vastorbit.VastColumn.spider

VastColumn.spider(by: str | None = None, method: Literal[None, 'density', 'count', 'avg', 'min', 'max', 'sum'] | str = 'density', of: str | None = None, max_cardinality: tuple[int, int] = (6, 6), h: tuple[Annotated[int | float | Decimal, 'Python Numbers'], Annotated[int | float | Decimal, 'Python Numbers']] = (None, None), chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Figure

Draws the spider plot of the input VastColumn based on an aggregation.

Parameters:

• by (str, optional) – VastColumn used to partition the data.

• 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 (ex: AVG(column1) + 5).

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

• max_cardinality (int, optional) – Maximum number of distinct elements for VastColumns to be used as categorical. For these elements, no h is picked or computed.

• h (PythonNumber, optional) – Interval width of the bar. If empty, an optimized h is computed.

• 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 Spider

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(
    {
        "category": [np.random.choice(['A','B','C']) for _ in range(N)],
        "score1": np.random.normal(5, 1, N),
    }
)

Now we are ready to draw the plot:

data["score1"].spider()

See also

VastFrame.pie() : Pie Chart.

VastColumn.pie() : Pie Chart.