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

Dpananos · 2018-03-10T18:38:00Z

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

PR title is "DOC: update the docstring"
The validation script passes: scripts/validate_docstrings.py <your-function-or-method>
The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
The html version looks good: python doc/make.py --single <your-function-or-method>
It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'kwargs', 'args'} not documented
		Unknown parameters {'*args, **kwargs'}
		Parameter "*args, **kwargs" has no type
(pandas_dev) Demetris-Retina-MacBook-Pro:pandas demetri$ git diff upstream/master -u -- "*.py" | flake8 --diff
(pandas_dev) Demetris-Retina-MacBook-Pro:pandas demetri$ python scripts/validate_docstrings.py pandas.DataFrame.clip

################################################################################
###################### Docstring (pandas.DataFrame.clip)  ######################
################################################################################

Trim values at input threshold(s).

Elements above/below the upper/lower thresholds will be changed to
upper/lower thresholds. Clipping data is a method for dealing with
out-of-range elements. If some elements are too large or too small,
clipping is one way to transform the data into a reasonable range.

Parameters
----------
lower : float, array-like or None, default None
    Lower threshold for clipping.  Values smaller than `lower` will be
    converted to `lower`.
upper : float, array-like or None, default None
    Upper threshold for clipping.  Values larger than `upper` will be
    converted to `upper`.
axis : {0 or 'index', 1 or 'columns', None}, default None
    Apply clip by index (i.e. by rows) or columns.
inplace : boolean, default False
    Whether to perform the operation in place on the data
        .. versionadded:: 0.21.0.
*args, **kwargs
    Additional keywords have no effect but might be accepted
    for compatibility with numpy.

Returns
-------
`Series` or `DataFrame`.
    Original input with those values above/below the
    `upper`/`lower` thresholds set to the threshold values.

References
-----
.. [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).
Series.clip : Trim values at input threshold(s).
Series.clip_lower : Return copy of the input with values below given
    value(s) truncated.
Series.clip_upper : Return copy of input with values above given
    value(s) truncated.
DataFrame.clip_lower : Return copy of the input with values below given
    value(s) truncated.
DataFrame.clip_upper : Return copy of input with values above given
    value(s) truncated.
DataFrame.quantile : Return values at the given quantile over requested
    axis, a la numpy.percentile.

Examples
--------
>>> df = pd.DataFrame({'a': [-1, -2, -100],
...                    'b': [1, 2, 100]},
...                    index=['foo', 'bar', 'foobar'])
>>> df
     a  b
foo -1  1
bar -2  2
foobar  -100    100

>>> df.clip(lower=-10, upper=10)
     a  b
foo -1  1
bar -2  2
foobar  -10 10

You can clip each column or row with different thresholds by passing
a ``Series`` to the lower/upper argument. Use the axis argument to clip
by column or rows.

>>> col_thresh = pd.Series({'a': -5, 'b': 5})
>>> df.clip(lower=col_thresh, axis='columns')
     a  b
foo -1  5
bar -2  5
foobar  -5  100

Clip the foo, bar, and foobar rows with lower thresholds 5, 7, and 10.

>>> row_thresh = pd.Series({'foo': 0, 'bar': 1, 'foobar': 10})
>>> df.clip(lower=row_thresh, axis='index')
    a   b
foo 0   1
bar 1   2
foobar  10  100

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

################################################################################
################################## Validation ##################################
################################################################################

Errors found:
	Errors in parameters section
		Parameters {'kwargs', 'args'} not documented
		Unknown parameters {'*args, **kwargs'}
		Parameter "*args, **kwargs" has no type

Changes:

*args and **kwargs description taken from DOC: update the docstring of pandas.Series.clip #20256 in the name of consistency

Added winsoriztion example.

datapythonista

Goo contribution, added some comments

datapythonista · 2018-03-10T18:56:19Z

pandas/core/generic.py

@@ -5601,53 +5601,99 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False,
        """
        Trim values at input threshold(s).

+        Elements above the upper threshold will be changed to upper threshold.
+        Elements below the lower threshold will be changed to lower threshold.


This seems duplicated.

datapythonista · 2018-03-10T18:57:59Z

pandas/core/generic.py

@@ -5601,53 +5601,99 @@ def clip(self, lower=None, upper=None, axis=None, inplace=False,
        """
        Trim values at input threshold(s).

+        Elements above the upper threshold will be changed to upper threshold.
+        Elements below the lower threshold will be changed to lower threshold.
+
        Parameters
        ----------
        lower : float or array_like, default None


not sure if it's better or worse, but I think the standard we defined is to use float, array-like or None, default None (so the None is duplicated). Also note the hyphen and not underscore in array-like

datapythonista · 2018-03-10T18:59:48Z

pandas/core/generic.py

        Parameters
        ----------
        lower : float or array_like, default None
+            Lower threshold for clipping.  Values smaller than upper will be


For naming parameters it's better to have them in backticks. In this case I think it adds value, making it clearer that upper is a parameter (and lower).

datapythonista · 2018-03-10T19:01:52Z

pandas/core/generic.py

        upper : float or array_like, default None
+            Upper threshold for clipping.  Values larger than upper will be
+            converted to upper.
        axis : int or string axis name, optional


We added a standard for axis to the documentation, at the end of the Paramters section. Basically it's axis : {0 or 'index', 1 or 'columns', None}, default None

datapythonista · 2018-03-10T19:02:54Z

pandas/core/generic.py

-                .. versionadded:: 0.21.0
+                .. versionadded:: 0.21.0.
+        args : dictionary of arguments arguments passed to pandas.compat.numpy
+        kwargs : dictionary of keyword arguments passed to pandas.compat.numpy


*args and **kwargs with the stars (even if the validate script complaints), and without the type (the description part goes in the nest line)

datapythonista · 2018-03-10T19:03:14Z

pandas/core/generic.py


        Returns
        -------
-        clipped : Series
+        clipped : DataFrame/Series


Series or DataFrame

datapythonista · 2018-03-10T19:03:54Z

pandas/core/generic.py


        Examples
        --------
+        >>> df=pd.DataFrame({'a':[1, 2, 3], 'b':[4, 5, 6], 'c':[7, 8, 9001]})


This have several PEP-8 issues because of missing spaces

datapythonista · 2018-03-10T19:04:57Z

pandas/core/generic.py

+        a ``Series`` to the lower/upper argument.
+
+        >>> some_data={'A':[-19, 12, -5],'B':[1, 100, -5]}
+        >>> df=pd.DataFrame(data=some_data, index=['foo', 'bar', 'bizz'])


cmdelatorre

args and kwargs are passed to validate_clip_with_axis, that is not trivial. It means that args and kwargs actually make sense. These should be clear in the docstring. Check here: https://github.com/pandas-dev/pandas/blob/master/pandas/compat/numpy/function.py#L137

jreback · 2018-03-10T19:47:07Z

pandas/core/generic.py

+
+        Notes
+        -----
+        Clipping data is a method for dealing with dubious elements.


prefer, out-of-range to dubious

jreback · 2018-03-10T19:47:17Z

pandas/core/generic.py

+        See Also
+        --------
+        pandas.DataFrame.clip_upper : Return copy of input with values
+            above given value(s) truncated.


add Series.clip

this is in generic, isn't it being reused by Series.clip?

jreback · 2018-03-10T19:47:42Z

pandas/core/generic.py

+        of removing outliers from data.  Columns of a DataFrame can be
+        winsorized by using clip.
+
+        >>> import numpy as np


don't need the numpy import

jreback · 2018-03-10T19:47:58Z

pandas/core/generic.py

+        >>> import numpy as np
+        >>> x=np.random.normal(size=(1000,3))
+        >>> df=pd.DataFrame(x, columns=['a','b','c'])
+        >>> #Winsorize columns at 5% and 95%


you can add this as part of the text above

jreback · 2018-03-10T19:48:05Z

pandas/core/generic.py

+        >>> x=np.random.normal(size=(1000,3))
+        >>> df=pd.DataFrame(x, columns=['a','b','c'])
+        >>> #Winsorize columns at 5% and 95%
+        >>> U=df.quantile(0.95)


spaces around equals

Dpananos · 2018-03-10T20:45:27Z

@cmdelatorre validate_clip_with_axis is going over my head. I'm not sure what validate_clip_with_axis does. Open to suggestions on what to add to docstring, or would appreciate reading suggestions.

Possible documentation for args and kwargs

datapythonista

I really like this contribution. Made some comments.

datapythonista · 2018-03-10T22:32:06Z

pandas/core/generic.py

            Align object with lower and upper along the given axis.
        inplace : boolean, default False
            Whether to perform the operation in place on the data
-                .. versionadded:: 0.21.0
+                .. versionadded:: 0.21.0.


I find the description of axis a bit complex.

datapythonista · 2018-03-10T22:33:42Z

pandas/core/generic.py

-                .. versionadded:: 0.21.0
+                .. versionadded:: 0.21.0.
+        *args : arguments passed to pandas.compat.numpy
+        **kwargs : keyword arguments passed to pandas.compat.numpy


It's not in the documentation yet, but after some discussion some minutes ago, we'll have *args, **kwargs in a single line (no colon), and a single description in the next, as it's rarely different between them.

datapythonista · 2018-03-10T22:35:05Z

pandas/core/generic.py

-        clipped : Series
+        clipped : `Series` or `DataFrame`.
+            Elements above or below the upper and lower thresholds converted to
+            threshold values.


We can just have the type in the first row of Returns, providing a name doesn't add much value.

The description sounds a bit like if we could be returning only part of the original values.

Sure thing.

datapythonista · 2018-03-10T22:37:01Z

pandas/core/generic.py

+        -----
+        Clipping data is a method for dealing with out-of-range elements.
+        If some elements are too large or too small, clipping is one way to
+        transform the data into a reasonable range.


This sounds more like part of the extended summary to me, than Notes, which is usually left for details on the implementaiton (e.g. calling this function makes a copy of the data)

Should I just remove it then?

I think it's an interesting comment. May be you can also mention about the outlier thing you show in the example. But I'd have it in the extended summary. After moving it, make sure the whole summary makes sense and doesn't sound repetitive. Usually happens when you move blocks.

datapythonista · 2018-03-10T22:40:17Z

pandas/core/generic.py

+        See Also
+        --------
+        pandas.DataFrame.clip_upper : Return copy of input with values
+            above given value(s) truncated.


this is in generic, isn't it being reused by Series.clip?

datapythonista · 2018-03-10T22:41:50Z

pandas/core/generic.py

+        a ``Series`` to the lower/upper argument.
+
+        >>> some_data = {'A': [-19, 12, -5], 'B': [1, 100, -5]}
+        >>> df = pd.DataFrame(data=some_data, index=['foo', 'bar', 'bizz'])


I would create df just once at the beginning (with 2 cols), and reuse it all the time.

I've usually seen foo, bar and foobar, not important, but I'd use that unless there is another convention that uses bizz

datapythonista · 2018-03-10T22:43:49Z

pandas/core/generic.py

+        bizz -5  -5
+
+        Use the axis argument to clip by column or rows.  Clip column A with
+        lower threshold of -10 and column B has lower threshold of 10.


two spaces after the dot?

Yep, bad habit.

datapythonista · 2018-03-10T22:53:04Z

pandas/core/generic.py

+        >>> x = np.random.normal(size=(1000,3))
+        >>> U = df.quantile(0.95)
+        >>> L = df.quantile(0.5)
+        >>> winsorized_df = df.clip(lower=L, upper=U, axis=1)


I like having this here. But couple of thoughts (feel free to disagree in both, they are open questions):

Should we add quantile to See Also, if we consider this a common case?

I'd probably like more to use the previous df even if this makes more sense with normally distributed data

Regardless of these two points, this example has some typos:

Missing space after the comma in the size tuple

x is defined but later you're using df

I don't see a reason to use capital letters for L and U, I don't see them as constants even if they are not modified again in the example

I would use descriptive names, not a single letter

Makes more sense to define lower first

I'll work on these tomorrow and update the PR then. Thanks for the feedback.

pep8speaks · 2018-03-11T04:26:59Z

Hello @Dpananos! Thanks for updating the PR.

Cheers ! There are no PEP8 issues in this Pull Request. 🍻

Comment last updated on March 13, 2018 at 15:29 Hours UTC

datapythonista

Looking great, I few more comments.

datapythonista · 2018-03-11T10:11:36Z

pandas/core/generic.py

+        *args : Additional keywords have no effect but might be accepted
+            for compatibility with numpy.
+        **kwargs : Additional keywords have no effect but might be accepted
+            for compatibility with numpy.


It was some change to the convention during the sprint, and args and kwargs should be in the same line *args, **kwargs and a common description in the next line. We didn't find any case where args and kwargs have different description, and it's repetitive the way it was originally defined.

Also, note that *args are positional arguments, I don't think it's correct to name they keywords.

datapythonista · 2018-03-11T10:13:25Z

pandas/core/generic.py

-        clipped : Series
+        `Series` or `DataFrame`.
+            DataFrame is returned with those values above/below the
+            `upper`/`'lower` thresholds set to the threshold values.


If return can be Series, the description is not accurate, saying the DataFrame is returned. May be something like "Original input with values above/below..."?

datapythonista · 2018-03-11T10:15:28Z

pandas/core/generic.py

+
+        See Also
+        --------
+        pandas.Series.clip : Trim values at input threshold(s).



I think the prefix pandas is not needed, just Series.clip. Also, not sure if that was already discussed, but don't we want clip_lower and clip_upper here?

I think you previously mentioned that those were generic. Did we want them included still?

Sorry, I think I wasn't clear enough. You should check it, but as this docstring is in generic.py I assume it'll be used by both Series and DataFrame. That's why I said it was (the docstring) was generic.

So, as your assigned docstring was DataFrame.clip (and it's what it's in the title of the PR) but you're actually working also in Series.clip, I don't think it makes sense to only add Series.clip as @jreback suggested. I'd say that we probably want also DataFrame.clip. Even if it'll be a bit weird to have in the See Also the same method which is being documented, but I think it's all right. In a separate PR we could use a template and just have the right one.

So, summarizing, the see also should contain Series.clip, DataFrame.clip, and the clip_lower and clip_upper for each of them (I assume both classes have it). And assuming that clip is commonly used with quantile as in your example (I don't know), I would also add it.

@jreback do you agree?

Ah, yes I see, I was mistaken. Sure, I can add all those.

datapythonista · 2018-03-11T10:16:55Z

pandas/core/generic.py


        Examples
        --------
+        >>> some_data = {'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9001]}
+        >>> df = pd.DataFrame(some_data, index = ['foo', 'bar', 'foobar'])


The spaces around = are not PEP-8 compliant. Sorry the validation script doesn't check it yet.

datapythonista · 2018-03-11T10:25:50Z

pandas/core/generic.py

+            a   b   c
+        foo 1   4   7
+        bar 2   5   8
+        foobar  3   6   9


I think this example could be more concise, and help people understand the concept faster.

I think 2 columns is enough, have 3 doesn't add much value, but takes some extra time to check what's going on with the example.

Also, the lower trimming by 1 not having effect, makes me a bit confused, on whether I'm understanding it or not. Also it's somethimg really minor, but I found the 9001 unnecessarily big and arbitrary (using 100 wouldn't make me thing there is any special on it..

Feel free to use the example you want, but using something like -1, -2, -100, 1, 2, 100 and trimming at -10 and 10 would make it more clear and straight to the point.

datapythonista · 2018-03-11T10:26:22Z

pandas/core/generic.py

+        a ``Series`` to the lower/upper argument. Use the axis argument to clip
+        by column or rows.
+
+        >>> col_thresh = pd.Series({'a':4, 'b':5, 'c':6})


spaces after colons for PEP-8

datapythonista · 2018-03-11T10:30:50Z

pandas/core/generic.py

+
+        >>> lwr_thresh = df.quantile(0.05)
+        >>> upr_thresh = df.quantile(0.95)
+        >>> dfw = df.clip(lower=lwr_thresh, upper=upr_thresh, axis='columns')


as you're using df now, I think you should show the output, instead of saving it to a variable.

Also, it's more a personal taste, feel free to ignore the commend, but these abbreviations feel a bit cryptic. lower_threshold would be better, but I think just lower it's probably even better.

And just an idea, in case you like it (it's how I'd write it and find more readable, but feel free to leave it as it):

>>> lower, upper = df.quantile(0.05), df.quantile(0.95)

PEP8

datapythonista

Looks great, just a small PEP-8 issue, and I think it's perfect.

datapythonista · 2018-03-11T22:17:57Z

pandas/core/generic.py


        Examples
        --------
+        >>> some_data = {'a': [-1, -2, -100], 'b': [1, 2, 100]}
+        >>> df = pd.DataFrame(some_data, index = ['foo', 'bar', 'foobar'])


PEP-8 issue with spaces around =.

Also, as you need to change this, I'd directly have the dictionary in the constructor, and avoid creating some_data. Just for the sake of consistency, as all examples I've seen use this way. With one key of the dictionary in one line, and the index in a third one, I think line width should be a problem.

datapythonista · 2018-03-11T22:21:13Z

pandas/core/generic.py

+        the 5th and 95th percentiles.
+
+        >>> lower, upper = df.quantile(0.05), df.quantile(0.95)
+        >>> df.clip(lower=lower, upper=upper, axis='columns')


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?

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.

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

datapythonista

lgtm.

Really nice documentation, I find it very useful.

Dpananos

Edited

jreback · 2018-03-13T00:18:18Z

pandas/core/generic.py

+        DataFrame.clip : Trim values at input threshold(s).
+        Series.clip : Trim values at input threshold(s).
+        Series.clip_lower : Return copy of the input with values below given
+            value(s) truncated.


would just mention Series.clip here (not upper/lower)

Why don't you find clip_upper and clip_lower related? I suggested them, but actually I was wondering if I was missing something, or if it was being considered deprecating them. It seems to me that clip(upper=X) could be used for clip_upper.

going to deprecate lower/upper but that’s not the reason
when showing a DataFrame method only like to show 1 Series method and and any related DataFrame method

just a preference to not have too many things in See Also

Makes sense to me.

But if I'm not wrong, the title is misleading but this docstring is for the Series.clip method too.

ahh ok in that case this is ok

jreback · 2018-03-13T00:18:51Z

pandas/core/generic.py

-        3  0.230930  0.000000
-        4  1.100000  0.570967
+            a   b
+        foo -1  1


alignment doesn’t look right here

jreback · 2018-03-13T00:19:33Z

pandas/core/generic.py

+        foobar  10  100
+
+        `Winsorizing <https://en.wikipedia.org/wiki/Winsorizing>`__ is a
+        related method, whereby the data are clipped at


put this reference in the Notes section

Do you want me to move the entire part about winsorization or just the link?

Just the link, you can see the format in the 13. References section of this document: http://numpydoc.readthedocs.io/en/latest/format.html

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datapythonista · 2018-03-13T15:25:26Z

pandas/core/generic.py

@@ -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


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

Yea, I agree. Done.

datapythonista

lgtm

jorisvandenbossche · 2018-03-15T14:38:36Z

@Dpananos I merged the other PR that also edited the same docstring (#20256). This will cause conflicts for this one, so you will need to resolve them (sorry for that trouble, but wanted to acknowledge both PRs, and yours adds some extra than the other).

If you need any help with updating the PR (merge upstream/master), let me know!

Dpananos · 2018-03-15T14:54:31Z

@jorisvandenbossche I'm not really certain what happened. So you merged another docstring and now I have a conflict? How should I go about updating the PR?

jorisvandenbossche · 2018-03-15T15:17:29Z

So to update the PR with the latest changes on master, you can do:

git checkout docstring_clip
git fetch upstream
git merge upstream/master

This will give conflicts in the generic.py file.

Solving these conflicts will mean merging somehow the two docstring (you can just use your judgement on which of the two versions you find best for each element of the docstring, certainly your examples were more elaborate I think).
Once you solved the conflicts, you need to do:

git add pandas/core/generic.py
git commit

and push again to the branch on origin.

I would say: try it out, and if you have troubles with, I can also do it.

Dpananos added 9 commits March 10, 2018 10:52

docstring for clip

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Added winsoriztion example.

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DOC: update the docstring of pandas.Series.clip #20256

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Possible documentation for args and kwargs

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datapythonista mentioned this pull request Mar 11, 2018

DOC: update the pandas.DataFrame.clip_lower docstring #20289

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Dpananos closed this Mar 15, 2018

Dpananos deleted the docstring_clip branch March 15, 2018 15:23

Dpananos mentioned this pull request Mar 15, 2018

DOC: update the pandas.DataFrame.clip docstring #20368

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

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

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Dpananos commented Mar 10, 2018 • edited Loading

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pep8speaks commented Mar 11, 2018 • edited Loading

Comment last updated on March 13, 2018 at 15:29 Hours UTC

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