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December 18, 2021 12:16 pm GMT

9 Open Source Python Projects to join in 2022!

Contributing to open source projects is great for your reputation, skill development and knowledge as a developer.
In this article, I will be going through 9 open source Python projects that you can join today!

9. Django

Ah yes, the famous web development framework made for Python. It has more than 60k stars on Github and is used by millions of Python developers around the world.

GitHub logo django / django

The Web framework for perfectionists with deadlines.

Django

Django is a high-level Python web framework that encourages rapid developmentand clean, pragmatic design. Thanks for checking it out.

All documentation is in the "docs" directory and online athttps://docs.djangoproject.com/en/stable/. If you're just getting startedhere's how we recommend you read the docs:

  • First, read docs/intro/install.txt for instructions on installing Django.
  • Next, work through the tutorials in order (docs/intro/tutorial01.txtdocs/intro/tutorial02.txt, etc.).
  • If you want to set up an actual deployment server, readdocs/howto/deployment/index.txt for instructions.
  • You'll probably want to read through the topical guides (in docs/topics)next; from there you can jump to the HOWTOs (in docs/howto) for specificproblems, and check out the reference (docs/ref) for gory details.
  • See docs/README for instructions on building an HTML version of the docs.

Docs are updated rigorously. If you find any problems in the docs, or thinkthey should be

If you have experience with web development in Python and are looking to join an open source project, Django is the project for you!
Start contributing to Django here.

8. Scrapy

Scrapy is the most popular Python web scraping library with over 40k stars on github.

GitHub logo scrapy / scrapy

Scrapy, a fast high-level web crawling & scraping framework for Python.

/artwork/scrapy-logo.jpg

Scrapy

PyPI VersionSupported Python VersionsUbuntumacOSWindowsWheel StatusCoverage reportConda Version

Overview

Scrapy is a fast high-level web crawling and web scraping framework, used tocrawl websites and extract structured data from their pages. It can be used fora wide range of purposes, from data mining to monitoring and automated testing.

Scrapy is maintained by Zyte (formerly Scrapinghub) and many othercontributors.

Check the Scrapy homepage at https://scrapy.org for more informationincluding a list of features.

Requirements

  • Python 3.6+
  • Works on Linux, Windows, macOS, BSD

Install

The quick way:

pip install scrapy

See the install section in the documentation athttps://docs.scrapy.org/en/latest/intro/install.html for more details.

Documentation

Documentation is available online at https://docs.scrapy.org/ and in the docsdirectory.

Releases

You can check https://docs.scrapy.org/en/latest/news.html for the release notes.

Community (blog, twitter, mail list, IRC)

See https://scrapy.org/community/ for details.

Contributing

See https://docs.scrapy.org/en/master/contributing.html for details.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct(see https://github.com/scrapy/scrapy/blob/master/CODE_OF_CONDUCT.md

If you're into web scraping with Python and want to work on improving the web scraping library used by thousands of Python developers, start contributing to Scrapy through this page.

7. Scikit-Learn

If you've been involved in machine learning with Python for some time, you've probably come across this library.

GitHub logo scikit-learn / scikit-learn

scikit-learn: machine learning in Python

Azure Travis Codecov CircleCI Nightly wheels Black PythonVersion PyPi DOI Benchmark

https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png

scikit-learn is a Python module for machine learning built on top ofSciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summerof Code project, and since then many volunteers have contributed. Seethe About us pagefor a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 3.7)
  • NumPy (>= 1.14.6)
  • SciPy (>= 1.1.0)
  • joblib (>= 0.11)
  • threadpoolctl (>= 2.0.0)

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.scikit-learn 0.23 and later require Python 3.6 or newerscikit-learn 1.0 and later require Python 3.7 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ andclasses end with "Display") require Matplotlib (>= 2.2.3)For running the examples Matplotlib >= 2.2.3 is required.A few examples require scikit-image >= 0.14.5, a

If you have experience with machine learning and data visualization with Python and want to contribute to one of the most popular Python machine learning libraries, start contributing to scikit-learn here.

6. Pandas

Pandas is the most popular data analysis/manipulation library for Python.

GitHub logo pandas-dev / pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more


pandas: powerful Python data analysis toolkit

PyPI Latest ReleaseConda Latest ReleaseDOIPackage StatusLicenseAzure Build StatusCoverageDownloadsGitterPowered by NumFOCUSCode style: blackImports: isort

What is it?

pandas is a Python package that provides fast, flexible, and expressive datastructures designed to make working with "relational" or "labeled" data botheasy and intuitive. It aims to be the fundamental high-level building block fordoing practical, real world data analysis in Python. Additionally, it hasthe broader goal of becoming the most powerful and flexible open source dataanalysis / manipulation tool available in any language. It is already well onits way towards this goal.

Main Features

Here are just a few of the things that pandas does well:

  • Easy handling of missing data (represented asNaN, NA, or NaT) in floating point as well as non-floating point data
  • Size mutability: columns can be inserted anddeleted from DataFrame and higher dimensionalobjects
  • Automatic and explicit data alignment: objects canbe explicitly aligned

If you know how to work with data in Python and want to help build the future of data analysis/manipulation in Python, start contributing to pandas here.

5. Flask

Flask is another popular Python web development library with over 50k stars on Github.

GitHub logo pallets / flask

The Python micro framework for building web applications.

Flask

Flask is a lightweight WSGI web application framework. It is designedto make getting started quick and easy, with the ability to scale up tocomplex applications. It began as a simple wrapper around Werkzeugand Jinja and has become one of the most popular Python webapplication frameworks.

Flask offers suggestions, but doesn't enforce any dependencies orproject layout. It is up to the developer to choose the tools andlibraries they want to use. There are many extensions provided by thecommunity that make adding new functionality easy.

Installing

Install and update using pip:

$ pip install -U Flask

A Simple Example

# save this as app.pyfrom flask import Flaskapp = Flask(__name__)@app.route("/")def hello():    return "Hello, World!"
$ flask run  * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Contributing

For guidance on setting

If you're looking to help build the future of web development with Python, start contributing to flask here.

4. Requests

Requests, the OG library used by millions that is used for making HTTP requests with Python. This might be pretty underwhelming, but you see, the requests library is used to connect to API endpoints, authenticate web connections, scrape data from the web, test web endpoints and more!
Without the requests library, Python wouldn't be where it is today.

GitHub logo psf / requests

A simple, yet elegant, HTTP library.

Requests

Requests is a simple, yet elegant, HTTP library.

>>> import requests>>> r = requests.get('https://api.github.com/user', auth=('user', 'pass'))>>> r.status_code200>>> r.headers['content-type']'application/json; charset=utf8'>>> r.encoding'utf-8'>>> r.text'{"type":"User"...'>>> r.json(){'disk_usage': 368627, 'private_gists': 484, ...}

Requests allows you to send HTTP/1.1 requests extremely easily. Theres no need to manually add query strings to your URLs, or to form-encode your PUT & POST data but nowadays, just use the json method!

Requests is one of the most downloaded Python packages today, pulling in around 30M downloads / week according to GitHub, Requests is currently depended upon by 500,000+ repositories. You may certainly put your trust in this code.

DownloadsSupported VersionsContributors

Installing Requests

Start contributing to requests here.

3. Matplotlib

Matplotlib is the most popular data visualization library for Python.

GitHub logo matplotlib / matplotlib

matplotlib: plotting with Python

PyPi Downloads NUMFocus

DiscourseBadge Gitter GitHubIssues GitTutorial

GitHubActions AzurePipelines AppVeyor Codecov LGTM

Matplotlib is a comprehensive library for creating static, animated, andinteractive visualizations in Python.

Check out our home page for more information.

https://matplotlib.org/_static/readme_preview.png

Matplotlib produces publication-quality figures in a variety of hardcopyformats and interactive environments across platforms. Matplotlib can be usedin Python scripts, the Python and IPython shell, web application servers, andvarious graphical user interface toolkits.

Install

For installation instructions and requirements, see the install documentation orinstalling.rst in the source.

Contribute

You've discovered a bug or something else you want to change - excellent!

You've worked out a way to fix it even better!

You want to tell us about it best of all!

Start at the contributing guide!

Contact

Discourse is the discussion forum forgeneral questions and discussions and our recommended starting point.

Our active mailing lists (which are mirrored on Discourse) are:

If you're involved with data visualization with Python and want to contribute to the most used and versatile data visualization library in Python, start contributing to Matplotlib here.

2. Keras

With over 50k stars on Github, Keras is a simple, versatile and robust library for building neural networks with Python.

GitHub logo keras-team / keras

Deep Learning for humans

Keras: Deep Learning for humans

Keras logo

This repository hosts the development of the Keras libraryRead the documentation at keras.io.

About Keras

Keras is a deep learning API written in Pythonrunning on top of the machine learning platform TensorFlowIt was developed with a focus on enabling fast experimentation.Being able to go from idea to result as fast as possible is key to doing good research.

Keras is:

  • Simple -- but not simplistic. Keras reduces developer cognitive loadto free you to focus on the parts of the problem that really matter.
  • Flexible -- Keras adopts the principle of progressive disclosure of complexitysimple workflows should be quick and easy, while arbitrarily advanced workflowsshould be possible via a clear path that builds upon what you've already learned.
  • Powerful -- Keras provides industry-strength performance and scalabilityit is used by organizations and companies including NASA, YouTube,

Start contributing to Keras here.

1. TensorFlow

TensorFlow is a sophisticated Python neural network, deep learning and machine learning library used by millions with over 160k stars on Github.

GitHub logo tensorflow / tensorflow

An Open Source Machine Learning Framework for Everyone

PythonPyPIDOI

Documentation
Documentation

TensorFlow is an end-to-end open source platformfor machine learning. It has a comprehensive, flexible ecosystem oftoolslibraries, andcommunity resources that letsresearchers push the state-of-the-art in ML and developers easily build anddeploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on theGoogle Brain team within Google's Machine Intelligence Research organization toconduct machine learning and deep neural networks research. The system isgeneral enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Pythonand C++ APIs, as well asnon-guaranteed backward compatible API forother languages.

Keep up-to-date with release announcements and security updates by subscribingto[email protected]See all the mailing lists.

Install

See the TensorFlow install guide for thepip package, toenable GPU support, use aDocker container, andbuild from source.

Start contributing to TensorFlow here.

Conclusion

I hope that in this article, you've found the open source project that you would like to contribute to, and help build the future of Python.

Educative

Before I end this article, I'd like to recommend Educative for developers looking to learn.
Why Educative?
It is home to hundreds of development courses, hands on tutorials, guides and demonstrations to help you stay ahead of the curve in your development journey.

You can get started with Educative here.

Byeeee


Original Link: https://dev.to/code_jedi/9-open-source-python-projects-to-join-in-2022-3c8o

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