Maxime Liquet

hvPlot adapts and extends the `.plot()` API made popular by Pandas and Xarray to easily create interactive and static maps.

If you have done data analysis with Pandas before, then you have likely encountered the pandas `.plot()` API that renders static images using Matplotlib. The pandas `.plot` API has emerged as a de-facto standard for high-level plotting APIs in Python, and is now supported by multiple data libraries (including GeoPandas, Xarray, and Dask) and multiple underlying plotting engines (via pandas-bokeh, cufflinks, hvPlot, etc.) that provide additional power and flexibility. Learning this API allows you to access capabilities provided by a wide variety of underlying tools, with relatively little additional effort. In this presentation we’ll introduce you to the high-level `.plot()` API offered by hvPlot, a Python library that is part of the HoloViz ecosystem and built on top of powerful data visualization libraries like HoloViews, GeoViews, Datashader, and Panel. You will see how easy it is to create interactive Bokeh, Matplotlib, or Plotly maps: * from your Pandas, GeoPandas or Xarray objects, * to help you understand your columnar or multidimensional data fully, * to render very large datasets faithfully to show both patterns and outliers, * to explore temporal datasets with an automatically generated dashboard-like view.