pandas.io.formats.style.Styler.apply#

Styler.apply(func, axis=0, subset=None, **kwargs)[source]#

Apply a CSS-styling function column-wise, row-wise, or table-wise.

Updates the HTML representation with the result.

Parameters:

funcfunction: func should take a Series if axis in [0,1] and return a list-like object of same length, or a Series, not necessarily of same length, with valid index labels considering subset. func should take a DataFrame if axis is None and return either an ndarray with the same shape or a DataFrame, not necessarily of the same shape, with valid index and columns labels considering subset.

Changed in version 1.3.0.

Changed in version 1.4.0.
axis{0 or ‘index’, 1 or ‘columns’, None}, default 0: Apply to each column (axis=0 or 'index'), to each row (axis=1 or 'columns'), or to the entire DataFrame at once with axis=None.
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.map: Apply a CSS-styling function elementwise.

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.

This is similar to DataFrame.apply, except that axis=None applies the function to the entire DataFrame at once, rather than column-wise or row-wise.

Examples

>>> def highlight_max(x, color):
...     return np.where(x == np.nanmax(x.to_numpy()), f"color: {color};", None)
>>> df = pd.DataFrame(np.random.randn(5, 2), columns=["A", "B"])
>>> df.style.apply(highlight_max, color="red")  
>>> df.style.apply(highlight_max, color="blue", axis=1)  
>>> df.style.apply(highlight_max, color="green", axis=None)  

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

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

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

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

Using a function which returns a Series / DataFrame of unequal length but containing valid index labels

>>> df = pd.DataFrame([[1, 2], [3, 4], [4, 6]], index=["A1", "A2", "Total"])
>>> total_style = pd.Series("font-weight: bold;", index=["Total"])
>>> df.style.apply(lambda s: total_style)  

See Table Visualization user guide for more details.