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REF: de-duplicate groupby_helper code #28934

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Merged
merged 7 commits into from
Oct 16, 2019
Merged

REF: de-duplicate groupby_helper code #28934

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af8f8c3
REF: implement _treat_as_na to de-duplicate na checks
jbrockmendel Oct 11, 2019
43772af
use nan_val pattern
jbrockmendel Oct 11, 2019
bea1cc5
Merge branch 'master' of https://github.com/pandas-dev/pandas into gb…
jbrockmendel Oct 11, 2019
e638c89
Merge branch 'master' of https://github.com/pandas-dev/pandas into gb…
jbrockmendel Oct 15, 2019
fd3a673
Merge branch 'master' of https://github.com/pandas-dev/pandas into gb…
jbrockmendel Oct 16, 2019
a61e7f5
post-merge fixup
jbrockmendel Oct 16, 2019
1803844
Merge branch 'master' of https://github.com/pandas-dev/pandas into gb…
jbrockmendel Oct 16, 2019
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3 changes: 2 additions & 1 deletion pandas/_libs/groupby.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -372,7 +372,8 @@ def group_any_all(uint8_t[:] out,
const uint8_t[:] mask,
object val_test,
bint skipna):
"""Aggregated boolean values to show truthfulness of group elements
"""
Aggregated boolean values to show truthfulness of group elements.

Parameters
----------
Expand Down
139 changes: 52 additions & 87 deletions pandas/_libs/groupby_helper.pxi.in
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Expand Up @@ -20,6 +20,18 @@ ctypedef fused rank_t:
object


cdef inline bint _treat_as_na(rank_t val, bint is_datetimelike) nogil:
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Is this only applicable in groupby or should it go in util?

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Only here. As I mention in comments below, I'm not wild about the fact that is_datetimelike is effectively hard-coded to True in several places. Will try to see if that causes problems in follow-up(s)

if rank_t is object:
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Somewhat counter-intuitive that this works - out of curiosity what was the complaint?

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AFAICT the val != val check requires calling val.__ne__ which requires the gil

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shouldn’t need the gil if these are c level object (eg ints or floats)

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Right, we just need to exclude the object case from getting to the val == val step, thats all this check is doing

# Should never be used, but we need to avoid the `val != val` below
# or else cython will raise about gil acquisition.
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this is pretty odd, why dont you return an enum here (true, false, raise) and handle in code appropriately.

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the vicissitudes of cython fused types

raise NotImplementedError

elif rank_t is int64_t:
return is_datetimelike and val == NPY_NAT
else:
return val != val


@cython.wraparound(False)
@cython.boundscheck(False)
def group_last(rank_t[:, :] out,
Expand Down Expand Up @@ -61,24 +73,16 @@ def group_last(rank_t[:, :] out,
for j in range(K):
val = values[i, j]

# not nan
if rank_t is int64_t:
# need a special notna check
if val != NPY_NAT:
nobs[lab, j] += 1
resx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
resx[lab, j] = val
if val == val:
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Is this totally equivalent to what's in place? Looks like we are losing the NPY_NAT check?

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This is in the rank_t is object case where we cant use _treat_as_na. (comment just below here)

# NB: use _treat_as_na here once
# conditional-nogil is available.
nobs[lab, j] += 1
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
if nobs[i, j] == 0:
if rank_t is int64_t:
out[i, j] = NPY_NAT
else:
out[i, j] = NAN
out[i, j] = NAN
else:
out[i, j] = resx[i, j]
else:
Expand All @@ -92,16 +96,10 @@ def group_last(rank_t[:, :] out,
for j in range(K):
val = values[i, j]

# not nan
if rank_t is int64_t:
# need a special notna check
if val != NPY_NAT:
nobs[lab, j] += 1
resx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
resx[lab, j] = val
if not _treat_as_na(val, True):
# TODO: Sure we always want is_datetimelike=True?
nobs[lab, j] += 1
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
Expand All @@ -113,6 +111,7 @@ def group_last(rank_t[:, :] out,
break
else:
out[i, j] = NAN

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

Expand All @@ -121,7 +120,6 @@ def group_last(rank_t[:, :] out,
# 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 Down Expand Up @@ -169,8 +167,9 @@ def group_nth(rank_t[:, :] out,
for j in range(K):
val = values[i, j]

# not nan
if val == val:
# NB: use _treat_as_na here once
# conditional-nogil is available.
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val
Expand All @@ -193,18 +192,11 @@ def group_nth(rank_t[:, :] out,
for j in range(K):
val = values[i, j]

# not nan
if rank_t is int64_t:
# need a special notna check
if val != NPY_NAT:
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val
if not _treat_as_na(val, True):
# TODO: Sure we always want is_datetimelike=True?
nobs[lab, j] += 1
if nobs[lab, j] == rank:
resx[lab, j] = val

for i in range(ncounts):
for j in range(K):
Expand Down Expand Up @@ -487,17 +479,11 @@ def group_max(groupby_t[:, :] out,
for j in range(K):
val = values[i, j]

# not nan
if groupby_t is int64_t:
if val != nan_val:
nobs[lab, j] += 1
if val > maxx[lab, j]:
maxx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
if val > maxx[lab, j]:
maxx[lab, j] = val
if not _treat_as_na(val, True):
# TODO: Sure we always want is_datetimelike=True?
nobs[lab, j] += 1
if val > maxx[lab, j]:
maxx[lab, j] = val

for i in range(ncounts):
for j in range(K):
Expand Down Expand Up @@ -563,17 +549,11 @@ def group_min(groupby_t[:, :] out,
for j in range(K):
val = values[i, j]

# not nan
if groupby_t is int64_t:
if val != nan_val:
nobs[lab, j] += 1
if val < minx[lab, j]:
minx[lab, j] = val
else:
if val == val:
nobs[lab, j] += 1
if val < minx[lab, j]:
minx[lab, j] = val
if not _treat_as_na(val, True):
# TODO: Sure we always want is_datetimelike=True?
nobs[lab, j] += 1
if val < minx[lab, j]:
minx[lab, j] = val

for i in range(ncounts):
for j in range(K):
Expand Down Expand Up @@ -643,21 +623,13 @@ def group_cummin(groupby_t[:, :] out,
for j in range(K):
val = values[i, j]

# val = nan
if groupby_t is int64_t:
if is_datetimelike and val == NPY_NAT:
out[i, j] = NPY_NAT
else:
mval = accum[lab, j]
if val < mval:
accum[lab, j] = mval = val
out[i, j] = mval
if _treat_as_na(val, is_datetimelike):
out[i, j] = val
else:
if val == val:
mval = accum[lab, j]
if val < mval:
accum[lab, j] = mval = val
out[i, j] = mval
mval = accum[lab, j]
if val < mval:
accum[lab, j] = mval = val
out[i, j] = mval


@cython.boundscheck(False)
Expand Down Expand Up @@ -712,17 +684,10 @@ def group_cummax(groupby_t[:, :] out,
for j in range(K):
val = values[i, j]

if groupby_t is int64_t:
if is_datetimelike and val == NPY_NAT:
out[i, j] = NPY_NAT
else:
mval = accum[lab, j]
if val > mval:
accum[lab, j] = mval = val
out[i, j] = mval
if _treat_as_na(val, is_datetimelike):
out[i, j] = val
else:
if val == val:
mval = accum[lab, j]
if val > mval:
accum[lab, j] = mval = val
out[i, j] = mval
mval = accum[lab, j]
if val > mval:
accum[lab, j] = mval = val
out[i, j] = mval