hvPlot.errorbars#

hvPlot.errorbars(x=None, y=None, yerr1=None, yerr2=None, **kwds)[source]#

errorbars provide a visual indicator for the variability of the plotted data on a graph. They are usually overlaid with other plots such as scatter , line or bar plots to indicate the variability.

Reference: https://hvplot.holoviz.org/reference/tabular/errorbars.html

Parameters:

xstring, optional: Field name to draw the x-position from. If not specified, the index is used. Can refer to continuous and categorical data.
ystring, optional: Field name to draw the y-position from
yerr1string, optional: Field name to draw symmetric / negative errors from
yerr2string, optional: Field name to draw positive errors from
**kwdsoptional: Additional keywords arguments are documented in hvplot.help(‘errorbars’). See Plotting Options for more information.

Returns:

holoviews.element.ErrorBars / Panel object

You 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

Bokeh: https://docs.bokeh.org/en/latest/docs/user_guide/annotations.html#whiskers
HoloViews: https://holoviews.org/reference/elements/bokeh/ErrorBars.html
Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html
Pandas: https://pandas.pydata.org/docs/user_guide/visualization.html#visualization-errorbars
Plotly: https://plotly.com/python/error-bars/
Wikipedia: https://en.wikipedia.org/wiki/Error_bar

Examples

import hvplot.pandas
import pandas as pd

df = pd.DataFrame(
    {
        "actual": [100, 150, 125, 140, 145, 135, 123],
        "forecast": [90, 160, 125, 150, 141, 141, 120],
        "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
        "date": pd.date_range("2022-01-03", "2022-01-09"),
        "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
    },
)
df["min"] = df[["actual", "forecast"]].min(axis=1)
df["max"] = df[["actual", "forecast"]].max(axis=1)
df["mean"] = df[["actual", "forecast"]].mean(axis=1)
df["yerr2"] = df["max"] - df["mean"]
df["yerr1"] = df["mean"] - df["min"]

errorbars = df.hvplot.errorbars(
    x="numerical",
    y="mean",
    yerr1="yerr1",
    yerr2="yerr2",
    legend="bottom",
    height=500,
    alpha=0.5,
    line_width=2,
)
errorbars

Normally you would overlay the errorbars on for example a scatter plot.

mean = df.hvplot.scatter(x="numerical", y=["mean"], color=["#55a194"], size=50)
errorbars * mean

Backend-specific styling options#

Bokeh

alpha, color, line_alpha, line_cap, line_color, line_dash, line_dash_offset, line_join, line_width, lower_head, muted, upper_head, visible

Matplotlib

alpha, barsabove, c, capsize, capthick, color, dashes, ec, ecolor, edgecolor, elinewidth, errorevery, linestyle, linewidth, lolims, lw, markeredgecolor, markeredgewidth, markerfacecolor, markersize, mec, mew, mfc, ms, solid_capstyle, solid_joinstyle, uplims, xlolims, xuplims

Examples#

TBD

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