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# pandas.io.formats.style.Styler.highlight_quantile

Styler.highlight_quantile(subset=None, color='yellow', axis=0, q_left=0.0, q_right=1.0, interpolation='linear', inclusive='both', props=None)

  Highlight values defined by a quantile with a style.

  Parameters:
    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.

    color str, default ‘yellow’

      Background color to use for highlighting.

    axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

      Axis along which to determine and highlight quantiles. If `None` quantiles are measured over the entire DataFrame. See examples.

    q_left float, default 0

      Left bound, in [0, q_right), for the target quantile range.

    q_right float, default 1

      Right bound, in (q_left, 1], for the target quantile range.

    interpolation{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

      Argument passed to `Series.quantile` or `DataFrame.quantile` for quantile estimation.

    inclusive{‘both’, ‘neither’, ‘left’, ‘right’}

      Identify whether quantile bounds are closed or open.

    props str, default None

      CSS properties to use for highlighting. If `props` is given, `color` is not used.

  Returns:
    Styler

      Instance of class where values in quantile highlighted with given style.

  See also

  `Styler.highlight_null`

    Highlight missing values with a style.

  `Styler.highlight_max`

    Highlight the maximum with a style.

  `Styler.highlight_min`

    Highlight the minimum with a style.

  `Styler.highlight_between`

    Highlight a defined range with a style.

  Notes

  This function does not work with `str` dtypes.

  Examples

  Using `axis=None` and apply a quantile to all collective data

      >>> df = pd.DataFrame(np.arange(10).reshape(2, 5) + 1)
      >>> df.style.highlight_quantile(axis=None, q_left=0.8, color="#fffd75")
      ...

  Or highlight quantiles row-wise or column-wise, in this case by row-wise

      >>> df.style.highlight_quantile(axis=1, q_left=0.8, color="#fffd75")
      ...

  Use `props` instead of default background coloring

      >>> df.style.highlight_quantile(
      ...     axis=None,
      ...     q_left=0.2,
      ...     q_right=0.8,
      ...     props="font-weight:bold;color:#e83e8c",
      ... )