Skip to main content
`Ctrl`+`K`

# pandas.io.formats.style.Styler.format_index

Styler.format_index(formatter=None, axis=0, level=None, na_rep=None, precision=None, decimal='.', thousands=None, escape=None, hyperlinks=None)

  Format the text display value of index labels or column headers.

  Added in version 1.4.0.

  Parameters:
    formatter str, callable, dict or None

      Object to define how values are displayed. See notes.

    axis{0, “index”, 1, “columns”}

      Whether to apply the formatter to the index or column headers.

    level int, str, list

      The level(s) over which to apply the generic formatter.

    na_rep str, optional

      Representation for missing values. If `na_rep` is None, no special formatting is applied.

    precision int, optional

      Floating point precision to use for display purposes, if not determined by the specified `formatter`.

    decimal str, default “.”

      Character used as decimal separator for floats, complex and integers.

    thousands str, optional, default None

      Character used as thousands separator for floats, complex and integers.

    escape str, optional

      Use ‘html’ to replace the characters `&`, `<`, `>`, `'`, and `"` in cell display string with HTML-safe sequences. Use ‘latex’ to replace the characters `&`, `%`, `$`, `#`, `_`, `{`, `}`, `~`, `^`, and `\` in the cell display string with LaTeX-safe sequences. Escaping is done before `formatter`.

    hyperlinks{“html”, “latex”}, optional

      Convert string patterns containing https://, http://, ftp:// or www. to HTML <a> tags as clickable URL hyperlinks if “html”, or LaTeX href commands if “latex”.

  Returns:
    Styler

  See also

  `Styler.format`

    Format the text display value of data cells.

  Notes

  This method assigns a formatting function, `formatter`, to each level label in the DataFrame’s index or column headers. If `formatter` is `None`, then the default formatter is used. If a callable then that function should take a label value as input and return a displayable representation, such as a string. If `formatter` is given as a string this is assumed to be a valid Python format specification and is wrapped to a callable as `string.format(x)`. If a `dict` is given, keys should correspond to MultiIndex level numbers or names, and values should be string or callable, as above.

  The default formatter currently expresses floats and complex numbers with the pandas display precision unless using the `precision` argument here. The default formatter does not adjust the representation of missing values unless the `na_rep` argument is used.

  The `level` argument defines which levels of a MultiIndex to apply the method to. If the `formatter` argument is given in dict form but does not include all levels within the level argument then these unspecified levels will have the default formatter applied. Any levels in the formatter dict specifically excluded from the level argument will be ignored.

  When using a `formatter` string the dtypes must be compatible, otherwise a ValueError will be raised.

  Warning

  Styler.format_index is ignored when using the output format Styler.to_excel, since Excel and Python have inherrently different formatting structures. However, it is possible to use the number-format pseudo CSS attribute to force Excel permissible formatting. See documentation for Styler.format.

  Examples

  Using `na_rep` and `precision` with the default `formatter`

      >>> df = pd.DataFrame([[1, 2, 3]], columns=[2.0, np.nan, 4.0])
      >>> df.style.format_index(axis=1, na_rep='MISS', precision=3)
          2.000    MISS   4.000
      0       1       2       3

  Using a `formatter` specification on consistent dtypes in a level

      >>> df.style.format_index('{:.2f}', axis=1, na_rep='MISS')
           2.00   MISS    4.00
      0       1      2       3

  Using the default `formatter` for unspecified levels

      >>> df = pd.DataFrame([[1, 2, 3]],
      ...     columns=pd.MultiIndex.from_arrays([["a", "a", "b"],[2, np.nan, 4]]))
      >>> df.style.format_index({0: lambda v: v.upper()}, axis=1, precision=1)
      ...
                     A       B
            2.0    nan     4.0
      0       1      2       3

  Using a callable `formatter` function.

      >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
      >>> df.style.format_index(func, axis=1, na_rep='MISS')
      ...
                STRING  STRING
          FLOAT   MISS   FLOAT
      0       1      2       3

  Using a `formatter` with HTML `escape` and `na_rep`.

      >>> df = pd.DataFrame([[1, 2, 3]], columns=['"A"', 'A&B', None])
      >>> s = df.style.format_index('$ {0}', axis=1, escape="html", na_rep="NA")
      ...
      <th .. >$ &#34;A&#34;</th>
      <th .. >$ A&amp;B</th>
      <th .. >NA</td>
      ...

  Using a `formatter` with LaTeX `escape`.

      >>> df = pd.DataFrame([[1, 2, 3]], columns=["123", "~", "$%#"])
      >>> df.style.format_index("\\textbf{{{}}}", escape="latex", axis=1).to_latex()
      ...
      \begin{tabular}{lrrr}
      {} & {\textbf{123}} & {\textbf{\textasciitilde }} & {\textbf{\$\%\#}} \\
      0 & 1 & 2 & 3 \\
      \end{tabular}