vastorbit.VastFrame.pie

VastFrame.pie(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'], method: str = 'count', of: str | None = None, max_cardinality: None | int | tuple = None, h: None | int | tuple = None, chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, categoryorder: Literal['trace', 'category ascending', 'category descending', 'total ascending', 'total descending'] = 'trace', **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Figure

Draws the nested pie chart of the input VastColumns.

Parameters:

• columns (SQLColumns) – list of the VastColumns names.

• method (str, optional) –

  The method used to aggregate the data.

  • count:

    Number of elements.

  • density:

    Percentage of the distribution.

  • mean:

    Average of the VastColumns of.

  • min:

    Minimum of the VastColumns of.

  • max:

    Maximum of the VastColumns of.

  • sum:

    Sum of the VastColumns of.

  • q%:

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

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

• of (str, optional) – The VastColumns used to compute the aggregation.

• max_cardinality (int | tuple, optional) – Maximum number of distinct elements for VastColumns 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:

Plotting Object.

Return type:

obj

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 vastorbit.

import vastorbit as vo

Let’s also import numpy to create a dataset.

import numpy as np

Let’s generate a dataset using the following data.

data = vo.VastFrame(
    {
        "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

Regular

data.pie(["grade"])

Nested

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

See also

VastFrame.hist() : Histogram.

VastColumn.bar() : Bar Chart.

VastColumn.pie() : Pie Chart.