parameters: (self, threshold, axis=None, inplace=False)
return_statement: return self._clip_with_one_bound(threshold, method=self.ge,
                                           axis=axis, inplace=inplace)
docstring: 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 : boolean, 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
docstring_summary: Trim values below a given threshold.
docstring_tokens: Trim values below a given threshold .
function: def clip_lower(self, threshold, axis=None, inplace=False):
          """
          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 : boolean, 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
          """
          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)
function_tokens: def clip_lower ( self , threshold , axis = None , inplace = False ) : 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 )