@@ -6572,7 +6572,10 @@ def clip_upper(self, threshold, axis=None, inplace=False):
65726572
65736573 def clip_lower (self , threshold , axis = None , inplace = False ):
65746574 """
6575- Return copy of the input with values below a threshold truncated.
6575+ Trim values below a given threshold.
6576+
6577+ Elements below the `threshold` will be changed to match the
6578+ `threshold` value(s).
65766579
65776580 Parameters
65786581 ----------
@@ -6597,17 +6600,22 @@ def clip_lower(self, threshold, axis=None, inplace=False):
65976600
65986601 See Also
65996602 --------
6600- Series.clip : Return copy of input with values below and above
6601- thresholds truncated.
6602- Series.clip_upper : Return copy of input with values above
6603- threshold truncated.
6603+ DataFrame.clip : General purpose method to trim `DataFrame` values to
6604+ given threshold(s)
6605+ DataFrame.clip_upper : Trim `DataFrame` values above given
6606+ threshold(s)
6607+ Series.clip : General purpose method to trim `Series` values to given
6608+ threshold(s)
6609+ Series.clip_upper : Trim `Series` values above given threshold(s)
66046610
66056611 Returns
66066612 -------
6607- clipped : same type as input
6613+ clipped
6614+ Original data with values trimmed.
66086615
66096616 Examples
66106617 --------
6618+
66116619 Series single threshold clipping:
66126620
66136621 >>> s = pd.Series([5, 6, 7, 8, 9])
@@ -6659,17 +6667,18 @@ def clip_lower(self, threshold, axis=None, inplace=False):
66596667 `threshold` should be the same length as the axis specified by
66606668 `axis`.
66616669
6662- >>> df.clip_lower(np.array( [3, 3, 5]) , axis='index')
6670+ >>> df.clip_lower([3, 3, 5], axis='index')
66636671 A B
66646672 0 3 3
66656673 1 3 4
66666674 2 5 6
66676675
6668- >>> df.clip_lower(np.array( [4, 5]) , axis='columns')
6676+ >>> df.clip_lower([4, 5], axis='columns')
66696677 A B
66706678 0 4 5
66716679 1 4 5
66726680 2 5 6
6681+
66736682 """
66746683 return self ._clip_with_one_bound (threshold , method = self .ge ,
66756684 axis = axis , inplace = inplace )
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