arviz.plot_dot(values, binwidth=None, dotsize=1, stackratio=1, hdi_prob=None, rotated=False, dotcolor='C0', intervalcolor='C3', markersize=None, markercolor='C0', marker='o', figsize=None, linewidth=None, point_estimate='auto', nquantiles=50, quartiles=True, point_interval=False, ax=None, show=None, plot_kwargs=None, backend=None, backend_kwargs=None, **kwargs)[source]#

Plot distribution as dot plot or quantile dot plot.

This function uses the Wilkinson’s Algorithm [1] to allot dots to bins. The quantile dot plots was inspired from [2].


Values to plot from an unknown continuous or discrete distribution.

binwidthfloat, optional

Width of the bin for drawing the dot plot.

dotsizefloat, default 1

The size of the dots relative to the bin width. The default makes dots be just about as wide as the bin width.

stackratiofloat, default 1

The distance between the center of the dots in the same stack relative to the bin height. The default makes dots in the same stack just touch each other.

point_intervalbool, default False

Plots the point interval. Uses hdi_prob to plot the HDI interval

point_estimatestr, optional

Plot point estimate per variable. Values should be mean, median, mode or None. Defaults to auto i.e. it falls back to default set in rcParams.

dotcolorstr, optional

The color of the dots. Should be a valid matplotlib color.

intervalcolorstr, optional

The color of the interval. Should be a valid matplotlib color.

linewidthint, default None

Line width throughout. If None it will be autoscaled based on figsize.

markersizeint, default None

Markersize throughout. If None it will be autoscaled based on figsize.

markercolorstr, optional

The color of the marker when plot_interval is True. Should be a valid matplotlib color.

markerstr, default “o”

The shape of the marker. Valid for matplotlib backend.

hdi_probfloat, optional

Valid only when point_interval is True. Plots HDI for chosen percentage of density. Defaults to stats.hdi_prob rcParam. See this section for usage examples.

rotatedbool, default False

Whether to rotate the dot plot by 90 degrees.

nquantilesint, default 50

Number of quantiles to plot, used for quantile dot plots.

quartilesbool, default True

If True then the quartile interval will be plotted with the HDI.

figsize(float,float), optional

Figure size. If None it will be defined automatically.

plot_kwargsdict, optional

Keywords passed for customizing the dots. Passed to matplotlib.patches.Circle in matplotlib and in bokeh.

backend :{“matplotlib”, “bokeh”}, default “matplotlib”

Select plotting backend.

axaxes, optional

Matplotlib_axes or bokeh_figure.

showbool, optional

Call backend show function.

backend_kwargsdict, optional

These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure. For additional documentation check the plotting method of the backend.

axesmatplotlib Axes or Bokeh Figure

See also


Plot distribution as histogram or kernel density estimates.



Leland Wilkinson (1999) Dot Plots, The American Statistician, 53:3, 276-281, DOI: 10.1080/00031305.1999.10474474


Matthew Kay, Tara Kola, Jessica R. Hullman, and Sean A. Munson. 2016. When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. DOI:


Plot dot plot for a set of data points

>>> import arviz as az
>>> import numpy as np
>>> values = np.random.normal(0, 1, 500)
>>> az.plot_dot(values)

Manually adjust number of quantiles to plot

>>> az.plot_dot(values, nquantiles=100)

Add a point interval under the dot plot

>>> az.plot_dot(values, point_interval=True)

Rotate the dot plots by 90 degrees i.e swap x and y axis

>>> az.plot_dot(values, point_interval=True, rotated=True)