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verticapy.vDataFrame.pie#

vDataFrame.pie(columns: str | list[str], method: str = 'count', of: str | None = None, max_cardinality: None | int | tuple = None, h: None | int | tuple = None, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, categoryorder: Literal['trace', 'category ascending', 'category descending', 'total ascending', 'total descending'] = 'trace', **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure#

Draws the nested pie chart of the input vDataColumns.

Parameters#

columns: SQLColumns

list of the vDataColumns names.

method: str, optional

The method used to aggregate the data.

  • count:

    Number of elements.

  • density:

    Percentage of the distribution.

  • mean:

    Average of the vDataColumns of.

  • min:

    Minimum of the vDataColumns of.

  • max:

    Maximum of the vDataColumns of.

  • sum:

    Sum of the vDataColumns of.

  • q%:

    q Quantile of the vDataColumns of (ex: 50% to get the median).

It can also be a cutomized aggregation, for example: AVG(column1) + 5

of: str, optional

The vDataColumns used to compute the aggregation.

max_cardinality: int | tuple, optional

Maximum number of distinct elements for vDataColumns 1 and 2 to be used as categorical. For these elements, no ``h is picked or computed. If of type tuple, represents the ‘max_cardinality’ of each column.

h: int | tuple, optional

Interval width of the bar. If empty, an optimized h will be computed. If of type tuple, it must represent each column’s h.

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 Pie Chart

Let’s begin by importing VerticaPy.

import verticapy as vp

Let’s also import numpy to create a dataset.

import numpy as np

Let’s generate a dataset using the following data.

data = vp.vDataFrame(
    {
        "gender": ['M', 'M', 'M', 'F', 'F', 'F', 'F'],
        "grade": ['A','B','C','A','B','B', 'B'],
    }
)

Below are examples of two types of pie plots:

  • Regular

  • Nested

data.pie(["grade"])

data.pie(columns = ["grade", "gender"])

See also

vDataFrame.hist() : Histogram.
vDataColumn.bar() : Bar Chart.
vDataColumn.pie() : Pie Chart.