API

Utilities

help([kind, docstring, generic, style])	Print a docstring with all valid options which apply to the plot type.
hvplot_extension(*, compatibility, logo, name)	Helper utility used to load hvPlot/HoloViews extensions and control the notebook environment.
extension	alias of hvplot_extension
output(*args, **params)	Helper used to set HoloViews display options.
render(obj[, backend])	Renders the HoloViews object to the corresponding object in the specified backend, e.g. a Matplotlib or Bokeh figure.
save(obj, filename[, fmt, backend, ...])	Saves the supplied object to file.
show(obj[, title, port])	Starts a Bokeh server and displays the plot in a new tab.

Plotting

hvPlot / .hvplot

hvPlot’s plotting API is most often invoked by installing the hvplot namespace on a data source via a special import:

import pandas as pd
import hvplot.pandas  # noqa
df = pd.DataFrame()

df.hvplot.scatter()
# or
df.hvplot(kind='scatter')

Under the hood, these special imports like import hvplot.pandas register an accessor that returns an instance of an hvPlotBase class. Tabular-like data sources rely on the hvPlotTabular class and gridded-like sources on hvPlot (subclass of hvPlotTabular extended with methods like image):

• hvPlotTabular: cuDF, Dask, Fugue, Ibis, Pandas, Streamz

• hvPlotTabularDuckDB: DuckDB

• hvPlotTabularPolars: Polars

• hvPlot: Xarray

Instead of using the hvplot namespace, one can directly generate hvPlot plots with these classes:

import pandas as pd
from hvplot import hvPlot
df = pd.DataFrame()

hvPlot(df).scatter()
# or
hvPlot(df)(kind='scatter')

This section documents all the plotting methods of the hvPlot class, which as described above are also available via the hvplot namespace.

Common

hvPlot.area([x, y, y2, stacked])	The area plot can be used to color the area under a line or to color the space between two lines.
hvPlot.bar([x, y, stacked])	A vertical bar plot
hvPlot.barh([x, y, stacked])	A horizontal bar plot
hvPlot.box([y, by])	The box plot gives you a visual idea about the locality, spread and skewness of numerical data through their quartiles.
hvPlot.bivariate([x, y, colorbar, ...])	A bivariate, density plot uses nested contours (or contours plus colors) to indicate regions of higher local density.
hvPlot.dataset([columns])	The 'dataset' wraps a tabular or gridded dataset and can be further transformed and annotated via methods from HoloViews.
hvPlot.density([y, by])	Alias of hvplot.hvPlot.kde().
hvPlot.errorbars([x, y, yerr1, yerr2])	errorbars provide a visual indicator for the variability of the plotted data on a graph.
hvPlot.heatmap([x, y, C, colorbar, logz])	heatmap visualises tabular data indexed by two key dimensions as a grid of colored values.
hvPlot.hexbin([x, y, C, colorbar, gridsize, ...])	The hexbin plot uses hexagons to split the area into several parts and attribute a color to it.
hvPlot.hist([y, by, bins, bin_range, ...])	A histogram displays an approximate representation of the distribution of continuous data.
hvPlot.kde([y, by])	The Kernel density estimate (kde) plot shows the distribution and spread of the data.
hvPlot.labels([x, y, text])	Labels plot.
hvPlot.line([x, y])	The line plot connects the points with a continuous curve.
hvPlot.ohlc([x, y])	The ohlc plot visualizes the open, high, low and close prices of stocks and other assets.
hvPlot.paths([x, y, c])	LineString and LineRing plot for geopandas dataframes.
hvPlot.points([x, y])	A points plot visualizes positions in a 2D space.
hvPlot.polygons([x, y, c])	Polygon plot for geopandas dataframes.
hvPlot.scatter([x, y])	The scatter plot visualizes your points as markers in 2D space.
hvPlot.step([x, y, where])	The step plot connects the points with piece-wise constant curves.
hvPlot.table([columns])	Displays a 'table'.
hvPlot.vectorfield([x, y, angle, mag])	vectorfield visualizes vectors given by the (x `, `y) starting point, a magnitude (mag) and an angle.
hvPlot.violin([y, by])	violin plots are similar to box plots, but they provide a better sense of the distribution of data.

Gridded

hvPlot.contour([x, y, z, colorbar, levels, logz])	Line contour plot
hvPlot.contourf([x, y, z, colorbar, levels, ...])	Filled contour plot
hvPlot.image([x, y, z, colorbar])	Image plot
hvPlot.quadmesh([x, y, z, colorbar])	QuadMesh plot
hvPlot.rgb([x, y, z, bands])	RGB plot

plotting module

hvPlot’s structure is based on Pandas’ plotting API and as such provides special plotting functions in the hvplot.plotting module.

andrews_curves(data, class_column[, ...])	Generate a plot of Andrews curves, for visualising clusters of multivariate data.
lag_plot(data[, lag])	Lag plot for time series.
parallel_coordinates(data, class_column[, ...])	Parallel coordinates plotting.
scatter_matrix(data[, c, chart, diagonal, ...])	Scatter matrix of numeric columns.

NetworkX

The hvPlot NetworkX plotting API is meant as a drop-in replacement for the networkx.draw methods.

Note

Please chime in this issue if you have opinions about NetworkX plotting API in hvPlot.

draw(G[, pos])	Draw the graph G using hvPlot.
draw_networkx(G[, pos])	Draw a networkx graph.
draw_networkx_nodes(G, pos, **kwargs)	Draw networkx graph nodes.
draw_networkx_edges(G, pos, **kwargs)	Draw networkx graph edges.
draw_networkx_labels(G, pos, **kwargs)	Draw networkx graph node labels.
draw_circular(G, **kwargs)	Draw networkx graph with circular layout.
draw_kamada_kawai(G, **kwargs)	Draw networkx graph with circular layout.
draw_random(G, **kwargs)	Draw networkx graph with random layout.
draw_planar(G, **kwargs)	Draw networkx graph with planar layout.
draw_shell(G, **kwargs)	Draw networkx graph with shell layout.
draw_spectral(G, **kwargs)	Draw networkx graph with spectral layout.
draw_spring(G, **kwargs)	Draw networkx graph with spring layout.

Explorer

The explorer interface can easily be created from the hvPlot / .hvplot namespace:

hvPlot.explorer([x, y])

The explorer plot allows you to interactively explore your data.

It is also available from the top-level explorer function:

explorer(data, **kwargs)

Explore your data and design your plot via an interactive user interface.

Calling the explorer function/method returns an hvPlotExplorer object:

hvPlotExplorer(df, **params)
hvPlotExplorer.hvplot()	Return the plot as a HoloViews object.
hvPlotExplorer.plot_code([var_name])	Return a string representation that can be easily copy-pasted in a notebook cell to create a plot from a call to the .hvplot accessor, and that includes all the customized settings of the explorer.
hvPlotExplorer.save(filename, **kwargs)	Save the plot to file.
hvPlotExplorer.servable([title, location, ...])	Serves the object or adds it to the configured pn.state.template if in a panel serve context, writes to the DOM if in a pyodide context and returns the Panel object to allow it to display itself in a notebook context.
hvPlotExplorer.settings()	Return a dictionary of the customized settings.
hvPlotExplorer.show([title, port, address, ...])	Starts a Bokeh server and displays the Viewable in a new tab.