arviz.plot_dist#

arviz.plot_dist(values, values2=None, color='C0', kind='auto', cumulative=False, label=None, rotated=False, rug=False, bw='default', quantiles=None, contour=True, fill_last=True, figsize=None, textsize=None, plot_kwargs=None, fill_kwargs=None, rug_kwargs=None, contour_kwargs=None, contourf_kwargs=None, pcolormesh_kwargs=None, hist_kwargs=None, is_circular=False, ax=None, backend=None, backend_kwargs=None, show=None, **kwargs)[source]#

Plot distribution as histogram or kernel density estimates.

By default continuous variables are plotted using KDEs and discrete ones using histograms

Parameters
valuesarray-like

Values to plot.

values2array-like, optional

Values to plot. If present, a 2D KDE or a hexbin will be estimated.

colorstring

valid matplotlib color.

kindstring

By default (“auto”) continuous variables will use the kind defined by rcParam plot.density_kind and discrete ones will use histograms. To override this use “hist” to plot histograms and “kde” for KDEs.

cumulativebool

If true plot the estimated cumulative distribution function. Defaults to False. Ignored for 2D KDE.

labelstring

Text to include as part of the legend.

rotatedbool

Whether to rotate the 1D KDE plot 90 degrees.

rugbool

If True adds a rugplot. Defaults to False. Ignored for 2D KDE.

bw: Optional[float or str]

If numeric, indicates the bandwidth and must be positive. If str, indicates the method to estimate the bandwidth and must be one of “scott”, “silverman”, “isj” or “experimental” when is_circular is False and “taylor” (for now) when is_circular is True. Defaults to “default” which means “experimental” when variable is not circular and “taylor” when it is.

quantileslist

Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. Defaults to None.

contourbool

If True plot the 2D KDE using contours, otherwise plot a smooth 2D KDE. Defaults to True.

fill_lastbool

If True fill the last contour of the 2D KDE plot. Defaults to True.

figsizetuple

Figure size. If None it will be defined automatically.

textsize: float

Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. Not implemented for bokeh backend.

plot_kwargsdict

Keywords passed to the pdf line of a 1D KDE. Passed to arviz.plot_kde() as plot_kwargs.

fill_kwargsdict

Keywords passed to the fill under the line (use fill_kwargs={‘alpha’: 0} to disable fill). Ignored for 2D KDE. Passed to arviz.plot_kde() as fill_kwargs.

rug_kwargsdict

Keywords passed to the rug plot. Ignored if rug=False or for 2D KDE Use space keyword (float) to control the position of the rugplot. The larger this number the lower the rugplot. Passed to arviz.plot_kde() as rug_kwargs.

contour_kwargsdict

Keywords passed to the contourplot. Ignored for 1D KDE.

contourf_kwargsdict

Keywords passed to matplotlib.axes.Axes.contourf(). Ignored for 1D KDE.

pcolormesh_kwargsdict

Keywords passed to matplotlib.axes.Axes.pcolormesh(). Ignored for 1D KDE.

hist_kwargsdict

Keyword arguments used to customize the histogram. Ignored when plotting a KDE. They are passed to matplotlib.axes.Axes.hist() if using matplotlib, or to bokeh.plotting.Figure.quad() if using bokeh. In bokeh case, the following extra keywords are also supported:

  • color: replaces the fill_color and line_color of the quad method

  • bins: taken from hist_kwargs and passed to numpy.histogram() instead

  • density: normalize histogram to represent a probability density function, Defaults to True

  • cumulative: plot the cumulative counts. Defaults to False

is_circular{False, True, “radians”, “degrees”}. Default False.

Select input type {“radians”, “degrees”} for circular histogram or KDE plot. If True, default input type is “radians”. When this argument is present, it interprets the values passed are from a circular variable measured in radians and a circular KDE is used. Inputs in “degrees” will undergo an internal conversion to radians. Only valid for 1D KDE. Defaults to False.

ax: axes, optional

Matplotlib axes or bokeh figures.

backend: str, optional

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

backend_kwargs: bool, 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.

showbool, optional

Call backend show function.

Returns
axesmatplotlib axes or bokeh figures

See also

plot_posterior

Plot Posterior densities in the style of John K. Kruschke’s book.

plot_density

Generate KDE plots for continuous variables and histograms for discrete ones.

plot_kde

1D or 2D KDE plot taking into account boundary conditions.

Examples

Plot an integer distribution

>>> import numpy as np
>>> import arviz as az
>>> a = np.random.poisson(4, 1000)
>>> az.plot_dist(a)
../../_images/arviz-plot_dist-1.png

Plot a continuous distribution

>>> b = np.random.normal(0, 1, 1000)
>>> az.plot_dist(b)
../../_images/arviz-plot_dist-2.png

Add a rug under the Gaussian distribution

>>> az.plot_dist(b, rug=True)
../../_images/arviz-plot_dist-3.png

Segment into quantiles

>>> az.plot_dist(b, rug=True, quantiles=[.25, .5, .75])
../../_images/arviz-plot_dist-4.png

Plot as the cumulative distribution

>>> az.plot_dist(b, rug=True, quantiles=[.25, .5, .75], cumulative=True)
../../_images/arviz-plot_dist-5.png

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