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DOC: update the pandas.DataFrame.clip docstring #20212

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DOC: update the pandas.DataFrame.clip docstring #20212

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17 changes: 11 additions & 6 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5629,6 +5629,12 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False,
Original input with those values above/below the
`upper`/`lower` thresholds set to the threshold values.

Notes
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I think it should be in References and not in Notes. It wasn't in the documentation for the sprint, for simplicity, but you can check this document: http://numpydoc.readthedocs.io/en/latest/format.html

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Yea, I agree. Done.

-----

.. [1] Tukey, John W. "The future of data analysis." The annals of
mathematical statistics 33.1 (1962): 1-67.

See Also
--------
DataFrame.clip : Trim values at input threshold(s).
Expand All @@ -5650,13 +5656,13 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False,
... 'b': [1, 2, 100]},
... index=['foo', 'bar', 'foobar'])
>>> df
a b
a b
foo -1 1
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alignment doesn’t look right here

bar -2 2
foobar -100 100

>>> df.clip(lower=-10, upper=10)
a b
a b
foo -1 1
bar -2 2
foobar -10 10
Expand All @@ -5667,7 +5673,7 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False,

>>> col_thresh = pd.Series({'a': -5, 'b': 5})
>>> df.clip(lower=col_thresh, axis='columns')
a b
a b
foo -1 5
bar -2 5
foobar -5 100
Expand All @@ -5681,15 +5687,14 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False,
bar 1 2
foobar 10 100

`Winsorizing <https://en.wikipedia.org/wiki/Winsorizing>`__ is a
related method, whereby the data are clipped at
Winsorizing [1]_ is a related method, whereby the data are clipped at
the 5th and 95th percentiles. The ``DataFrame.quantile`` method returns
a ``Series`` with column names as index and the quantiles as values.
Use ``axis='columns'`` to apply clipping to columns.

>>> lower, upper = df.quantile(0.05), df.quantile(0.95)
>>> df.clip(lower=lower, upper=upper, axis='columns')
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Not sure if it's just because I'm too tired, but is axis useful here? If the threshold is a scalar, doesn't seem to have an effect, right?

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Axis is required here since df.quantile returns a series with the quantiles for each column. Running without the axis argument results in an error.

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I'd find useful having this comment in the explanations before the test. :)

a b
a b
foo -1.1 1.1
bar -2.0 2.0
foobar -90.2 90.2
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