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Many data sets are beautiful in themselves, but how do we make their beauty obvious? Data visualization, of course. This hands-on tutorial will explore the craft, once described by industry expert Ben Fry as a clear multi-step process oriented around data: acquire, parse, filter, mine, represent, refine, & interact. Python -- especially with new analysis tools like Pandas -- excels at the first few steps. However, other tools beyond Python must be used in order to represent, refine, and interact with data. The best toolset for this lives in the modern browser. Many PyData attendees are familiar with IPython Notebook. It provides an ideal place for us to build out a "read-evaluate-print loop" (REPL) for the visual data exploration process. Join us as we unify Python with browser technologies like JavaScript, CSS, and SVG, in a single space. The tutorial will cover how to iteratively produce visualizations from a raw data set of online news articles & web traffic metrics. You will use IPython Notebook to discover hidden patterns in the data, then you will convert your own explorations into production visualizations ready for interaction (& publication!) on the web. The tools covered will include: Pandas; D3.js; NVD3.js; Vega / Vincent; and PhantomJS.

Andrew Montalenti

Andrew is the co-founder and CTO of, a Python-built tech startup that helps top online publishers understand what content their audience is interested in -- and why. Prior to starting, Andrew was a technologist with nearly a decade of experience in finance, high tech, and online media. He earned a degree in Computer Science from NYU. A dedicated Pythonista, JavaScript hacker, and open source advocate, Andrew is also a published technical author and editor. He has presented at PyData NYC 2012, PyCon 2013, and several other technology conferences.

What is PyData? is the home for all things related to the use of Python in data management and analysis. This site aims to make open source data science tools easily accessible by listing the links in one location. If you would like to submit a download link or any items to be listed in PyData News, please let us know at:

PyData conferences are a gathering of users and developers of data analysis tools in Python. The goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply the language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization.

We aim to be an accessible, community-driven conference, with tutorials for novices, advanced topical workshops for practitioners, and opportunities for package developers and users to meet in person.

A major goal of PyData events and conferences is to provide a venue for users across all the various domains of data analysis to share their experiences and their techniques, as well as highlight the triumphs and potential pitfalls of using Python for certain kinds of problems.

PyData is organized by NumFOCUS with the generous help and support of our sponsors. Proceeds from PyData are donated to NumFOCUS and used for the continued development of the open-source tools used by data scientists If you would like to volunteer to be a part of the PyData team contact us at:


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