At the end of the first of the Curriculum Development Hackathon on Reproducible Research using Jupyter Notebooks held at BIDS in Berkeley, yesterday, discussion turned on whether we should include a short how-to on the use of interactive IPython widgets to support exploratory data analysis. This would provide workshop participants with an example of how to rapidly prototype a simple exploratory data analysis application such as an interactive chart, enabling them to explore a range of parameter values associated with the data being plotted in a convenient way.
In summarising how the ipywidgets interact() function works, Fernando Perez made a comment that made wonder whether we could use the idea of creating simple interactive chart explorers as a way of motivating the use of functions.
More specifically, interact() takes a function name and the set of parameters passed into that function and creates a set of appropriate widgets for setting the parameters associated with the function. Changing the widget setting runs the function with the currently selected values of the parameters. If the function returns a chart object, then the function essentially defines an interactive chart explorer application.
So one reason for creating a function is that you may be able to automatically convert into an interactive application using interact().
Here’s a quick first sketch notebook that tries to set up a motivating example: An Alternative Way of Motivating Functions?
PS to embed an image of a rendered widget in the notebook, select the Save notebook with snapshots option from the Widgets menu:
See also: Simple Interactive View Controls for pandas DataFrames Using IPython Widgets in Jupyter Notebooks