8000 DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. by pandres · Pull Request #20374 · pandas-dev/pandas · GitHub
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DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. #20374

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Merged
merged 11 commits into from
Nov 14, 2018
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DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. #20374

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59355d6
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.
pandres Mar 15, 2018
deea3c7
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring.
pandres Mar 20, 2018
2fcabf7
DOC: update pandas.core.groupby.DataFrameGroupBy.resample docstring. …
pandres Aug 22, 2018
fdce932
Merge remote-tracking branch 'upstream/master' into docstring_pandas.…
datapythonista Nov 3, 2018
4b55b5f
Minor fixes
datapythonista Nov 3, 2018
e7905b2
Replace Offset with DateOffset.
pandres Nov 4, 2018
a0c5329
Merge branch 'docstring_pandas.core.groupby.DataFrameGroupBy.resample…
pandres Nov 4, 2018
4880d3b
Change args and kwargs description.
pandres Nov 11, 2018
084ac39
Change args and kwargs description.
pandres Nov 11, 2018
73b480b
Merge remote-tracking branch 'upstream/master' into docstring_pandas.…
Nov 13, 2018
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Merge remote-tracking branch 'upstream/master' into docstring_pandas.…
Nov 13, 2018
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Minor fixes
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@datapythonista
datapythonista committed Nov 3, 2018
commit 4b55b5fd51447c43ab380cd7027860994c77bec9
27 changes: 12 additions & 15 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -1294,13 +1294,11 @@ def describe(self, **kwargs):
return result.T
return result.unstack()

@Substitution(name='groupby')
@Appender(_doc_template)
def resample(self, rule, *args, **kwargs):
"""
Provide resampling when using a TimeGrouper.

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

See the :ref:`frequency aliases <timeseries.offset-aliases>`
Expand All @@ -1310,15 +1308,16 @@ def resample(self, rule, *args, **kwargs):
----------
rule : str or Offset
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Offset --> DateOffset

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.
*args, **kwargs
For compatibility with other groupby methods. Available keywor
arguments are:

* closed : {'right', 'left'}
Which side of bin interval is closed.
* label : {'right', 'left'}
Which bin edge label to label bucket with.
* loffset : timedelta
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is "loffset" right? I don't know this section of the code all that well.

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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?

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

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@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!

Adjust the resampled time labels.

Returns
-------
Expand All @@ -1327,15 +1326,13 @@ def resample(self, rule, *args, **kwargs):

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

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,
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