Find The Best Frameworks and Libraries for Your Python Projects.

Scikit-Learn

Description

Scikit-learn is a free and open-source software library with a number of supervised and unsupervised algorithms for machine learning.
Scikit-learn is built on NumPy, and makes use of other libraries like Pandas, Scipy and Matplotlib.

Scikit-learn is a powerful machine learning library for Python that uses NumPy extensively to deliver fast linear algebra and array operations. Some of its core algorithms are written in Cython, which improves performance.

It's designed to make the process as simple as possible, with a minimal learning curve.

Features of Scikit-Learn

Scikit-learn offers a range of supervised and unsupervised learning algorithms for

1. Classification
2. Regression
3. Clustering
4. Dimensionality reduction
5. Model Selection
6. Preprocessing

Scikit-learn has been designed to be easy-to-use, accessible to everyone and reusable in various contexts.

Uses of Scikit-Learn

1. Predictive and statistical modelling.
2. Data analysis
3. It is used to create, investigate, and evaluate models.
4. Numerical optimization
5. Data mining

Installation

pip install scikit-learn

Scikit-Learn Resources

$ Homepage
$ Documentation

TensorFlow

Description

TensorFlow is a platform that allows machine learning to be done openly. It provides a wide range of flexible tools, libraries, and community resources that allow researchers to push the current state of ML research and allows developers to quickly build and deploy ML-powered apps.

Installation

pip install tensorflow

TensorFlow Resources

$ Homepage
$ Documentation

PyTorch

pip install torch

conda install -c pytorch pytorch

PyTorch Resources

$ Homepage
$ Documentation

Theano

Description

Theano is a python library and an optimizing compiler for manipulating and evaluating mathematical expressions involving multi-dimensional arrays. It is built on Numpy and named after a Greek mathematician and philosopher.
In Theano, computations are expressed using a syntax very similar to NumPy's and compiled to run efficiently on either CPU or GPU architectures.

Theano was developed by the Montreal Institute for Learning Algorithms (MILA), the University of Montreal in 2007 and It has been powering large-scale computationally intensive scientific research since then. It is one of the most mature deep learning frameworks in existence and is open-sourced under the BSD license for all to use.

Developed specifically to handle the types of computation required for large neural network algorithms used in Deep Learning.

pip install Theano

Theano Resources

$ Homepage
$ Documentation

Django

pip install Django

conda install -c anaconda django

Websites or Organizations using Django

Here are a few of the large organizations using Django on their sites or in various projects:

1. Disqus
2. Instagram
3. Knight Foundation
4. MacArthur Foundation
5. Mozilla
6. National Geographic
7. Open Knowledge Foundation
8. Pinterest
9. Open Stack

Django is used by countless organizations and companies, large and small, around the world.

Django Resources

$ Homepage
$ Documentation

Flask

Who Uses Flask

Flask is a popular choice for Python projects of all shapes and sizes and is used by a wide range of companies and organizations such as Pinterest and LinkedIn.

Installation

pip install flask

A Basic Flask App

A minimal Flask application looks something like this

from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

Visit the Quickstart Guide to understand what this code does and how to run it.

Flask Resources

$ Homepage
$ Documentation

Bottle

Description

Bottle is a super-simple, fast and lightweight WSGI micro-framework for small web applications. Other than the Python Standard Library, it is distributed as a single file module with no dependencies.

Bottle is built on top of the simplicity and minimalism of the web. It offers a fast and lightweight structure to build simple web applications and services.

Bottle was developed by Marcel Hellkamp in 2009 and it is intended to be fast, convenient, and lightweight, and to reduce the complexity of developing web applications.
Bottle is not as full-featured as other frameworks, but then it doesn’t have as big a learning curve either, also does not require any kind of configuration, and is perfect for quickly hacking together a simple app that requires a database.

It tries to make the best trade-off between simplicity, efficiency, and capability.

Features

Bottle includes the following features:

1. Built-in template engine.
2. Built-in HTTP development server (WSGI server)
3. Support for JSON client data
4. Plugins for popular databases and other features

Installation

Stable releases of bottle.py are available on the Python Package Index and can be installed via pip (recommended)

pip install bottle

To install the bottle framework with conda run:

conda install -c conda-forge bottle 

Basics

This tutorial assumes you already have Bottle installed. Let's begin with a simple "Hello World" example:

from bottle import route, run

@route('/hello')
def hello():
    return "Hello World!"

run(host='localhost', port=8080, debug=True)

This is how simple a bottle app can be. Just run the script and open http://localhost:8080/hello and you will see “Hello World!” in your browser.

Bottle Resources

$ Homepage
$ Documentation

Web2py

Description

Web2py is an open-source full-stack web framework written in Python. It’s designed for the rapid development of database-driven web applications. Web2py is scalable, comes with a web-based IDE for creating and managing applications. It is easy to install and configure and supports multiple protocols. 

Installation

pip install web2py

Web2py Resources

$ Homepage
$ Documentation

Pyramid

Pyramid is an open-source WSGI web framework implemented in python. It is based on the Model-View-Controller (MVC) architectural pattern and makes it easy for python developers to create web apps.

Pyramid makes it easier to create complex software as your application grows.

Installation

pip install pyramid

This is a basic example of a pyramid web app:

from wsgiref.simple_server import make_server
from pyramid.config import Configurator
from pyramid.response import Response

def hello_world(request):
    return Response('Hello World!')

if __name__ == '__main__':
    with Configurator() as config:
        config.add_route('hello', '/')
        config.add_view(hello_world, route_name='hello')
        app = config.make_wsgi_app()
    server = make_server('0.0.0.0', 6543, app)
    server.serve_forever()

Pyramid Resources

$ Homepage
$ Documentation

FastAPI

Description

FastAPI is a modern and high-performance Python web framework for building APIs. It makes use of python's standard type hints and was designed to simplify the development experience for developers so you can write simple code and build production-ready APIs using best practices.

Features

1. It is fast to run
2. Fast and easy to code
3. Fewer number of bugs when coding
4. Robust
5. Very Intuitive

FastAPI Resources

$ Homepage
$ Documentation

PyGame

Pygame is an open-source and cross-platform set of Python modules for creating video games. It also includes sound libraries and computer graphics designed for use with the Python programming language.

pygame serves as a python wrapper for the SDL library, (Simple DirectMedia Layer). The SDL provides cross-platform access to your system's underlying multimedia hardware components, such as sound, video, mouse, keyboard, and joystick.

Installation

To install pygame on your system, use the pip command:

pip install pygame

PyGame Resources

$ Homepage
$ Documentation

Pyglet

Pyglet is a powerful and easy-to-use library that allows you to develop games and other visually rich applications. It is written in python and provides a friendly pythonic  API to allow users to create games, simulations, and other graphical applications without having to worry about the underlying windowing toolkit or operating system. Pyglet can be used on Windows, Mac OS and Linux.

Installation

pip install pyglet

Pyglet Resources

$ Homepage
$ Documentation

Cocos2d

cocos2d is a software framework for building 2D games, demos, and other graphical and interactive applications. It is cross platform and used by applications written in the python programming language.

Cocos2d Resources

$ Homepage
$ Documentation

Panda3D

Panda3D is a game engine. It provides a framework for 3D rendering, game development and programming in Python and C++.

Panda3D was designed for commercial game development. This engine should emphasize four key areas: power and speed, completeness and error tolerance.

Panda3D is free and open-source, making it available for commercial projects.

Installation with pip

pip install panda3d

Panda3D Resources

$ Homepage
$ Documentation

Scipy

SciPy is a Python-based ecosystem of open-source libraries used for scientific computing. It is built on these core packages of scipy are Numpy, SymPy, Matplotlib, Pandas, IPython and scipy library.

Scipy Resources

$ Homepage
$ Documentation

NumPy

NumPy is a python library for high-performance, numerical computing. It is the fundamental package needed for scientific computing with python and performing advanced data science and analytics using tools like pandas, matplotlib, scikit-learn, and many others.

It extends Python by adding support for powerful n-dimensional arrays and matrices along with a large collection of high-level mathematical functions to operate on these arrays. It includes optimized code for computationally expensive linear algebra operations, basic linear algebra primitives such as matrix multiplication, inversion, determinants, eigenvalues and singular values; interpolation; minimization problems including least-squares problems; polynomial approximations, etc.

NumPy Resources

$ Homepage
$ Documentation

Pandas

pandas is an open-source python library designed to make it easier to work with data. It provides fast, flexible and expressive data structures for data analysis and manipulation.

It has tools and operations for working with relational and labelled data, numerical tables, time-series, as well as statistical computation and graphics.

Installation

pip install pandas

Pandas Resources

$ Homepage
$ Documentation

Scrapy

Scrapy is a fast, powerful and open-source web scraping and web crawling framework written in python. It is used to crawl websites and extract structured data from their pages.

It can be used for a wide range of purposes, from data mining to monitoring, automated testing and even building web spiders. 

Installation

pip install scrapy

Scrapy Resources

$ Homepage
$ Documentation

Beautiful Soup

Beautiful Soup is a Python library for parsing and pulling data out of HTML and XML files/structures. In other words, It is a popular python library used for web scraping. It can be used to either extract or access specific HTML tags and attributes, like the title of a webpage or find all links on a page and follow them sequentially.

Its different features make it easier to address the challenges we might face when we scrape websites and parse the data we find there. Beautiful Soup comes with excellent documentation and is also a very popular choice among python developers for web scraping.

Installation

pip install beautifulsoup4

Beautiful Soup Resources

$ Homepage
$ Documentation

Selenium

Selenium is a set of tools for automating web browsers. It is one of the most popular automation tools among developers. Selenium allows you to script interactions on various websites. Selenium can be used across many different languages, including Java, Python, Ruby, and JavaScript.

It provides a collection of language-specific bindings called WebDrivers to drive a browser. With selenium's WebDriver, you can create robust, browser-based regression automation suites and tests, scale and distribute scripts across many environments.

Selenium excels at cross-platform compatibility and web-based functional testing. Sometimes, selenium is also used for web scraping.

Installation

pip install selenium

Selenium Resources

$ Homepage
$ Documentation

Requests

Requests is one of the most popular python libraries due to its simplicity and ease of use. It is an elegant and simple HTTP library used for making all kinds of  HTTP requests in python.

The requests library is an easy-to-use library with a lot of features ranging from passing parameters in URLs to sending custom headers and SSL Verification.

Installation

pip install requests

Requests Resources

$ Homepage
$ Documentation

ScientificPython

ScientificPython is an open-source python library made up of a collection of Python modules that are useful for scientific computing.

Features of ScientificPython

1. Advanced mathematical tools for differentiation, numerical integration and many more
2. It has support for parallel computing.
3. It has support for several input/output interfaces.

Installation

pip install ScientificPython

ScientificPython Resources

$ Homepage
$ Documentation

Statsmodels

Statsmodels is a Python module that allows you to explore statistical models and make predictions using data. It provides classes for linear regressions, generalized linear models, time series analysis, classical parametric and non-parametric tests, clustering, smoothing, decomposition and for conducting other statistical tests, and statistical data exploration.

Installation

pip install statsmodels

Statsmodels Resources

$ Homepage
$ Documentation

CuPy

CuPy is a NumPy-compatible array library accelerated by CUDA. CUDA, short for Compute Unified Device Architecture is an architecture and programming model invented by NVIDIA that enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) and parallel computing.

CuPy allows for the execution of NumPy-style computations on CUDA-capable GPUs, with a wide range of data transfer mechanisms to make the most of the available hardware.

NumPy is a library for managing arrays of numbers efficiently in Python. It is a great library for managing arrays of numbers, but it doesn’t have built-in support for CUDA.
CuPy library is designed to take advantage of the computational resources of CUDA-enabled GPUs.

In short, CuPy is a python package that provides fast and efficient operations on NumPy arrays accelerated by CUDA.

Features

1. High performance with CUDA
2. Compatible with NumPy
3. Easy to install
4. Easy custom kernel implementation.

Uses

1. For enhanced array computing

CuPy Resources

$ Homepage
$ Documentation

PsychoPy

PsychoPy is a free cross-platform package that allows you to run a variety of behavioral science experiments (neuroscience, psychology, psychophysics, and linguistics, etc).

Installation

pip install psychopy

PsychoPy Resources

$ Homepage
$ Documentation

PyRobot

PyRobot is a lightweight, high-level interface that provides hardware-independent APIs for robotic manipulation and navigation.

It is a Python tool for benchmarking and executing robot learning experiments. The purpose of this project is to easily isolate low-level controls for individual robots from high-level motion generation and learning. Using PyRobot allows you to run robots without having to deal with robot-specific software, as well as perform better comparisons.

Installation

pip install pyrobot

PyRobot Resources

$ Homepage
$ Documentation

PySide

PySide is the Python Qt bindings project, which provides access to the entire Qt 4.8 framework as well as generator tools for quickly constructing bindings for any C++ library.

Installation

pip install PySide

PySide Resources

$ Homepage

OpenCV

Open Source Computer Vision (OpenCV) is an open-source computer programming library designed to assist computer vision applications. It offers hundreds of functions for the acquisition, analysis, and manipulation of visual data and can alleviate some of the difficulties that programmers experience while designing computer vision applications.

Installation

pip install opencv-python

OpenCV Resources

$ Homepage
$ Documentation

Kivy

Kivy is a cross-platform C++/Python software framework for the development of mobile apps and other multitouch application software with a natural user interface (NUI). It runs on Linux, Windows, Mac OS X, Android, iOS and even Raspberry pi.
Kivy is a free and open-source software, released under the MIT License. It is designed to work with a variety of input devices and provides a fully-featured, user-friendly, and customizable interface builder, along with a long list of supported UI controls.

It allows you to write the same app for Android, iOS, and Windows Phone with a single codebase.

Features

1. Cross-platform.
2. Multitouch support.
3. GPU accelerated
4. Open-source and business friendly.
5. Stable and well documented

Kivy Resources

$ Homepage
$ Documentation

BeeWare

BeeWare is an open-source Python framework for building cross-platform applications, such as desktop applications and mobile apps. It is basically a collection of tools and libraries for building and distributing native applications in Python. It consists of a number of components that help to ease the development of applications that function identically on different platforms and devices. (One Codebase. Multiple Apps)

Applications written with BeeWare can easily be packaged for a number of different platforms.

Uses of BeeWare

1. Used to build cross-platform apps with a rich, native user interface.
2. It contains tools for GUI development for desktop, mobile and web platforms.
3. It contains a set of tools for packaging a Python project for multiple platforms.

Installing BeeWare

pip install beeware

BeeWare Resources

$ Homepage
$ Documentation

Matplotlib

The matplotlib is a library used for creating static, animated and interactive visualizations in Python. It allows users to create plots, histograms, power spectrums, bar charts, scatter plots, and many more graphical representations of data. 

Matplotlib offers a multitude of options for visualization which makes easy things easy and hard things possible.

Installation

pip install matplotlib

To learn more, checkout the resources below.

Matplotlib Resources

$ Homepage
$ Documentation

Pytesseract

Python-tesseract is a Python-based optical character recognition (OCR) tool. In other words, it will recognize and "read" text encoded in images.

Python-tesseract is a library that wraps Google's Tesseract-OCR Engine. It can also be used as a standalone tesseract invocation script because it can read any image types supported by the Pillow and Leptonica imaging libraries, such as jpeg, png, gif, bmp, tiff, and others. Furthermore, when used as a script, Python-tesseract will print the detected text rather of writing it to a file.

Installation

pip install pytesseract

Pytesseract Resources

$ Homepage
$ Documentation

Mahotas

Mahotas is a Python computer vision and image processing toolkit that incorporates several algorithms written in C++ for speed, operate in numpy arrays, and has a very clean Python interface.

Installation

pip install mahotas

Mahotas Resources

$ Homepage
$ Documentation

Bokeh

Bokeh is a Python data visualization package that generates high-performance interactive charts and graphs. It allows for the beautiful and concise creation of adaptable images, as well as high-performance interactivity across big or streaming datasets.
Unlike popular Python visualization libraries such as Matplotlib and Seaborn, Bokeh renders its graphics using HTML and JavaScript.

Installation

pip install bokeh

Bokeh Resources

$ Homepage
$ Documentation

BioPython

A multinational team of developers created Biopython, a suite of freely available tools for biological computation written in Python.

It is a widespread collaborative effort to create Python modules and apps to meet the needs of present and future bioinformatics work. The source code is distributed under the Biopython License, which is incredibly permissive and compatible with nearly every license in the world.

Installation

pip install biopython

BioPython Resources

$ Homepage
$ Documentation

PySB

Contents

Description

PySB is a Python modeling framework for creating, simulating and analyzing biological models with a high-level, action-oriented vocabulary that encourages clarity, extensibility, and reusability. In other words, PySB is a Python library that implements a variety of mathematical models of biochemical systems in living cells. It also includes tools to generate, visualize, and explore the models.

PySB models are mathematically similar to the ordinary differential equations often used to model natural phenomena. In particular, they are based on the equations of mass action kinetics and conservation of mass, energy, charge, and volume.

PySB also works with standard scientific Python libraries including NumPy, SciPy, and SymPy to simulate and analyze models.

Members of the Lopez Lab at Vanderbilt University and the Sorger Lab at Harvard Medical School are the primary developers and maintainers of PySB:

Features

Here are a few features of the PySB:

1. Free and open source
2. Optimized simulation
3. Resuble modular specification
4. Integration with scientific python

Uses

Though PySB is primarily used for modeling biological models, It also interoperates other standard scientific Python libraries for

1. Compatibility with SBML(Systems Biology Markup Language) tools
2. Efficient array and matrix operations
3. Developing scientific algorithms, e.g., ODE integration, statistics, and optimization
4. Symbolic manipulation of mathematical expressions
5. Layout and rendering of node‐edge graphs
6. General‐purpose scientific computing

Installation

For detailed instructions on installation processes please visit http://pysb.org/download. Anyways, the installation is very straightforward with conda - run the the following in a terminal:

conda install -c alubbock pysb 

Basics

This a code example from pysb.org.

from pysb import *
from pysb.simulator import ScipyOdeSimulator
from pylab import linspace, plot, xlabel, ylabel, show

# A simple model with a reversible binding rule
Model()

# Declare the monomers
Monomer('L', ['s'])
Monomer('R', ['s'])

# Declare the parameters
Parameter('L_0', 100)
Parameter('R_0', 200)
Parameter('kf', 1e-3)
Parameter('kr', 1e-3)

# Declare the initial conditions
Initial(L(s=None), L_0)
Initial(R(s=None), R_0)

# Declare the binding rule
Rule('L_binds_R', L(s=None) + R(s=None) | L(s=1) % R(s=1), kf, kr)

# Observe the complex
Observable('LR', L(s=1) % R(s=1))

if __name__ == '__main__':
    # Simulate the model through 40 seconds
    time = linspace(0, 40, 100)
    print "Simulating..."
    sim_result = ScipyOdeSimulator(model, time).run()

    # Plot the trajectory of LR
    plot(time, sim_result.observables['LR'])
    xlabel('Time (seconds)')
    ylabel('Amount of LR')
    show()

Output:

More Resources

Keywords from google

pysb simulator
pysb tutorial
pysb github
pysb chase
pysb install
programming biological models in python using pysb
bionetgen
python biochemistry

PySB Resources

$ Homepage

IPython

IPython short for "Interactive Python" is a command shell for interactive computing originally developed for the python programming language. It provides a rich toolkit to help you make the most out of using Python interactively.

Apart from a powerful interactive shell, Ipython also comes with a kernel for Jupyter and support for interactive data visualization.

Installation

pip install ipython

IPython Resources

$ Homepage
$ Documentation

Jupyter Notebook

The Jupyter Notebook is a free and open-source web application that lets you create and share documents with live code, equations, visualizations, and narrative text. Data cleansing and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and many other applications are possible.
It is very popular among data science and machine learning practitioners.

Installation

pip install notebook

Jupyter Notebook Resources

$ Homepage
$ Documentation

IMPacket

Impacket is a Python module that allows you to work with network protocols. Currently, the library focuses on supporting low-level socket programming, TCP/IP, and a variety of higher-level protocols. It is a Python module that allows programmers to easily construct and decode network packets.

Many hackers and penetration testers utilize Impacket to create unique network-based intrusion attacks such as man in the middle and session hijacking. It was created as an internal tool to aid in testing, but it has evolved into a strong hacker's tool that can be used against networks.

Installation

pip install impacket

IMPacket Resources

$ Homepage
$ Documentation

Cryptography

cryptography is a python package that provides cryptographic primitives and cryptographic recipes to developers. It is intended to become your "cryptographic standard library".

Cryptography includes both high-level recipes and low-level interfaces to common cryptographic algorithms such as message digests, symmetric ciphers and key derivation functions.

Cryptography Resources

$ Homepage
$ Documentation

Nmap

Python-nmap is a Python package that aids in the use of the nmap port scanner. It enables simple manipulation of nmap scan results and will be an ideal tool for system administrators looking to automate scanning tasks and reports. It also supports the output of nmap scripts.

Installation

pip install python-nmap

Nmap Resources

$ Homepage
$ Documentation

Shapely

pip install shapely

conda install -c conda-forge shapely

Shapely Resources

$ Homepage
$ Documentation

SymPy

Sympy is an open-source Python library for symbolic mathematics. It has been developed to provide a more robust alternative to Python’s built-in math library. It provides several features that are missing from the default library, such as functions to compute limits, find derivatives or find integrals, among others.

Sympy is written entirely in python and it aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible.

SymPy Resources

$ Homepage
$ Documentation

Qiskit

This toolkit is designed to make it easier for engineers, scientists, and programmers to work with quantum computing, without having to worry about the underlying technology. Qiskit’s goal is to make quantum computing accessible to a broader range of people.

The world of quantum computing might seem a little intimidating to someone who hasn’t had any experience with it. But, the Qiskit Toolkit is a great place to get started – and it’s open-source so anyone can use it.
In this post, I’ll give you an introduction to quantum computing and introduce you to the Qiskit Toolkit in detail.

Qiskit Resources

$ Homepage
$ Documentation

BitCoinLib

Bitcoinlib is a python cryptocurrency library, it is used to create and manage wallets for bitcoin, litecoin, dash, etc. This library can be used at a high level to build and control wallets from the command line, or at a low level to create your own custom transactions, files, keys, or wallets.

It supports most of the features that are required for building applications using Bitcoin, Bitcoin transactions, bitcoin addresses, private keys and multiple other cryptocurrencies based on the same protocol. It uses pure python implementation; so many applicable platforms are available to use (cross-platform).

Using simple and straightforward Python code, you can create and manage transactions, addresses/keys, wallets, mnemonic password phrases, and blocks.

To know more about what this library can do for you or your organization please refer to the documentation page. The library comes with a  fully functioning wallet, complete with multiple signatures, multiple currencies, and multiple accounts.

BitCoinLib Resources

$ Homepage
$ Documentation

Pytest

The pytest framework simplifies the creation of short tests while also scaling to handle large functional testing for apps and libraries.

Installation

pip install pytest

Pytest Resources

$ Homepage
$ Documentation

CoreNLP

CoreNLP is a python wrapper for the Stanford CoreNLP Java library.

Installation

pip install corenlp

CoreNLP Resources

$ Homepage
$ Documentation

spaCY

spaCy is an open-source software library written in Python and Cython for advanced natural language processing. The library was created by Matthew Honnibal and Ines Montani, the founders of the software company Explosion, and is available under the MIT license.

Unlike NLTK, which is widely used in education and research, spaCy focuses on providing software for industrial use.

Installation

pip install spacy

spaCY Resources

$ Homepage
$ Documentation

Gensim

Gensim is an open-source library that uses modern statistical machine learning to perform unsupervised topic modeling and natural language processing.

For performance, Gensim is written in Python and Cython. Gensim is intended to handle large text collections using data streaming and incremental online algorithms, which distinguishes it from most other machine learning software packages that are only intended for in-memory processing.

Installation

pip install gensim

Gensim Resources

$ Homepage
$ Documentation

Cirq

Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. It's available on PyPI and GitHub.

Cirq is a Python library that allows you to write, manipulate, and optimize quantum circuits which you can then run them on quantum computers or quantum simulators. Cirq provides useful abstraction for dealing with noisy, intermediate-scale quantum computers.

Cirq Resources

$ Homepage
$ Documentation