fperez

Polyglot Data Science with IPython & friends

Author: Fernando Pérez.

A demonstration of how to use Python, Julia, Fortran and R cooperatively to analyze data, in the same process.

This is supported by the IPython kernel and a few extensions that take advantage of IPython's magic system to provide low-level integration between Python and other languages.

See the companion notebook for data preparation and setup.

parmentf

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

iandanforth

How to capture WebGL/Canvas by piping data over a websocket.

This Gist builds on https://gist.github.com/unconed/4370822 from @unconed.

Instead of the original method which writes to the browsers sandboxed filesystem here we use a websocket connection provided by websocketd to pipe image data to a short python script that writes out the .png files to disk.

Install websocketd

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t