ehrapy.plot.missing_values_heatmap

ehrapy.plot.missing_values_heatmap(edata, *, filter=None, max_cols=0, max_percentage=0, sort=None, figsize=(20, 12), fontsize=16, labels=True, label_rotation=45, cmap='RdBu', vmin=-1, vmax=1, cbar=True, categoricals=False, layer=None)

Presents a seaborn heatmap visualization of nullity correlation in the given data object.

Note that this visualization has no special support for large datasets. For those, try the dendrogram instead.

Parameters:

• edata (EHRData) – Central data object.

• filter (str | None, default: None) – The filter to apply to the heatmap. Should be one of “top”, “bottom”, or None.

• max_cols (int, default: 0) – The max number of columns from the data object to include.

• max_percentage (float, default: 0) – The max percentage fill of the columns from the data object.

• sort (str | None, default: None) – The row sort order to apply. Can be “ascending”, “descending”, or None.

• figsize (tuple, default: (20, 12)) – The size of the figure to display.

• fontsize (float, default: 16) – The figure’s font size.

• labels (bool, default: True) – Whether or not to display the column names.

• label_rotation (float, default: 45) – What angle to rotate the text labels to.

• cmap (str, default: 'RdBu') – What matplotlib colormap to use.

• vmin (int, default: -1) – The normalized colormap threshold.

• vmax (int, default: 1) – The normalized colormap threshold.

• cbar (bool, default: True) – Whether to draw a colorbar.

• categoricals (bool, default: False) – Whether to include “ehrapycat” columns to the plot.

• layer (str | None, default: None) – The layer to use.

Returns:

The plot axis.

Examples

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pl.missing_values_heatmap(edata, filter="bottom", max_cols=15, max_percentage=0.999)

Preview: