DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. #20374

pandres · 2018-03-15T23:18:12Z

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:

################################################################################
########## Docstring (pandas.core.groupby.DataFrameGroupBy.resample)  ##########
################################################################################

Provide resampling when using a TimeGrouper.

Given a grouper the function resamples it according to a string
"string" -> "frequency".

See the :ref:`frequency aliases <timeseries.offset-aliases>`
documentation for more details.

Parameters
----------
rule : str or Offset
    The offset string or object representing target grouper conversion.
*args, **kwargs : [closed, label, loffset]
    For compatibility with other groupby methods. See below for some
    example parameters.
closed : {‘right’, ‘left’}
    Which side of bin interval is closed.
label : {‘right’, ‘left’}
    Which bin edge label to label bucket with.
loffset : timedelta
    Adjust the resampled time labels.

Returns
-------
Grouper
    Return a new grouper with our resampler appended.

Examples
--------
Start by creating a length-4 DataFrame with minute frequency.

>>> idx = pd.date_range('1/1/2000', periods=4, freq='T')
>>> df = pd.DataFrame(data=4 * [range(2)],
...                   index=idx,
...                   columns=['a', 'b'])
>>> df.iloc[2, 0] = 5
>>> df
                     a  b
2000-01-01 00:00:00  0  1
2000-01-01 00:01:00  0  1
2000-01-01 00:02:00  5  1
2000-01-01 00:03:00  0  1

Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.

>>> df.groupby('a').resample('3T').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  2
    2000-01-01 00:03:00  0  1
5   2000-01-01 00:00:00  5  1

Upsample the series into 30 second bins.

>>> df.groupby('a').resample('30S').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  1
    2000-01-01 00:00:30  0  0
    2000-01-01 00:01:00  0  1
    2000-01-01 00:01:30  0  0
    2000-01-01 00:02:00  0  0
    2000-01-01 00:02:30  0  0
    2000-01-01 00:03:00  0  1
5   2000-01-01 00:02:00  5  1

Resample by month. Values are assigned to the month of the period.

>>> df.groupby('a').resample('M').sum()
                a  b
a
0   2000-01-31  0  3
5   2000-01-31  5  1

Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.

>>> df.groupby('a').resample('3T', closed='right').sum()
                         a  b
a
0   1999-12-31 23:57:00  0  1
    2000-01-01 00:00:00  0  2
5   2000-01-01 00:00:00  5  1

Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.

>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  1
    2000-01-01 00:03:00  0  2
5   2000-01-01 00:03:00  5  1

Add an offset of twenty seconds.

>>> df.groupby('a').resample('3T', loffset='20s').sum()
                         a  b
a
0   2000-01-01 00:00:20  0  2
    2000-01-01 00:03:20  0  1
5   2000-01-01 00:00:20  5  1


See also
--------
pandas.Series.groupby
pandas.DataFrame.groupby
pandas.Panel.groupby

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

Errors found:
	Use only one blank line to separate sections or paragraphs
	Errors in parameters section
		Parameters {'args', 'kwargs'} not documented
		Unknown parameters {'*args, **kwargs', 'loffset', 'label', 'closed'}

args and kwargs are described with asterisks as requested, even if the validator does not recognizes it.

See also complains of duplication if left as in the commit in the docstring. Not showed above.

TomAugspurger · 2018-03-16T11:12:43Z

pandas/core/groupby.py

-        Return a new grouper with our resampler appended
+        Provide resampling when using a TimeGrouper.
+
+        Given a grouper the function resamples it according to a string and an


"string" -> "frequency" and link to the frequency aliases:

:ref:`frequency aliases <timeseries.offset_aliases>`

TomAugspurger · 2018-03-16T11:13:16Z

pandas/core/groupby.py

+        Provide resampling when using a TimeGrouper.
+
+        Given a grouper the function resamples it according to a string and an
+        optional list and dictionary of parameters. Returns a new grouper with


"optional list and dictionary of parameters" doesn't mean much to me as a user. I'd say remove that bit.

TomAugspurger · 2018-03-16T11:14:07Z

pandas/core/groupby.py

+
+        Parameters
+        ----------
+        rule : str


rule : str or Offset I think. @jorisvandenbossche does that look OK for Offset, instead of pandas.core.offsets.Offset? We used just Offset in a couple places.

TomAugspurger · 2018-03-16T11:15:41Z

pandas/core/groupby.py

+        ----------
+        rule : str
+            The offset string or object representing target grouper conversion.
+        *args


This feels a bit too specific to the implementation to me. Do you have any specific args / kwargs that the user would care about? Otherwise, I'd just write it as

*args, **kwargs For compatibility with other groupby methods.

Some keywords to think about (explicitly document and show examples for)?

closed : {'right', 'left'} label : {'right', 'left'} loffset : timedelta

See pandas.core.generic.NDFrame.resample for some others that may or may not have an effect. I'm not sure.

TomAugspurger · 2018-03-16T11:18:04Z

pandas/core/groupby.py

+        -------
+        Grouper
+            Return a new grouper with our resampler appended.
+


See Also -------- pandas.Grouper : specify a frequency to resample with when grouping by a key. DatetimeIndex.resample : Frequency conversion and resampling of time series.

TomAugspurger · 2018-03-16T11:19:15Z

pandas/core/groupby.py

+
+        Examples
+        --------
+        Start by creating a DataFrame with 9 one minute timestamps.


"9 one" is a bit awkward. Maybe "a length-9 DataFrame with minute frequency."

TomAugspurger · 2018-03-16T11:19:30Z

pandas/core/groupby.py

+        Start by creating a DataFrame with 9 one minute timestamps.
+
+        >>> idx = pd.date_range('1/1/2000', periods=9, freq='T')
+        >>> df = pd.DataFrame(data=9*[range(4)],


PEP8: space around *.

TomAugspurger · 2018-03-16T11:20:03Z

pandas/core/groupby.py

+        >>> df = pd.DataFrame(data=9*[range(4)],
+        ...                   index=idx,
+        ...                   columns=['a', 'b', 'c', 'd'])
+        >>> df.iloc[[6], [0]] = 5  # change a value for grouping


Don't need the inner lists, just df.iloc[6, 0] = 5. Don't need the comment.

codecov · 2018-04-03T19:57:31Z

Codecov Report

Merging #20374 into master will decrease coverage by <.01%.
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pandas/core/groupby/groupby.py	`96.5% <ø> (-0.01%)`	⬇️
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pandres · 2018-04-04T19:52:38Z

################################################################################
########### Docstring (pandas.core.groupby.groupby.GroupBy.resample) ###########
################################################################################

Provide resampling when using a TimeGrouper.

Given a grouper the function resamples it according to a string
"string" -> "frequency".

See the :ref:`frequency aliases <timeseries.offset-aliases>`
documentation for more details.

Parameters
----------
rule : str or Offset
    The offset string or object representing target grouper conversion.
*args, **kwargs
    For compatibility with other groupby methods. See below for some
    example parameters.
closed : {‘right’, ‘left’}
    Which side of bin interval is closed.
label : {‘right’, ‘left’}
    Which bin edge label to label bucket with.
loffset : timedelta
    Adjust the resampled time labels.

Returns
-------
Grouper
    Return a new grouper with our resampler appended.

Examples
--------
Start by creating a length-9 DataFrame with minute frequency.

>>> idx = pd.date_range('1/1/2000', periods=9, freq='T')
>>> df = pd.DataFrame(data=9 * [range(4)],
...                   index=idx,
...                   columns=['a', 'b', 'c', 'd'])
>>> df.iloc[6, 0] = 5
>>> df
                     a  b  c  d
2000-01-01 00:00:00  0  1  2  3
2000-01-01 00:01:00  0  1  2  3
2000-01-01 00:02:00  0  1  2  3
2000-01-01 00:03:00  0  1  2  3
2000-01-01 00:04:00  0  1  2  3
2000-01-01 00:05:00  0  1  2  3
2000-01-01 00:06:00  5  1  2  3
2000-01-01 00:07:00  0  1  2  3
2000-01-01 00:08:00  0  1  2  3

Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.

>>> df.groupby('a').resample('3T').sum()
                         a  b  c  d
a
0   2000-01-01 00:00:00  0  3  6  9
    2000-01-01 00:03:00  0  3  6  9
    2000-01-01 00:06:00  0  2  4  6
5   2000-01-01 00:06:00  5  1  2  3

Upsample the series into 30 second bins.

>>> df.groupby('a').resample('30S').sum()
                         a  b  c  d
a
0   2000-01-01 00:00:00  0  1  2  3
    2000-01-01 00:00:30  0  0  0  0
    2000-01-01 00:01:00  0  1  2  3
    2000-01-01 00:01:30  0  0  0  0
    2000-01-01 00:02:00  0  1  2  3
    2000-01-01 00:02:30  0  0  0  0
    2000-01-01 00:03:00  0  1  2  3
    2000-01-01 00:03:30  0  0  0  0
    2000-01-01 00:04:00  0  1  2  3
    2000-01-01 00:04:30  0  0  0  0
    2000-01-01 00:05:00  0  1  2  3
    2000-01-01 00:05:30  0  0  0  0
    2000-01-01 00:06:00  0  0  0  0
    2000-01-01 00:06:30  0  0  0  0
    2000-01-01 00:07:00  0  1  2  3
    2000-01-01 00:07:30  0  0  0  0
    2000-01-01 00:08:00  0  1  2  3
5   2000-01-01 00:06:00  5  1  2  3

Resample by month. Values are assigned to the month of the period.

>>> df.groupby('a').resample('M').sum()
                a  b   c   d
a
0   2000-01-31  0  8  16  24
5   2000-01-31  5  1   2   3

Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.

>>> df.groupby('a').resample('3T', closed='right').sum()
                         a  b  c  d
a
0   1999-12-31 23:57:00  0  1  2  3
    2000-01-01 00:00:00  0  3  6  9
    2000-01-01 00:03:00  0  2  4  6
    2000-01-01 00:06:00  0  2  4  6
5   2000-01-01 00:03:00  5  1  2  3

Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.

>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
                         a  b  c  d
a
0   2000-01-01 00:00:00  0  1  2  3
    2000-01-01 00:03:00  0  3  6  9
    2000-01-01 00:06:00  0  2  4  6
    2000-01-01 00:09:00  0  2  4  6
5   2000-01-01 00:06:00  5  1  2  3

Add an offset of twenty seconds.

>>> df.groupby('a').resample('3T', loffset='20s').sum()
                         a  b  c  d
a
0   2000-01-01 00:00:20  0  3  6  9
    2000-01-01 00:03:20  0  3  6  9
    2000-01-01 00:06:20  0  2  4  6
5   2000-01-01 00:06:20  5  1  2  3

See Also
--------
pandas.Grouper : specify a frequency to resample with when
    grouping by a key.
DatetimeIndex.resample : Frequency conversion and resampling of
    time series.

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

Errors found:
	Use only one blank line to separate sections or paragraphs
	Errors in parameters section
		Parameters {'args', 'kwargs'} not documented
		Unknown parameters {'loffset', 'label', 'closed', '*args, **kwargs'}
		Parameter "*args, **kwargs" has no type

Better summary and parameters description.

pandres · 2018-04-04T20:13:14Z

I'm confused about the 'See also' section. It auto-generates the message:

See also
--------
pandas.Series.groupby
pandas.DataFrame.groupby
pandas.Panel.groupby

With the errors:
Missing description for See Also "pandas.Series.groupby" reference
Missing description for See Also "pandas.DataFrame.groupby" reference
Missing description for See Also "pandas.Panel.groupby" reference

I've replaced it with the commentary noted above in the responses.

datapythonista · 2018-07-22T00:47:47Z

pandas/core/groupby/groupby.py

+        label : {‘right’, ‘left’}
+            Which bin edge label to label bucket with.
+        loffset : timedelta
+            Adjust the resampled time labels.


These parameters are not in the signature, are they the possible kwargs? If that's the case, we can add them as a list in the kwargs description.

OK, the are just some samples of the kwargs, as they were requested.

pandas/core/groupby/groupby.py

datapythonista · 2018-07-22T00:51:18Z

pandas/core/groupby/groupby.py

+        2000-01-01 00:05:00  0  1  2  3
+        2000-01-01 00:06:00  5  1  2  3
+        2000-01-01 00:07:00  0  1  2  3
+        2000-01-01 00:08:00  0  1  2  3


Do you think it'd be possible to use a more compact example. May be 4 rows of 1 minute intervals, that can be downsampled to 2 rows of 30 seconds? Also, I think 2 columns should be enough.

…Smaller sample. Kwargs review. See also review.

…core.groupby.DataFrameGroupBy.resample

pep8speaks · 2018-11-03T06:02:56Z

Hello @pandres! Thanks for updating the PR.

There are no PEP8 issues in the file pandas/core/groupby/groupby.py !

datapythonista

Pushed some minor fixes.

@jbrockmendel can you take a quick look and merge on green if you're happy?

jbrockmendel · 2018-11-03T22:08:52Z

pandas/core/groupby/groupby.py

+
+        Parameters
+        ----------
+        rule : str or Offset


Offset --> DateOffset

pandas/core/groupby/groupby.py

jbrockmendel · 2018-11-03T22:09:38Z

pandas/core/groupby/groupby.py

+                 Which side of bin interval is closed.
+               * label : {'right', 'left'}
+                 Which bin edge label to label bucket with.
+               * loffset : timedelta


is "loffset" right? I don't know this section of the code all that well.

Not an expert myself, I guess the type should be DateOffset, tseries.offsets, timedelta, or str?

For what I can see, valid keywords should be how, fill_method, limit, kind and on (in get_resampler_for_grouping), closed, label, how, axis, fill_method, limit, loffset, kind, convention, base (in TimeGrouper), and key, level, freq, axis, sort (in Grouper).

What I'd do is to add the ones from get_resampler_for_grouping as explicit arguments, and then document that **kwargs will be passed to TimeGrouper.

@jreback is it ok to change the signature?

level, axis, freq, key, sort are all part of the grouper and not args to .resample() or any aggregation function.

@pandres can you replace the description by something like:

Possible arguments are `how`, `fill_method`, `limit`, `kind` and `on`, and other arguments of `TimeGrouper`.

We can improve that later in a separate PR, but I think we can merge all the rest of the changes for now.

Thanks!

jbrockmendel · 2018-11-03T22:10:43Z

@datapythonista I gave this a read and made some comments, but don't know this section of the code well enough to form an informed opinion.

…' of github.com:pandres/pandas into docstring_pandas.core.groupby.DataFrameGroupBy.resample

…core.groupby.DataFrameGroupBy.resample

mroeschke

LGTM. Just rebased master. I think should be good to merge on green.

…core.groupby.DataFrameGroupBy.resample

TomAugspurger · 2018-11-14T20:55:32Z

Thanks @pandres!

pandres · 2018-11-14T23:18:31Z

Thank you guys!

* upstream/master: (25 commits) DOC: Delete trailing blank lines in docstrings. (pandas-dev#23651) DOC: Change release and whatsnew (pandas-dev#21599) DOC: Fix format of the See Also descriptions (pandas-dev#23654) DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. (pandas-dev#20374) ENH: Allow export of mixed columns to Stata strl (pandas-dev#23692) CLN: Remove unnecessary code (pandas-dev#23696) Pin flake8-rst version (pandas-dev#23699) Implement _most_ of the EA interface for DTA/TDA (pandas-dev#23643) CI: raise clone depth limit on CI BUG: Fix Series/DataFrame.rank(pct=True) with more than 2**24 rows (pandas-dev#23688) REF: Move Excel names parameter handling to CSV (pandas-dev#23690) DOC: Accessing files from a S3 bucket. (pandas-dev#23639) Fix errorbar visualization (pandas-dev#23674) DOC: Surface / doc mangle_dupe_cols in read_excel (pandas-dev#23678) DOC: Update is_sparse docstring (pandas-dev#19983) BUG: Fix read_excel w/parse_cols & empty dataset (pandas-dev#23661) Add to_flat_index method to MultiIndex (pandas-dev#22866) CLN: Move to_excel to generic.py (pandas-dev#23656) TST: IntervalTree.get_loc_interval should return platform int (pandas-dev#23660) CI: Allow to compile docs with ipython 7.11 pandas-dev#22990 (pandas-dev#23655) ...

…fixed * upstream/master: DOC: Delete trailing blank lines in docstrings. (pandas-dev#23651) DOC: Change release and whatsnew (pandas-dev#21599) DOC: Fix format of the See Also descriptions (pandas-dev#23654) DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. (pandas-dev#20374) ENH: Allow export of mixed columns to Stata strl (pandas-dev#23692) CLN: Remove unnecessary code (pandas-dev#23696) Pin flake8-rst version (pandas-dev#23699) Implement _most_ of the EA interface for DTA/TDA (pandas-dev#23643) CI: raise clone depth limit on CI BUG: Fix Series/DataFrame.rank(pct=True) with more than 2**24 rows (pandas-dev#23688) REF: Move Excel names parameter handling to CSV (pandas-dev#23690) DOC: Accessing files from a S3 bucket. (pandas-dev#23639) Fix errorbar visualization (pandas-dev#23674) DOC: Surface / doc mangle_dupe_cols in read_excel (pandas-dev#23678) DOC: Update is_sparse docstring (pandas-dev#19983) BUG: Fix read_excel w/parse_cols & empty dataset (pandas-dev#23661) Add to_flat_index method to MultiIndex (pandas-dev#22866) CLN: Move to_excel to generic.py (pandas-dev#23656) TST: IntervalTree.get_loc_interval should return platform int (pandas-dev#23660)

…andas-dev#20374)

jreback added Docs Resample resample method labels Mar 16, 2018

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DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.

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DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.

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Better summary and parameters description.

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DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. …

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…Smaller sample. Kwargs review. See also review.

Merge remote-tracking branch 'upstream/master' into docstring_pandas.…

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Replace Offset with DateOffset.

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…' of github.com:pandres/pandas into docstring_pandas.core.groupby.DataFrameGroupBy.resample

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Change args and kwargs description.

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DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. #20374

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codecov bot commented Apr 3, 2018 • edited Loading

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pep8speaks commented Nov 3, 2018

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jbrockmendel commented Nov 3, 2018

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TomAugspurger commented Nov 14, 2018

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pandres commented Mar 15, 2018 •

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pandres commented Apr 4, 2018 •

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