Talk entitled: Collaborative, streaming, 3D, and interactive matplotlib, ggplot2, and MATLAB plots in an IPython Notebook with Plotly by Chris Parmer & Carole Griffiths

The idea behind was to bring web standards to graphing and data analysis.

Reason for being: Experiencing teams making graphs in various tools and trying to share it them.  This could literally be a case of working with a dedicated graphing tool and then creating and emailing screen shots, as their colleagues all used different tools.

The team wrote a wrapper for iPython to translate graphs into a based graphs.

Graphing news feed-


  • creates shareable d3.js graphs links that are interactive
  • includes plotting streaming data
  • limited by the browser – 50,000 points before browser slows – can get to 200,000 points in some cases using optimisation of rendering overlapping points
  • like github for graphs
  • aimed at small to medium data at present
  • currently around 20 developers – doubling ever few months over the last 6 months
  • 18 month old project
  • can have it installed locally on a private network – paid for model. Free tool, can create a limited number of private graphs, say 20.


Various wrappers exists –

  • ggplotly for R
  • matplotlib
  • – has a spreadsheet like interface online too.
  • any graph can be edited via code or through online GUI

User base

  • data journalists
  • engineers
  • etc

Various output formats, such as svg, png, available as RESTful calla.

Open source library – cached request model – to handle connectivity breaks.

Can pull graphs back into Python. Also can extract JSON output of the data used in the graph.

No limit on the data storage at this stage.  1/2 million rows have been uploaded say


  • d3.js for the graphs
  • Uses map.js – for adding notations/formulas to graphs
  • is  a layer written on top of d3.js

JavaScript makes this graphing tool available to more people, as the technology is already in their browser.


  • Better data API
  • Adding datasets directly to – several graphs referencing a single dataset.
  • Symbolic formulas, to plot transformations say in Physics
  • To keep a trace of the transformations, all the steps to transformation.
  • Improve 3D maps