pandas.io.formats.style.Styler.map#

Styler.map(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.map_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 or None.

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.map(color_negative, color="red")  

Using subset to restrict application to a single column or multiple columns

>>> df.style.map(color_negative, color="red", subset="A")
... 
>>> df.style.map(color_negative, color="red", subset=["A", "B"])
... 

Using a 2d input to subset to select rows in addition to columns

>>> df.style.map(
...     color_negative, color="red", subset=([0, 1, 2], slice(None))
... )  
>>> df.style.map(color_negative, color="red", subset=(slice(0, 5, 2), "A"))
... 

See Table Visualization user guide for more details.