plotnine.stat_density_2d
Compute 2D kernel density estimation
stat_density_2d(
mapping=None,
data=None,
*,
geom="density_2d",
position="identity",
na_rm=False,
contour=True,
package="statsmodels",
kde_params=None,
n=64,
levels=5,
**kwargs
)Parameters
mapping : aes = None-
Aesthetic mappings created with aes. If specified and
inherit_aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.Aesthetic Default value x The bold aesthetics are required.
Options for computed aesthetics
"level" # density level of a contour "density" # Computed density at a point "piece" # Numeric id of a contour in a given grouplevelis only relevant when contours are computed.densityis available only when no contours are computed.pieceis largely irrelevant. data : DataFrame = None-
The data to be displayed in this layer. If
None, the data from from theggplot()call is used. If specified, it overrides the data from theggplot()call. geom : str | geom = "density_2d"-
The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.
position : str | position = "identity"-
Position adjustment. If it is a string, it must be registered and known to Plotnine.
na_rm : bool = False-
If
False, removes missing values with a warning. IfTruesilently removes missing values. contour : bool = True-
Whether to create contours of the 2d density estimate.
n : int = 64-
Number of equally spaced points at which the density is to be estimated. For efficient computation, it should be a power of two.
levels : int | array_like = 5-
Contour levels. If an integer, it specifies the maximum number of levels, if array_like it is the levels themselves.
package : Literal["statsmodels", "scipy", "sklearn"] = "statsmodels"-
Package whose kernel density estimation to use.
kde_params : dict-
Keyword arguments to pass on to the kde class.
**kwargs : Any-
Aesthetics or parameters used by the
geom.
See Also
geom_density_2d-
The default
geomfor thisstat. KDEMultivariategaussian_kdeKernelDensity