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

Styler.apply(func, axis=0, subset=None, **kwargs)

  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.