A couple of months ago, I stumbled upon this interesting presentation Jupyter Notebooks: interactive visualization approaches. The presentation showed how you can use bqplot to build interactive visualizations.
Bqplot contains a set of 2D plotting widgets built on top of the ipywidgets framework for Jupyter notebooks. The bqplot package aims to bring d3.js visualizations to Python while retaining the flexibility and ease of use of ipywidgets and was developed by the quantitative research team at Bloomberg. You can install bqplot using conda or pip.
One of the examples built by the team that you can find on Github is a Jupyter notebook which shows US equity market performance (using the S&P 500 index) where you can select an interval on a time series chart - for the selected area you get the total return as well as a histogram of the daily returns.
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