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BUG: Unable to pass additional arguments to resample().apply() #14635

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BUG: Unable to pass additional arguments to resample().apply() #14635

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16 changes: 13 additions & 3 deletions pandas/tseries/resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -319,9 +319,19 @@ def aggregate(self, arg, *args, **kwargs):
self._set_binner()
result, how = self._aggregate(arg, *args, **kwargs)
if result is None:
return self._groupby_and_aggregate(arg,
*args,
**kwargs)
# It seems there is an ambiguity in having both named args
# and variadic in the same function, so we need to distinguish here,
# otherwise the first element of args is "eaten" by grouper in
# _groupby_and_aggregate.
if len(args) == 0:
return self._groupby_and_aggregate(arg,
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no you need to figure out the reason and not put in hacks
if u can't then can simply construct the args (you make a tuple) and pass to the function
but would like to see where it's not getting passed thru

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Sorry, I disappeared for a long time!
Yes, I know it was a hack and I agree it's not a real solution... but I don't know the internals of the code, so I'm not sure I can figure this out...

The problem in my opinion is the definition of the function itself: it seems like you cannot have a function with a named argument with a default value, immediately followed by a variadic part, because the interpreter has no idea where to put the first argument (in the named one or as the first parameter of the variadic?).

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I think that *args can be removed from _groupby_and_aggregate. this should solve the issue I think. IOW, you can only pass kwargs except for named args. The grouper arg is really internally anyhow.

*args,
**kwargs)
else:
return self._groupby_and_aggregate(arg,
None,
*args,
**kwargs)

return result

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11 changes: 11 additions & 0 deletions pandas/tseries/tests/test_resample.py
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Expand Up @@ -2039,6 +2039,17 @@ def test_resample_datetime_values(self):
res = df['timestamp'].resample('2D').first()
tm.assert_series_equal(res, exp)

def test_resample_additional_args(self):
# Issue 14615

rng = date_range('1/1/2011', periods = 72, freq = 'H')
rng_exp = date_range('1/1/2011', periods = 3, freq = 'D')
vals = np.tile(np.arange(0, 24), 3)
ts = Series(vals, index = rng)
ts_exp = Series([286, 286, 286], index = rng_exp)
res = ts.resample("D").apply(lambda x, y: np.sum(x) + y, 10)
tm.assert_series_equal(res, ts_exp)


class TestPeriodIndex(Base, tm.TestCase):
_multiprocess_can_split_ = True
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