hvPlot.bivariate#
- hvPlot.bivariate(x=None, y=None, colorbar=True, bandwidth=None, cut=3, filled=False, levels=10, **kwds)[source]#
A bivariate, density plot uses nested contours (or contours plus colors) to indicate regions of higher local density.
bivariate plots can be a useful alternative to scatter plots, if your data are too dense to plot each point individually.
Reference: https://hvplot.holoviz.org/reference/tabular/bivariate.html
- Parameters:
- xstring, optional
Field name to draw x-positions from. If not specified, the index is used.
- ystring, optional
Field name to draw y-positions from
- colorbar: boolean
Whether to display a colorbar
- bandwidth: int, optional
The bandwidth of the kernel for the density estimate. Default is None.
- cut: int, optional
Draw the estimate to cut * bw from the extreme data points. Default is 3.
- filledbool, optional
If True the contours will be filled. Default is False.
- levels: int or list, optional
The number of contour lines to draw or a list of scalar values used to specify the contour levels. Default is 10.
- **kwdsoptional
Additional keywords arguments are documented in hvplot.help(‘bivariate’). See Plotting Options for more information.
- Returns:
holoviews.element.Bivariate/ Panel objectYou can print the object to study its composition and run:
import holoviews as hv hv.help(the_holoviews_object)
to learn more about its parameters and options.
References
ggplot: https://bio304-class.github.io/bio304-fall2017/ggplot-bivariate.html
HoloViews: https://holoviews.org/reference/elements/bokeh/Bivariate.html
Plotly: https://plotly.com/python/2d-histogram-contour/
Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html
Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html
Wiki: https://en.wikipedia.org/wiki/Bivariate_analysis
Examples
import hvplot.pandas from bokeh.sampledata.autompg import autompg_clean as df bivariate = df.hvplot.bivariate("accel", "mpg", filled=True, cmap="blues") bivariate
To get a better intuitive understanding of the bivariate plot, you can try overlaying the corresponding scatter plot.
scatter = df.hvplot.scatter("accel", "mpg") bivariate * scatter
Backend-specific styling options#
alpha, cmap, color, fill_alpha, fill_color, hover_alpha, hover_color, hover_fill_alpha, hover_fill_color, hover_line_alpha, hover_line_cap, hover_line_color, hover_line_dash, hover_line_dash_offset, hover_line_join, hover_line_width, line_alpha, line_cap, line_color, line_dash, line_dash_offset, line_join, line_width, muted, muted_alpha, muted_color, muted_fill_alpha, muted_fill_color, muted_line_alpha, muted_line_cap, muted_line_color, muted_line_dash, muted_line_dash_offset, muted_line_join, muted_line_width, nonselection_alpha, nonselection_color, nonselection_fill_alpha, nonselection_fill_color, nonselection_line_alpha, nonselection_line_cap, nonselection_line_color, nonselection_line_dash, nonselection_line_dash_offset, nonselection_line_join, nonselection_line_width, selection_alpha, selection_color, selection_fill_alpha, selection_fill_color, selection_line_alpha, selection_line_cap, selection_line_color, selection_line_dash, selection_line_dash_offset, selection_line_join, selection_line_width, visible
alpha, c, capstyle, cmap, color, ec, ecolor, edgecolor, facecolor, fc, fill, hatch, joinstyle, linestyle, linewidth, lw
Examples#
TBD