data_describe.core.heatmap ================================= .. py:module:: data_describe.core.heatmap .. autoapisummary:: data_describe.core.heatmap.data_heatmap .. py:class:: HeatmapWidget(input_data=None, colnames=None, std_data=None, missing=False, missing_data=None, **kwargs) Bases: :class:`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. .. attribute:: input_data The input data. .. attribute:: colnames Names of numeric columns. .. attribute:: std_data The transposed, standardized data after scaling. .. attribute:: missing If True, the heatmap shows missing values as indicators instead of standardized values. .. attribute:: missing_data The missing value indicator data. .. method:: show(self, viz_backend=None, **kwargs) The default display for this output. Shows the data heatmap plot. :param viz_backend: The visualization backend. :param \*\*kwargs: Keyword arguments. :raises ValueError: Computed data is missing. :returns: The correlation matrix plot. .. function:: 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. :param data: A pandas data frame :param missing: If True, show only missing values :type missing: bool :param compute_backend: The compute backend. :param viz_backend: The visualization backend. :param \*\*kwargs: Keyword arguments :returns: The data heatmap.