Jupyter DeclarativeWidgets make data exploration easy!
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.