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verticapy.vDataColumn.spider#

vDataColumn.spider(by: str | None = None, method: str = 'density', of: str | None = None, max_cardinality: tuple[int, int] = (6, 6), h: tuple[int | float | Decimal, int | float | Decimal] = (None, None), chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure#

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

Parameters#

by: str, optional

vDataColumn 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 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 (ex: AVG(column1) + 5).

of: str, optional

The vDataColumn used to compute the aggregation.

max_cardinality: int, optional

Maximum number of distinct elements for vDataColumns 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#

obj

Plotting Object.

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 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(
    {
        "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

vDataFrame.pie() : Pie Chart.
vDataColumn.pie() : Pie Chart.