vastorbit.VastColumn.hist

VastColumn.hist(by: str | None = None, method: Literal[None, 'density', 'count', 'avg', 'min', 'max', 'sum'] | str = 'density', of: str | None = None, h: Annotated[int | float | Decimal, 'Python Numbers'] | None = None, h_by: Annotated[int | float | Decimal, 'Python Numbers'] = 0, max_cardinality: int = 8, cat_priority: None | Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] | Annotated[list | ndarray, 'Array Like Structure'] = None, chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Figure

Draws the histogram 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.

• h (PythonNumber, optional) – Interval width of the input VastColumns. Optimized h will be computed if the parameter is empty or invalid.

• h_by (PythonNumber, optional) – Interval width if the ‘by’ VastColumn is numerical or of a date-like type. Optimized h will be computed if the parameter is empty or invalid.

• max_cardinality (int, optional) – Maximum number of distinct elements for VastColumns to be used as categorical. The less frequent elements are gathered together to create a new category : ‘Others’. This parameter is used to discretize the VastColumn ‘by’ when the main input nVastColumn is nnumerical. Otherwise, it is used to discretize all the VastColumn inputs.

• cat_priority (PythonScalar / ArrayLike, optional) – ArrayLike list of the different categories to consider when drawing the box plot. The other categories are filtered.

• 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 Histogram

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 = 50

Let’s generate a dataset using the following data.

data = vo.VastFrame(
    {
        "score1": np.random.normal(5, 1, N),
    }
)

Now we are ready to draw the plot:

data["score1"].hist()

See also

VastFrame.hist() : Histogram.

VastFrame.barh() : Horizontal Bar Chart.

VastColumn.bar() : Bar Chart.