Rob Story - Up and Down the Python Data and Web Visualization Stack

PyData SV 2014 In the past two years, there has been incredible progress in Python data visualization libraries, particularly those built on client-side JavaScript tools such as D3 and Leaflet. This talk will give a brief demonstration of many of the newest charting libs: mpld3 (using Seaborn/ggplot), nvd3-python, ggplot, Vincent, Bearcart, Folium,and Kartograph will be used to visualize a newly-released USGS/FAA wind energy dataset (with an assist from Pandas and the IPython Notebook). After a demo of the current state of Python and web viz, it will discuss the future of how the Python data stack can have seamless interoperability and interactivity with JavaScript visualization libraries.

Related Talks

PyPy.js: What? How? Why? by Ryan Kelly

PyPy.js is an experiment in building a fast, compliant, in-browser python interpreter. By compiling the PyPy interpreter into javascript, and retargeting its JIT compiler to emit asmjs code at runtime, it is possible to run python code in the browser at speeds competitive with a native python environment. ...

Thomas Pfaff: Advanced Data Storage

In this tutorial we will give an introduction to two advanced data storage formats. HDF5 and NetCDF were designed to efficiently store the results of supercomputing applications like climate model outputs, or the data streams received from NASA's fleet of earth observing satellites. They provide a lot of optimizations concerning ...

Facts and Myths about Python names and values

Ned Batchelder
25 minutes
The behavior of names and values in Python can be confusing. Like many parts of Python, it has an underlying simplicity that can be hard to discern, especially if you are used to other programming languages. Here I'll explain how it all works, and present some facts and myths along ...

Bugra Akyildiz - Outlier Detection in Time Series Signals

PyData SV 2014 Many real-world datasets have missing observations, noise and outliers; usually due to logistical problems, component failures and erroneous procedures during the data collection process. Although it is easy to avoid missing points and noise to some level, it is not easy to detect wrong measurements and outliers ...