vastorbit.VastFrame.pivot_table

VastFrame.pivot_table(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'], method: Literal[None, 'density', 'count', 'avg', 'min', 'max', 'sum'] | str = 'count', of: str | None = None, max_cardinality: tuple[int, int] = (20, 20), h: tuple[Annotated[int | float | Decimal, 'Python Numbers'], Annotated[int | float | Decimal, 'Python Numbers']] = (None, None), fill_none: float = 0.0, mround: int = 3, with_numbers: bool = True, chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Figure

Draws the pivot table of one or two columns based on an aggregation.

Parameters:

• columns (SQLColumns) – List of the VastColumns names. The list must have one or two elements.

• 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 (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.

• h (tuple, optional) – Interval width of the VastColumns 1 and 2 bars. Only valid if the VastColumns are numerical. Optimized h will be computed if the parameter is empty or invalid.

• fill_none (float, optional) – The empty values of the pivot table are filled by this number.

• mround (int, optional) – Rounds the coefficient using the input number of digits. It is only used to display the final pivot table.

• with_numbers (bool, optional) – If set to True, no number is displayed in the final drawing.

• 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 Pivot Table

Let’s begin by importing vastorbit.

import vastorbit as vo

Let’s also import numpy to create a dataset.

import numpy as np

We can create a variable N to fix the size:

N = 30

Let’s generate a dataset using the following data.

data = vo.VastFrame(
    {
        "category1": [np.random.choice(['A','B','C']) for _ in range(N)],
        "category2": [np.random.choice(['D','E']) for _ in range(N)],
    }
)

Below are examples of one types of pivot_table plots:

• Pivot Plot

Pivot Plot

data.pivot_table(columns = ["category1", "category2"])

See also

VastFrame.contour() : Contour Plot.

VastFrame.scatter_matrix() : Scatter Matrix.