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

Styler.map(func, subset=None, **kwargs)

  Apply a CSS-styling function elementwise.

  Updates the HTML representation with the result.

  Parameters:
    func function

      `func` should take a scalar and return a string.

    subset label, 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.

    **kwargs dict

      Pass along to `func`.

  Returns:
    Styler

      Instance of class with CSS-styling function applied elementwise.

  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.