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adatasetadayjorisvandenbossche
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DOC: update the pandas.DataFrame.clip_lower docstring (#20289)
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pandas/core/generic.py

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@@ -6572,7 +6572,10 @@ def clip_upper(self, threshold, axis=None, inplace=False):
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def clip_lower(self, threshold, axis=None, inplace=False):
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"""
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Return copy of the input with values below a threshold truncated.
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Trim values below a given threshold.
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Elements below the `threshold` will be changed to match the
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`threshold` value(s).
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Parameters
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----------
@@ -6597,17 +6600,22 @@ def clip_lower(self, threshold, axis=None, inplace=False):
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See Also
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--------
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Series.clip : Return copy of input with values below and above
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thresholds truncated.
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Series.clip_upper : Return copy of input with values above
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threshold truncated.
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DataFrame.clip : General purpose method to trim `DataFrame` values to
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given threshold(s)
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DataFrame.clip_upper : Trim `DataFrame` values above given
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threshold(s)
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Series.clip : General purpose method to trim `Series` values to given
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threshold(s)
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Series.clip_upper : Trim `Series` values above given threshold(s)
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Returns
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-------
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clipped : same type as input
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clipped
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Original data with values trimmed.
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Examples
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--------
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Series single threshold clipping:
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>>> s = pd.Series([5, 6, 7, 8, 9])
@@ -6659,17 +6667,18 @@ def clip_lower(self, threshold, axis=None, inplace=False):
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`threshold` should be the same length as the axis specified by
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`axis`.
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>>> df.clip_lower(np.array([3, 3, 5]), axis='index')
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>>> df.clip_lower([3, 3, 5], axis='index')
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A B
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0 3 3
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1 3 4
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2 5 6
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>>> df.clip_lower(np.array([4, 5]), axis='columns')
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>>> df.clip_lower([4, 5], axis='columns')
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A B
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0 4 5
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1 4 5
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2 5 6
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"""
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return self._clip_with_one_bound(threshold, method=self.ge,
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axis=axis, inplace=inplace)

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