This blog post was originally posted here. View the code on Gist.

Design of a sequential dual-loop controller for a bicycle using NumPy, SciPy, Python Control, and Plotly.

A multi-core Python HTTP server that is about 40% to 110% faster than Go can be built by relying on the Cython language and LWAN C library. A proof of concept validates the possibility of high performance system programming in the Cython language.

This post walks you through building a simple Flask web app with user registration and login.

Learn how to identify and fix website link rot errors using Python, BeautifulSoup and Requests in this tutorial.

This is a lecture for MATH 4100/CS 5160: Introduction to Data Science, offered at the University of Utah, introducing time series data analysis applied to finance. This is also an update to my earl…

Viewsets and serializers

Hamburglar -- collect useful information from urls, directories, and files - needmorecowbell/Hamburglar

Learn how to extract data from the web with Beautiful Soup, manipulate and clean data using Python's Pandas library, and data visualization using Python's Matplotlib library.

Jupyter Notebook Kernel for running Ansible Tasks and Playbooks - ansible/ansible-jupyter-kernel

Jupyter notebooks for teaching/learning Python 3. Contribute to jerry-git/learn-python3 development by creating an account on GitHub.

Strokes is a project meant to help you learn Chinese characters. - GitHub - d33tah/strokes: Strokes is a project meant to help you learn Chinese characters.

This package is deprecated - use Import Linter instead. - seddonym/layer_linter

Serialization / De-serialization support for the SQLAlchemy Declarative ORM - GitHub - insightindustry/sqlathanor: Serialization / De-serialization support for the SQLAlchemy Declarative ORM

Caffe: a fast open framework for deep learning. Contribute to BVLC/caffe development by creating an account on GitHub.

A flexible framework of neural networks for deep learning - chainer/chainer

Intel® Nervana™ reference deep learning framework committed to best performance on all hardware - NervanaSystems/neon

What is it about pandas that has data scientists, analysts, and engineers raving? This is a guide to using pandas Pythonically to get the most out of its powerful and easy-to-use built-in features. Additionally, you will learn a couple of practical time-saving…