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BUG/TST: fix and test for timezone drop in GroupBy.shift/bfill/ffill #27992

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merged 7 commits into from
Sep 9, 2019
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BUG/TST: fix and test for timezone drop in GroupBy.shift/bfill/ffill #27992

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8b38d7a
BUG/TST: fix and test for timezone drop in GroupBy.shift/bfill/ffill
noritada Aug 17, 2019
887c6e7
Fill issue number placeholders with a PR number (#27992)
noritada Aug 18, 2019
0812ccf
Relocate a test function as suggested by Matthew Roeschke (@mroeschke)
noritada Aug 19, 2019
43b42d5
Parameterize methods as per a suggestion from Matthew Roeschke (@mroe…
noritada Aug 19, 2019
b5a3735
Use a predefined fixture function for tz parameterization thanks to @…
noritada Aug 19, 2019
4b93034
Merge with master and move whatsnew entry to v0.25.2
noritada Sep 9, 2019
6eb0d28
Document additionally an existing issue number #19995 for GroupBy.shift
noritada Sep 9, 2019
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.25.2.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -78,6 +78,7 @@ Groupby/resample/rolling
^^^^^^^^^^^^^^^^^^^^^^^^

- Bug incorrectly raising an ``IndexError`` when passing a list of quantiles to :meth:`pandas.core.groupby.DataFrameGroupBy.quantile` (:issue:`28113`).
- Bug in :meth:`pandas.core.groupby.GroupBy.shift`, :meth:`pandas.core.groupby.GroupBy.bfill` and :meth:`pandas.core.groupby.GroupBy.ffill` where timezone information would be dropped (:issue:`19995`, :issue:`27992`)
-
-
-
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12 changes: 7 additions & 5 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -2263,26 +2263,28 @@ def _get_cythonized_result(
base_func = getattr(libgroupby, how)

for name, obj in self._iterate_slices():
values = obj._data._values
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Hmm I'm not sure relying on underlying block values is the best way to go about this. Is it possible to just work with the obj here instead?

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Sorry for my lack of understanding about underlying block values.
Should I leave the code using obj.values except for the last result = algorithms.take_nd(obj.values, result)?


if aggregate:
result_sz = ngroups
else:
result_sz = len(obj.values)
result_sz = len(values)

if not cython_dtype:
cython_dtype = obj.values.dtype
cython_dtype = values.dtype

result = np.zeros(result_sz, dtype=cython_dtype)
func = partial(base_func, result, labels)
inferences = None

if needs_values:
vals = obj.values
vals = values
if pre_processing:
vals, inferences = pre_processing(vals)
func = partial(func, vals)

if needs_mask:
mask = isna(obj.values).view(np.uint8)
mask = isna(values).view(np.uint8)
func = partial(func, mask)

if needs_ngroups:
Expand All @@ -2291,7 +2293,7 @@ def _get_cythonized_result(
func(**kwargs) # Call func to modify indexer values in place

if result_is_index:
result = algorithms.take_nd(obj.values, result)
result = algorithms.take_nd(values, result)

if post_processing:
result = post_processing(result, inferences)
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66 changes: 66 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -1882,3 +1882,69 @@ def test_groupby_axis_1(group_name):
results = df.groupby(group_name, axis=1).sum()
expected = df.T.groupby(group_name).sum().T
assert_frame_equal(results, expected)


@pytest.mark.parametrize(
"op, expected",
[
(
"shift",
{
"time": [
None,
None,
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
]
},
),
(
"bfill",
{
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
]
},
),
(
"ffill",
{
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
]
},
),
],
)
def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
# GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill
tz = tz_naive_fixture
data = {
"id": ["A", "B", "A", "B", "A", "B"],
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
],
}
df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz))

grouped = df.groupby("id")
result = getattr(grouped, op)()
expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz))
assert_frame_equal(result, expected)