pandas.io.formats.style.Styler.applymap#
- Styler.applymap(func, subset=None, **kwargs)[source]#
Apply a CSS-styling function elementwise.
Updates the HTML representation with the result.
- Parameters
- funcfunction
func
should take a scalar and return a string.- subsetlabel, array-like, IndexSlice, optional
A valid 2d input to DataFrame.loc[<subset>], or, in the case of a 1d input or single key, to DataFrame.loc[:, <subset>] where the columns are prioritised, to limit
data
to before applying the function.- **kwargsdict
Pass along to
func
.
- Returns
- Styler
See also
Styler.applymap_index
Apply a CSS-styling function to headers elementwise.
Styler.apply_index
Apply a CSS-styling function to headers level-wise.
Styler.apply
Apply a CSS-styling function column-wise, row-wise, or table-wise.
Notes
The elements of the output of
func
should be CSS styles as strings, in the format ‘attribute: value; attribute2: value2; …’ or, if nothing is to be applied to that element, an empty string orNone
.Examples
>>> def color_negative(v, color): ... return f"color: {color};" if v < 0 else None >>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"]) >>> df.style.applymap(color_negative, color='red')
Using
subset
to restrict application to a single column or multiple columns>>> df.style.applymap(color_negative, color='red', subset="A") ... >>> df.style.applymap(color_negative, color='red', subset=["A", "B"]) ...
Using a 2d input to
subset
to select rows in addition to columns>>> df.style.applymap(color_negative, color='red', ... subset=([0,1,2], slice(None))) >>> df.style.applymap(color_negative, color='red', subset=(slice(0,5,2), "A")) ...
See Table Visualization user guide for more details.