Python is one of the most versatile and popular programming languages used by developers today. Its design principles emphasize code readability, and its easy-to-learn syntax enables coders to express ideas in fewer lines compared to languages like Java or C++.
But what exactly is Python used for? What are some of its most popular applications and use cases?
In this comprehensive guide, we‘ll explore some of the top domains and areas where Python excels. We‘ll also look at specific examples and statistics that showcase Python‘s capabilities for various tasks.
Web Development
Python is one of the most preferred languages for web development. Its wide range of web frameworks cater to various needs and make backend web development very convenient for programmers.
Some of the most popular web frameworks for Python include:
- Django – Used by companies like Instagram and Mozilla, Django is great for building complex, database-driven sites. It follows the model-view-template (MVT) architectural pattern and provides many built-in features like admin screens out of the box.
- Flask – Flask offers a lightweight framework good for smaller apps. It has very minimal components allowing you to add other packages as needed. Sites like Pinterest use Flask.
- Pyramid – Built to be very flexible, Pyramid lets you choose components to match your project needs rather than being a full-stack solution. It provides freedom to tailor things as you want.
Image source: JetBrains
As per the JetBrains Python Developers Survey 2024, Django and Flask are by far the most popular Python web frameworks used by developers.
Python makes web development very rapid because of its vast collection of libraries and tools for common web dev tasks. It is great for developing APIs, web scraping, implementing web protocols, and automating web-based workflows.
Machine Learning and Data Science
Python plays a pivotal role in machine learning and data science due to its versatility and specialized libraries suited for these fields.
Some of the most popular Python libraries used for machine learning include:
- NumPy – Provides efficient arrays and mathematical operations for working with numeric data.
- Pandas – Offers easy data manipulation and analysis tools for modeling and analytics.
- SciKit-Learn – Provides a vast range of machine learning algorithms and models for common tasks like classification, regression, clustering, etc.
- TensorFlow – An end-to-end open source platform for machine learning from Google.
Many industry experts consider Python to be an ideal programming language for machine learning. Here are some key reasons why:
- Available libraries like NumPy, SciKit-Learn, Pandas, TensorFlow provide ready-to-use ML capabilities
- Supports all forms of machine learning – supervised, unsupervised, reinforcement, deep learning
- General purpose nature allows non-engineers to adopt Python for ML
- Interoperability with other languages like C, C++, Java
- Vibrant community provides learning resources and support
According to a KDnuggets survey, Python was used by approximately 64% data scientists and machine learning developers in 2024, far ahead of R (19%) and SQL (11%).
Image source: KDNuggets
Python is also very popular for data analysis and visualization using libraries like Pandas, Matplotlib and Seaborn. Overall, Python offers a complete platform for the data science workflow – from data loading and preprocessing to model building, evaluation and deployment.
Automation and Scripting
Python is commonly used to automate repetitive IT tasks, business processes and workflows through scripting. Here are some examples of automation use cases where Python scripts are very helpful:
- Monitoring IT systems and infrastructure – Scripts can regularly check system metrics like CPU usage, disk space, network bandwidth etc. and send alerts on issues.
- Managing backups and workflows – Automate backups to cloud services like AWS S3, schedule ETL pipelines, manage Airflow workflows etc.
- Cloud automation – Automate deployment of cloud infrastructure using providers like AWS, GCP, Azure.
- Business reporting – Scripts can connect to APIs, databases and Excel/CSV files to generate scheduled reports.
- Web scraping – Download and process content from websites automatically.
Python provides several advantages for automation like rapid script development, ability to mix procedural and OOP code, integration with APIs, built-in schedulers like Cron, libraries like psutil, paramiko that make system-level automation easier.
IT automation helps improve efficiency, reduce human errors and allow staff to focus on value-added tasks. Python‘s versatility and scalability make it a very popular choice for implementing automation.
Game Development
For game development, Python offers some unique benefits:
- Python has several game frameworks like Pygame, Panda3D, Pyglet, PySFML that provide the necessary tools for building games using Python.
- The language‘s high-level nature and dynamic typing allow for rapid prototyping of game ideas. Developers can quickly test gameplay logic without boilerplate code.
- Python has excellent community support for game development. Resources like Python Discord servers, Reddit forums, blogs aid learning.
- The clear syntax of Python makes game code easier to maintain compared to lower-level languages like C++. Less coding complexity helps focus on gameplay features.
- Python has good integration with other languages like C, C++, Cython allowing performance-critical parts to be optimized while keeping high-level game logic simple.
Some examples of popular games built with Python include Civilization IV, EVE Online, Disney‘s Toontown Online, Battlefield 2, World of Tanks. Independent developers building 2D games also often prefer Python for its flexibility.
While AAA game studios tend to use C++ for the highest performance needs, Python plays a key role in prototyping game ideas before full development with other languages. The rapid workflows and community resources make Python a very capable language for building games.
Web Scraping
For web scraping tasks like extracting data from websites, Python is the most popular choice among developers. Reasons for this include:
- Python has many libraries tailored specifically for web scraping like BeautifulSoup, Scrapy, Selenium, Requests etc.
- The syntax makes it relatively easy to write web scrapers to extract relevant information from HTML/XML responses.
- Scraped data can be easily manipulated using Pandas for filtering, analysis, visualization.
- Python code can interact with browser automation tools like Selenium/Playwright to scrape dynamic content.
Let‘s look at a quick example to scrape a table from a Wikipedia page:
import requests
from bs4 import BeautifulSoup
url = ‘https://en.wikipedia.org/wiki/List_of_largest_technology_companies_by_revenue‘
response = requests.get(url)
soup = BeautifulSoup(response.content, ‘html.parser‘)
table = soup.find(‘table‘, {‘class‘:‘wikitable sortable‘})
for row in table.findAll(‘tr‘)[1:]:
col = row.findAll(‘td‘)
company = col[1].text
revenue = col[2].text
print(company + " : " + revenue)This is just a small sample of Python‘s web scraping capabilities. Some common applications include:
- Aggregating data from multiple sites into one place
- Comparing prices and inventory levels across e-commerce sites
- Monitoring websites for content changes
- Academic/business research using public data
Due to the versatility, scalability and available tools, Python has become the go-to language for web scraping related projects.
Artificial Intelligence
Python plays a very important role in AI and machine learning applications. Its extensive libraries and framework support have made Python a popular choice for developing intelligent systems and training machine learning models.
Some of the most widely used Python AI libraries and tools include:
- TensorFlow – End-to-end platform by Google for building and training neural network models
- PyTorch – Provides tools for deep learning and neural net architectures
- Keras – High-level API focused on fast prototyping of neural net models
- scikit-learn – Provides a range of machine learning algorithms and modeling capabilities
- NLTK – Natural language toolkit widely used for building Python programs to work with human language data
- OpenCV – Algorithms focused on real-time computer vision and image/video analysis
- SpaCy – Performs advanced natural language processing tasks like named entity recognition
Here are some examples of AI applications where Python offers great capabilities:
- Building chatbots and virtual assistants using NLP libraries like NLTK.
- Image and video recognition using neural networks implemented in TensorFlow/Keras.
- Self-driving technology using computer vision libraries like OpenCV.
- Predictive modeling and forecasting of business metrics using scikit-learn.
- Sentiment analysis of social media conversations using NLP tools.
Leading technology companies like Google, Facebook, Amazon use Python extensively for their AI-related products and services. The language provides the right blend of performance, scalability and ease of use for developing intelligent algorithms and training robust models.
As we have seen, Python is a versatile general-purpose programming language used for a very wide range of applications – from building web applications to game development to machine learning models.
Some of its key strengths are code readability, vast libraries and frameworks tailored for specific domains, active community support and interoperability with other languages like C/C++.
Whether you are looking to get into web development, data science, automation or AI, learning Python is a great choice to unlock your programming potential. The demand for Python skills also continues to grow rapidly as organizations adopt it for critical business systems.
