vastorbit.VastFrame.heatmap¶
- VastFrame.heatmap(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, h: tuple = (None, None), chart: PlottingBase | TableSample | Axes | mFigure | Figure | None = None, **style_kwargs) PlottingBase | TableSample | Axes | mFigure | Figure¶
Draws the Heatmap of the two input VastColumns.
- Parameters:
columns (SQLColumns) – List of the VastColumns names. The list must have 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
VastColumnsof.
- min:
Minimum of the
VastColumnsof.
- max:
Maximum of the
VastColumnsof.
- sum:
Sum of the
VastColumnsof.
- q%:
q Quantile of the
VastColumnsof(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 (tuple, optional) – Interval width of the VastColumns 1 and 2 bars. Optimized h will be computed if the parameter is empty or invalid.
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 Chart Gallery
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
Nto fix the size:N = 30
Let’s generate a dataset using the following data.
data = vo.VastFrame( { "x": np.random.normal(5, 1, N), "y": np.random.normal(8, 1.5, N), } )
Below is an examples of one type of heatmap plots:
Heatmap
data.heatmap(columns = ["x", "y"])