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BUG: avoid RuntimeError in groupby.max #46408

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Merged
merged 1 commit into from
Mar 18, 2022
Merged

BUG: avoid RuntimeError in groupby.max #46408

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69 changes: 38 additions & 31 deletions pandas/_libs/groupby.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -1047,6 +1047,7 @@ def group_last(
const uint8_t[:, :] mask,
uint8_t[:, ::1] result_mask=None,
Py_ssize_t min_count=-1,
bint is_datetimelike=False,
) -> None:
"""
Only aggregates on axis=0
Expand All @@ -1056,7 +1057,6 @@ def group_last(
iu_64_floating_obj_t val
ndarray[iu_64_floating_obj_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
bint runtime_error = False
bint uses_mask = mask is not None
bint isna_entry

Expand Down Expand Up @@ -1116,35 +1116,38 @@ def group_last(
if uses_mask:
isna_entry = mask[i, j]
else:
isna_entry = _treat_as_na(val, True)
# TODO: Sure we always want is_datetimelike=True?
isna_entry = _treat_as_na(val, is_datetimelike)

if not isna_entry:
nobs[lab, j] += 1
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
# TODO(cython3): the entire next block can be shared
# across 3 places once conditional-nogil is available
if nobs[i, j] < min_count:
# if we are integer dtype, not is_datetimelike, and
# not uses_mask, then getting here implies that
# counts[i] < min_count, which means we will
# be cast to float64 and masked at the end
# of WrappedCythonOp._call_cython_op. So we can safely
# set a placeholder value in out[i, j].
if uses_mask:
result_mask[i, j] = True
elif iu_64_floating_obj_t is int64_t:
# TODO: only if datetimelike?
# Per above, this is a placeholder in
# non-is_datetimelike cases.
out[i, j] = NPY_NAT
elif iu_64_floating_obj_t is uint64_t:
runtime_error = True
break
# placeholder, see above
out[i, j] = 0
else:
out[i, j] = NAN

else:
out[i, j] = resx[i, j]

if runtime_error:
# We cannot raise directly above because that is within a nogil
# block.
raise RuntimeError("empty group with uint64_t")


# TODO(cython3): GH#31710 use memorviews once cython 0.30 is released so we can
# use `const iu_64_floating_obj_t[:, :] values`
Expand All @@ -1159,6 +1162,7 @@ def group_nth(
uint8_t[:, ::1] result_mask=None,
int64_t min_count=-1,
int64_t rank=1,
bint is_datetimelike=False,
) -> None:
"""
Only aggregates on axis=0
Expand All @@ -1168,7 +1172,6 @@ def group_nth(
iu_64_floating_obj_t val
ndarray[iu_64_floating_obj_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
bint runtime_error = False
bint uses_mask = mask is not None
bint isna_entry

Expand Down Expand Up @@ -1230,8 +1233,7 @@ def group_nth(
if uses_mask:
isna_entry = mask[i, j]
else:
isna_entry = _treat_as_na(val, True)
# TODO: Sure we always want is_datetimelike=True?
isna_entry = _treat_as_na(val, is_datetimelike)

if not isna_entry:
nobs[lab, j] += 1
Expand All @@ -1241,25 +1243,27 @@ def group_nth(
for i in range(ncounts):
for j in range(K):
if nobs[i, j] < min_count:
# if we are integer dtype, not is_datetimelike, and
# not uses_mask, then getting here implies that
# counts[i] < min_count, which means we will
# be cast to float64 and masked at the end
# of WrappedCythonOp._call_cython_op. So we can safely
# set a placeholder value in out[i, j].
if uses_mask:
result_mask[i, j] = True
out[i, j] = 0
elif iu_64_floating_obj_t is int64_t:
# TODO: only if datetimelike?
# Per above, this is a placeholder in
# non-is_datetimelike cases.
out[i, j] = NPY_NAT
elif iu_64_floating_obj_t is uint64_t:
runtime_error = True
break
# placeholder, see above
out[i, j] = 0
else:
out[i, j] = NAN
else:
out[i, j] = resx[i, j]

if runtime_error:
# We cannot raise directly above because that is within a nogil
# block.
raise RuntimeError("empty group with uint64_t")


@cython.boundscheck(False)
@cython.wraparound(False)
Expand Down Expand Up @@ -1386,7 +1390,6 @@ cdef group_min_max(
Py_ssize_t i, j, N, K, lab, ngroups = len(counts)
iu_64_floating_t val, nan_val
ndarray[iu_64_floating_t, ndim=2] group_min_or_max
bint runtime_error = False
int64_t[:, ::1] nobs
bint uses_mask = mask is not None
bint isna_entry
Expand All @@ -1403,7 +1406,6 @@ cdef group_min_max(
group_min_or_max[:] = _get_min_or_max(<iu_64_floating_t>0, compute_max, is_datetimelike)

if iu_64_floating_t is int64_t:
# TODO: only if is_datetimelike?
nan_val = NPY_NAT
elif iu_64_floating_t is uint64_t:
# NB: We do not define nan_val because there is no such thing
Expand Down Expand Up @@ -1442,25 +1444,30 @@ cdef group_min_max(
for i in range(ngroups):
for j in range(K):
if nobs[i, j] < min_count:
# if we are integer dtype, not is_datetimelike, and
# not uses_mask, then getting here implies that
# counts[i] < min_count, which means we will
# be cast to float64 and masked at the end
# of WrappedCythonOp._call_cython_op. So we can safely
# set a placeholder value in out[i, j].
if uses_mask:
result_mask[i, j] = True
# set out[i, j] to 0 to be deterministic, as
# it was initialized with np.empty. Also ensures
# we can downcast out if appropriate.
out[i, j] = 0
elif iu_64_floating_t is int64_t:
# Per above, this is a placeholder in
# non-is_datetimelike cases.
out[i, j] = nan_val
elif iu_64_floating_t is uint64_t:
runtime_error = True
break
# placeholder, see above
out[i, j] = 0
else:
out[i, j] = nan_val
else:
out[i, j] = group_min_or_max[i, j]

if runtime_error:
# We cannot raise directly above because that is within a nogil
# block.
raise RuntimeError("empty group with uint64_t")


@cython.wraparound(False)
@cython.boundscheck(False)
Expand Down
12 changes: 1 addition & 11 deletions pandas/core/groupby/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -519,7 +519,7 @@ def _call_cython_op(
result = maybe_fill(np.empty(out_shape, dtype=out_dtype))
if self.kind == "aggregate":
counts = np.zeros(ngroups, dtype=np.int64)
if self.how in ["min", "max", "mean"]:
if self.how in ["min", "max", "mean", "last", "first"]:
func(
out=result,
counts=counts,
Expand All @@ -530,16 +530,6 @@ def _call_cython_op(
result_mask=result_mask,
is_datetimelike=is_datetimelike,
)
elif self.how in ["first", "last"]:
func(
out=result,
counts=counts,
values=values,
labels=comp_ids,
min_count=min_count,
mask=mask,
result_mask=result_mask,
)
elif self.how in ["add"]:
# We support datetimelike
func(
Expand Down
20 changes: 8 additions & 12 deletions pandas/tests/resample/test_datetime_index.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1856,15 +1856,11 @@ def test_resample_unsigned_int(any_unsigned_int_numpy_dtype):
)
df = df.loc[(df.index < "2000-01-02") | (df.index > "2000-01-03"), :]

if any_unsigned_int_numpy_dtype == "uint64":
with pytest.raises(RuntimeError, match="empty group with uint64_t"):
df.resample("D").max()
else:
result = df.resample("D").max()

expected = DataFrame(
[1, np.nan, 0],
columns=["x"],
index=date_range(start="2000-01-01", end="2000-01-03 23", freq="D"),
)
tm.assert_frame_equal(result, expected)
result = df.resample("D").max()

expected = DataFrame(
[1, np.nan, 0],
columns=["x"],
index=date_range(start="2000-01-01", end="2000-01-03 23", freq="D"),
)
tm.assert_frame_equal(result, expected)