kde

Generates a Kernel Density Estimate (KDE) plot using Gaussian kernels.

In statistics, kernel density estimation is a non-parametric way to estimate the probability density function (PDF) of a random variable. This function uses Gaussian kernels and includes automatic bandwidth determination.

Syntax

kde(bw_method, column=None, ind=None, **kwargs)

Parameters

Parameter	Type	Description
bw_method	int or float	The method used to calculate the estimator bandwidth. See KernelDensity in PySpark for more information.
column	str or list of str, optional	Column name or list of names to use for creating the KDE plot. If None (default), all numeric columns are used.
ind	list of float, NumPy array, or int, optional	Evaluation points for the estimated PDF. If None (default), 1000 equally spaced points are used. If a NumPy array, the KDE is evaluated at those points. If an integer, that many equally spaced points are used.
**kwargs	optional	Additional keyword arguments.

Returns

plotly.graph_objs.Figure

Examples

Python

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data = [(5.1, 3.5, 0), (4.9, 3.0, 0), (7.0, 3.2, 1), (6.4, 3.2, 1), (5.9, 3.0, 2)]
columns = ["length", "width", "species"]
df = spark.createDataFrame(data, columns)
df.plot.kde(bw_method=0.3, ind=100)