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PERF: lib.generate_slices #42097

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Merged
jreback merged 1 commit into from
Jun 18, 2021
Merged

PERF: lib.generate_slices #42097

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32 changes: 17 additions & 15 deletions pandas/_libs/lib.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -906,12 +906,13 @@ def count_level_2d(ndarray[uint8_t, ndim=2, cast=True] mask,
return counts


@cython.wraparound(False)
@cython.boundscheck(False)
def generate_slices(const intp_t[:] labels, Py_ssize_t ngroups):
cdef:
Py_ssize_t i, group_size, n, start
intp_t lab
object slobj
ndarray[int64_t] starts, ends
int64_t[::1] starts, ends

n = len(labels)

Expand All @@ -920,19 +921,20 @@ def generate_slices(const intp_t[:] labels, Py_ssize_t ngroups):

start = 0
group_size = 0
for i in range(n):
lab = labels[i]
if lab < 0:
start += 1
else:
group_size += 1
if i == n - 1 or lab != labels[i + 1]:
starts[lab] = start
ends[lab] = start + group_size
start += group_size
group_size = 0

return starts, ends
with nogil:
for i in range(n):
lab = labels[i]
if lab < 0:
start += 1
else:
group_size += 1
if i == n - 1 or lab != labels[i + 1]:
starts[lab] = start
ends[lab] = start + group_size
start += group_size
group_size = 0

return np.asarray(starts), np.asarray(ends)
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@jbrockmendel jbrockmendel Jun 18, 2021

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FYI there is a tiny perf bump from using starts.base instead of np.asarray(starts) in these cases.

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@mzeitlin11 mzeitlin11 Jun 18, 2021

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Ah thanks, good to know



def indices_fast(ndarray[intp_t] index, const int64_t[:] labels, list keys,
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