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add uint64 support for some libgroupby funcs #28931

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Merged
merged 3 commits into from
Oct 16, 2019
Merged

add uint64 support for some libgroupby funcs #28931

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0de0628
add uint64 support for some libgroupby funcs
jbrockmendel Oct 11, 2019
3d66557
Merge branch 'master' of https://github.com/pandas-dev/pandas into fa…
jbrockmendel Oct 14, 2019
77a0b7e
clarify comment, add to parametrization
jbrockmendel Oct 14, 2019
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54 changes: 54 additions & 0 deletions pandas/_libs/groupby_helper.pxi.in
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ ctypedef fused rank_t:
float64_t
float32_t
int64_t
uint64_t
object


Expand All @@ -34,6 +35,7 @@ def group_last(rank_t[:, :] out,
rank_t val
ndarray[rank_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
bint runtime_error = False

assert min_count == -1, "'min_count' only used in add and prod"

Expand Down Expand Up @@ -106,11 +108,20 @@ def group_last(rank_t[:, :] out,
if nobs[i, j] == 0:
if rank_t is int64_t:
out[i, j] = NPY_NAT
elif rank_t is uint64_t:
runtime_error = True
break
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
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where are these caught?

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this would get caught by one of the except Exceptions in the groupby code that i'm trying to make more specific.

# block.
raise RuntimeError("empty group with uint64_t")


group_last_float64 = group_last["float64_t"]
group_last_float32 = group_last["float32_t"]
group_last_int64 = group_last["int64_t"]
Expand All @@ -132,6 +143,7 @@ def group_nth(rank_t[:, :] out,
rank_t val
ndarray[rank_t, ndim=2] resx
ndarray[int64_t, ndim=2] nobs
bint runtime_error = False

assert min_count == -1, "'min_count' only used in add and prod"

Expand Down Expand Up @@ -199,11 +211,19 @@ def group_nth(rank_t[:, :] out,
if nobs[i, j] == 0:
if rank_t is int64_t:
out[i, j] = NPY_NAT
elif rank_t is uint64_t:
runtime_error = True
break
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")


group_nth_float64 = group_nth["float64_t"]
group_nth_float32 = group_nth["float32_t"]
Expand Down Expand Up @@ -282,12 +302,16 @@ def group_rank(float64_t[:, :] out,
if ascending ^ (na_option == 'top'):
if rank_t is int64_t:
nan_fill_val = np.iinfo(np.int64).max
elif rank_t is uint64_t:
nan_fill_val = np.iinfo(np.uint64).max
else:
nan_fill_val = np.inf
order = (masked_vals, mask, labels)
else:
if rank_t is int64_t:
nan_fill_val = np.iinfo(np.int64).min
elif rank_t is uint64_t:
nan_fill_val = 0
else:
nan_fill_val = -np.inf

Expand Down Expand Up @@ -397,6 +421,7 @@ def group_rank(float64_t[:, :] out,
group_rank_float64 = group_rank["float64_t"]
group_rank_float32 = group_rank["float32_t"]
group_rank_int64 = group_rank["int64_t"]
group_rank_uint64 = group_rank["uint64_t"]
# Note: we do not have a group_rank_object because that would require a
# not-nogil implementation, see GH#19560

Expand All @@ -410,6 +435,7 @@ ctypedef fused groupby_t:
float64_t
float32_t
int64_t
uint64_t


@cython.wraparound(False)
Expand All @@ -426,6 +452,7 @@ def group_max(groupby_t[:, :] out,
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
groupby_t val, count, nan_val
ndarray[groupby_t, ndim=2] maxx, nobs
bint runtime_error = False

assert min_count == -1, "'min_count' only used in add and prod"

Expand All @@ -439,6 +466,9 @@ def group_max(groupby_t[:, :] out,
# Note: evaluated at compile-time
maxx[:] = -_int64_max
nan_val = NPY_NAT
elif groupby_t is uint64_t:
maxx[:] = 0
nan_val = 0 # We better not need this! TODO: Can we use e.g. NULL?
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Can you clarify what this is?

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We have to define a value to treat as NA, and it has to be of the appropriate dtype. But for uint64 there is no such value, so defining it here is just as a placeholder.

I'll clarify the comment in the code.

else:
maxx[:] = -np.inf
nan_val = NAN
Expand Down Expand Up @@ -470,10 +500,18 @@ def group_max(groupby_t[:, :] out,
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
if groupby_t is uint64_t:
runtime_error = True
break
out[i, j] = nan_val
else:
out[i, j] = maxx[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 All @@ -489,6 +527,7 @@ def group_min(groupby_t[:, :] out,
Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
groupby_t val, count, nan_val
ndarray[groupby_t, ndim=2] minx, nobs
bint runtime_error = False

assert min_count == -1, "'min_count' only used in add and prod"

Expand All @@ -501,6 +540,9 @@ def group_min(groupby_t[:, :] out,
if groupby_t is int64_t:
minx[:] = _int64_max
nan_val = NPY_NAT
elif groupby_t is uint64_t:
minx[:] = np.iinfo(np.uint64).max
nan_val = 0 # We better not need this! TODO: Can we use e.g. NULL?
else:
minx[:] = np.inf
nan_val = NAN
Expand Down Expand Up @@ -532,10 +574,18 @@ def group_min(groupby_t[:, :] out,
for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
if groupby_t is uint64_t:
runtime_error = True
break
out[i, j] = nan_val
else:
out[i, j] = minx[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 @@ -575,6 +625,8 @@ def group_cummin(groupby_t[:, :] out,
accum = np.empty((ngroups, K), dtype=np.asarray(values).dtype)
if groupby_t is int64_t:
accum[:] = _int64_max
elif groupby_t is uint64_t:
accum[:] = np.iinfo(np.uint64).max
else:
accum[:] = np.inf

Expand Down Expand Up @@ -642,6 +694,8 @@ def group_cummax(groupby_t[:, :] out,
accum = np.empty((ngroups, K), dtype=np.asarray(values).dtype)
if groupby_t is int64_t:
accum[:] = -_int64_max
elif groupby_t is uint64_t:
accum[:] = 0
else:
accum[:] = -np.inf

Expand Down
8 changes: 8 additions & 0 deletions pandas/core/groupby/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1361,7 +1361,15 @@ def f(self, **kwargs):
return self._cython_agg_general(alias, alt=npfunc, **kwargs)
except AssertionError as e:
raise SpecificationError(str(e))
except DataError:
pass
except Exception:
# TODO: the remaining test cases that get here are from:
# - AttributeError from _cython_agg_blocks bug passing
# DataFrame to make_block; see GH#28275
# - TypeError in _cython_operation calling ensure_float64
# on object array containing complex numbers;
# see test_groupby_complex, test_max_nan_bug
pass

# apply a non-cython aggregation
Expand Down