HoloViews is an open-source Python library for data analysis and visualization. Python already has excellent tools like numpy, pandas, and xarray for data processing, and bokeh and matplotlib for plotting, so why yet another library?
HoloViews helps you understand your data better, by letting you work seamlessly with both the data and its graphical representation.
HoloViews focuses on bundling your data together with the appropriate metadata to support both analysis and visualization, making your raw data and its visualization equally accessible at all times. This process can be unfamiliar to those used to traditional data-processing and plotting tools, and this getting-started guide is meant to demonstrate how it all works at a high level. More detailed information about each topic is then provided in the User Guide.
With HoloViews, instead of building a plot using direct calls to a plotting library, you first describe your data with a small amount of crucial semantic information required to make it visualizable, then you specify additional metadata as needed to determine more detailed aspects of your visualization. This approach provides immediate, automatic visualization that can be effortlessly requested at any time as your data evolves, rendered automatically by one of the supported plotting libraries (such as Bokeh or Matplotlib).
Tabulated data: subway stations#
To illustrate how this process works, we will demonstrate some of the key features of HoloViews using a collection of datasets related to transportation in New York City. First let’s run some imports to make numpy and pandas accessible for loading the data. Here we start with a table of subway station information loaded from a CSV file with pandas:
import pandas as pd import numpy as np import holoviews as hv from holoviews import opts hv.extension('bokeh')