Python Libraries provide a collection of useful functions that make it easy to create code from scratch. Today, there are more than 137,000 Python libraries. They play an important role in machine learning, data science, and image manipulation, as well as data science and data visualization. First, let’s briefly discuss Python Programming Language. Then we will dive directly into the most well-known Python libraries.
Guido Van Rossum’s genius invention – Python, which dates back to the ’80s has been a game changer. It is widely used in a variety of applications and is one of the most popular coding languages. How to create an app with Python. Let’s see what happens.
What is a Library?
A library is a collection of pre-combined code that can be used iteratively in order to decrease the time needed to code. These libraries are especially useful because they allow you to access the most frequently used codes, rather than writing them all from scratch. These are similar to physical libraries. They contain a variety of reusable resources. Every library has a source. This is what enables Python to have so many open-source libraries.
What is a Python Library?
Python library is a collection module that contains functions and classes that can then be used by other programs for various tasks.
Also read: What is a Programming Library: A Complete Guide?
Top 20 Python Libraries List
1. Scikit-learn
It is a free machine-learning library that can be used with the Python programming language. It is effective in a number of applications, including classification, regression, clustering and model selection, Preprocessing, grade boosting, K–means, and naive Bayes’.
Spotify uses Scikit–learn to build its classifiers and Evernote to make music recommendations. The pip method is the best way to install Scikit-learn if you have an existing working installation of NumPy or scipy.
2. NuPIC
The Numenta Platform for Intelligent Computing, NuPIC is a platform that implements an HTM learning algorithm. It also makes them available as a public source. It provides the basis for machine learning algorithms that are based on the biology and neocortex. To view their code on GitHub, click here.
3. Ramp
It’s a Python library used to quickly prototype machine learning models. RAMP is a declarative syntax that allows you to explore features, algorithms, or transformations. It’s a lightweight, pandas-based machine intelligence framework that can be seamlessly integrated with existing python statistics and machine learning tools.
4. NumPy
When it comes to scientific computing, NumPy is one of the most fundamental packages for Python. It supports large multidimensional arrays as well matrices are accompanied by a set of high-level mathematical functions that allow you to quickly execute these functions. NumPy uses BLAS, and LAPACK to perform efficient linear algebra computations. NumPy is also a multi-dimensional container for generic data.
5. Pipenv
The official recommended Python tool in 2017, Pipenv is a production-ready tool. It aims to bring together the best of all packaging to the Python world. Its primary purpose is to make it easy for users to work in a comfortable environment. Pipenv is the “Python Development Workflow for Humans”, Kenneth Reitz created it to manage package discrepancies.
6. TensorFlow
TensorFlow’s most famous deep learning framework, TensorFlow’s open-source software library is for high-performance numerical computation. It is a well-known math library that is used in Python machine learning and deep learning algorithms. Tensorflow was created by researchers from the Google Brain team, which is part of the Google AI organization. It is currently being used by scientists for machine learning algorithms as well as physicists to perform complex mathematical computations. TensorFlow is supported by the following operating systems: macOS 10.12.6 (Sierra); Ubuntu 16.04; Windows 7; Raspbian or 9.0 later.
7. Bob
Bob, a free machine learning and signal processing toolbox, was developed at Idiap Research Institute (Switzerland). This toolbox is written in Python and C++. Bob contains many packages that can be used to quickly make everything happen, including image recognition and image and video processing with machine learning algorithms.
8. PyTorch
In 2017 Facebook introduced PyTorch, a Python package. It combines two high-level features: Tensor computation (like NumPy), strong GPU acceleration, and the development of Deep Neural Networks using a tape-based auto-diff system. PyTorch is a great platform for Deep Learning models. It has increased flexibility and speed and can be easily integrated with Python.
Are you ready to get started using PyTorch These PyTorch courses will help you get started quickly?
9. PyBrain
PyBrain provides algorithms for neural networks. These algorithms can be used by students at the entry-level, but can also be used for advanced research. Our goal is to provide simple, flexible, yet powerful, machine learning algorithms with multiple pre-determined environments for testing and comparing your algorithms. PyBrain is available to researchers, students, developers, lecturers, and other users.
Also read: Top 10 Python Frameworks for Game Development
10. MILK
This machine learning toolkit for Python focuses on supervised classification with a variety of classifiers: SVM (k-NN), random forests, decision trees, and random forests. There are many combinations of these classifiers that can be used to create different classification systems. Unsupervised learning can be achieved using affinity propagation and k-means clustering. The emphasis is on speed and minimal memory usage. Therefore, most of the performance-sensitive code is in C++. Learn more.
11. Keras
It is an open-source library of neural network libraries written in Python that allows for fast experimentation with deep learning neural networks. Deep learning is becoming more common. Keras is the best choice, as it’s an API that was created for humans and not machines. More than 200,000 users as of November 2017 Keras is more popular in the industry than it is in the research community. Even over TensorFlow and Theano, It is recommended to install TensorFlow before installing Keras.
12. Dash
Dash can be used to explore data and monitor your experiments. It’s like the front end of the analytical Python backend. This Python framework is perfect for data visualization tools, and it’s a great fit for all Python users. This is the result of a lot of hard work and dedication.
13. Pandas
It is open-source and BSD-licensed. Pandas allow Python to provide a simple data structure and faster data analysis. Pandas allow you to perform operations such as data analysis and modeling without having to switch to a more domain-specific language like R. Conda is the best method to install Pandas.
14. Scipy
Another open-source Python software that is used for scientific computing. Scipy can also be used for Data Computation and productivity as well as quality assurance. You can find the various installation packages. The core Scipy packages include Numpy and SciPy library.
15. Matplotlib
While all the libraries we’ve discussed can perform a wide range of numeric operations (including dimensional plotting), Matplotlib is the clear winner. This Python open-source library is used to publish high-quality figures in a variety of formats. It can also be used in interactive environments. With just a few lines, you can create charts, graphs, and pie charts as well scatterplots. Histograms, error tables, and other types of charts.
16. Theano
This library is open-source and allows you to quickly define, optimize, evaluate, and test mathematical expressions that involve multi-dimensional arrays. Handcrafted C codes are slower for large amounts of data. Theano allows for quick code implementations. Theano is able to recognize instabilities and still compute them using stable algorithms. This gives it an advantage over NumPy. Sympy is the closest Python package to Theano. Let’s talk about it.
17. SymPy
SymPy is the solution for all your symbolic mathematics. The Python library for symbolic mathematics is an efficient aid for computer algebra systems (CAS). It keeps the code simple and easy to understand. SimPy can be embedded into other applications or extended with custom functions. The source code can be found on GitHub.
Also read: Top 10 Programming Languages For Machine Learning
18. Caffe2
Caffe2, the new kid on the block, is a lightweight, modular, and scalable deep learning framework. Caffe2 is designed to make deep learning easy for anyone. Caffe2’s Python and C++ APIs allow us to quickly create our prototype and then optimize it later. This step-by-step installation guide will help you get started with Caffe2.
19. NLTK
One of the most popular Python NLP Libraries is the Natural Language Toolkit (NLTK). This toolkit contains a collection of processing libraries that offer processing solutions for numerical or symbolic language processing in English only. You can discuss any issue related to NLTK in the dynamic discussion forum.
20. SQLAlchemy
SQLAcademy is a Python Database abstraction library, that provides incredible support for a variety of layouts and databases. It is simple to use, provides consistent patterns, and can be understood by beginners. It speeds up communication between Python language and databases, and it supports all platforms, including Jython, Python 2.5, and Pypy. SQLAcademy allows you to create database schemes completely from scratch.
