Jupyter DeclarativeWidgets make data exploration easy!

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Over the past year, we have built many demonstrations using declarativewidgets to create dashboards and good-enough applications in the Jupyter Notebook. We’ve discovered several patterns that are in great need of simplification. One of these patterns is data exploration. By data exploration, I mean the ability for the user to query and visualize a data set.

In declarativewidgets, we make it simple to bind visual elements to data represented by DataFrames. Until recently, elements could only read the data from a DataFrame, but now, we’ve enhanced our DataFrame support to allow for queries. These queries are represented by a set of declarative elements that allow the Notebook user to build custom interfaces that can manipulate the data (see this example).

The new declarativewidgets_explorer project takes advantage of the above and provides a powerful UI that allows the user to perform queries on the DataFrame and select how to visualize the data. This is all neatly wrapped by the explore function. Pass in a DataFrame of any type (Pandas, R, and Spark in all supported languages) and away you go!

 

Come checkout out the projects and let us know what you think.

 

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Gino Bustelo

Gino Bustelo

Senior Technical Staff Member at IBM
Gino works at IBM Emerging Technologies. He enjoys learning all sorts of new technologies and building things that turn complex things into simple things. At home he is a tinkerer... getting into FPV racing quads and building Legos with the family.
Gino Bustelo
Gino Bustelo

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