verticapy.vDataColumn.boxplot#

Draws the box plot of the vDataColumn.

Parameters#

by: str, optional: vDataColumn used to partition the data.
q: tuple, optional: Tuple including the 2 quantiles used to draw the BoxPlot.
h: PythonNumber, optional: Interval width if the ‘by’ vDataColumn is numerical or of a date-like type. Optimized h will be computed if the parameter is empty or invalid.
max_cardinality: int, optional: Maximum number of distinct vDataColumn elements to be used as categorical. The less frequent elements are gathered together to create a new category : ‘Others’.
cat_priority: PythonScalar / ArrayLike, optional: ArrayLike list of the different categories to consider when drawing the box plot. The other categories are filtered.
max_nb_fliers: int, optional: Maximum number of points used to represent the fliers of each category. Drawing fliers slows down the graphic computation.
whis: float, optional: The position of the whiskers.
chart: PlottingObject, optional: The chart object to plot on.
**style_kwargs: Any optional parameter to pass to the plotting functions.

Note

The below example is a very basic one. For other more detailed examples and customization options, please see Boxplot

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"].boxplot()