Navigating the Magical Data Visualisation Forest IBM
Navigating the Magical Data Visualisation Forest IBM


Data visualization is fun but can take up a lot of time, especially when you are exploring new data. The magic forest is much easier to navigate with PixieDust, a free open-source Python library that makes it quick and simple to explore data with any visualization library without writing code in a Jupyter notebook. Learn how PixieDust takes out some of the coding, how to contribute, and how to make and share visualizations in seconds.
There are many different Python libraries available for data visualization. These all have different philosophies, syntax and ways to create charts. To make charts in a Jupyter notebook in seconds, PixieDust integrates with any library and visualizes data in a Pandas or Spark DataFrame. matplotlib, Bokeh, seaborn and Brunel are the renderers added so far, but there are many more! In this session you will learn how to find your way in the magical forest of visualization libraries through examples creating charts, plots and maps of real forest data.
When you have created a chart it is time to leave the forest. You can share your visualizations with anyone through the PixieGateway, which lets you share charts as web applications.
In the final part of this session you will learn how you can contribute to PixieDust by adding new visualization libraries or suggesting improvements for the existing ones. Let PixieDust be your compass to navigate the data visualisation forest!


Margriet is a Data Scientist and Developer Advocate for the IBM Watson Data Platform. She has a background in Climate Science where she explored large observational datasets of carbon uptake by forests and the output of global scale weather and climate models. Now she uses this knowledge to create clear visualisations and models from diverse data sets using cloud databases, data warehouses, Spark, and Python notebooks.

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