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=None, 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 the paper When (ish) is My Bus? [2].


Values to plot

binwidthfloat, optional

Width of the bin for drawing the dot plot.

dotsizefloat, optional

The size of the dots relative to the bin width. The default, 1, makes dots be just about as wide as the bin width.

stackratiofloat, optional

The distance between the center of the dots in the same stack relative to the bin height. The default, 1, makes dots in the same stack just touch each other.

point_intervalbool, optional

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.

dotcolorstring, optional

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

intervalcolorstring, optional

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

linewidthint, optional

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

markersizeint, optional

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

markercolor: string, optional

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

marker: string, optional

The shape of the marker. Valid for matplotlib backend Defaults to “o”.

hdi_probfloat, optional

Valid only when point_interval is True. Plots HDI for chosen percentage of density. Defaults to stats.hdi_prob rcParam.

rotatedbool, optional

Whether to rotate the dot plot by 90 degrees.

nquantilesint, optional

Number of quantiles to plot, used for quantile dot plots Defaults to 50.

quartilesbool, optional

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

figsizetuple, 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: str, optional

Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.

axaxes, optional

Matplotlib axes or bokeh figures.

show: bool, optional

Call backend show function.

backend_kwargs: dict, 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 figures

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)