game Development

Python-powered rapid scripting and logic design make it a versatile tool used to develop captivating games. Its Pygame and Panda3D libraries are perfect for 3D game development. Integration with engines such as Unity and Unreal boosts performance vital for such projects. Additionally, support for game modding fosters creativity and impacts long-term player retention.

One of the vivid examples of using Python for game development is Civilization IV. In this game, Python is utilized to modify game behavior and create new game scenarios. Thus, it allows for complex scripting, such as triggering events under specific conditions, editing the game interface, generating new map types, and altering AI behavior — all of which enabled modders to flexibly customize and extend the game.

Additional common real-world scenarios and solutions built using Python, as per Mobilnuity experts’ observations, are:

• 2D and 3D game development supporting physics and collision detection;
  Educational and simulation games for training and skill-building applications;
• AI-powered NPC behavior modeling for interactive gaming experiences.

Whatever the project is, these best practices will help you enhance the efficiency of code in Python for game development:

> Design modular game components

Ensure reusability of scripts and components through modular design.

> Document mechanics and structure

Thoroughly document game mechanics and code structures, thus streamlining debugging and future updates.

> Incorporate dynamic gameplay elements

Utilize AI or procedural generation tools like TensorFlow or SciPy to create engaging, dynamic gameplay experiences with Python.

How Developers Use Python for Web Scraping and Data Extraction

Web scraping and data extraction require factoring in a range of aspects, including handling dynamic content, anti-scraping measures, unstructured data, large datasets, frequent website changes, concurrency, legal compliance, and others.

Python effectively addresses these challenges with its rich tools and robust capabilities for crawling and parsing web data. Plus, with support for diverse data formats and parallel processing, Python can also be used to ensure script scalability and efficiency, even for large-scale data extraction tasks.

Zooming in on real-life scenarios, the project Scraping Google Flights with Python demonstrates how to handle dynamic websites for valuable data extraction, such as flight prices and schedules, using libraries like Selenium and BeautifulSoup. In this case, tools like proxies and rotating user agents efficiently handle dynamic content, CAPTCHAs, and rate-limiting issues. Besides, developers can navigate JavaScript-heavy content, automate browser actions, and parse HTML effectively.

Python is also a common choice for these tasks:

• E-commerce price monitoring to track product prices and availability;
• Aggregating job listings from sites like LinkedIn for market trend analysis;
• Social media data extraction for sentiment analysis or marketing insights.

To ensure a high level of efficiency, Mobilunity recommends following this advice when using Python for web scraping and data extraction:

> Avoid detection and blocking

Set proper request headers, use proxies, and implement rate limiting to prevent bot detection and comply with website policies.

> Clean and preprocess data

Use libraries like Pandas or NumPy to clean and preprocess extracted data, ensuring it is ready for analysis and further use.

> Ensure legal and ethical compliance

Follow website terms of service and legal requirements to avoid potential issues with data usage.

Python Usage in Desktop Application Development

For those building desktop applications, Python provides extensive GUI libraries and allows for quick prototyping with tools that support drag-and-drop UI design and simplified event handling. Advanced features like cloud synchronization, leveraged with boto3, allow Python developers to create modern, connected desktop applications. On top of that, its cross-platform compatibility ensures minimal code modifications for robust and adaptable apps.

One of the famous Python-powered desktop application development examples is Dropbox, where the language’s cross-platform compatibility, readability, and rapid development cycle were key to Dropbox’s early scaling and quick implementation and testing of features. The Dropbox desktop client includes a full Python installation, and the company has even created Pyston, a custom Python implementation for improved performance.

Other industry examples where Python can play a significant role, according to the Mobilunity insight, include:

• Cross-platform GUIs with frameworks like PyQt for tools like file managers or text editors;
• Data visualization dashboards for financial or scientific data;
• Custom productivity tools such as time trackers or note-taking applications.

Use these best practices gathered by Mobilunity from our successful desktop application development projects:

> Streamline deployment

Use packaging tools like PyInstaller, cx_Freeze, or PyOxidizer to create platform-specific executables and installers.

> Test on all platforms

Thoroughly test your Python app across target platforms to deliver a consistent and bug-free user experience.

> Design accessible GUIs

Leverage accessibility features, ensuring the needs of various users are met.

Python Offers for Scientific and Numeric Computing

Speaking of numeric computing, Python provides robust visualization capabilities, libraries to efficiently support scientific workflows, and seamless integration with tools like MATLAB and R for hybrid workflows. Its open-source tools also support symbolic mathematics and solving optimization problems, making it a versatile choice for researchers and analysts.

A vivid example of a Python application is the Large Hadron Collider at CERN, where Python supports data management workflows. In particular, to support data research and analysis at CERN, the ROOT framework was created for big data processing, statistical analysis, visualization, and storage. Besides, for managing the vast number of research publications, CERN uses Invenio, a digital framework enabling the creation of customized repositories like Zenodo and CERN Analysis Preservation.

Other common examples of using Python in this domain, based on Mobilunity’s project portfolio, include:

• Numerical simulations for physics, engineering, or finance;
• Genomic data insights for bioinformatics research;
• Climate modeling using Python for atmospheric and oceanic simulations.

The efficiency of the above-mentioned and other Python projects for scientific and numeric computing can be enhanced with these best practices:

> Ensure reproducibility

Write modular and well-documented code to facilitate repeatable experiments and analyses.

> Test on sample datasets

Validate algorithms and computations on smaller datasets before scaling to real-world data.

> Optimize computational performance

Use tools like Cython or Numba to convert critical Python functions to C for enhanced efficiency.

Python for Enterprise and ERP Software Development

When it comes to ERP software development, Python aligns perfectly with such project needs — offering a high level of customizability and seamless integration with legacy systems. It also overcomes ERP development challenges of handling complex workflows and managing large datasets thanks to its robust libraries and the ability to integrate seamlessly with databases and third-party tools.

To better review Python’s application in enterprise and ERP software development, let’s review a real-life use case. Flask-ERP is an ERP system developed using Flask, a Python-based web framework, designed for business process management to integrate and automate functions like technology, services, and human resources. The ERP solution offered user and admin management features like login/signup, credential updates, profile editing, online fee payment with CSV downloads, system-wide announcements, and request management for supplies, visible to admins and exportable in CSV format.

Other than that, best use cases of Python include:

• Custom ERP systems using frameworks like Odoo or Tryton for accounting, HR, and CRM;
• Automating business workflows for inventory tracking or billing;
• API integrations to connect ERP systems with third-party platforms like Salesforce.

To ensure a successful project, apply these best practices for Python during enterprise and ERP application development:

> Adopt modular ERP development

Split ERP functionalities into independent modules, such as inventory tracking or payroll, to enhance scalability and ease maintenance.

> Ensure robust database management

Utilize extra tools (SQLAlchemy, Django ORM, etc.) to manage complex database structures typical in enterprise systems.

> Leverage stringent security protocols

Use PyCrypto and cryptography to encrypt sensitive enterprise data and ensure compliance.

> Implement CI/CD pipelines

Automate testing and deployment of ERP updates with CI/CD tools.

Python Applications in Education and Training Programs

The simplicity of Python syntax enables developers to create user-friendly learning tools with minimal technical barriers. With a robust suite of tools — NumPy for math, TensorFlow for AI, and Django for web projects — this popular language serves as a versatile asset in modern education.

When it comes to education and training programs, Python boasts a plethora of use cases. One of them is Moodle, a learning management system that supports Python-based plugins to extend its functionality. Plus, Python scripts are used to build interactive quizzes, manage assignments, and analyze student performance.

Other potential applications of Python for education include:

• Interactive coding platforms for teaching programming concepts;
• Simulations and visualizations for physics, biology, or finance education;
• AI-powered personalized learning systems for tailoring content to individual learner needs.

To leverage efficient Python-based solutions in the education domain, follow these practices:

> Adopt modular design

Develop educational tools in modular components to ensure scalability and simplify updates.

> Create interactive content

Focus on creating visually engaging and interactive learning materials by leveraging the right libraries (Plotly, Dash, etc.)

> Leverage data-driven insights

Utilize Python-based data analysis libraries to monitor learner engagement and assess outcomes effectively.

Python vs. Other Popular Programming Languages

Having analyzed its potential for various projects, let’s review in more detail how Python performed against specific other major languages.

PHP vs Python

When it comes to PHP vs Python, Python is the winner in beginner-friendliness and usability. It takes pride in diverse areas of application thanks to its simple syntax, active community, and extensive educational resources. However, PHP outperforms Python in speed for server-side scripts. Thus, it is a more preferred choice for web development projects, especially those involving CMS platforms like WordPress.

Rust vs Python

In the debate of Rust vs Python, Python wins in ease of use and versatility, particularly in AI and data science, thanks to TensorFlow, NumPy, and other libraries that simplify workflows. Meanwhile, Rust is known for offering higher efficiency and memory safety, thus being a more preferred choice for resource-intensive tasks like system development.

R vs Python

In the comparison of R vs Python, Python is widely versatile due to its extensive libraries, flexible programming paradigms, and integration capabilities. In the meantime, R and its advanced tools like ggplot2 and dplyr make it highly specialized for statistical modeling and data visualization.

Python vs Java

When comparing Python vs Java, Python is generally more beginner and user-friendly than Java which has a steeper learning curve due to its verbose syntax, strict static typing, and complex setup. However, Java outperforms Python in runtime performance, scalability, and its strong type system, which makes it more commonly applied in enterprise systems and Android development.

Final Thoughts

Whatever your project goals are, Python has something to offer. Its versatility comes from simple syntax, ease of use, and the powerful ecosystem of tools. This makes Python one of the most popular coding languages and a preferred choice for various purposes: dynamic web applications, exploratory data analysis, machine learning models, and automated workflows.

However, for those considering Python for their project, it’s important to consider this language might not always be the best choice due to its performance limitations, potential scalability challenges, and dependency management complexities.

Written by:

Anastasia Stefanuk

Market Researcher