We have collection of more than 1 Million open source products ranging from Enterprise product to
small libraries in all platforms. We aggregate information from all open source repositories.
Search and find the best for your needs. Check out projects section.
If you have specific questions, please add an issue or ask on Stack Overflow with the label python-jedi. Jedi is a static analysis tool for Python that can be used in IDEs/editors. Its historic focus is autocompletion, but does static analysis for now as well. Jedi is fast and is very well tested. It understands Python on a deeper level than all other static analysis frameworks for Python.
This is a package with preferences and syntax highlighter for cutting edge Python 3, although Python 2 is well supported, too. The syntax is compatible with Sublime Text, Atom and Visual Studio Code. It is meant to be a drop-in replacement for the default Python package. We are proud to say that MagicPython is used by GitHub to highlight Python.
This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.
Binarytree is a Python library which provides a simple API to generate, visualize, inspect and manipulate binary trees. It allows you to skip the tedious work of setting up test data, and dive straight into practising your algorithms. Heaps and BSTs (binary search trees) are also supported. You may need to use sudo depending on your environment.
This repository contains a collection of materials for teaching/learning Python 3 (3.5+). If you can not access Python and/or Jupyter Notebook on your machine, you can still follow the web based materials. However, you should be able to use Jupyter Notebook in order to complete the exercises.
A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.
This is a collection of Python scripts that are split by topics and contain code examples with explanations, different use cases and links to further readings. It is a playground because you may change or add the code to see how it works and test it out using assertions. It also allows you to lint the code you've wrote and check if it fits to Python code style guide. Altogether it might make your learning process to be more interactive and it might help you to keep code quality pretty high from very beginning.
Hawkpost lets you create unique links that you can share with the person that desires to send you important information but doesn't know how to deal with PGP. You can deploy your own server using the code from this repository or use the official server (that is running an exact copy of this repo) at https://hawkpost.co.
This is a program to cast your macOS audio, or Linux audio to your Google Cast devices or Sonos speakers. It can also cast video files. It is written for Python3, and it can stream via node.js, parec (Linux), ffmpeg, or avconv. Mkchromecast is capable of using lossy and lossless audio formats provided that ffmpeg, avconv (Linux), or parec (Linux) are installed. It also supports Multi-room group playback, and 24-bit/96kHz high audio resolution. Additionally, a system tray menu is available.
you are all set and ready to go. All configuration can be done in setup.cfg like changing the description, url, classifiers, installation requirements and so on as defined by setuptools. That means in most cases it is not necessary to tamper with setup.py.