As we have already discovered, Elements are simple wrappers around your data that provide a semantically meaningful visual representation. HoloViews can work with a wide variety of data types, but many of them can be categorized as either:
Tabular: Tables of flat columns, or
Gridded: Array-like data on 2-dimensional or N-dimensional grids
These two general data types are explained in detail in the Tabular Data and Gridded Data user guides, including all the many supported formats (including Python dictionaries of NumPy arrays, pandas
DataFrames, and xarray
In this Getting-Started guide we provide a quick overview and introduction to two of the most flexible and powerful formats: columnar pandas DataFrames (in this section), and gridded xarray Datasets (in the next section).
Tabular data (also called columnar data) is one of the most common, general, and versatile data formats, corresponding to how data is laid out in a spreadsheet. There are many different ways to put data into a tabular format, but for interactive analysis having tidy data provides flexibility and simplicity. For tidy data, the columns of the table represent variables or dimensions and the rows represent observations. The best way to understand this format is to look at such a dataset:
import numpy as np import pandas as pd import holoviews as hv from holoviews import opts hv.extension('bokeh', 'matplotlib')