The text presents a summary of the top 30 GitHub Python projects at the start of 2024. It discusses various categories, such as machine learning frameworks, AI-driven applications, programming frameworks, development productivity boosters, information catalogs, educational content, and real-world applications. The author emphasizes the use of GitHub API to acquire the ranked list and provides insights into the top projects. Additionally, there’s a categorization of projects and descriptions of various repositories, each grouped under the relevant categories. The article aims to shed light on the most popular Python projects based on the number of stars on GitHub, showcasing the significance of educational content and information organization repositories.
“`html
Top 30 GitHub Python Projects At The Beginning Of 2024
Repositories with the most stars!
Happy New Year 2024! As the first post in the new year, just like what I did before, I’m very curious about what were the most popular Python projects so far. GitHub is definitely the most suitable place to have these statistics. Although not all the open-sourced projects will be maintained here, there won’t be any other single places that are better than here.
This rank is meant to be easy because I’ll share my code. Now, let’s have a look at how we can get the ranked list from GitHub API with a few lines of code. After that, I’ll categorize these projects using my terminology and then add some short introductions to them.
The Top 30 GitHub projects are categorized as follows:
GitHub Search API
The official API documentation can be found on this page:
GitHub API Documentation
The most beautiful thing is that we don’t need to register or apply for an API key to use this endpoint. Of course, it has a rate limit which is up to 10 requests per minute, but it is not a problem for us as we just want to get several top-ranked repos. A few times of API calls will be enough for us to debug.
First of all, we need to use the requests module of Python. It is built-in and I believe most of you should be familiar with it. Then, we need Pandas to do some transformation of the data.
Machine Learning Frameworks
Machine Learning Frameworks refers to those essential tools and libraries for developing and training machine learning models. They are used by Data Scientists, Machine Learning Engineers, and Researchers day by day.
1. PyTorch (7th ranked, 74k stars)
PyTorch is one of the most popular Machine Learning Frameworks that was developed by Facebook’s AI Research lab. It is very commonly used in any deep-learning workload. Compared to other popular frameworks such as TensorFlow, it is generally more flexible and easier to use.
2. Scikit-Learn (14th ranked, 57k stars)
Sci-kit Learn, which is commonly known as sklearn, is famous for its foundational capabilities such as classification, regression, clustering, and dimensionality reduction. It is highly recommended that the newbies should learn the classical algorithms before diving into deep learning, in order to have a consolidated fundamental understanding about this domain.
AI-Driven Applications
These repos are innovative projects that make use of recent AI breakthroughs.
1. Real-Time-Voice-Cloning (18th ranked, 49k stars)
This project is an implementation of the SV2TTS (Speaker Verification to Multispeaker Text-To-Speech Synthesis) model with a real-time vocoder, originally developed by the author as a master’s thesis.
2. gpt-engineer (19th ranked, 48k stars)
GPT-Engineer allows users to specify what they want to do with the program in natural language. Then, it leverages AI to build the software gradually by understanding the problem space and asking follow-up questions to clarify the requirements.
Programming Frameworks
These repositories could be essential tools in software development.
1. Django (6th ranked, 75k stars)
Django is a high-level Python web framework that allows Web Developers to rapidly develop comprehensive web services including both backend and front-end UI.
2. FastAPI (8th ranked, 66k stars)
FastAPI can be an alternative to Django. As its name, it would allow developers to implement web services even faster.
Development Productivity Boosters
These projects are not directly involved in the coding itself. However, they could significantly enhance the efficiency and productivity of developers by addressing those common inconveniences that happen during the general development processes.
1. thef**k (4th ranked, 81k stars)
Unlike its funny name, this tool is actually a highly practical application. It is designed to correct errors in previous console commands.
2. mitmproxy (25th ranked, 33k stars)
Mitmproxy is an interactive tool designed for HTTP traffic analysis. It supports SSL/TLS protocol.
Information Catalogs
These repositories provide comprehensive collections of valuable information in a systematic and organized format.
1. The Algorithms — Python (2nd ranked, 174k stars)
This repo collected a wide range of algorithms and implemented them in Python, which makes it a great educational resource.
2. HelloGitHub (5th ranked, 80k stars)
This repo gathered a collection of interesting projects on GitHub. Most of them are at entry level.
Educational Content
These repos are generally not focused on source code. Think of them as an open-source “book”. The information they contain is pretty useful for educational purposes.
1. The System Design Primer (1st ranked, 239k stars)
It provides a comprehensive guide to learning system design.
2. Hugging Face — Transformers (3rd ranked, 118k stars)
This repo provides numerous pre-trained models that can be used for text/image/audio-related ML tasks.
Real-World Applications
These repos implement applications that are used in real life using Python. They can either save us time or enable us to do some very cool stuff.
1. Home Assistant (9th ranked, 65k stars)
This repo is a project for home automation. It highlighted that user privacy is seriously considered.
2. yt-dlp (11th ranked, 63k stars)
The repo is a fork of the popular project called youtube-dl which had been abandoned. So, it focuses on incorporating additional features and bug fixes.
Summary
Given that “popularity” in this context is measured by the number of stars a repository has, it’s clear that repositories focusing on educational content and information organization rank highly. This is often because users add stars to these repositories as a means of bookmarking them for future reference. While the code for these repositories is readily available, it’s important to apply further discernment to these rankings to cater to specific interests and satisfy curiosity.
If you want to evolve your company with AI, stay competitive, use for your advantage Top 30 GitHub Python Projects At The Beginning Of 2024.
Discover how AI can redefine your way of work. Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
Select an AI Solution: Choose tools that align with your needs and provide customization.
Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram or Twitter @itinaicom.
Spotlight on a Practical AI Solution: Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.