def clip_lower(self, threshold, axis=None, inplace=False):
    '\n        Trim values below a given threshold.\n\n        .. deprecated:: 0.24.0\n            Use clip(lower=threshold) instead.\n\n        Elements below the `threshold` will be changed to match the\n        `threshold` value(s). Threshold can be a single value or an array,\n        in the latter case it performs the truncation element-wise.\n\n        Parameters\n        ----------\n        threshold : numeric or array-like\n            Minimum value allowed. All values below threshold will be set to\n            this value.\n\n            * float : every value is compared to `threshold`.\n            * array-like : The shape of `threshold` should match the object\n              it\'s compared to. When `self` is a Series, `threshold` should be\n              the length. When `self` is a DataFrame, `threshold` should 2-D\n              and the same shape as `self` for ``axis=None``, or 1-D and the\n              same length as the axis being compared.\n\n        axis : {0 or \'index\', 1 or \'columns\'}, default 0\n            Align `self` with `threshold` along the given axis.\n\n        inplace : bool, default False\n            Whether to perform the operation in place on the data.\n\n            .. versionadded:: 0.21.0\n\n        Returns\n        -------\n        Series or DataFrame\n            Original data with values trimmed.\n\n        See Also\n        --------\n        Series.clip : General purpose method to trim Series values to given\n            threshold(s).\n        DataFrame.clip : General purpose method to trim DataFrame values to\n            given threshold(s).\n\n        Examples\n        --------\n\n        Series single threshold clipping:\n\n        >>> s = pd.Series([5, 6, 7, 8, 9])\n        >>> s.clip(lower=8)\n        0    8\n        1    8\n        2    8\n        3    8\n        4    9\n        dtype: int64\n\n        Series clipping element-wise using an array of thresholds. `threshold`\n        should be the same length as the Series.\n\n        >>> elemwise_thresholds = [4, 8, 7, 2, 5]\n        >>> s.clip(lower=elemwise_thresholds)\n        0    5\n        1    8\n        2    7\n        3    8\n        4    9\n        dtype: int64\n\n        DataFrames can be compared to a scalar.\n\n        >>> df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]})\n        >>> df\n           A  B\n        0  1  2\n        1  3  4\n        2  5  6\n\n        >>> df.clip(lower=3)\n           A  B\n        0  3  3\n        1  3  4\n        2  5  6\n\n        Or to an array of values. By default, `threshold` should be the same\n        shape as the DataFrame.\n\n        >>> df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]]))\n           A  B\n        0  3  4\n        1  3  4\n        2  6  6\n\n        Control how `threshold` is broadcast with `axis`. In this case\n        `threshold` should be the same length as the axis specified by\n        `axis`.\n\n        >>> df.clip(lower=[3, 3, 5], axis=\'index\')\n           A  B\n        0  3  3\n        1  3  4\n        2  5  6\n\n        >>> df.clip(lower=[4, 5], axis=\'columns\')\n           A  B\n        0  4  5\n        1  4  5\n        2  5  6\n        '
    warnings.warn('clip_lower(threshold) is deprecated, use clip(lower=threshold) instead', FutureWarning, stacklevel=2)
    return self._clip_with_one_bound(threshold, method=self.ge, axis=axis, inplace=inplace)
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
def clip_lower(self, threshold, axis=None, inplace=False):
    '\n        Trim values below a given threshold.\n\n        .. deprecated:: 0.24.0\n            Use clip(lower=threshold) instead.\n\n        Elements below the `threshold` will be changed to match the\n        `threshold` value(s). Threshold can be a single value or an array,\n        in the latter case it performs the truncation element-wise.\n\n        Parameters\n        ----------\n        threshold : numeric or array-like\n            Minimum value allowed. All values below threshold will be set to\n            this value.\n\n            * float : every value is compared to `threshold`.\n            * array-like : The shape of `threshold` should match the object\n              it\'s compared to. When `self` is a Series, `threshold` should be\n              the length. When `self` is a DataFrame, `threshold` should 2-D\n              and the same shape as `self` for ``axis=None``, or 1-D and the\n              same length as the axis being compared.\n\n        axis : {0 or \'index\', 1 or \'columns\'}, default 0\n            Align `self` with `threshold` along the given axis.\n\n        inplace : bool, default False\n            Whether to perform the operation in place on the data.\n\n            .. versionadded:: 0.21.0\n\n        Returns\n        -------\n        Series or DataFrame\n            Original data with values trimmed.\n\n        See Also\n        --------\n        Series.clip : General purpose method to trim Series values to given\n            threshold(s).\n        DataFrame.clip : General purpose method to trim DataFrame values to\n            given threshold(s).\n\n        Examples\n        --------\n\n        Series single threshold clipping:\n\n        >>> s = pd.Series([5, 6, 7, 8, 9])\n        >>> s.clip(lower=8)\n        0    8\n        1    8\n        2    8\n        3    8\n        4    9\n        dtype: int64\n\n        Series clipping element-wise using an array of thresholds. `threshold`\n        should be the same length as the Series.\n\n        >>> elemwise_thresholds = [4, 8, 7, 2, 5]\n        >>> s.clip(lower=elemwise_thresholds)\n        0    5\n        1    8\n        2    7\n        3    8\n        4    9\n        dtype: int64\n\n        DataFrames can be compared to a scalar.\n\n        >>> df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]})\n        >>> df\n           A  B\n        0  1  2\n        1  3  4\n        2  5  6\n\n        >>> df.clip(lower=3)\n           A  B\n        0  3  3\n        1  3  4\n        2  5  6\n\n        Or to an array of values. By default, `threshold` should be the same\n        shape as the DataFrame.\n\n        >>> df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]]))\n           A  B\n        0  3  4\n        1  3  4\n        2  6  6\n\n        Control how `threshold` is broadcast with `axis`. In this case\n        `threshold` should be the same length as the axis specified by\n        `axis`.\n\n        >>> df.clip(lower=[3, 3, 5], axis=\'index\')\n           A  B\n        0  3  3\n        1  3  4\n        2  5  6\n\n        >>> df.clip(lower=[4, 5], axis=\'columns\')\n           A  B\n        0  4  5\n        1  4  5\n        2  5  6\n        '
    warnings.warn('clip_lower(threshold) is deprecated, use clip(lower=threshold) instead', FutureWarning, stacklevel=2)
    return self._clip_with_one_bound(threshold, method=self.ge, axis=axis, inplace=inplace)