def clip_lower(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.ge,<EOL>axis=axis, inplace=inplace)<EOL>
Trim values below a given threshold.

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

Elements below 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
    Minimum value allowed. All values below 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 `self` 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
--------

Series single threshold clipping:

>>> s = pd.Series([5, 6, 7, 8, 9])
>>> s.clip(lower=8)
0    8
1    8
2    8
3    8
4    9
dtype: int64

Series clipping element-wise using an array of thresholds. `threshold`
should be the same length as the Series.

>>> elemwise_thresholds = [4, 8, 7, 2, 5]
>>> s.clip(lower=elemwise_thresholds)
0    5
1    8
2    7
3    8
4    9
dtype: int64

DataFrames can be compared to a scalar.

>>> df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]})
>>> df
   A  B
0  1  2
1  3  4
2  5  6

>>> df.clip(lower=3)
   A  B
0  3  3
1  3  4
2  5  6

Or to an array of values. By default, `threshold` should be the same
shape as the DataFrame.

>>> df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]]))
   A  B
0  3  4
1  3  4
2  6  6

Control how `threshold` is broadcast with `axis`. In this case
`threshold` should be the same length as the axis specified by
`axis`.

>>> df.clip(lower=[3, 3, 5], axis='index')
   A  B
0  3  3
1  3  4
2  5  6

>>> df.clip(lower=[4, 5], axis='columns')
   A  B
0  4  5
1  4  5
2  5  6