Top Python Libraries You Should Know in 2022

Python libraries are a set of useful functions that eliminate the need to write code from scratch. There are currently more than 137,000 python libraries and they play a vital role in the development of machine learning, data science, data visualization, image and data manipulation applications, and more. Let’s start with a brief introduction to the Python programming language and then dive right into the most popular Python libraries.

The spirit of Guido Van Rossum – Python, which dates back to the 1980s, has become a passionate game changer. It is one of the most popular coding languages ​​today and is widely used for a variety of applications. In this article, we have listed 34 machine learning Python libraries you should know about.

  • What is a Library?
  • What is a python library?
  • Top 26 Python Libraries List

What is a Library?

A library is a collection of pre-combined codes that can be used iteratively to reduce coding time. They are especially useful for accessing pre-written frequently used code, instead of writing it from scratch each time. Similar to physical libraries, it is a collection of reusable resources, meaning that each library has a root resource. This is the basis of many open source libraries available in Python.

What is a Python Library?

What is a Python Library?

Python library is a collection of modules that contain functions and classes that can be used by other programs to perform various tasks.

Top 26 Python Libraries List

Below are the list of top Python Libraries :

  • Scikit-learn
  • NuPIC
  • Ramp
  • NumPy
  • Pipenv
  • TensorFlow
  • Bob
  • PyTorch
  • PyBrain
  • MILK
  • Keras
  • Dash
  • Pandas
  • Scipy
  • Matplotlib
  • Theano
  • SymPy
  • Caffe2
  • Seaborn
  • Hebel
  • Chainer
  • OpenCV Python
  • Theano
  • NLTK
  • SQLAlchemy
  • Bokeh

1. Scikit- learn

It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.
Scikit-learn requires:

  • Python (>= 2.7 or >= 3.3),
  • NumPy (>= 1.8.2),
  • SciPy (>= 0.13.3).

Spotify uses Scikit-learn for its music recommendations and Evernote for building its classifiers. If you already have a working installation of NumPy and scipy, the easiest way to install sci-kit-learn is using pip.

2. NuPIC

The Numenta Platform for Intelligent Computing (NuPIC) is a platform that aims to implement an HTM learning algorithm and make them a public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex.

3. Ramp

It is a Python library that is used for the rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. It is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools.

4. NumPy

When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used as an efficient multi-dimensional container of generic data.

The various NumPy installation packages can be found here.

5. Pipenv

The officially recommended tool for Python in 2017 – Pipenv is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. The cardinal purpose is to provide users with a working environment that is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies. The instructions to install Pipenv can be found here.

6. TensorFlow

The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for Python in machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within the Google AI organization. Today, it is used by researchers for machine learning algorithms and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.

7. Bob

Developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.

8. PyTorch

Introduced by Facebook in 2017, PyTorch is a Python package that gives the user a blend of 2 high-level features – Tensor computation (like NumPy) with strong GPU acceleration and the development of Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

9. PyBrain

PyBrain contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. The goal is to offer simple, flexible yet sophisticated, and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms. Researchers, students, developers, lecturers, you and me – we can all use PyBrain.

10. MILK

This machine learning toolkit in Python focuses on supervised classification with a gamut of classifiers available: SVM, k-NN, random forests, and decision trees. A range of combinations of these classifiers gives different classification systems. For unsupervised learning, one can use k-means clustering and affinity propagation. There is a strong emphasis on speed and low memory usage. Therefore, most of the performance-sensitive code is in C++. Read more about it here.

11. Keras

It is an open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API designed for humans and not machines according to the creators. With over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is advised to install the TensorFlow backend engine.

12. Dash

From exploring data to monitoring your experiments, Dash is like the frontend to the analytical Python backend. This productive Python framework is ideal for data visualization apps particularly suited for every Python user. The ease which we experience is a result of extensive and exhaustive effort.

13. Pandas

It is an open-source, BSD-licensed library. Pandas enable the provision of easy data structure and quicker data analysis for Python. For operations like data analysis and modeling, Pandas makes it possible to carry these out without needing to switch to more domain-specific language like R. The best way to install Pandas is by Conda installation.

14. Scipy

This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, high-performance computing, and quality assurance. The various installation packages can be found here. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

15. Matplotlib

All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. This open-source library in Python is widely used for the publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code.

The various installation packages can be found here.

16. Theano

This open-source library enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. For a humongous volume of data, handcrafted C codes become slower. Theano enables swift implementations of code. Theano can recognize unstable expressions and yet compute them with stable algorithms which gives it an upper hand over NumPy. The closest Python package to Theano is Sympy. So let us talk about it.

17. SymPy

For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra systems (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SimPy is written in Python only and can be embedded in other applications and extended with custom functions. You can find the source code on GitHub. 

18. Caffe2

The new boy in town – Caffe2 is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning. Thanks to Python and C++ APIs in Caffe2, we can create our prototype now and optimize it later. You can get started with Caffe2 now with this step-by-step installation guide.

19. Seaborn

When it comes to the visualization of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and is closely integrated with Pandas data structures. Visit the installation page to see how this package can be installed.

20. Hebel

This Python library is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future. Follow the link to explore Hebel.

21. Chainer

A competitor to Hebel, this Python package aims at increasing the flexibility of deep learning models. The three key focus areas of Chainer include :
a. Transportation system: The makers of Chainer have consistently shown an inclination toward automatic driving cars and they have been in talks with Toyota Motors about the same.

b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.

c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for the early diagnosis of cancer cells.
The installation, projects and other details can be found here.
So here is a list of the common Python Libraries which are worth taking a peek at and if possible familiarizing yourself with. If you feel there is some library that deserves to be on the list do not forget to mention it in the comments.

22. OpenCV Python

Open Source Computer Vision or OpenCV is used for image processing. It is a Python package that monitors overall functions focused on instant computer vision. OpenCV provides several inbuilt functions, with the help of this you can learn Computer Vision. It allows both to read and write images at the same time. Objects such as faces, trees, etc., can be diagnosed in any video or image. It is compatible with Windows, OS-X, and other operating systems. You can get it here

23. Theano

Along with being a Python Library, Theano is also an optimizing compiler. It is used for analyzing, describing, and optimizing different mathematical declarations at the same time. It makes use of multi-dimensional arrays, ensuring that we don’t have to worry about the perfection of our projects. Theano works well with GPUs and has an interface quite similar to Numpy. The library makes computation 140x faster and can be used to detect and analyze any harmful bugs. You can get it here

24. NLTK

Natural Language toolkit or NLTK is said to be one of the popular Python NLP Libraries. It contains a set of processing libraries that provide processing solutions for numerical and symbolic language processing in English only. The toolkit comes with a dynamic discussion forum that allows you to discuss and bring up any issues relating to NLTK.

25. SQLAlchemy

SQLAcademy is a Database abstraction library for Python that comes with astounding support for a range of databases and layouts. It provides consistent patterns, is easy to understand, and can be used by beginners too. It improves the speed of communication between Python language and databases and supports most platforms such as Python 2.5, Jython, and Pypy. Using SQLAcademy, you can develop database schemes from scratch.

26. Bokeh

A Data Visualisation library for Python, Bokeh allows interactive visualization. It makes use of HTML and Javascript to provide graphics, making it reliable for contributing web-based applications. It is highly flexible and allows you to convert visualization written in other libraries such as ggplot or matplotlib. Bokeh makes use of straightforward commands to create composite statistical scenarios.

By geekycodesco

One thought on “Top Python Libraries You Should Know in 2022

  • Rohak Jain -

    Hey, great read! I particularly enjoyed your in-depth discussion of the Pipenv Library, since it was something I hadn’t really thought of before. Being a fellow tech blogger myself, I also really appreciate how organized and well-formatted everything was – it definitely made the content much more digestible overall. Keep up the awesome work!

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