Top 42 🐍 Python libraries you need to know 🦾

TL;DR

Dive deep into Python with this cheat list featuring the only libraries any Pythonista needs to know.
From data manipulation to Machine Learning and creating web applications, these libraries are essential in your Python coding journey.

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Web applications

1. Taipy

Taipy is the new kid on the block. The simplest Python app builder.
It was designed for easy development for both front-end (GUI) and your ML/Data pipeline(s).

Create the application of your dreams thanks to:

• complete customization & interactivity
• multipage & multi-user applications
• pipeline graphical editor
• and so much more!

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Star ⭐ the Taipy repository

Your support means a lot🌱, and really helps us in so many ways, like writing articles! 🙏

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2. Streamlit

Streamlit is a well-established library to quickly create web applications for pilots. Very easy to use!

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Essentials

3. Pandas

This library brings two core concepts, dataframes, and series, making data cleaning and preparation a painless process.

4. Numpy

While Pandas has data frames, Numpy has arrays.
They are known for allowing fast data manipulation, making Numpy an essential tool for scientific computing.

5. Requests

This library makes dealing with HTTPS requests a breeze.
Requests provides functions for interacting with web APIs and managing HTTP responses.

6. Scipy

Based on Numpy, Scipy’s core functions focus on mathematical computing with features around optimization, signal processing, and interpolation.

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Date & Time

7. DateTime

DateTime is a standard Python library that is essential for dealing with any DateTime format.

8. Pendulum

Pendulum has additional features necessary for more advanced date and time handling.
They have better time zone support as well as better formatting options.

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Machine Learning

9. Scikit-Learn

This library doesn’t need an introduction anymore, and rightfully so.
Scikit Learn is the reference for Machine Learning with algorithms from clustering to classification.
It also includes functions for everything from data validation to data selection.

10. XGboost

This library is well-known for its efficient results for regression and classification algorithms.

11. Catboost

Catboost is a Machine Learning library specifically designed to deal with datasets displaying mostly categorical data.

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Deep Learning

12. TensorFlow

TensorFlow is a well-established deep-learning library specializing in Natural Language Processing and image classification.

13. PyTorch

Pytorch or TensorFlow, that is the question.
Ultimately, you choose your team, but PyTorch differentiates with a more significant focus on Natural Language Processing and a more Pythonic feel, reducing the learning curve known to be steep for TensorFlow.

14. Keras

Keras is a great way to start with Deep Learning as it runs on top of TensorFlow but with a simplified implementation process.

15. OpenCV

OpenCV provides various algorithms around real-time computer vision.
You can process multiple formats including objects, humans, and even handwriting.

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Natural Language Processing

16. NLTK

NLTK is the go-to library for Natural Language Processing.
NLTKs' key features are: processing and manipulating text( tokenization, stemming, etc.…) and classifications with NLP tasks for sentiment analysis, for example.

17. SpaCy

Is the newer kid on the block, with a focus on making NLP more accessible and user-friendly.
The library optimized the process to guarantee greater speed and efficiency.

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Testing

18. Pytest

Pytest is a framework that simplifies test writing and execution. It is user-friendly with its concise syntax.

19. Unitest

Unistest is Python’s built-in testing framework.
Its key features are: test discovery, support for fixtures, effortless organization, and management of test suites.

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Audio

20. AudioFlux

The go-to library in Python for audio signal processing, but made easy.
AudioFlux has a plethora of features including sound analysis and can be used for deep learning training.

21. Librosa

This Python library allows for analyzing and extracting features from audio sources.

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Code Analysis

22. Black

It is an automated code formatter.
It will format your code automatically for a consistent style throughout your projects.

23. Pylint

As the name infers, Pylint is a linter.
It is a static code analysis tool that checks for code quality and errors.

24. Flake8

It is another linting library that will check your code against the PEP8 coding convention.

25. Ruff

Ruff is the fastest option to equivalent linters.
It adds effectiveness and speed, making the process ten times faster.

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Distributed Computing

26. Dask

Dask is a popular Python package for distributed computing, as it is particularly helpful in dealing with large datasets.
It is easy to use as Dask integrates Pandas, Numpy, and Scikit-learn APIs . 

27. PySpark

As the name implies, PySpark is a Python API for Apache Spark and allows us to harness Spark’s capabilities directly in Python.

28. Polars

Polars is a DataFrame library created to handle and process large datasets.
It was inspired by python royalty - Pandas, but with a (fast) twist, it’s 10 to 100 times faster.

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Documentation

29. Mkdocs

Mkdocs is the most accessible library to generate straightforward documentation.
Suitable for smaller projects and has almost no learning curve.

30. Sphinx

Sphinx is usually preferred for larger-scale projects.
It includes support for multiple formats and allows for specific customization.

31. Pydoc

Pydoc is integrated into the Python ecosystem. It directly generates your documentation from your modules.

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Geographical data

32. Geopy

Geopys’ key features are: distance calculations, geocoding & reverse geocoding.

33. Folium

This library allows you to create interactive maps in Python. A game-changer.

34. Geopandas

The way to go when you have geospatial data.
As the title states, Geopandas is Pandas but for geospatial data. This library has functions for easy manipulation and analysis of geo-data.

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Games

35. Pygame

Pygame is the go-to, straightforward library that makes creating 2D and interactive video games in Python easy.

36. Arcade

Just like PyGame, Arcade, makes creating video games a fun process in Python.
They have a more modern twist to the classical Pygame, so choosing is really based on personal preference.

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Web scraping

37. Scrapy

Scrapy is a well-established library known for web scraping.
Some key features are: support for asynchronous/synchronous operations, HTTPS request handling, etc.
It has an extensive array of functionalities, which may justify the library has a steep learning curve.

38. Beautiful Soup

Beautiful Soup is all you need to deal with pulling data out of XML and HTML files.
It is appreciated by developers thanks to its Pythonic feel.

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Visualizations

39. Matplotlib

Matplotlib is the main widget library in Python and for a good reason.
Matplotlib allows the plotting of 2D graphs with a wide range of chart types and also allows for significant customization.
The fine-grain control of the elements is a real advantage of this library.

40. Bokeh

Bokeh, contrary to Matplotlib, has its focus on interactive charts.

41. Seaborn

Seaborn is built on top of Matplotlib.
While Matplolib has an emphasis on preciseness and simplicity, Seaborn has real added value in their sleek visuals while creating complex statistical visualizations.

42. Vizzu

Vizzu found a niche in visualization and do it very well.
They’ve put storytelling and graphs all in one with their highly animated visualizations—a great way to have more dynamic graphs.

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Conclusion

Whether you’re a senior Pythonista or dabbling with Python, with this list of indispensable libraries you will be able to undertake any challenge. Have fun coding!

I’m a rookie writer and would welcome any suggestions for improvement!

Feel free to reach out if you have any questions.