verticapy.vDataColumn.density¶

Draws the vDataColumn Density Plot.

Parameters¶

by: str, optional

vDataColumn used to partition the data.

bandwidth: PythonNumber, 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 scoring phases.

xlim: 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),
    }
)

Now we are ready to draw the plot:

data["score1"].density()