API to style categorical variables

It is often useful to style columns having a pd.CategoricalDtype, by either:

• being supplied a mapping from category name/code to background colour.
• being supplied a mapping from category name/code to Styler.
• selecting len(dtype.categories) colours from a matplotlib colormap (qualitative or otherwise).

So for example we might allow for:

StyleFrame(df).add_categorical_conditional_formatting(style_map={"a": Styler(font_color="red"), "b": Styler(font_color="green"), "c": Styler(font_color="blue")})

or

StyleFrame(df).add_categorical_conditional_formatting(style_bg_cmap="Pastel1"})

which would perform the following for each column

cmap = matplotlib.cm.get_cmap(style_bg_cmap)
col_style_map = dict(zip(col.dtype.categories, cmap(len(col.dtype.categories))))

Conditional formatting would be applied using a CellIsRule("=", category) for each category value in each column in range.

[DeepSpace2]

Ah, I see.

The current API does support conditional formatting, but only using ColorScaleRule (wrapped by StyleFrame.add_color_scale_conditional_formatting).

However, the above example can be implemented without it, using quite an explicit code:

style_map = {"a": Styler(font_color="red"), "b": Styler(font_color="green"), "c": Styler(font_color="blue")}
sf = StyleFrame({'1': ['a', 'b'],
                 '2': ['c', 'd']})
for value, styler in style_map.items():
    for col in sf.columns:
        sf.apply_style_by_indexes(sf[sf[col] == value], styler, cols_to_style=col)

generating

[stale[bot]]

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