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 from an unknown continuous or discrete distribution.

values2array_like, optional

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

colorstr

valid matplotlib color.

kindstr, default “auto”

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, default False

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

labelstr

Text to include as part of the legend.

rotatedbool, default False

Whether to rotate the 1D KDE plot 90 degrees.

rugbool, default False

Add a rug plot for a specific subset of values. Ignored for 2D KDE.

bwfloat or str, optional

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 “experimental” when variable is not circular and “taylor” when it is.

quantileslist, optional

Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles.

contourbool, default True

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

fill_lastbool, default True

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

figsize(float, float), optional

Figure size. If None it will be defined automatically.

textsizefloat, optional

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.

axmatplotlib Axes or Bokeh Figure, optional

Matplotlib or bokeh targets on which to plot. If not supplied, Arviz will create its own plot area (and return it).

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

Select plotting backend.

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.

showbool, optional

Call backend show function.

Returns
axesmatplotlib axes or bokeh figure

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