Pillow was initially based upon Python Image Library (PIL) but later it was modified to be friendlier and better. Experts describe pillow as the modern version of PIL. Pillow is the most reliable option while dealing with images or any type of image format. It allows the user to not only open and save an image but also redefine the surroundings of the image as well.

It is fairly simple to create the thumbnails for an image using a pillow. File formats like PDF, WebP, PCX, PNG, JPEG, GIF, PSD, WebP, PCX, GIF, IM, EPS, ICO, BMP, and many others are supported on a pillow. It has an extensive collection of image filters that help in the modification of the images.

Installation: pip3 install Pillow


This Python library uses a Python script to write two-dimensional graphs and plots. Frequently, mathematics and scientific data require more than single-dimensional representations. This library helps the users to manipulate various characteristics of figures and build multiple plots as well. For publication purpose, high-quality figures are needed.

Matplotlib can create such high-quality figures which are available in hardcopy formats on different interactive platforms. A number of third-party libraries like gplot, base map, and seaborn can be integrated into Matplotlib applications. Matplotlib can be used with different toolkits like Python Scripts, IPython Shells, Jupyter Notebook and many others for the purpose of the graphical user interface.

Installation: python3 -m pip install matplotlib


This is a common array-processing package of Python. It provides excellent support for n-dimensional array objects as well as matrices. Numpy also provides various tools that are required for the management of these arrays which makes it fast and efficient in handling both arrays and matrices.

Arrays and matrices of Numpy provide modern mathematical implementation on a vast amount of data hence making the implementation of such projects simple, versatile and fast.

It also contains functionalities like manipulation of logical shapes, discrete Fourier transform, general linear algebra and many others. This Python library offers various tools for integration. The users can easily integrate Numpy with various programming languages like C, C++, and FORTRAN.

Installation: pip3 install numpy


Released under Apache 2.0 license, the request is a rich library that pays attention to making HTTP requests more friendly and approachable. Addition of parameters, multi-part files, headers and forms can be done using basic Python dictionaries in Requests. It is a simple library with plenty of characteristics that allows the user to address custom headers, SSL, sweep parameters towards URL’s, and verify certification.

This library allows the user to upload multiple files at the same time. It provides the user with a rapid and resourceful environment to work and offers the feature of automatic decompression that helps in restoring and retrieving data in no time. It offers the users with benefits of HTTP proxy support and value cookies.

Installation: pip3 install requests


Open Source Computer Vision is a Python library for image processing. It examines all the functions that are concerned with instant computer vision. OpenCV is the ultimate image processing package that allows the user to perform read and write operations on an image at the same time. It offers the user to reconstruct and understand the three-dimensional environment from a two-dimensional image.

This package helps the user in recognizing special objects in an image like faces, eyes, trees etc. In other words, with the help of this library, the user can isolate objects from an image. It also offers the facility to capture and save any moment in a video and analyze its characteristics like motion, background, etc.

Installation: pip3 install opencv-python


This is an open-source neural network library that is written in Python. It is user-friendly, has a modular structure and offers an efficient inspection policy over detailed networks. Keras is an extremely powerful and successful Python library that is capable of running on Microsoft Cognitive Toolkit, PaidML, TensorFlow and others. This library provides a large number of implementations from the forming blocks of neural networks – layers, optimizers, objectives, functions and others.

It also helps in working with different types of images and texts very easily. Keras also provides a completely supportive environment for a complicated and recurrent neural network. It can build deep models for smartphones – both for android and iOS and also for Java Virtual Machine.

Installation: pip3 install keras


This is not only a Python library but also an optimizing compiler. Theano uses multi-dimensional arrays which ensure the perfection of the project. It can influence different mathematical declarations, analyze, describe and optimize at the same time. It works extremely well with GPUs and has the ability to execute different symbolic differentiations with one or many inputs.

Its interface is similar to that of Numpy and hence Numpy.ndarrays is also available internally in Theano. While working with expressions, it allows the user to avoid bugs and work flawlessly without wasting any time. It makes computation 140x times faster and helps the user to find and examine harmful bugs and other serious problems.

Installation: Check this link for the installation process


This is a free, open-source Python learning library that has a handful collection of useful tools. TensorFlow is not limited only to machine learning but can also be used for dataflow and programs that are differentiable. It provides an immediate iteration of machine learning models and uses automatic high-performance API such as Keras.

With this library, the user can transfer their machine learning models over the cloud, on any device and on-premises of any browser. It has an easy to learn architecture which allows the user to develop their own concepts into codes that make publications easier.


Natural Language Toolkit is a group of language processing libraries and additional programs that collectively provide a numerical and symbolic language processing solution. It is one of the most popular Python NLP libraries and works only for the English language. NLTK has text processing libraries that allow classification, parsing, tagging, tokenization and semantic reasoning as well.

This library is open source and contains more than fifty corpora and lexical resources. It also contains graphical illustrations for data science and includes structure types, structure strings parsing and also includes various pathways. With the use of NLTK, natural language processing through Python has become more normal and ideal.

Installation: pip3 install nltk


This is an open-source Python library that automatically generates command-line interfaces. It is an extremely powerful library that can generate command-line interfaces for any Python object. The command-line interfaces generated through the fire can adapt to any change that the user makes in its code automatically. It is a very easy library that can send commands at the call request fire ().

It comes with a linear input and works with various Python objects like classes, modules, objects, lists, etc. The command-line interfaces appear within an exceedingly interactive system and are incomplete form with automated help-pages and completion tabs.

Installation: pip3 install fire


This is a practical and friendly Python library which works with time and dates and comes with a smart API that supports many general schemes. It can create, effect and change dates and time. It supports different versions of Python and performs fast updates on date and time changes plugin gaps and many more.

This python library provides the most simple creation methods and helps the user in creating a number of general input scenarios. Since it is time-sensitive, this library is set to UTC by default and can easily convert time zones. Arrow offers timestamp as general property and can remove and resolve strings within a natural process. It can create time frames that can range from microseconds to years.

Installation: pip3 install arrow


This is a Python library that provides the user with a simple search and replacement of words in a document.  As input it needs a collection of words and strings and then it recognizes some words as keywords and replaces them from Text Data.

In order to reserve keywords, it uses a well-organized and dynamic form of a data structure called a Trie data structure. FlashText library not only has speed but also provides a number of string manipulations. It is excellent for a large number of inquiries but does not support the search of special characters such as *,), #, @ and others.

Installation: pip3 install flashtext


This is a free and open-source Python library that is used for both mathematical and technical computations and is extremely useful in machine learning. However, it is highly recommended for image manipulations as well. This library contains different modules that are appropriate for linear algebra, optimization, integration, and statistics as well.

Numpy is an integrated part of scipy as it uses Numpy arrays for general data structures.  There are two techniques scipy uses in order to handle one-dimensional polynomials. In the first technique, the user can select poly1d class from Numpy and use it. In the second technique, the user can select co-efficient arrays for the job.

Installation: pip3 install scipy


This is a free and open-source Python library that is used for both mathematical and technical computations and is extremely useful in machine learning. However, it is highly recommended for image manipulations as well. This library contains different modules that are appropriate for linear algebra, optimization, integration, and statistics as well.

Numpy is an integrated part of scipy as it uses Numpy arrays for general data structures.  There are two techniques scipy uses in order to handle one-dimensional polynomials. In the first technique, the user can select poly1d class from Numpy and use it. In the second technique, the user can select co-efficient arrays for the job.

Installation: pip3 install sqlalchemy


This powerful wrapper for many computer software is a GUI toolkit for Python that can be implemented on many digital platforms. It is very effective and applied as an extension module of Python. This library is the optimal choice for cross-platform Python as it supports all famous operating systems. Depending upon the Python being used, users need to make some changes in the GUI code.

In order to handle and modify layout this Python wrapper uses nested HBOX and VBOX which are very simple to implement. It comes with a lot of features and a very easy installation process, unlike other Python wrappers.

Installation: pip3 install wxPython


This Python library focuses on revealing the details of computer hardware and is generally used for noisy intermediate-scale quantum (NISQ) circuits. It allows the user to write, update and manipulate quantum circuits and then it compares them with different computers and simulators that can perform quantum computing. Essential details regarding the possibility of a circuit execution are uncovered by Cirq.

This python library is designed in such a way so that it can support several quantum-based hardware and cloud processors. In order to make the most of NISQ circuits this library optimizes data structures to write and assemble quantum circuits. Users can also use native gates to analyze gate behaviour.

Installation: pip3 install cirq


This is an open-source Python machine learning library based on the Torch library initially developed by the A.I researcher group of Facebook. It can also be used for multi – variational applications like computer vision and NLP as well. In graph mode, this library offers absolute transitioning, fast optimizations and C++ run time environment.

The use of TorchScript in this library allows a flexible and simple eager mode along with an instant evaluation of different functions and operations.  The PyTorch library allows both C++ and Python to gain P2P (Peer to Peer) communication. This library provides users with direct access to platforms, visualizers, and runtimes that are compatible with ONNX.

Installation: Follow the link for installation


This Python built toolkit is dedicated to computer vision. It is easy to use and allows seamless detection of an object in an image. With the help of customization according to user requirement, it can also classify models. Built with TensorFlow and Sonnet, it offers a built-in Google Cloud Platform to train user models. Luminoth makes visualization of an image extremely simple with built-in UI or by using a CLI. In order to track regular progress, users can integrate tensor board and examine results with a various number of data splits.


This Python library for enhancing date-time allows the user to easily organize a time for their Python projects. It provides the facility to shift DateTime from one zone to another and the use of NL processing to manipulate DateTime and time as well. Delorean allows users to manipulate and create their own DateTime. It can use strings to fix a time-zone which makes this library easier to use. 

The installation process of Delorean is quite simple and comes this library comes with features that allow the user to go forward and backwards in time zones easier. The next_day() function makes this zone transfer process easier for the user.


One of the best Python libraries for parsing, BeautifulSoup can parse various Python documents as well as broken HTML and XML documents. By direct extraction of data from HTML page, this library offers a simple way for web scraping. It can easily parse data from HTML and XML documents but needs an additional package and an exterior parser.

This python library saves user time as it can be easily trained and learned. It has two versions BeautifulSoup3 and BeautifulSoup4. BeautifulSoup4 works with both Python 2 and 3 whereas BeautifulSoup3 works only with Python 2. BeautifulSoup comes with proper documentation of the package that helps the user to learn fast.


This is a data visualization library for Python that offers interactive data visualization. Bokeh uses HTML and Javascript to provide its graphics which makes this a special package, different from other data visualization libraries. Use of HTML and Javascript also makes this library a reliable platform for contributing to dashboards and applications that are web-based.

With the use of this library, the user can give straight-forward commands in order to create composite statistical scenarios. It can easily render project outputs on different media platforms such as HTML, Notebook, servers, etc. Bokeh is a highly flexible, compatible library that can easily convert visualizations that are written in other libraries.


This is a simple tool for Python that allows the user to supervise Python packaging and dependencies. A project can depend on several libraries and Poetry helps the user to maintain them easily. Poetry handles projects in a systematic way and contains all the essential tools that a project may need. With a single line command, a user can package and create projects.

In case there are any comprehensive dependencies present in user projects, Poetry resolves any issue with the help of an exhaustive-dependency-resolver. It is always isolated from the system of the user and helps them to keep track of their projects.


With a moderate level of functionality, Genism is a Python-based natural language processing library. This is a smart library for unsystematic topic modelling and document resemblance analysis. It uses advanced statistical ML for solving any issues. Genism contains a simple interface and is highly expandable with any Vector Space algorithm. It is an influential, efficient and highly scalable library.

Genism can execute Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) on various devices. The feature of LDA provided by Genism is one of a kind. This comes with complete documentation and a bunch of tutorials as well.


For data science, this Python software package is a must. This library is a high-speed, expressive and adaptable platform that provides intuitive data-structures. It provides the user with control of any type of structured or time-series data easily. Pandas allow the user to easily arrange, search, characterize and control data.

It provides the user with various series and data frames and contains features like smart alignment and indexing. Pandas contain other special features that allow the user to handle missing values and data properly. Users can read and write data in different web services, data structures and data services with the help of built-in tools collection provided by Pandas. 


Previously known as a chicken turtle util, this is a utility library for Python. This is a useful package that is always client-focused and offers a huge amount of support to customers. Easy solutions for data mining, KDD (Knowledge Discovery in Data) simulation and modelling are also provided by Pytil.

It comes with a simple automation solution for the business organization of a user. From expert guidance to have a standard quality image and video processing to contours, face detection and filter everything is available on Pytil. Every single feature of this utility library has been well tested and documented. It also plays a key role in educational platforms.

Scikit Learn 

Another simple and effective Python machine learning library, Scikit Learn is written Python, cython, C and C++ (mostly in Python). This is a free and flexible Python package that works in complete agreement with other Python libraries and packages like Scipy and Numpy. It comes with a pristine API that provides very useful documentation to the beginners.

It contains different algorithms like classification, clustering, regression and is easily adaptable. Simple methods for data representations, support to random forests, k-means, gradient boosting, and DBSCAN are some specialized features of Scikit Learn. It allows data representation in both table format and matrix format. This library allows the user to not only upload but also visualize digits-data as well.


This Python package allows the user to form, control and learns the structure, functions and dynamics of complex networks. It provides a great number of solutions for learning and diagnosing graphs of all levels. Networkx also helps the user to create and manipulate the architecture, motion, and functionalities of high-quality networks. It is a free package that is released under the BSD license.

This library maintains data structures for graphs, digraphs, and multigraphs and a number of ideal graph standards. Users also enjoy the benefits of arbitrary data such as a timestamp. It has numerous standard graph algorithms and can easily create perfect graphs and simulated networks.


This wrapper module for Python is a collection of Python functions and classes dedicated mainly to write video games. It can also be used to write other multi-media applications as well. This is a highly consistent and easy to learn package that contains both computer graphics and audio libraries.

These elements are designed in such a way that they work together with Python language. Featured with Simple Direct Media (SDL), Pygame allows the user to build real-time graphics games by avoiding poor mechanisms. This library also supports the management of pixel-camera, MIDI, collision detection, modern FreeType font, camera, drawing, etc.


One of the most simplified NLP libraries used for textual data processing is available in both Python 2.0 and Python 3.0. It provides quite direct tokenization and makes it easier for the words to be converted in their original form as present in the library.

This process of conversion is also called as Lemmatization. It offers the users a simple pluralize and singularize procedures to transform singular to plural and vice-versa. It can easily extract different noun phrases and provides Parts of Speech tagging.


This is another Python image processing also known as computer vision library. It is very fast and comes with a well-organized code. It provides least dependencies to any other third party platform and can perform complex tasks with simpler forms of code. The interface of this library is written in Python due to which it offers fast and dynamic development of user projects.

Mahotas is flexible and easily compatible with many other scientific software environments. This library provides smart computer vision features like calculation, point detection, local binary patterns, and many more.


This Python library is fast in execution and guarantees high production efficiency. It is very intuitive which makes it extremely user friendly. Unlike other machine learning libraries, it is easily trained and does not produce errors when the user considers Not a Number (NaN) values and other canonical values.

The gradient boosting provided by LightGBM is highly flexible, advanced and fast in implementations. These features make this library extremely popular among machine learners.

Spark MLlib 

This is an Apache Spark’s flexible machine learning library. Spark MLlib enables easy scaling of computations for the user. It is fast, simple to use, easy to set up and provides smooth integration. This library is a suitable tool for developing machine learning algorithms and applications.

The machine learning tools available in this Python library are ML algorithms, Featurization, Pipelines, Persistence, and Utilities. The most popular algorithms and API’s programmers make use of while working on Spark MLlib are Regression, Clustering, Optimization, Dimensional Reduction, Classification, Feature Extraction, and Basic Statistics.


This is a standard machine learning library for Python with the main objective to provide users with a flexible, easy-to-use yet powerful algorithms and a number of predefined environments to test and compare these machine learning algorithms. PyBrain can be used by entry-level users and still provide adaptable and state-of-the-art algorithms. This is a free open-source library licensed under BSD.

PyBrain stands for Python-Based Reinforcement Learning, Artificial Intelligence, Neural Network library. It contains algorithms for neural networks, reinforced learning, unsupervised learning and evolution. PyBrain is a powerful tool for real-life tasks as it is built around neural networks in the kernel.


This Python library, built on top of Numpy and Scipy, is the finest package for creating statistical models, data handling, and model evaluation. StatsModel is most popular for statistical computing, statistical testing and data exploration. This is the best library to perform hypothesis testing as it is not available in either Numpy or Scipy.

For better statistical analysis, it provides the implementation of R-style formulas. This is more associated with the R language that is frequently used by statisticians. Due to its vast support of statistical computations, this library is often used for Generalised Linear Models (GLM) and Ordinary least-square Linear Regression (OLM) models.


The base of this library is formed by Matplotlib library. Although, in comparison, seaborn creates more attractive and expressive statistical graphs. Seaborn contains an inbuilt data set oriented API and provides extensive support to data visualization. The inbuilt API is used for studying the relationships between multiple variables. It offers users various choices for analyzing and visualizing univariate and bivariate data points.

It also helps the user in comparing the data with other subsets of data. This library supports automated statistical estimations and graphical representations of linear regression models for multiple types of variables. For structuring multi-plot grids, it builds complex visualizations using functions that perform high-level abstraction.


One of the most popular graphical Python libraries, Plotly offers interactive graphs for understanding dependencies between output and input variables. This library can be used to interpret and visualize financial, statistical, commerce and scientific data to generate unambiguous and concise graphs, sub-plots, heatmaps, 3D graphs etc.

It consists of more than thirty chart types, complete with 3D charts, scientific and statistical graphs, SVG maps, and so on for distinct visualization. Users can create both public and private dashboards consisting of graphs, plots, charts and web images with the help of Plotly’s Python API. Plotly consists of an in-built API called Plotly Grid which allows the user to import data directly in the plotly environment.


One of the best Python packages for boosting machine learning, XGBoost stands for Extreme Gradient Boosting. Mainly built for the purpose of executing a gradient boosting machine, this library is used to improve the performance and accuracy of machine learning models. Originally written in C++, XGBoost is considered to be one of the fastest and most effective libraries for improving machine learning models.

The core algorithm is parallelizable and can efficiently use the power of multi-core computers. This feature makes XGBoost library robust enough to deal with massive amounts of data sets. Often used in top Data Science and Machine Learning competitions, this library has outperformed other algorithms over and over again.


Another Python library focused on improving the performance of machine learning models, Eli5 is relatively new and usually used alongside other machine learning libraries. Like others, this library is concerned with improving the accuracy and performance of various machine learning models.

In order to express important characteristics and describe predictions of decision trees, Eli5 can be integrated with Scikit learn. It studies and explains predictions made by XGBClassifier, XGBRegressor, LGBMClassifier, LGBMRegressor, CatBoostClassifier, CatBoostRegressor and catboost. In order to inspect black-box models, Eli5 offers support in executing several algorithms including TextExplainer module that allows the user to describe predictions made by text classifiers.


This is an open-source automation tool that is web-based and offers the users with a simple API to write functional or acceptance tests. These tests are written using Selenium WebDriver. Selenium is basically a collection of various software tools, each of which comes with a different approach to support test automation. This entire collection of tools results in a group of rich testing functions.

These testing functions are specially geared to fulfil the needs of testing of web applications of all types. The Selenium Python API helps the user in accessing all the functionalities of a Selenium WebDriver in an intuitive manner. This module allows the developers to open webpages, enter fields, click buttons, and submit forms in a programmable way.


This open-source web scraping framework is written in Python and is concerned with everything from downloading HTML to restoring in a format suitable to the user. Since it is built on top of twister which is an asynchronous framework the requests made on Scrapy are scheduled and processed in an asynchronous manner. It not only reduces CPU usage but also consumes less memory.

It is extremely resourceful in comparison and its well-designed architecture offers the user both robustness and flexibility. Users can easily create custom middle-ware or pipeline to add custom functionalities. This library works best if the user needs to build a real spider or web-crawler for large web scraping needs, instead of just scraping a few pages here and there. It can provide extensibility and flexibility to projects.


This Python package provides attachment for GObject based libraries such as GTK, GStreamer, Glib and others. PyGobject is extremely useful whenever the user wants to write either a Python application for gnome or a Python GUI application for GTK. PyGObject uses glib, gobject, girepository, libffi and other libraries to access the C library (libgtk-3.so) in combination with the additional metadata from the associated typelib file (Gtk-3.0.typelib) and dynamically offers a Python interface based on that information.

It works with Python 3.5+and PyPy3 and supports Linux, Windows and MacOS. PyGObject is by libraries like Anaconda, Orca, Lollypop, MyPaint and others.


This is a cross windowing and multimedia library for Python that is powerful yet easy to use in developing games and other applications that are rich in visual effects. The applications made by PYGLET can run on Linux, Windows, and MacOS. It not only offers support in loading images, songs and other multimedia content but also sustains user interface event handling, Joysticks, and OpenGL graphics.

PYGLET is written purely in Python and utilizes standard library ctypes module to interface with system libraries. Users can modify the codebase or make a contribution without any compilation steps, or knowledge of another language. Even though it is completely written in Python it provides excellent performance this is due to the fact that it uses advanced batching and CPU rendering. It requires no external dependencies and has built-in support for audio and images.


This is a Numpy compatible matrix library accelerated by CUDA. This is an open-source library that offers GPU accelerated computing with Python. In order to make full use of its architecture, CuPy uses CUDA related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL. CuPy speeds up execution a hundred times than normal for some operations.

This is because its interface is extremely compatible with Numpy. In most cases, CuPy can be used as a drop-in replacement. It sustains a number of methods, indexing, data types, broadcasting and many more. CuPy can easily be installed and offers various pre-compiled binary packages for the suggested environments.


This Python module, as the name suggests, is used for the manipulation and statistical analysis of graphs or networks. Algorithms and core data structures are implemented in C++ in this library hence making wide use of metaprogramming. It supports creation and manipulation of directed and undirected graphs, generation of random graphs with arbitrary degree distribution, correlation and advanced visualizations.

Graph-tool is easy to use, powerful and handles well-established network models with ease. This library can be used to work with very large graphs in a variety of contexts, including simulation of cellular tissue, data mining, analysis of social networks and many more.


Quantum toolbox in Python is an open-source for computational physics software library for simulating quantum. It provides the user to simulate the Hamiltonians with arbitrary time-dependence, allowing simulation of situations of interest in quantum optics, ion trapping, superconducting circuits and quantum nanomechanical resonators.

The library contains extensive visualization facilities for content under simulations. QuTiP’s API offers a Python interface that uses Cython to allow run-time compilation and extensions via C and C++. QuTiP is designed in such a way so that it works well with popular Python packages like Numpy, SciPy, Matplotlib and IPython.


RDFLib is a Python library for working with RDF which is a simple but powerful language for representing information. This library consists of parsers/serializers for approximately all the known RDF serializations, such as RDF/XML, Turtle, N-Triples, & JSON-LD, many of which are now supported in their updated form.

It also contains both in-memory and constant graph back-ends for storing RDF information and several useful functions for declaring graph namespaces, lodging SPARQL queries and so on. Its use of various Python idioms means it is fairly simple for programmers with only junior Python skills to influence RDF. The main or core class in RDFLib is Graph which is a Python dictionary used to accumulate sets of RDF triples in memory. It redefines a number of built-in Python object methods to show simple graph behaviour, such as simple graph merging via addition.

There are RDFLib classes represent RDF terms in a graph that are inherited from a common Identifier class. These classes extend Python Unicode where the instances of the classes are nodes in an RDF graph. These classes are URIRef, BNode, Literal, and Variable.


Pronounced as “Pineapple”, this Python library is used for Natural Language Processing. This library contains a number of modules for both common NLP tasks and less common NLP tasks. It can be used for extraction or isolation of n-grams, frequency lists and to build a simple language model.

The most prominent feature or characteristic that this library has is the presence of a highly extensive library for working with FoLiA XML which is the format for Linguistic Annotation. It includes modules for basic tasks, clients for interfacing with the server, and modules for parsing several file formats that are common in NLP, particularly FoLiA.


This Python library for used natural language processing is basically a dictionary in the form of a Python module. For a given word, upon the use of vocabulary users can get its meaning, synonyms, antonyms, part of speech, pronunciation and hyphenation.

It also provides the user with the facility to translate a phrase from one language to another and how a word or a phrase can be used on an example. It is written in a simple, uncomplicated way and returns JSON objects, Python dictionaries and lists. This library is fast, easy to use, and provides the users with a decent alternative for Wordnet. Vocabulary is easy to install and has minimum dependencies.


Written in Python and Cython, this library is used for advanced Natural Language Processing. Built on the very latest research, it was designed in such a way so as to be used in real products. It contains pre-trained statistical models and word vectors. It supports non-destructive tokenization for over fifty languages.

spaCy is characterized with state-of-the-art speed, intricate neural network models which can be used for tagging, parsing, named entity recognition, and simple deep learning integration. spaCy offers high accuracy, efficient binary serialization and suitable string-to-hash mapping. It contains built-in visualizers and can export data to Numpy data arrays.

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