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####################### Docstring (pandas.DataFrame.any) #######################
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Return whether any element is True over requested axis.

Unlike :meth:`DataFrame.all`, this performs an *or* operation. If any of the
values along the specified axis is True, this will return True.

Parameters
----------
axis : int, default 0
    Select the axis which can be 0 for indices and 1 for columns.
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result
    will be NA.
level : int or level name, default None
    If the axis is a MultiIndex (hierarchical), count along a
    particular level, collapsing into a Series.
bool_only : boolean, default None
    Include only boolean columns. If None, will attempt to use everything,
    then use only boolean data. Not implemented for Series.
**kwargs : any, default None
    Additional keywords have no affect but might be accepted for
    compatibility with numpy.

Returns
-------
any : Series or DataFrame (if level specified)

See Also
--------
pandas.DataFrame.all : Return whether all elements are True.

Examples
--------
**Series**

For Series input, the output is a scalar indicating whether any element
is True.

>>> pd.Series([True, False]).any()
True

**DataFrame**

Whether each column contains at least one True element (the default).

>>> pd.DataFrame({
...     "A": [1, 2, 3],
...     "B": [4, 5, 6]
... }).any()
A    True
B    True
dtype: bool

Aggregating over the columns.

>>> pd.DataFrame({
...     "A": [True, False, True],
...     "B": [4, 5, 6]
... }).any(axis='columns')
0    True
1    True
2    True
dtype: bool

>>> pd.DataFrame({
...     "A": [True, False, True],
...     "B": [4, 0, 6]
... }).any(axis='columns')
0    True
1    False
2    True
dtype: bool

`any` for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)

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Errors found:
	Use only one blank line to separate sections or paragraphs
	Errors in parameters section
		Parameters {'kwargs'} not documented
		Unknown parameters {'**kwargs'}