Python libraries and frameworks are one of the major benefits of Python. A framework is like a blueprint while a library is more like a module. They’re both chunks of reusable code that are secure and standard. Sometimes the words library and framework are used interchangeably.
You can consider a framework a house, it can be used to create a whole app from foundation to ceiling. A library can be compared to the appliances and furniture inside the house. They make using Python faster, more secure, and compatible with other technologies because Python programmers don’t have to code everything from scratch.
Python libraries and frameworks are used for web development, data science, fintech, machine learning, and more. The most popular Python libraries are used for data science and the most downloaded frameworks are used for web development. With over 137,000 Python libraries and frameworks in the world, you can find a library for just about anything.
Sometimes the words library, framework, and package are all used synonymously to describe a collection of pre-written code. In actuality, a framework is a big-picture package while a library provides smaller chunks of code for specific tools or functions. A package can refer to a library, framework, or standard Python itself.
TensorFlow is used to create speech recognition systems, image and video recognition and tagging, self-driving cars, text summarization, analyze handwriting, recommendation systems, fintech predictions, and search engine ranking. Because TensorFlow is open-source and used by a large community, you’ll find a massive global support network online for this library.
Check out these TensorFlow projects to learn more:
- Use Teachable Machine to teach your computer using your webcam live in the browser.
- Play with Nsynth Super and make music using special sounds developed by the Nsynth algorithm in TensorFlow.
- Try creating your own neural network powered handwritten text recognition system using TensorFlow with this tutorial!
Also developed via a Google program, Scikit-Learn is written in Python, C, and C++ and it’s primarily used for supervised and unsupervised machine learning algorithms. It was designed to be easy to use, produce quality code, used in collaboration with other data scientists, and perform efficiently with larger amounts of data.
Scikit-Learn is built on top of other libraries to provide a complete package including NumPy, SciPy, Matplotlib, IPython, Sympy, and Pandas. It can execute classification, regression, and clustering algorithms as well as work with math operations, scientific computing, data visualization, symbolic mathematics, and data manipulation.
Who uses Scikit-Learn?
- Spotify uses Scikit-Learn for music recommendations.
- Evernote built a classifier tool using Scikit-Learn.
- J.P. Morgan executes predictive analytics with Scikit-Learn.
- Birchbox uses Scikit-Learn for managing customer preferences.
- Change.org drives email campaigns with Scikit-Learn.
- OkCupid uses Scikit-Learn to improve matchmaking.
Check out these Scikit-Learn projects to learn more:
- Try your hand at Analyzing stocks with Scikit-Learn and Pandas.
- Predict Wine Quality with this Scikit-Learn project.
- Try predicting the demand for a bike-sharing terminal with this SciKit-Learn playground competition.
Numpy is a numerical library used for machine learning and data science. It’s written in C and Python. Numpy can be used with TensorFlow and Scikit-Learn. It’s used for data manipulation and mathematical operations.
Numpy makes big data projects easier. It is used to work with arrays, linear algebra, Fourier transform, and matrices. These are all math processes commonly used to manipulate data to make predictions, understand margins of error, and make using arrays in Python faster.
Who uses Numpy?
- Instacart uses Numpy to predict how many shoppers they need to schedule each week.
- Walmart uses Numpy to analyze and predict customer and sales data.
- Trivago uses Numpy to label images for an individualized search engine and the optimal user experience.
Check out these Numpy projects to learn more:
- Try your hand at analyzing passenger data from the Titanic with Numpy!
- Predict customer outcomes and optimize customer retention with Numpy.
- Make decisions using Random Forest and Numpy.
PyTorch is a machine learning Python framework that also has a C++ interface. It was designed to make the journey from research prototyping to production deployment more efficient. It’s based on the Torch library, a collection of deep learning algorithms, which is no longer being actively developed.
Pytorch supports computer vision and natural language processing. It provides tensor computing, similar to NumPy, and strong acceleration as well as deep neural networks. It’s primarily used to develop and train neural networks based on deep learning models.
Who uses PyTorch?
- Facebook developed PyTorch in their AI Research Lab (FAIR) based on Torch.
- Tesla used PyTorch to create Tesla Autopilot, deep learning software for self-driving cars.
- Uber created Pyro using PyTorch. Pyro is a tool used for deep probabilistic modeling.
Check out these Pytorch projects to learn more:
- Learn how to create neural networks in PyTorch.
- Learn how a deep learning network classifies images with Grad-CAM.
Beautiful Soup is a Python package that is used to parse HTML and XML documents and extract data from HTML. While technically considered a data science package, it’s useful for more than just data scientists.
Beautiful Soup is usually used for web scraping. Web scraping is used by many digital businesses and data scientists for things like search engine bots, price comparison sites, market research companies, fintech analysts, and more.
Who uses Beautiful Soup?
Beautiful Soup is used by many individual developers and data scientists but isn’t listed by any specific company in their tech stack.
Check out these Beautiful Soup projects to learn more:
- Learn how to scrape the web with Beautiful Soup in our Python for Data Science Course!
Django is a high-level Python framework for web development. By providing almost everything a developer needs to create a website or web app, it encourages quick and efficient web development. Django’s code is secure, scalable, and maintainable so you can focus on building your website, not writing every line of code.
Django can be used to create content management systems and wikis, social networks, and news sites. It can be used with almost any client-side framework and works with most web formats. Django is a free and open-source framework with a large, supportive online developer community.
Who uses Django?
- Instagram uses Django for error reporting, using an app developed by Disqus.
- Disqus runs its commenting system on Django.
- National Geographic’s custom content management system for their magazine’s website was built with Django.
Check out these Django projects to learn more:
- Build a website for your local library.
- Poll your friends through a simple polling application created with Django.
- Make a real-time chat app with Django and Vue.
Flask is a microframework written in Python for web development. It’s considered a micro framework because, unlike Django, it does not provide tools and libraries for everything. This gives the developer more freedom to choose the third-party apps that best fit their needs without suggestions from their framework.
Flask can be used to build web pages, blogs, wikis, web applications, commercial websites, or modules within a web application. Flask is often compared with Django, which was written several years before Flask. Flask is used for simple web apps, while Django is usually used in more complex scenarios.
Who uses Flask?
- Airbnb uses Flask for Airflow, a proprietary workforce management platform.
- Lyft uses Flask’s signaling module to supply data to the web user interface.
- Uber’s marketplace team uses Flask to optimize dynamic pricing, supply positioning, and intelligent match.
Check out these Flask projects to learn more:
- Create a flight tracking app with Flask
- Plan a road trip with this beautiful app built with Flask and Mapbox
- Get coffee-obsessed with Bean Counter, a Flask app for specialty coffee enthusiasts
- Search jobs that have been posted on Reddit with Junter
Pandas is a Python library written in Python and C that’s used for data science. It offers data structures and operations which can be used as tools for executing data handling, data manipulation, indexing, reading and writing data, and data analysis.
Pandas is open-source and has a massive online support community. Pandas can be used in combination with many other libraries. It’s commonly used to import and manipulate data from Excel files, manage data for recommendation engines, personalize advertisements, and predict stocks.
Who uses pandas?
- Instacart uses pandas to cluster data like deliveries
- J.P. Morgan uses pandas to create yield curve charts to inform their decisions
- Netflix uses pandas for their recommendation engine
Check out these pandas projects to learn more:
- Make your job search more efficient by building your own personalized job board with pandas
- Create your own recommendation engine using pandas