def any(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
    "\n        Return whether any element is True.\n\n        Returns False unless there is at least one element within a series that is\n        True or equivalent (e.g. non-zero or non-empty).\n\n        Parameters\n        ----------\n        axis : {0 or 'index'}, default 0\n            Indicate which axis or axes should be reduced.\n\n            * 0 / 'index' : reduce the index, return a Series whose index is the\n              original column labels.\n\n        bool_only : bool, default None\n            Include only boolean columns. If None, will attempt to use everything,\n            then use only boolean data.\n\n        Returns\n        -------\n        Series\n\n        Examples\n        --------\n        Create a dataframe from a dictionary.\n\n        >>> df = ps.DataFrame({\n        ...    'col1': [False, False, False],\n        ...    'col2': [True, False, False],\n        ...    'col3': [0, 0, 1],\n        ...    'col4': [0, 1, 2],\n        ...    'col5': [False, False, None],\n        ...    'col6': [True, False, None]},\n        ...    columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])\n\n        Default behaviour checks if column-wise values all return True.\n\n        >>> df.any()\n        col1    False\n        col2     True\n        col3     True\n        col4     True\n        col5    False\n        col6     True\n        dtype: bool\n\n        Include only boolean columns when set `bool_only=True`.\n\n        >>> df.any(bool_only=True)\n        col1    False\n        col2     True\n        dtype: bool\n        "
    axis = validate_axis(axis)
    if (axis != 0):
        raise NotImplementedError('axis should be either 0 or "index" currently.')
    column_labels = self._internal.column_labels
    if bool_only:
        column_labels = self._bool_column_labels(column_labels)
    if (len(column_labels) == 0):
        return ps.Series([], dtype=bool)
    applied = []
    for label in column_labels:
        scol = self._internal.spark_column_for(label)
        any_col = F.max(F.coalesce(scol.cast('boolean'), SF.lit(False)))
        applied.append(F.when(any_col.isNull(), False).otherwise(any_col))
    return self._result_aggregated(column_labels, applied)
Return whether any element is True.

Returns False unless there is at least one element within a series that is
True or equivalent (e.g. non-zero or non-empty).

Parameters
----------
axis : {0 or 'index'}, default 0
    Indicate which axis or axes should be reduced.

    * 0 / 'index' : reduce the index, return a Series whose index is the
      original column labels.

bool_only : bool, default None
    Include only boolean columns. If None, will attempt to use everything,
    then use only boolean data.

Returns
-------
Series

Examples
--------
Create a dataframe from a dictionary.

>>> df = ps.DataFrame({
...    'col1': [False, False, False],
...    'col2': [True, False, False],
...    'col3': [0, 0, 1],
...    'col4': [0, 1, 2],
...    'col5': [False, False, None],
...    'col6': [True, False, None]},
...    columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])

Default behaviour checks if column-wise values all return True.

>>> df.any()
col1    False
col2     True
col3     True
col4     True
col5    False
col6     True
dtype: bool

Include only boolean columns when set `bool_only=True`.

>>> df.any(bool_only=True)
col1    False
col2     True
dtype: bool
def any(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
    "\n        Return whether any element is True.\n\n        Returns False unless there is at least one element within a series that is\n        True or equivalent (e.g. non-zero or non-empty).\n\n        Parameters\n        ----------\n        axis : {0 or 'index'}, default 0\n            Indicate which axis or axes should be reduced.\n\n            * 0 / 'index' : reduce the index, return a Series whose index is the\n              original column labels.\n\n        bool_only : bool, default None\n            Include only boolean columns. If None, will attempt to use everything,\n            then use only boolean data.\n\n        Returns\n        -------\n        Series\n\n        Examples\n        --------\n        Create a dataframe from a dictionary.\n\n        >>> df = ps.DataFrame({\n        ...    'col1': [False, False, False],\n        ...    'col2': [True, False, False],\n        ...    'col3': [0, 0, 1],\n        ...    'col4': [0, 1, 2],\n        ...    'col5': [False, False, None],\n        ...    'col6': [True, False, None]},\n        ...    columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])\n\n        Default behaviour checks if column-wise values all return True.\n\n        >>> df.any()\n        col1    False\n        col2     True\n        col3     True\n        col4     True\n        col5    False\n        col6     True\n        dtype: bool\n\n        Include only boolean columns when set `bool_only=True`.\n\n        >>> df.any(bool_only=True)\n        col1    False\n        col2     True\n        dtype: bool\n        "
    axis = validate_axis(axis)
    if (axis != 0):
        raise NotImplementedError('axis should be either 0 or "index" currently.')
    column_labels = self._internal.column_labels
    if bool_only:
        column_labels = self._bool_column_labels(column_labels)
    if (len(column_labels) == 0):
        return ps.Series([], dtype=bool)
    applied = []
    for label in column_labels:
        scol = self._internal.spark_column_for(label)
        any_col = F.max(F.coalesce(scol.cast('boolean'), SF.lit(False)))
        applied.append(F.when(any_col.isNull(), False).otherwise(any_col))
    return self._result_aggregated(column_labels, applied)