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The Possibilities Of Plotting With pandas and IPython


A wave of complimentary new tools allow developers to quickly access, analyze, and plot data. IPython Notebooks let you harness these libraries and code in a web-based, language agnostic Notebook. Pandas lets you wrangle your data. And matplotlib, ggplot for Python, Plotly, bokeh, and Seaborn let you make beautiful, interactive plots. This talk shows how to use and deploy these tools together.

A new wave of Python libraries enable interactive, scientific figures, and web shareability. From Python we can access and manipulate data with pandas, make 2D, 3D, and live-streaming graphs inside IPython Notebooks, and translate plots from static images into apps we can deploy with WebGL and D3.js.

A number of libraries support--to various degrees--plotting directly from pandas inside IPython Notebooks--matplotlib, ggplot for Python, Plotly, bokeh, cufflinks, mpld3, and Seaborn. Some of these plots can also be turned into interactive web-based plots. This talk will show the volume of plotting options available for Python users from within these libraries, and highlight relevant technologies along the way.

Materials available here:


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