Python is a powerful high-level, interactive, object-oriented scripting language created by Guido Van Rossum in the late 1980s. It is an object-oriented language, meaning it can model real-world entities.
Python is considered one of the most popular and in-demand programming languages, and Python codes are compiled line-by-line, which makes debugging errors much easier and more efficient. A recent Stack Overflow survey showed that Python has taken over languages such as Java, C, and C++ and has made its way to the top.
Python Programming language is widely used by companies and individuals in wide applications like building web apps, data science and analytics, automating operations via Develops, and building scalable and reliable enterprise applications. This most versatile language in the programming world applies in almost every software development domain. It helps us take care of our current programming task and lets us focus on the core functionality of Python programming languages.
This article lists the top Python libraries used by top companies in different industries for various applications.
Python Library for Natural Language Processing (NLP)
1. NLTK: Natural language toolkit (NLTK) is the most popular library for natural language processing (NLP) which was written in Python and had a big community behind it. NLTK is also very easy to learn; it’s the easiest natural language processing (NLP) library you’ll use.
2. Spacy: Spacy is an open-source library for industrial-strength Natural Language Processing, built using Python and Cython. It combines the best of both languages, the convenience of Python, and the speed of Cython to deliver the best-in-class NLP experiences.
3. CoreNLP: CoreNLP is a suite of tools for implementing NLP into a project. CoreNLP was initially written in Java but also functioned with many other languages, including Python and JavaScript. It offers several linguistic features such as extracting relations between entities, indicating sentiment, marking up sentence structures, providing the base form of words, translating to Lemmatization, Part-of-Speech, and Morphological Tagging, Named Entity Recognition, Tokenization, and Sentence Split.
4. Gensim: Gensim is a library for Topic Modelling, Similarity Retrieval, and Natural Language Processing written in Python. Gensim provides memory-independent implementation capabilities for several popular algorithms such as Random Projections (RP), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA/LSI/LVD), and Hierarchical Dirichlet Process (HDP).
5. PyNLPI: Pronounced as “Pineapple,” it is an open-source Natural Language Processing library for Python. PyNLPl is ideal for various tasks ranging from building simplistic models and extracting n-grams and frequency lists, with support for complex data types and algorithms.
6. Pattern: Pattern is primarily a web mining module for Python with the inclusion of tools for various purposes such as Data Mining, Natural Language Processing, Machine Learning, and Network Analysis. Focusing on the NLP component of Pattern, it is well-equipped with all necessary NLP components, including Sentiment Analysis, WordNet, Text Classification, Part-of-Speech Taggers, and N-grams.
7. TextBlob: Textblob is another open-source Python library for processing text-based data. Through its APIs, it provides easy integration with other programming languages. If you need to complete common NLP tasks quickly, Textblob can save the day. It offers several NLP features that are common to all NLP libraries, including Sentiment Analysis, Classification, n-grams, Tokenization, Word Inflection, WordNet Integration, Language Translation, and Detection Powered by Google Translate, Word and Phrase Frequency, Parsing, Spelling Correction, and Add New Models or Languages through Extensions.
Python Library for Computer Vision
8. OpenCV: OpenCV is a popular open-source computer vision library focused on real-time applications. The library contains several thousand computer vision algorithms and is organized in a modular structure. OpenCV includes several modules, including image processing, video analysis, 2D feature framework, object detection, camera calibration, and 3D reconstruction.
9. Fastai: It is a deep learning library that offers high-level components capable of producing cutting-edge outcomes in conventional deep learning domains quickly and simply. Researchers are also given low-level components that they can combine and match to create new methodologies. Among the features of Fastai is a computer vision library that is GPU-optimized and extensible in pure Python, a new type dispatch system for Python, and a semantic type hierarchy for tensors.
10. IPSDK: IPSDK is an image processing library in C++ and Python, offering a complete range of image processing features to process datasets and a comprehensive and optimized range of functionalities for 2D and 3D image processing. It automatically adjusts itself to the architecture and capabilities of the processor. The features of this library include full PC cluster support, high performance, and high availability computing.
11. Imutils: It is a computer vision package that includes OpenCV + convenience functions to make basic image processing functions such as rotation, translation, resizing, skeletonization, displaying Matplotlib images, sorting contours, and detecting edges, among others, quite easy.
12. Pytessarct: Pytessarct (Python-tesseract) is an optical character recognition (OCR) tool for the Python language. A wrapper for Google’s Tesseract-OCR Engine, it helps recognize and read the text embedded in an image and can read all image formats supported by the Pillow and Leptonica imaging libraries, including jpeg, png, gif, bmp, tiff, and others, is also helpful as a standalone invocation script to tesseract.
13. PyTorchCV: It is a PyTorch-based framework for computer vision tasks. The framework collects image classification, segmentation, detection, and pose estimation models. This framework implements several models, including AlexNet, ResNet, ResNeXt, PyramidNet, SparseNet, DRN-C/DRN-D, and more.
14. SimpleCV: One of the popular machine vision frameworks for building computer vision applications, SimpleCV is a library written in Python that helps access several high-powered computer vision libraries, such as OpenCV. It is a collection of software and libraries that can be used to develop vision applications, providing a concise, readable interface for cameras, image manipulation, feature extraction, and format conversion. It also allows users to work with the images or video streams from webcams, Kinects, FireWire and IP cameras, or mobile phones.
Python Library for Graphical User Interface
15. Kivy: Kivy is an OpenGL ES 2 accelerated framework for creating new user interfaces. It supports multiple platforms: Windows, MacOSX, Linux, Android, iOS, and Raspberry Pi. It is open source and comes with over 20 widgets in its toolkit.
16. PyQT: PyQT is one of the most popular cross-platform Python bindings implementing the Qt library for the Qt application development framework. Windows, Mac OS X, Unix/Linux, and Sharp Zaurus all support PyQT. It combines the best of Qt and Python, and it is up to the programmer to choose whether to write a program by hand using the Qt Designer to create visual dialogues or the Qt coding environment. Both commercial and GPL licenses are offered for it. If your application is open source, you can use it under the free license even though some features might not be available in the free version.
17. Tkinter: Commonly bundled with Python, using Tk, Tkinter is Python’s standard GUI framework, which is popular for its graphical user interface and simplicity. It is accessible as open source and is covered by the Python License. Tkinter has a lot of resources, including codes and reference materials, because it is included by default, which is one of its benefits. Due to the community’s longevity and activity, numerous users can assist you if you have any questions.
18. WxPython: WxPython is an open-source implementation of the Python extension module for the cross-platform GUI library WxWidgets (previously known as WxWindows). You can develop native applications for Windows, Mac OS, and Unix using WxPython.
19. PyGUI: PyGUI is a graphical application cross-platform framework for Unix, Macintosh, and Windows. Because the API is only in sync with Python, PyGUI is the most straightforward and lightweight GUI framework. Since PyGUI inserts very little code between the GUI platform and the Python application, the platform’s default GUI is typically displayed on the application’s screen.
20. PySide: Nokia created and sponsored PySide, a free and open-source Qt GUI toolkit. Qt is a cross-platform program and UI framework. Currently, PySide supports Linux/X11, Mac OS X, Maemo, and Windows. Support for Android is forthcoming. PySide offers tools for working with databases, GUIs, networks, XML documents, and multimedia. The ease of migration to PySide is made possible by PySide’s API compatibility with PyQt4, which is a key feature of PySide.
Python Library for Game Development
21. Pygame: Pygame is an open-source Python library for making multimedia applications like games built on top of the excellent SDL library. This library combines C, Python, Native, and OpenGL, enabling the users to build fully featured games and multimedia programs. It runs on almost every platform and operating system and is very portable. Pygame features are multicore CPUs that can be used easily, uses optimized C and Assembly code for core functions, are Simple and Portable, and A small amount of code is needed.
22. Pyglet: Pyglet is an open-sourced, cross-platform windowing and multimedia library for Python that can be used for developing games and visually-rich applications on Windows, Mac OS X, and Linux. It supports windowing, user interface event handling, Joysticks, OpenGL graphics, loading images and videos, and playing sounds and music. Pyglet runs under Python 3.5+ and works on other Python interpreters such as PyPy.
23. PyOpenGL: PyOpenGL is the most common cross-platform Python binding to OpenGL and related APIs, in which the binding is created using the standard ctypes library. PyOpenGL is interoperable with many external GUI libraries for Python, including PyGame, PyQt, and Raw XLib.
24. Panda3D: Written in Python and C++, Panda3D is an open-source and free-to-use engine for real-time 3D games, visualizations, simulations, experiments, and more. Panda3D includes command-line tools for processing and optimizing source assets, allowing users to automate and script the content production pipeline. It also supports many popular third-party libraries, such as the Bullet physics engine, Assimp model loader, OpenAL, and FMOD sound libraries.
25. Kivy: Kivy is an open-source, cross-platform Python library for rapidly developing applications. Kivy runs on Linux, Windows, OS X, iOS, Android, and Raspberry Pi. It can natively use most inputs, protocols, and devices, including WM_Touch, Mac OS X Trackpad, WM_Pen, Magic Mouse, Mtdev, Linux Kernel HID, and TUIO. The library is GPU accelerated and comes with more than 20 highly extensible widgets.
Python Library for Web Application
26. Django: It is a free and open-source Python framework that enables developers to quickly develop complex code and apps. Django framework assists in developing quality web applications. It is among the best python frameworks and is used to quickly develop APIs and web applications. Key Features of Django are Assist you in defining patterns for the URLs in your app, a Built-in authentication system, a Simple yet powerful URL system, an Object-oriented programming language database that offers the best data storage and recovery; the automatic admin interface feature enables the functionality of editing, adding, and deleting things with customization, and Cache framework accompanies multiple cache mechanisms.
27. CherryPy: CherryPy is an open-source Python web development framework that embeds its multi-hung server and can run on any working framework supporting Python. It has a moderate web framework that empowers you to use any technology for data access, templating, etc. It can do everything a web framework can, for example, taking care of sessions, file uploads, static, and cookies. With CherryPy, programmers can create web applications just as they would any other object-oriented Python program. Smaller sources of code are produced faster as a result. CherryPy’s primary attributes are a reliable, WSGI thread-pooling, HTTP/1.1 compliant web server, simple to simultaneously run multiple HTTP servers (for instance, on different ports), and runs on Android, PyPy, Jython, and Python 2.7 and higher. Built-in tools for authentication, caching, sessions, encoding, and many other tasks, a robust configuration system suitable for developers and deployers, and support for testing, coverage, and profiling built in.
28. Pyramid: Pyramid’s popularity is progressively growing. Most experienced developers are embracing it. Pyramid frameworks run on Python 3. This flexible framework allows users to develop basic web apps via a minimalistic approach. Key Features of Pyramid are Seamless development of single-file applications, Generation of URLs, Extendable configuration, Alterable authentication and authorization, Asset specifications, and templates.
29. Sanic: Sanic is an open-source and one of the basic python frameworks. It was intended to offer fast HTTP responses by asynchronous request handling. It enables the use of the async and awaits syntax included in Python 3.5, making the code much simpler, fast, and non-blocking. The project’s purpose is to have an easy way to get up and run, simple to build, extend, and eventually scale the HTTP server. The key features of Sanic are providing plugin support, Class-based views, Able to write and read cookies, And various types of logging like error logs and access logs.
30. Grok: Grok framework is a web framework based on the Zope toolkit technology. It gives an agile development experience to developers by concentrating on two general principles – convention over configuration and DRY (Don’t Repeat Yourself). It is an open-source framework developed to speed up the application development process. Developers can choose from a wide scope of network and independent libraries as indicated by the task needs. Grok’s UI (user interface) is like other full-stack Python frameworks such as Pylons and TurboGears. Key Features of Grok offers a strong foundation for developing powerful and extensible web applications, Enables web developers to take advantage of the power of Zope 3, has a powerful object database for storage, and Integrated security to ensure your application and grant access to specific users. Grok component architecture helps developers lessen the unpredictability of development, Offers the building blocks and other essential assets to develop custom web applications for business needs.
31. Flask: Flask is a Python framework accessible under the BSD license inspired by the Sinatra Ruby framework. Flask relies upon the Werkzeug WSGI toolbox and Jinja2 template. The main purpose is to help develop a strong web application base. Developers can develop the Python backend framework any way they need; however, it was designed for open-ended applications. Flask has been used by big companies, which include LinkedIn and Pinterest. Compared to Django, Flask is best suited for small and easy projects. Thus, you can expect web server development, Google App Engine support, and in-built unit testing. Key Features of Flask are a built-in development server and debugger, RESTful request dispatching, Integrated unit testing support (code with quality), Uses Jinja2 templating (tags, filters, macros, and more), 100% WSGI 1.0 compliant, multiple extensions provided by the community that eases the integration of new functionalities.
Python Library for Data Science
32. TensorFlow: TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of about 1,500 contributors. It’s used across various scientific fields. TensorFlow is a framework for defining and running computations involving tensors, partially defined computational objects that eventually produce a value. Key features of TensorFlow are Better computational graph visualizations, reduced error by 50 to 60 percent in neural machine learning, Parallel computing to execute complex models, Seamless library management backed by Google, and Quicker updates and frequent new releases to provide you with the latest features, TensorFlow is particularly useful for Speech and image recognition, Text-based applications, Time-series analysis, and Video detection.
33. NumPy: NumPy (Numerical Python) is Python’s fundamental package for numerical computation; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors. It’s a general-purpose array-processing package that provides high-performance multi-dimensional objects called arrays and tools for working with them. NumPy also partly addresses the slowness problem by providing these multi-dimensional arrays and providing functions and operators that operate efficiently on these arrays. Key features of NumPy are providing fast, precompiled functions for numerical routines, Array-oriented computing for better efficiency, Support for an object-oriented approach, and Compact and faster computations with vectorization. NumPy is particularly useful for Extensively used in data analysis, Creating a powerful N-dimensional array, Forms the base of other libraries, such as SciPy and sci-kit-learn, and Replacement of MATLAB when used with SciPy and matplotlib
34. SciPy: SciPy (Scientific Python) is another free and open-source Python library extensively used in data science for high-level computations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It’s widely used for scientific and technical computations because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations. Key features of SciPy are a Collection of algorithms and functions built on the NumPy extension of Python, High-level commands for data manipulation and visualization, and Multi-dimensional image processing with the SciPy.ndimage submodule, and Includes built-in functions for solving differential equations. SciPy is particularly useful for Multi-dimensional image operations, Solving differential equations and the Fourier transform, Optimization algorithms, and Linear algebra
35. Pandas: Pandas (Python data analysis) are a must in the data science life cycle. It is the most popular Python library for data science and NumPy in matplotlib. With around 17,00 comments on GitHub and an active community of 1,200 contributors, it is heavily used for data analysis and cleaning. Pandas provide fast, flexible data structures, such as data frame CDs, designed to quickly and intuitively work with structured data. Key features of Pandas are Eloquent syntax and rich functionalities that allow you to deal with missing data, create your function and run it across a series of data, High-level abstraction, and Contain high-level data structures and manipulation tools. SciPy is particularly useful for General data wrangling and cleaning, ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format; used in a variety of academic and commercial areas, including statistics, finance, and neuroscience, Time-series-specific functionality, such as date range generation, moving window, linear regression, and date shifting.
36. Matplotlib: Matplotlib has powerful yet beautiful visualizations. It’s a plotting library for Python with around 26,000 comments on GitHub and a vibrant community of about 700 contributors. Because of the graphs and plots that it produces, it’s extensively used for data visualization. It also provides an object-oriented API, which can be used to embed those plots into applications. Key features of Matplotlib are Usable as a MATLAB replacement, with the advantage of being free and open-source, and Supports dozens of backends and output types, which means you can use it regardless of which operating system you’re using or which output format you wish to use, Pandas itself can be used as wrappers around MATLAB API to drive MATLAB like a cleaner, and Low memory consumption and better runtime behavior. Matplotlib is particularly useful for Correlation analysis of variables, visualizing 95 percent confidence intervals of the models, Outlier detection using a scatter plot, and Visualize the distribution of data to gain instant insights.
Python Library for Machine Learning
37. Keras: Kerasis is one of Python’s most popular and open-source neural network libraries. Initially designed by a Google engineer for ONEIROS, short for Open-Ended Neuro Electronic Intelligent Robot Operating System, Keras, was soon supported in TensorFlow’s core library, making it accessible on top of TensorFlow. Keras is a very popular Machine Learning library for Python. It is a high-level neural network API capable of running on top of TensorFlow, CNTK, or Theano. It can run seamlessly on both CPU and GPU. Keras makes it really for ML beginners to build and design a Neural Network. One of the best things about Keras is that it allows for easy and fast prototyping. Keras extends the usability of TensorFlow with these additional features for ML and DL programming. With a helpful community and a dedicated Slack channel, getting support is easy. Support for the convolutional and recurrent neural network also exists along with standard neural networks.
38. PyTorch: PyTorch is one of Python’s few machine learning libraries. Apart from Python, PyTorch also has support for C++ with its C++ interface if you’re into that. Considered among the top contenders in the race to be the best Machine Learning and Deep Learning framework, PyTorch faces tough competition from Tensor Flow. PyTorch is a popular open-source Machine Learning library for Python based on Torch, an open-source Machine Learning library implemented in C with a wrapper in Lua. It has an extensive choice of tools and libraries that support Computer Vision, Natural Language Processing (NLP), and many more ML programs. It allows developers to perform computations on Tensors with GPU acceleration and helps create computational graphs.
39. Scikit-learn: Scikit-learn is another actively used machine learning library for Python. It includes easy integration with different ML programming libraries like NumPy and Pandas. Skikit-learn is one of the most popular ML libraries for classical ML algorithms. It is built on top of two basic Python libraries, NumPy and SciPy. Scikit-learn supports most of the supervised and unsupervised learning algorithms. Scikit-learn can also be used for data mining and analysis, making it a great tool for starting with ML.
40. MLlib: Developed by Apache, Spark MLlib is a machine learning library that enables easy scaling of your computations. It is simple to use, quick, easy to set up, and offers smooth integration with other tools. Spark MLlib instantly became a convenient tool for developing machine learning algorithms and applications. Some popular algorithms and APIs that programmers are working on in Machine Learning using Spark MLlib can utilize are regression, Clustering, Optimization, Dimensional Reduction, Classification, Basic Statistics, and Feature Extraction.
41. Theano: Theano is a powerful Python library enabling easy defining, optimization, and evaluation of powerful mathematical expressions. Theano is a popular python library used to efficiently define, evaluate and optimize mathematical expressions involving multi-dimensional arrays. It is achieved by optimizing the utilization of CPU and GPU. It is extensively used for unit testing and self-verification to detect and diagnose different types of errors. Theano is a very powerful library that has been used in large-scale computationally intensive scientific projects for a long time but is simple and approachable enough to be used by individuals for their projects.
