pandas.io.formats.style.Styler.highlight_quantile — pandas 2.2.3 documentation (original) (raw)

Styler.highlight_quantile(subset=None, color='yellow', axis=0, q_left=0.0, q_right=1.0, interpolation='linear', inclusive='both', props=None)[source]#

Highlight values defined by a quantile with a style.

Added in version 1.3.0.

Parameters:

subsetlabel, array-like, IndexSlice, optional

A valid 2d input to DataFrame.loc[], or, in the case of a 1d input or single key, to DataFrame.loc[:, ] where the columns are prioritised, to limit data to before applying the function.

colorstr, default ‘yellow’

Background color to use for highlighting.

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Axis along which to determine and highlight quantiles. If None quantiles are measured over the entire DataFrame. See examples.

q_leftfloat, default 0

Left bound, in [0, q_right), for the target quantile range.

q_rightfloat, default 1

Right bound, in (q_left, 1], for the target quantile range.

interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

Argument passed to Series.quantile or DataFrame.quantile for quantile estimation.

inclusive{‘both’, ‘neither’, ‘left’, ‘right’}

Identify whether quantile bounds are closed or open.

propsstr, default None

CSS properties to use for highlighting. If props is given, coloris not used.

Returns:

Styler

Notes

This function does not work with str dtypes.

Examples

Using axis=None and apply a quantile to all collective data

df = pd.DataFrame(np.arange(10).reshape(2,5) + 1) df.style.highlight_quantile(axis=None, q_left=0.8, color="#fffd75") ...

Or highlight quantiles row-wise or column-wise, in this case by row-wise

df.style.highlight_quantile(axis=1, q_left=0.8, color="#fffd75") ...

Use props instead of default background coloring

df.style.highlight_quantile(axis=None, q_left=0.2, q_right=0.8, ... props='font-weight:bold;color:#e83e8c')