hvPlot.bar#

hvPlot.bar(x=None, y=None, stacked=False, **kwds)[source]#

A vertical bar plot

A bar plot represents categorical data with rectangular bars with heights proportional to the values that they represent. The x-axis represents the categories and the y axis represents the value scale. The bars are of equal width which allows for instant comparison of data.

Reference: https://hvplot.holoviz.org/reference/tabular/bar.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. If not specified, all numerical fields are used.

stackedbool, optional

If True, creates a stacked bar plot. Default is False.

colorstr or array-like, optional.

The color for each of the series. Possible values are:

The name of the field to draw the colors from. The field can contain numerical values or strings representing colors.

A single color string referred to by name, RGB or RGBA code, for instance ‘red’ or ‘#a98d19’.

A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each series recursively. For instance [‘red’, ‘green’,’blue’].

**kwdsoptional

Additional keywords arguments are documented in hvplot.help(‘bar’). See Plotting Options for more information.

Returns:
holoviews.element.Bars / 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

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"],
    },
)
bar = df.hvplot.bar(x="string", y="actual", color="#f16a6f", legend="bottom", xlabel="day", ylabel="value")
bar

You can overlay for example a line plot via

forecast_line = df.hvplot.line(x="string", y="forecast", color="#1e85f7", line_width=5, legend="bottom")
forecast_markers = df.hvplot.scatter(x="string", y="forecast", color="#1e85f7", size=100, legend="bottom")
bar * forecast_line * forecast_markers

df.hvplot.bar(stacked=True, rot=90, color=["#457278", "#615078"])

Backend-specific styling options#

alpha, bar_width, 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

align, alpha, c, capsize, color, ec, ecolor, edgecolor, error_kw, facecolor, fc, hatch, log, visible

Examples#

TBD

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