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Top 42 ๐Ÿ Python libraries you need to know ๐Ÿฆพ

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.

Introduction GIF


Web applications

Web applications

1. Taipy

Taipy is the new kid on the block.
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!

QueenB

Star โญ the Taipy repository

Your support means a lot๐ŸŒฑ, and really helps us in so many ways, like writing articles! ๐Ÿ™


2. Streamlit

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


Essentials

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, Scipyโ€™s core functions focus on mathematical computing with features around optimization, signal processing, and interpolation.


Date & Time

Datetime

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.


Machine Learning

Machine Learning

9. Scikit-Learn

This library doesnโ€™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.


Deep Learning

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.


Natural Language Processing

NLP

16. NLTK

NLTK is the go-to library for Natural Language Processing.
NLTKs' key features are: processing and manipulating text( tokenization, stemming, etc.โ€ฆ) 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.


Testing

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 Pythonโ€™s built-in testing framework.
Its key features are: test discovery, support for fixtures, effortless organization, and management of test suites.


Audio

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.


Code Analysis

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.


Distributed Computing

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 APIsโ€Š.โ€Š

27. PySpark

As the name implies, PySpark is a Python API for Apache Spark and allows us to harness Sparkโ€™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, itโ€™s 10 to 100 times faster.


Documentation

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.


Geographical data

Geodata

32. Geopy

Geopysโ€™ 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.


Games

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.


Web scraping

web scarping

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.


Visualizations

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.
Theyโ€™ve put storytelling and graphs all in one with their highly animated visualizationsโ€”a great way to have more dynamic graphs.


Conclusion

Whether youโ€™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!

Iโ€™m a rookie writer and would welcome any suggestions for improvement!

Rookie image

Feel free to reach out if you have any questions.

Top comments (13)

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proteusiq profile image
Prayson Wilfred Daniel • Edited

I would have grouped them into utility categories e.g.

  1. Data Tools (Viz & Processing & Computing)
  2. Web | Scraping Tools
  3. Machine Learning( Frame Works, Algorithms & Tools in NLP, Traditional ML)
  4. Geospartial/Map
  5. Code Development (Documentation & Tests)
  6. Games Development (Games | Audio)

In that way, I could present a package and its alternative. For example Pandas and Polars, Unittest and Pytest, NLTK and Spacy. Here we can see that Polars, Pytest and Spacy are tools designed to solve some issues the others had.

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marisogo profile image
Marine

That's a great idea actually!

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debadyuti profile image
Deb

Wow! This is huge list!

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fredericg78 profile image
FGPROD

No surprises on this list, but a good starting point !
Should be presented as arrays with comparative pros and cons.
thanks !

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jamesmurdza profile image
James Murdza

Here are a few missing great ones:

Flask โ€” for creating simple and lightweight web apps
FastAPI โ€” for building web APIs
Tornado โ€” for asynchronous networking
Plotly โ€” for interactive plotting and graphing
Pillow โ€” for image processing and manipulation
SymPy โ€” for symbolic mathematics and algebra
PyMongo โ€” for working with MongoDB

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rym_michaut profile image
Rym

love this summary! <3
Thank you!

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aleajactaest78 profile image
AleaJactaEst

Wow, 42 libraries to know! That's an impressive list. It seems like you could have put more but decided to stop at 42

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marisogo profile image
Marine

What can I say, lucky number!

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ngud0119 profile image
NGUD-0119: ACE

Awesome.

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fernandezbaptiste profile image
Bap

Now that's a GOOD LIST!

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matijasos profile image
Matija Sosic

wow, this is a big one! I love how you went for 42 things to cover :).

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puffhaus profile image
Puffhaus

Agree, Librosa is great

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elisedev profile image
Elise Hasenberg

Awesome share thank you!!