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Merged
merged 4 commits into from
Jun 8, 2021
Merged

PERF: is_bool_indexer #41861

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bcd74fa
PERF: is_bool_list
jbrockmendel Jun 7, 2021
f8a6f2a
typo fixup
jbrockmendel Jun 8, 2021
808a82f
mypy fixup
jbrockmendel Jun 8, 2021
63948a0
Merge branch 'master' into ref-infer
jbrockmendel Jun 8, 2021
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3 changes: 3 additions & 0 deletions pandas/_libs/lib.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -267,3 +267,6 @@ def get_reverse_indexer(
indexer: np.ndarray, # const intp_t[:]
length: int,
) -> np.ndarray: ... # np.ndarray[np.intp]


def is_bool_list(obj: list) -> bool: ...
38 changes: 38 additions & 0 deletions pandas/_libs/lib.pyx
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Expand Up @@ -2920,3 +2920,41 @@ def to_object_array_tuples(rows: object) -> np.ndarray:
result[i, j] = row[j]

return result


def is_bool_list(obj: list) -> bool:
"""
Check if this list contains only bool or np.bool_ objects.

This is appreciably faster than checking `np.array(obj).dtype == bool`

obj1 = [True, False] * 100
obj2 = obj1 * 100
obj3 = obj2 * 100
obj4 = [True, None] + obj1

for obj in [obj1, obj2, obj3, obj4]:
%timeit is_bool_list(obj)
%timeit np.array(obj).dtype.kind == "b"

340 ns ± 8.22 ns
8.78 µs ± 253 ns

28.8 µs ± 704 ns
813 µs ± 17.8 µs

3.4 ms ± 168 µs
78.4 ms ± 1.05 ms

48.1 ns ± 1.26 ns
8.1 µs ± 198 ns
"""
cdef:
object item

for item in obj:
if not util.is_bool_object(item):
return False
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Is there any concern with the benchmarks being somewhat misleading due to short circuiting? At least in the Python space similar functions don't seem to benefit much from this

In [6]: %timeit all(isinstance(x, bool) for x in obj)
6.7 µs ± 136 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [7]: %timeit np.array(obj).dtype.kind == "b"
5.98 µs ± 121 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

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Is there any concern with the benchmarks being somewhat misleading due to short circuiting?

Only one of the 4 cases posted short-circuits. Am I misunderstanding the question?

At least in the Python space similar functions don't seem to benefit much from this

I was surprised by this too, found that doing it in cython was much more performant after talking with @seberg yesterday about how numpy does this inference.

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Wow, the difference is big...

NumPy should maybe try to be a bit faster, but that will just make the gap a bit smaller (e.g. by assuming that usually all elements are the same type, I guess a true casting support np.asarray(..., dtype=bool, casting="equiv") would fill the gap, but no idea how likely that would be). Unlike NumPy, you already know what you want here (bools) and what you got (a 1-d list).

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Ah my bad on short circuiting - misread it. Thanks for comments


# Note: we return True for empty list
return True
4 changes: 2 additions & 2 deletions pandas/core/common.py
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Expand Up @@ -142,8 +142,8 @@ def is_bool_indexer(key: Any) -> bool:
elif is_bool_dtype(key.dtype):
return True
elif isinstance(key, list):
arr = np.asarray(key)
return arr.dtype == np.bool_ and len(arr) == len(key)
# check if np.array(key).dtype would be bool
return len(key) > 0 and lib.is_bool_list(key)

return False

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