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verticapy.vDataColumn.hist#

vDataColumn.hist(by: str | None = None, method: str = 'density', of: str | None = None, h: int | float | Decimal | None = None, h_by: int | float | Decimal = 0, max_cardinality: int = 8, cat_priority: None | bool | float | str | timedelta | datetime | list | ndarray = None, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure#

Draws the histogram 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.

h: PythonNumber, optional

Interval width of the input vDataColumns. Optimized h will be computed if the parameter is empty or invalid.

h_by: PythonNumber, optional

Interval width if the ‘by’ vDataColumn 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 vDataColumns 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 vDataColumn ‘by’ when the main input nvDataColumn is nnumerical. Otherwise, it is used to discretize all the vDataColumn 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#

obj

Plotting Object.

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 VerticaPy.

import verticapy as vp

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 = vp.vDataFrame(
    {
        "score1": np.random.normal(5, 1, N),
    }
)

Now we are ready to draw the plot:

data["score1"].hist()

See also

vDataFrame.hist() : Histogram.
vDataFrame.barh() : Horizontal Bar Chart.
vDataColumn.bar() : Bar Chart.