def clip_upper(self, threshold, axis=None, inplace=False):
warnings.warn('<STR_LIT>'<EOL>'<STR_LIT>',<EOL>FutureWarning, stacklevel=<NUM_LIT:2>)<EOL>return self._clip_with_one_bound(threshold, method=self.le,<EOL>axis=axis, inplace=inplace)<EOL>
Trim values above a given threshold.

.. deprecated:: 0.24.0
    Use clip(upper=threshold) instead.

Elements above the `threshold` will be changed to match the
`threshold` value(s). Threshold can be a single value or an array,
in the latter case it performs the truncation element-wise.

Parameters
----------
threshold : numeric or array-like
    Maximum value allowed. All values above threshold will be set to
    this value.

    * float : every value is compared to `threshold`.
    * array-like : The shape of `threshold` should match the object
      it's compared to. When `self` is a Series, `threshold` should be
      the length. When `self` is a DataFrame, `threshold` should 2-D
      and the same shape as `self` for ``axis=None``, or 1-D and the
      same length as the axis being compared.

axis : {0 or 'index', 1 or 'columns'}, default 0
    Align object with `threshold` along the given axis.
inplace : bool, default False
    Whether to perform the operation in place on the data.

    .. versionadded:: 0.21.0

Returns
-------
Series or DataFrame
    Original data with values trimmed.

See Also
--------
Series.clip : General purpose method to trim Series values to given
    threshold(s).
DataFrame.clip : General purpose method to trim DataFrame values to
    given threshold(s).

Examples
--------
>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s
0    1
1    2
2    3
3    4
4    5
dtype: int64

>>> s.clip(upper=3)
0    1
1    2
2    3
3    3
4    3
dtype: int64

>>> elemwise_thresholds = [5, 4, 3, 2, 1]
>>> elemwise_thresholds
[5, 4, 3, 2, 1]

>>> s.clip(upper=elemwise_thresholds)
0    1
1    2
2    3
3    2
4    1
dtype: int64