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Thursday 9 a.m.–12:20 p.m.

Making Beautiful Graphs in Python and Sharing Them

Andrew Seier, Étienne Tétreault-Pinard, Marianne Corvellec

Audience level:
Python Libraries


From Python basics to NYT-quality graphics, we walk through workflows to make beautiful, shareable data visualizations. We’ll also explore 3D plotting in the browser, cross-language collaboration, and matplotlib figure conversion. By using Python’s scientific stack and an IPython notebook--attendees may follow along. For data analysts, data journalists, and anyone who likes plots.


###overview This tutorial aims to teach participants how to use standard Python tools and Plotly to create stunning data visualizations which can be shared and leveraged as a platform for collaboration. ###takeaways After completion of the tutorial, students will be able to use their data to create matplotlib and Plotly figures from a Python script. They will be able to convert matplotlib figures into interactive graphs and subsequently share them on the web. Beginner Python programmers will get a feel for using the language and all will learn how to make beautiful data visualizations in the browser. ###requirements To participate in the entire tutorial, students will need to install Python and the following packages: numpy, matplotlib, ipython[notebook], and plotly. Students will also need to create an account here []( They may want to follow the getting started instructions here []( If students run into any difficulties getting their environments setup for this tutorial they may contact the presenters directly at: <>, <>, and <>. ###format The format of the tutorial is that of an interactive classroom. Deriving motivation from real-world data visualization needs in science and journalism, we begin by showing how to get from data to basic, informative visualizations quickly and easily. Students will then be introduced to different ways of improving and customizing their graphs. We will also show how to edit existing graphs and instructors will engage students to help them explore on their own. We will put forward an exploratory approach and address reproducibility, which is made possible by a seamless workflow in the Python world (use of Python scripts, the IPython Notebook, Plotly’s Python package).

Student Handout

No handouts have been provided yet for this tutorial

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