verticapy.vDataFrame.density#
- vDataFrame.density(columns: str | list[str] | None = None, bandwidth: float = 1.0, kernel: Literal['gaussian', 'logistic', 'sigmoid', 'silverman'] = 'gaussian', nbins: int = 50, xlim: list[tuple[float, float]] = None, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure #
Draws the vDataColumns Density Plot.
Parameters#
- columns: SQLColumns, optional
List of the vDataColumns names. If empty, all numerical vDataColumns are selected.
- bandwidth: float, optional
The bandwidth of the kernel.
- kernel: str, optional
The method used for the plot.
- gaussian:
Gaussian Kernel.
- logistic:
Logistic Kernel.
- sigmoid:
Sigmoid Kernel.
- silverman:
Silverman Kernel.
- nbins: int, optional
Maximum number of points used to evaluate the approximate density function. Increasing this parameter increases the precision but also increases the time of the learning and the scoring phases.
- xlim: list of tuple, optional
Set the x limits of the current axes.
- chart: PlottingObject, optional
The chart object to plot on.
- **style_kwargs
Any optional parameter to pass to the plotting functions.
Returns#
- obj
Plotting Object.
Examples#
Note
The below example is a very basic one. For other more detailed examples and customization options, please see Density
Let’s begin by importing VerticaPy.
import verticapy as vp
Let’s also import numpy to create a dataset.
import numpy as np
We can create a variable
N
to fix the size:N = 50
Let’s generate a dataset using the following data.
data = vp.vDataFrame( { "score1": np.random.normal(5, 1, N), "score2": np.random.normal(8, 1.5, N), "score3": np.random.normal(10, 2, N), } )
Below are examples of two types of density plots:
Single
Multi
data.density(["score1"])
data.density(columns = ["score1", "score2"])
See also
vDataFrame.
hist()
: Histogram.vDataFrame.
range_plot()
: Range Plot.vDataColumn.
density()
: Density Plot.