data_describe.core.heatmap

data_heatmap(data, missing=False, compute_backend=None, viz_backend=None, **kwargs)

Visualizes data patterns in the entire dataset by visualizing as a heatmap.

class data_describe.core.heatmap.HeatmapWidget(input_data=None, colnames=None, std_data=None, missing=False, missing_data=None, **kwargs)

Bases: data_describe._widget.BaseWidget

Container for data heatmap calculation and visualization.

This class (object) is returned from the data_heatmap function. The attributes documented below can be accessed or extracted.

input_data

The input data.

colnames

Names of numeric columns.

std_data

The transposed, standardized data after scaling.

missing

If True, the heatmap shows missing values as indicators instead of standardized values.

missing_data

The missing value indicator data.

show(self, viz_backend=None, **kwargs)

The default display for this output.

Shows the data heatmap plot.

Parameters

• viz_backend – The visualization backend.

• **kwargs – Keyword arguments.

Raises

ValueError – Computed data is missing.

Returns

The correlation matrix plot.

data_describe.core.heatmap.data_heatmap(data, missing=False, compute_backend=None, viz_backend=None, **kwargs) → HeatmapWidget

Visualizes data patterns in the entire dataset by visualizing as a heatmap.

This feature operates in two modes.

(Default): A data heatmap showing standardized values (bounded to [-3, 3]). This visualization is useful for showing unusual, ordered patterns in the data that would otherwise be unnoticeable in summary statistics or distribution plots.

Missing: Visualize only missing values.

Parameters

• data – A pandas data frame

• missing (bool) – If True, show only missing values

• compute_backend – The compute backend.

• viz_backend – The visualization backend.

• **kwargs – Keyword arguments

Returns

The data heatmap.