Top 20 Trending Python Repositories

Top 20 Trending Python Repositories on Github

See what the GitHub community is most excited about .

GitHub uses git which is a distributed version control system written by the creator of Linux, Linus Torvalds. Almost all open source projects utilize GitHub for project management, mainly because it’s free and includes nifty features like wikis and issue trackers for better documentation and feedback. let’s have look on top 20 Github Repo on Python to see what community is excited about to showcase it Real World!

 

Top 20 Trending Python Repositories

Github Trending Python Repositories does need any introduction as this is constantly updated with the curated list of most sought open-source projects which the developer community is most excited about.

“Trending Repos” is based on various factors such as how many times it has been starred by GitHub users/most fork or weekly & monthly commits. In this blog post, we have explored the top 20 trending repositories based on the Python programming language.

Following a new GitHub project is an import part of ‘Developer World’ as it provides a way to improve productivity by using modern tools.technology and approach. Not only this…community feedback & issues reported helps you out to select the set of tools for future or ongoing development.

Below is the curated list of Top 20 Trending Python Repositories you must check out!

tensorflow / tensorflow

Tensorflow (An Open Source Machine Learning Framework for Everyone https://tensorflow.org) backed by google development team it’s inconsistently top sought python repo on GitHub.

TensorFlow™ is an open source software library for high-performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

[Source – https://www.tensorflow.org/ ]

  • Currently been contributed by more than 1600+ Developers
  • Used by many tech giants such as Twitter, AMD, UBER, Dropbox, Bloomberg, Linkedin etc.
  • Tenserflow is rapidly moving and with the recent announcement for TensorFlow 2.0

Community support of tensor flow is excellent with a large no of developers asking and solving problems of each other!

You should also watch out for aymericdamien / TensorFlow-Examples (For a lot of TensorFlow Tutorial and Examples for Beginners with Latest APIs).

vinta / awesome-python

Awesome-Python‘ is really an awesome list of all the python resources available all over the internet. This is a perfect page to bookmark if you wish to have links of everything you need in python.

This repo contains a list of all popular frameworks, tutorials, how to, documentation, Kickstarter projects, books and everything about ‘Python’. (See here- https://awesome-python.com/ )

Thanks to @vinta  for building such as awesome list and saving time and effort to provide single page views of all around python development.

 

donnemartin/system-design-primer

The ‘System Design Primer’ is all about scaling your large system, practical views and discussion to design large-scale system.

System Design Principle is Board topic and the internet is full of resources scattered on how to implement in the correct way. This Repo is an Organised collection of all such topics based on various system design topics, depth analysis of pros and cons.

Design core components, constraints, high-level design and various practical exercises and solutions.

pallets / flask

The Python micro-framework for building web applications. ‘Flask’ (Website: https://www.palletsprojects.com/p/flask/) is a lightweight WSGI web applications framework with the ability to create a quick and easy web application.

The Pallets organization develops and supports Flask and the libraries it uses. it does not enforce any dependencies or project layout. It’s totally your choice how you want to structure and what libraries you want to use. there are many community driven extensions available to make your life easier.

 

jakubroztocil/httpie

Modern command line HTTP client – user-friendly curl alternative with intuitive UI, JSON support, syntax highlighting, wget-like downloads, extensions, etc. (https://httpie.org )

HTTPie (pronounced aitch-tee-tee-pie) is a command line provides a simple HTTP command that allows for sending arbitrary HTTP requests using a simple and natural syntax, and displays colourized output. HTTPie can be used for testing, debugging, and generally interacting with HTTP servers.

 

django/django

The Web framework for perfectionists with deadlines( https://www.djangoproject.com/) Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Django makes it easier to build better Web apps more quickly and with less code. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It’s free and open source.

Django Packages is a directory of reusable apps, sites, tools, and more for your Django projects.(https://djangopackages.org/)

 

requests/requests

Python HTTP Requests for Humans™ http://python-requests.org

Requests allow you to send organic, grass-fed HTTP/1.1 requests, without the need for manual labour. There’s no need to manually add query strings to your URLs or to form-encode your POST data. Keep-alive and HTTP connection pooling are 100% automatic, thanks to urllib3.

Requests is one of the most downloaded Python packages of all time, pulling in over 400,000 downloads each day. Requests provide access to almost the full range of HTTP verbs: GET, OPTIONS, HEAD, POST, PUT, PATCH and DELETE.

 

TheAlgorithms / Python

This is all time favourite for all the new learners and university students. This contains all the algorithms implemented in python. Be it a search or sorting algorithms or neural networks most of the well-known problems based on graph theory, machine learning, network flow are covered.

Although they are not production ready but gives a quick refresher for anyone who wants to implement same in on-going projects or preparing for the interview! This is community driven so you are welcome to contribute!

 

keras-team/keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlowCNTK, or Theano.

Keras has broad adoption in the industry and the research community, it offers consistent & simple APIs.

Keras model can be trained on a number of different hardware platforms beyond CPUs

This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster — which in turn helps you win machine learning competitions.

Keras development is backed primarily by Google, and the Keras API comes packaged in TensorFlow as tf.keras. Additionally, Microsoft maintains the CNTK Keras backend. Amazon AWS is developing MXNet support. Other contributing companies include NVIDIA, Uber, and Apple (with CoreML).

ansible / ansible

Ansible is a radically simple IT automation system. It handles configuration-management, application deployment, cloud provisioning, ad-hoc task-execution, and multinode orchestration — including trivializing things like zero-downtime rolling updates with load balancers.

Ansible is the only automation language that can be used across entire IT teams from systems and network administrators to developers and managers.

Avoid writing scripts or custom code to deploy and update your applications — automate in a language that approaches plain English, using SSH, with no agents to install on remote systems.

Ansible has a large and engaged community of users who can help answer your questions.

scikit-learn / scikit-learn

scikit-learn: machine learning – Python module for machine learning built on top of SciPy. The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. (Website: http://scikit-learn.org) 

  • Simple and efficient tools for data mining and data analysis,
  • Built on NumPy, SciPy, and matplotlib – Open source, commercially usable

scrapy/scrapy

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

Scrapy is a fast high-level web crawling and web scraping framework 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 and automated testing.

Its fast powerful easily extensible modules help you to extract data by writing simple rules.

The popularity of this tool can be gauged by looking at the no of open source tools has adopted its usages and freelancer projects posted every day.

Scrapy has a healthy and active community which helps you ask for code reviews and advice for your projects.

certbot/certbot

Certbot is EFF’s tool (https://certbot.eff.org/ )to obtain certs from Let’s Encrypt and (optionally) auto-enable HTTPS on your server.

Certbot is part of EFF’s effort to encrypt the entire Internet. Secure communication over the Web relies on HTTPS, which requires the use of a digital certificate that lets browsers verify the identity of web servers (e.g., is that really google.com?). Web servers obtain their certificates from trusted third parties called certificate authorities (CAs).

Check with your hosting provider for documentation about uploading certificates or using certificates issued by Let’s Encrypt.

Certbot and Let’s Encrypt can automate away the pain and let you turn on and manage HTTPS with simple commands.

Using Certbot and Let’s Encrypt is free, so there’s no need to arrange payment.

pytorch / pytorch

An open source deep learning platform that provides a seamless path from research prototyping to production deployment.

Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python.

PyTorch (https://pytorch.org/ ) is a Python package that provides two high-level features:

  • Tensor computation (like NumPy) with strong GPU acceleration
  • Deep neural networks built on a tape-based autograd system

You can reuse your favourite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

Still in Beta Phase so Expect some adventures and rough edges.

ageitgey / face_recognition

The world’s simplest facial recognition api for Python and the command line.
Built using dlib‘s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on theLabeled Faces in the Wild benchmark.

The commandface_recognition lets you recognize faces in a photograph or folder full for photographs.

You can import the face_recognition module and then easily manipulate faces with just a couple of lines of code. It’s super easy!

API Docs: https://face-recognition.readthedocs.io.

home-assistant/home-assistant

Open source home automation that puts local control and privacy first https://www.home-assistant.io

Home Assistant is a home automation platform running on Python 3. It is able to track and control all devices at home and offer a platform for automated control.

Many Integrations are available  such as Amazon Echo/Kodi/ Google Cast/Nest/Apple TV/Arduino etc.

The community provide a curated list of different ways to use Home Assistant. Most of these examples are using the automation component and other built-in automation related and organization components available.

Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server.

pandas-dev/pandas

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labelled” data both easy and intuitive.

It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

This is known as one of the MUST know python package for data analysis with Numpy.

Pandas: powerful Python data analysis toolkit provides an efficient DataFrame object for data manipulation along with interfaces for easy handling of missing data, data alignment, slicing, fancy indexing, merging, reshape, pivoting, hierarchical labelling, robust io with multiple data format.

Not only these pandas provide time series specific functionality such as date range generation and frequency conversion to make life easier for many data scientist.

Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language

Avik-Jain/100-Days-Of-ML-Code

 

As the name suggests ‘100 Days of ML Coding’ is collection machine learning algorithms, implementation, tutorials and infographics to provide you best step by step guide to learn ML.

It has also the collection of Code implemented and data set ready to play around.

It looks like it is still not been completed fully but lots many visualization and infographics make it easier for anyone who is starting fresh!

donnemartin/data-science-ipython-notebooks

Data science Python notebooks in form of ipython NOTEBOOKS.

Notebooks based on Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

To demonstrate usages and examples of how to start! This repository contains a variety of content; some developed by Donne Martin, and some from third-parties.

Contributions are welcome to add your content to be shared with this popular GitHub repo.

SeleniumHQ/selenium

Selenium is an umbrella project encapsulating a variety of tools and libraries enabling web browser automation. Selenium specifically provides infrastructure for the W3C WebDriver specification — a platform and language-neutral coding interface compatible with all major web browsers.

Selenium automates browsers. That’s it! What you do with that power is entirely up to you.

Primarily, it is for automating web applications for testing purposes but is certainly not limited to just that. Boring web-based administration tasks can (and should!) be automated as well.

 

Python is so much popular nowadays that this list is changing on the frequent basis.

We make our best effort to keep this page updated however if you see it’s not reflecting the top list from GitHub page you can directly refer to trending page at GITHUB- https://github.com/topics/python?o=desc&s=stars 

You’re highly encouraged to provide your feedback by commenting below. If you don’t like Comments (for some reason) you’re welcome to send regular email to us on techfossguru@gmail.com.

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