vastorbit.VastFrame.barh¶

VastFrame.barh(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'], 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), kind: Literal['auto', 'drilldown', 'fully_stacked', 'stacked', 'fully', 'fully stacked', 'pyramid', 'density'] = 'auto', categoryorder: Literal['trace', 'category ascending', 'category descending', 'total ascending', 'total descending', 'min ascending', 'min descending', 'max ascending', 'max descending', 'sum ascending', 'sum descending', 'mean ascending', 'mean descending', 'median ascending', 'median descending'] = 'trace', chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, **style_kwargs) → PlottingBase | TableSample | Axes | mFigure | Figure¶

Draws the horizontal bar chart of the input VastColumns based on an aggregation.

Parameters:

columns (SQLColumns) – list of the VastColumns names.
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).
- None:
  No Aggregations. Parameter of must be empty, otherwise it is ignored.
It can also be a cutomized aggregation, for example: AVG(column1) + 5
of (str, optional) – The VastColumns 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.

Important

This parameter is only used for categorical data types. For numerics use h to discretize them first
h (tuple, optional) –
Interval width of the VastColumns 1 and 2 bars.

Important

Only valid if the VastColumns are numerical. Optimized h will be computed if the parameter is empty or invalid.
kind (str, optional) –
The BarChart Type.
- auto:
  Regular Bar Chart based on 1 or 2 VastColumns.
- drilldown:
  Drilldown Bar Chart.
- pyramid:
  Pyramid Density Bar Chart. Only works if one of the two VastColumns is binary and the method='density'.
- stacked:
  Stacked Bar Chart based on 2 VastColumns.
- fully_stacked:
  Fully Stacked Bar Chart based on 2 VastColumns.
categoryorder (str, optional) –
How to sort the bars. One of the following options:
- trace (no transformation)
- category ascending
- category descending
- total ascending
- total descending
- min ascending
- min descending
- max ascending
- max descending
- sum ascending
- sum descending
- mean ascending
- mean descending
- median ascending
- median descending
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 Bar Chart

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(
    {
        "gender": ['M', 'M', 'M', 'F', 'F', 'F', 'F'],
        "grade": ['A','B','C','A','B','B', 'B'],
    }
)

Below are examples of two types of barh plots:

data.barh(["grade"])

data.barh(columns = ["grade", "gender"])