Developing a Timeseries Heatmap in Python Using Plotly

Using Plotly to create a heatmap visualization of monthly and hourly data. Anyone who has ever been exposed to the data, knows that time series data is arguably the most abundant type of datum that we deal with on a routine basis. Data that is indexed with date, time and/or both is thereby classified as a timeseries dataset. Often, it may be helpful to render our timeseries as a monthly and hourly heatmap visualization. Such powerful visualizations are supremely helpful in being able to digest data that is otherwise presented in form that may not be ingested into our highly visual selves. These renderings, will usually depict hour horizontally, month vertically, and will utilize color to communicate the intensity of the value the underlying cell represents. Here, we are going to transform a randomly generated timeseries dataset into an interactive heatmap useful some of Python's most powerful bindings.