20 Most Useful Python Libraries

Python LibrariesPython is a high-level programming language, simple to learn, object oriented, modular and emphasizes readability.

The applicability of Python is wide spread and it is used in education sector, scientific and numeric computing, big data and machine learning, web and internet development, graphics, GUI, games and much more.

Every python library enables one or more tasks to be done easily or without the need to write too much of code.

If you are into Python programming, it is good know about the most useful libraries available out there in the wild. Our team of Python experts has gone through most of the libraries, packages, and modules and compiled here is the list of 20 Most Useful Python Libraries for Python programmers.

1. Requests

With over 26K stars on GitHub and thousands of downloads every day, Requests ranks #1 in the list of most popular and useful libraries for Python development. This library is used in projects of Spotify, Microsoft, NPR, Heroku, Amazon, BuzzFeed, Reddit, Twitter, Mozzila, and the list is very long.

Requests takes off the manual labor work and automates the task of adding query strings to URLs, form encoding of post data, multipart files, keep alive and Http connection pooling etc. by utilizing urllib3.

Automatic content decoding is also provided to cut down the development efforts. It offers loads of other features like digest authentication, elegant/value cookies, Unicode response bodies, streaming downloads, connection timeouts, .netrc support and so on.


2. Simplejson

Simplejson is another library in the wild which is highly popular and happens to be one of the most downloaded python library. Simplejson is fast, correct and extensible json encoder and decoder, and supports Python 3.3+ with backward compatibility to Python 2.5.

Simplejson is written in python and has no external dependencies. It also packages an extension written in C for lightning fast performance. Apart from the default UTF-8, Simplejson decoder can handle JSON strings coming in with any encoding.

With the default Kwarg to dumps, simplejson encoder can be used to provide serialization and without the support of objects to be serialised.

Simplejson library can also post-process JSON objects with object_hook or object_pairs_hook kwargs. The implementation of protocols such as JSON-RPC are much easier while using simplejason.


3. SQL Alchemy

Databases are integral to application development, and in the world of Python, SQLAlchemy is the go to library for working with databases. It acts as SQL toolkit and Object Relational mapper by offering full suite of enterprise persistent patterns for high performance database access.

The cool thing is the data mapper pattern, in which different classes are mapped to the database in different ways, it allows the object model and database schema to be decoupled from the start itself.

Developers get full control and visibility of the SQL construction, nothing is hidden behind the walls of wrappers.

The approach of this library is far more efficient and modern when compared to other SQL / ORM tools available out there in the wild, and for that reason, it ranks much higher in our list of most useful Python libraries.

With the recent release, just a couple of months back, SQLAlchemy is far ahead in the game and is used by organizations like Freshbooks, Survey Monkey, Mozilla, reddit, Yelp and many more.


4. TensorFlow

TensorFlow goes beyond the basics and takes you to the world of machine intelligence. This is an open source python library that is useful for doing calculations using data flow graphs.

The computation is initially represented in the form of graphs with every node of the graph meant to do some mathematical operation. The actual computation, however, is done on demand, which allows for great performance boost in complex calculations.

The library caters to the need of complex computations by catering to distributed computing across CPUs/GPUs, and multiple systems while taking care of redundancy.

TensorFlow is available as open source library and free to use, it was originally developed by Google’s engineers working on Google Brain project.


5. Scrapy

Scrapy is an open source and collaborative framework which is used to extract structured data from websites. This library is fast, simple, extensible, easy to use and its application includes data mining, information processing, web crawler, historical archival, data extraction using APIs and much more

Scrapy provides out of the box support for extracting data from HTML/XML using Xpath expressions and CSS selectors, an interactive shell console, data export into json, csv, xml, backend storage ftp, s3, local storage, cookies and session handling, compression, caching, authentication, user-agent spoofing, crawl depth restriction and much more.

Scrapy lets you write the rules for extracting data, is written in Python and its design can be extended with new functionality, and you can port it to run on Linux, Windows, Mac and BSD.


6. Matplotlib

Matplotlib is 2D numerical plotting library for Python. It can be of great use for data analysis and produces high-quality figures in different formats including interactive graphs and plots.

It is possible to use Matplotlib in Python scripts, IPython shell and on a verity of different web application servers. It makes difficult plotting tasks much simpler and helps to generate plots, bar charts, histograms, error charts, scatterplots etc. by writing just a small piece of code.

The pyplot module and Ipython combo offers MATLAB like interface for plotting simple graphs. You can also have the full control of line styles, axes properties and so on and is undoubtedly one of the best numerical plotting libraries available for Python.

Mataplotlib powers are further multiplied with the help of toolkits like basemap, cartopy, mplot3d, axes_grid and many more.

Matplotlib is the baby of John Hunter and is used by thousands of researchers, scientists and publication projects.


7. Scikit-Learn

Scikit-learn is your high-level machine learning library and provides off the shelf algorithms like random forests, ready to be used in machine learning projects.

Scikit-learn is written mainly in Python but it also uses Cython for performance enhancements in some of the core algorithms. Cython wrapper around LIBSVM is used for support vector implementation and LIBLINEAR for logistical regression and linear support vector machines. Scikit-learn also makes use of CBLAS which is a C interface to utilize Basic Linear Algebra Subprograms (CBLAS) library.

This library is built on top of SciPy and distributed under the 3-Clause BSD license for open source, for research as well as for commercial use, and is one of the best python libraries.


8. Pygame

Like developing games? Pygame is the open source library that is for making multimedia applications including games, built on top of powerful Simple Directmedia Library (SDL), and yes, it is used to teach games development to kids.

Pygame is powerful since it doesn’t require OpenGL, allows to make use of multiple CPUs to work in multicore system for super performance, and makes use of optimized C and Assembly code for the core functions.

Pygame is highly portable and can run on almost all platforms and operating systems. Millions of people have downloaded it and arguably it is one of the best game library for Python.


9. Arrow

Arrow is meant to overcome the shortcomings of the built-in date/time functionality of Python, which is not that clean and easy to use. It can be used as a perfect replacement to datetime and time modules of Python.


10. wxPython

wxPython is one of the three popular GUI libraries for Python, the top three names are PyQT, Tkinter and wxPython.  wxPython is intuitive to python developers and simple to use, it is a perfect blend of C++ wxWidgets with Python programming.

wxPython is implemented as a Python extension module and is a cross-platform toolkit that runs on different platforms without the need for modification. It is supported on many platforms including Unix distributions, Macintosh OS X and Microsoft Windows (32 bit).

wxPython offers tons of features that allow to create robust and functional application GUIs in Python with ease.


11. Beautiful Soup

Beautiful Soup is used for parsing HTML and XML documents, in short you dissect the document and get the contents you need. It offers simple methods that are useful for navigating, searching, and modifying a parse tree.

Beautiful Soup can also be used for converting incoming documents to Unicode and outgoing documents to UTF-8. Beautiful Soup is not a full web scrapper like Scrapy, it offers basic features and is used for simple data parsing needs where you do not need a full features web crawler like Scrapy.


12. Natural Language Toolkit (NLTK)

This is an advanced python library used for creating Python programs that work with natural human language.

This library offers different interfaces to different corpora and lexical resources like WordNet. NLTK also offers a suite of text processing libraries which are used for classification, tokenization, parsing, semantic reasoning, tagging, stemming etc. as well as wrappers for most used NLP libraries in the market.

NLTK is open source, community driven and free to use for personal as well as commercial projects. It is available for Linux, Windows, Mac OS X and used heavily in universities and institutes for teaching.


13. Twisted

This library acts as an event-driven networking engine and makes the implementation of custom network applications easy.

It supports TCP, UDP, SSL/TLS, IP multicast, Unix domain sockets, and many other protocols like HTTP, XMPP, NNTP, IMAP, SSH, IRC, FTP, etc. It is event driven which gives an option to developers to write short callbacks to be further accessed by the framework.


14. Pyglet

Pyglet is a library that can be used for creating multimedia and windows applications in Python. You can load and play around with music, images video and other sounds in any format you prefer.

This library has no external dependencies nor does it have any installation requirements and needs core Python for most applications and multimedia projects.


15. Nose

Another library that targets the basic needs of any development project – unit testing. Nose is built on top of unittest and can be used as full featured testing framework for Python.

Pytest is another full featured test framework which is used heavily for python testing for last quite some time. The latest version of Nose, however, is giving tough competition to Pytest with its much simpler approach to unit testing, in short, it is back in the game again.

Nose gets its powers from the classless tests, assert introspection plugins, and many more other plugins. If test driven development is game on, then this library is for you.


16. Peewee

Peewee is a very simple no overhead ORM which has very expressive, has few concepts and very easy and intuitive to use. Peewee supports many databases including sqllite, postgresql and mysql, out of the box.

Peewee can be extended in functionality and support for other databases using extensions, there are many out there.


17. Scapy

Scapy is a used to forge and sniff packets in Python. It enables developers to decode packets of many different protocols, capture them, match requests and responses and so on.

Scapy also comes in very handy for doing tracerouting, probing, attacks and network discovery and scanning etc.


18. NumPy

It is difficulty to leave NumPy and SciPy from this list. NumPy is a library that provides math functionalities and a scientific computing package for Python that includes N-dimensional array object, sophisticated (broadcasting) functions and tools of integrating code from C, C++ and Fortran.

NumPy is useful for linear algebra, Fourier transform and random number capabilities. In addition, this library can also be used as a multi-dimensional container of generic data. It allows arbitrary data types to be defined and makes the integration of different databases seamless and fast.


19. SciPy

SciPy is provides a collection of open-source software needed for scientific computing. Scipy also utilizes NumPy for array and matrix processing.

SciPy contains Python modules useful for common tasks used in science and engineering like linear algebra, optimization, integration, interpolation, image processing and much more.


20. IPython

IPython is a different animal altogether, it is an interactive command shell for programming in Python and for many other languages. It supports coding, mathematical expressions, inline plots, data visualizations etc. right in the browser.

It also provides support for many GUI toolkits and can be embedded in any project as a interactive shell, includes tools for parallel computing.


21. SymPy

SymPy is an open source library, written in python itself and is useful for symbolic mathematics. A full featured CAS or Computer Algebra system is the goal of this library, not yet there, but not far of either.

It is used in many popular Python projects including names like Cadabra, Lcapy, galgebra, sageMath and many more.

In Summary

Python is the language for masses and is used in education, scientific research, machine learning, mathematical computations, GUI development, games development, web development, games development and has some application in mobile development as well.

The shear count of developers across the globe who work with and love python is beyond imagination, and for that reason, there are tons of libraries to automate one or other of programming tasks.

The libraries that we have seen in this article are among those which are widely used, are proven and well established. Hope you liked the article and got what you were looking for, do share your thoughts via comments. Cheers!

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