def pipe(self, func, *args, **kwargs):
    '\n        Apply ``func(self, *args, **kwargs)``, and return the result.\n\n        .. versionadded:: 0.24.0\n\n        Parameters\n        ----------\n        func : function\n            Function to apply to the Styler.  Alternatively, a\n            ``(callable, keyword)`` tuple where ``keyword`` is a string\n            indicating the keyword of ``callable`` that expects the Styler.\n        *args, **kwargs :\n            Arguments passed to `func`.\n\n        Returns\n        -------\n        object :\n            The value returned by ``func``.\n\n        See Also\n        --------\n        DataFrame.pipe : Analogous method for DataFrame.\n        Styler.apply : Apply a function row-wise, column-wise, or table-wise to\n            modify the dataframe\'s styling.\n\n        Notes\n        -----\n        Like :meth:`DataFrame.pipe`, this method can simplify the\n        application of several user-defined functions to a styler.  Instead\n        of writing:\n\n        .. code-block:: python\n\n            f(g(df.style.set_precision(3), arg1=a), arg2=b, arg3=c)\n\n        users can write:\n\n        .. code-block:: python\n\n            (df.style.set_precision(3)\n               .pipe(g, arg1=a)\n               .pipe(f, arg2=b, arg3=c))\n\n        In particular, this allows users to define functions that take a\n        styler object, along with other parameters, and return the styler after\n        making styling changes (such as calling :meth:`Styler.apply` or\n        :meth:`Styler.set_properties`).  Using ``.pipe``, these user-defined\n        style "transformations" can be interleaved with calls to the built-in\n        Styler interface.\n\n        Examples\n        --------\n        >>> def format_conversion(styler):\n        ...     return (styler.set_properties(**{\'text-align\': \'right\'})\n        ...                   .format({\'conversion\': \'{:.1%}\'}))\n\n        The user-defined ``format_conversion`` function above can be called\n        within a sequence of other style modifications:\n\n        >>> df = pd.DataFrame({\'trial\': list(range(5)),\n        ...                    \'conversion\': [0.75, 0.85, np.nan, 0.7, 0.72]})\n        >>> (df.style\n        ...    .highlight_min(subset=[\'conversion\'], color=\'yellow\')\n        ...    .pipe(format_conversion)\n        ...    .set_caption("Results with minimum conversion highlighted."))\n        '
    return com._pipe(self, func, *args, **kwargs)
Apply ``func(self, *args, **kwargs)``, and return the result.

.. versionadded:: 0.24.0

Parameters
----------
func : function
    Function to apply to the Styler.  Alternatively, a
    ``(callable, keyword)`` tuple where ``keyword`` is a string
    indicating the keyword of ``callable`` that expects the Styler.
*args, **kwargs :
    Arguments passed to `func`.

Returns
-------
object :
    The value returned by ``func``.

See Also
--------
DataFrame.pipe : Analogous method for DataFrame.
Styler.apply : Apply a function row-wise, column-wise, or table-wise to
    modify the dataframe's styling.

Notes
-----
Like :meth:`DataFrame.pipe`, this method can simplify the
application of several user-defined functions to a styler.  Instead
of writing:

.. code-block:: python

    f(g(df.style.set_precision(3), arg1=a), arg2=b, arg3=c)

users can write:

.. code-block:: python

    (df.style.set_precision(3)
       .pipe(g, arg1=a)
       .pipe(f, arg2=b, arg3=c))

In particular, this allows users to define functions that take a
styler object, along with other parameters, and return the styler after
making styling changes (such as calling :meth:`Styler.apply` or
:meth:`Styler.set_properties`).  Using ``.pipe``, these user-defined
style "transformations" can be interleaved with calls to the built-in
Styler interface.

Examples
--------
>>> def format_conversion(styler):
...     return (styler.set_properties(**{'text-align': 'right'})
...                   .format({'conversion': '{:.1%}'}))

The user-defined ``format_conversion`` function above can be called
within a sequence of other style modifications:

>>> df = pd.DataFrame({'trial': list(range(5)),
...                    'conversion': [0.75, 0.85, np.nan, 0.7, 0.72]})
>>> (df.style
...    .highlight_min(subset=['conversion'], color='yellow')
...    .pipe(format_conversion)
...    .set_caption("Results with minimum conversion highlighted."))
def pipe(self, func, *args, **kwargs):
    '\n        Apply ``func(self, *args, **kwargs)``, and return the result.\n\n        .. versionadded:: 0.24.0\n\n        Parameters\n        ----------\n        func : function\n            Function to apply to the Styler.  Alternatively, a\n            ``(callable, keyword)`` tuple where ``keyword`` is a string\n            indicating the keyword of ``callable`` that expects the Styler.\n        *args, **kwargs :\n            Arguments passed to `func`.\n\n        Returns\n        -------\n        object :\n            The value returned by ``func``.\n\n        See Also\n        --------\n        DataFrame.pipe : Analogous method for DataFrame.\n        Styler.apply : Apply a function row-wise, column-wise, or table-wise to\n            modify the dataframe\'s styling.\n\n        Notes\n        -----\n        Like :meth:`DataFrame.pipe`, this method can simplify the\n        application of several user-defined functions to a styler.  Instead\n        of writing:\n\n        .. code-block:: python\n\n            f(g(df.style.set_precision(3), arg1=a), arg2=b, arg3=c)\n\n        users can write:\n\n        .. code-block:: python\n\n            (df.style.set_precision(3)\n               .pipe(g, arg1=a)\n               .pipe(f, arg2=b, arg3=c))\n\n        In particular, this allows users to define functions that take a\n        styler object, along with other parameters, and return the styler after\n        making styling changes (such as calling :meth:`Styler.apply` or\n        :meth:`Styler.set_properties`).  Using ``.pipe``, these user-defined\n        style "transformations" can be interleaved with calls to the built-in\n        Styler interface.\n\n        Examples\n        --------\n        >>> def format_conversion(styler):\n        ...     return (styler.set_properties(**{\'text-align\': \'right\'})\n        ...                   .format({\'conversion\': \'{:.1%}\'}))\n\n        The user-defined ``format_conversion`` function above can be called\n        within a sequence of other style modifications:\n\n        >>> df = pd.DataFrame({\'trial\': list(range(5)),\n        ...                    \'conversion\': [0.75, 0.85, np.nan, 0.7, 0.72]})\n        >>> (df.style\n        ...    .highlight_min(subset=[\'conversion\'], color=\'yellow\')\n        ...    .pipe(format_conversion)\n        ...    .set_caption("Results with minimum conversion highlighted."))\n        '
    return com._pipe(self, func, *args, **kwargs)