def all(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
    "\n        Return whether all elements are True.\n\n        Returns True unless there is at least one element within a series that is\n        False or equivalent (e.g. zero or 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': [True, True, True],\n        ...    'col2': [True, False, False],\n        ...    'col3': [0, 0, 0],\n        ...    'col4': [1, 2, 3],\n        ...    'col5': [True, True, 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.all()\n        col1     True\n        col2    False\n        col3    False\n        col4     True\n        col5     True\n        col6    False\n        dtype: bool\n\n        Include only boolean columns when set `bool_only=True`.\n\n        >>> df.all(bool_only=True)\n        col1     True\n        col2    False\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)
        all_col = F.min(F.coalesce(scol.cast('boolean'), SF.lit(True)))
        applied.append(F.when(all_col.isNull(), True).otherwise(all_col))
    return self._result_aggregated(column_labels, applied)
Return whether all elements are True.

Returns True unless there is at least one element within a series that is
False or equivalent (e.g. zero or 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': [True, True, True],
...    'col2': [True, False, False],
...    'col3': [0, 0, 0],
...    'col4': [1, 2, 3],
...    'col5': [True, True, None],
...    'col6': [True, False, None]},
...    columns=['col1', 'col2', 'col3', 'col4', 'col5', 'col6'])

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

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

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

>>> df.all(bool_only=True)
col1     True
col2    False
dtype: bool
def all(self, axis: Axis=0, bool_only: Optional[bool]=None) -> 'Series':
    "\n        Return whether all elements are True.\n\n        Returns True unless there is at least one element within a series that is\n        False or equivalent (e.g. zero or 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': [True, True, True],\n        ...    'col2': [True, False, False],\n        ...    'col3': [0, 0, 0],\n        ...    'col4': [1, 2, 3],\n        ...    'col5': [True, True, 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.all()\n        col1     True\n        col2    False\n        col3    False\n        col4     True\n        col5     True\n        col6    False\n        dtype: bool\n\n        Include only boolean columns when set `bool_only=True`.\n\n        >>> df.all(bool_only=True)\n        col1     True\n        col2    False\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)
        all_col = F.min(F.coalesce(scol.cast('boolean'), SF.lit(True)))
        applied.append(F.when(all_col.isNull(), True).otherwise(all_col))
    return self._result_aggregated(column_labels, applied)