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

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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/v0.25.1.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -120,6 +120,7 @@ Groupby/resample/rolling
- Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` where applying a timezone conversion lambda function would drop timezone information (:issue:`27496`)
- Bug in windowing over read-only arrays (:issue:`27766`)
- Fixed segfault in `pandas.core.groupby.DataFrameGroupBy.quantile` when an invalid quantile was passed (:issue:`27470`)
- 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:`xxxxx`)
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If there is not an existing issue for this bug, use the number of this PR.

-

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12 changes: 7 additions & 5 deletions pandas/core/groupby/groupby.py
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Expand Up @@ -2220,26 +2220,28 @@ 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 @@ -2248,7 +2250,7 @@ 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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112 changes: 112 additions & 0 deletions pandas/tests/groupby/test_timezone.py
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@@ -0,0 +1,112 @@
import pytest

from pandas import DataFrame, Timestamp
from pandas.util.testing import assert_frame_equal


@pytest.mark.parametrize(
"data, expected_shift, expected_bfill, expected_ffill",
[
(
{
"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"),
],
},
{
"time": [
None,
None,
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
]
},
{
"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"),
]
},
{
"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"),
]
},
),
(
{
"id": ["A", "B", "A", "B", "A", "B"],
"time": [
Timestamp("2019-01-01 12:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 12:30:00", tz="Asia/Tokyo"),
None,
None,
Timestamp("2019-01-01 14:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:30:00", tz="Asia/Tokyo"),
],
},
{
"time": [
None,
None,
Timestamp("2019-01-01 12:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 12:30:00", tz="Asia/Tokyo"),
None,
None,
]
},
{
"time": [
Timestamp("2019-01-01 12:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 12:30:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:30:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:30:00", tz="Asia/Tokyo"),
]
},
{
"time": [
Timestamp("2019-01-01 12:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 12:30:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 12:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 12:30:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:00:00", tz="Asia/Tokyo"),
Timestamp("2019-01-01 14:30:00", tz="Asia/Tokyo"),
]
},
),
],
)
def test_shift_bfill_ffill_tz(data, expected_shift, expected_bfill, expected_ffill):
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Can you put this test in test_groupby.py

Also you can parameterize over shift, bfill, and ffill

# GHxxxxx: Check that timezone does not drop in shift, bfill, and ffill
df = DataFrame(data)

result = df.groupby("id").shift()
expected = DataFrame(expected_shift)
assert_frame_equal(result, expected)

result = df.groupby("id").bfill()
expected = DataFrame(expected_bfill)
assert_frame_equal(result, expected)

result = df.groupby("id").ffill()
expected = DataFrame(expected_ffill)
assert_frame_equal(result, expected)