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BUG: TimeGrouper doesnt exclude the column specified by key #7276

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Merged
merged 1 commit into from
May 30, 2014
Merged

BUG: TimeGrouper doesnt exclude the column specified by key #7276

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1 change: 1 addition & 0 deletions doc/source/v0.14.1.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -65,3 +65,4 @@ There are no experimental changes in 0.14.1
Bug Fixes
~~~~~~~~~

- Bug in ``TimeGrouper`` doesn't exclude column specified by ``key`` (:issue:`7227`)
5 changes: 4 additions & 1 deletion pandas/core/groupby.py
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Expand Up @@ -1927,7 +1927,10 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True):
# a passed in Grouper, directly convert
if isinstance(key, Grouper):
binner, grouper, obj = key._get_grouper(obj)
return grouper, [], obj
if key.key is None:
return grouper, [], obj
else:
return grouper, set([key.key]), obj

# already have a BaseGrouper, just return it
elif isinstance(key, BaseGrouper):
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26 changes: 26 additions & 0 deletions pandas/tseries/tests/test_resample.py
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Expand Up @@ -699,6 +699,32 @@ def test_resample_consistency(self):
assert_series_equal(s10_2, r10_2)
assert_series_equal(s10_2, rl)

def test_resample_timegrouper(self):
# GH 7227
dates = [datetime(2014, 10, 1), datetime(2014, 9, 3),
datetime(2014, 11, 5), datetime(2014, 9, 5),
datetime(2014, 10, 8), datetime(2014, 7, 15)]

df = DataFrame(dict(A=dates, B=np.arange(len(dates))))
result = df.set_index('A').resample('M', how='count')
exp_idx = pd.DatetimeIndex(['2014-07-31', '2014-08-31', '2014-09-30',
'2014-10-31', '2014-11-30'], freq='M', name='A')
expected = DataFrame({'B': [1, 0, 2, 2, 1]}, index=exp_idx)
assert_frame_equal(result, expected)

result = df.groupby(pd.Grouper(freq='M', key='A')).count()
assert_frame_equal(result, expected)

df = DataFrame(dict(A=dates, B=np.arange(len(dates)), C=np.arange(len(dates))))
result = df.set_index('A').resample('M', how='count')
expected = DataFrame({'B': [1, 0, 2, 2, 1], 'C': [1, 0, 2, 2, 1]},
index=exp_idx, columns=['B', 'C'])
assert_frame_equal(result, expected)

result = df.groupby(pd.Grouper(freq='M', key='A')).count()
assert_frame_equal(result, expected)


def _simple_ts(start, end, freq='D'):
rng = date_range(start, end, freq=freq)
return Series(np.random.randn(len(rng)), index=rng)
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