Links to the best IPython and Jupyter Notebooks.
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matplotlib - 2D and 3D plotting in Python
The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures .
Visualizing distributions of data
This notebook demonstrates different approaches to graphically representing distributions of data, specifically focusing on the tools provided by the seaborn package.
Neural networks are one approach to machine learning that attempts to deal with the problem of large data dimensionality. The neural network approach uses a fixed number of basis functions - in contrast to methods such as support vector machines ...
XKCD plots in Matplotlib
Update: the matplotlib pull request has been merged! See This post for a description of the XKCD functionality now built-in to matplotlib!
Web Scraping Indeed for Key Data Science Job Skills
As many of you probably know, being a data scientist can require a pretty large skill set . . .
Automating Microsoft Office with Python
Windows applications, for many years, have provided a COM API for automation. This includes Microsoft Office as well.
Advanced Data Visualization
There have been many examples of useful and exciting data visualizations for a variety of topics and applications.
Python for Data Science
This short primer on Python is designed to provide a rapid "on-ramp" to enable computer programmers who are already familiar with concepts and constructs in other programming languages learn enough about Python to facilitate the effective use of open-source and ...
A Primer on Bayesian Methods for Multilevel Modeling
Hierarchical or multilevel modeling is a generalization of regression modeling.
INTRODUCTION TO PYTHON FOR DATA MINING
I do most of my work from the command line, but Anaconda comes with a launcher app that can be found in the ~/anaconda directory. To get the launcher to work with a Mac, you need to do the following:
This notebook implements a numeric simulation of the three-body problem . This is done using the Julia Language and the Sundials library.
9.1 Reading data from SQL databases
So far we've only talked about reading data from CSV files. That's a pretty common way to store data, but there are many others! Pandas can read from HTML, JSON, SQL, Excel (!!!), HDF5, Stata, and a few other things. ...
Interactive Financial Analytics with Python & IPython
Tutorial with Examples based on the VSTOXX Volatility Index
6.4. Visualizing a NetworkX graph in the IPython notebook with d3.js
This is one of the 100 recipes of the IPython Cookbook , the definitive guide to high-performance scientific computing and data science in Python.
This web site does not host notebooks, it only renders notebooks available on other websites.
A D3 Viewer for Matplotlib Visualizations
This notebook originally appeared as a blog post by Jake Vanderplas on Pythonic Perambulations . Content is BSD licensed
Scatter plots in Lightning
Style options like color and size can be passed a single value, which affects all points
>>> The Traveling Salesperson Problem
Given a set of cities and the distances between each pair of cities, what is the shortest possible tour that visits each city exactly once, and returns to the starting city?
Theano is a software package which allows you to write symbolic code and compile it onto different architectures (in particular, CPU and GPU). It was developed by machine learning researchers at the University of Montreal. Its use is not limited ...
Faster data processing in Python
Let's discuss how to make these choices with the aim of running code faster.
Explorations of PCAP files from contagio malware dump
The goal here is to get a brief understanding of the amount of data that we're dealing with after reading all of the log files in. This includes understanding the "how-many-of-each", the "when", and the "what".
Datamining Twitter with tweepy
Twitter is a worldwide database of human sentiment. About 500 million new tweets go out per day( source ). The ability to stream, parse and understand Twitter data at large scale has huge implications for marketing, humanitarian efforts, social sciences ...