8000 PERF: masked ops for reductions (min/max) by jorisvandenbossche · Pull Request #33261 · pandas-dev/pandas · GitHub
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PERF: masked ops for reductions (min/max) #33261

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Merged
merged 7 commits into from
Apr 6, 2020
Merged

PERF: masked ops for reductions (min/max) #33261

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7ff6a4e
PERF: masked ops for reductions (min/max)
jorisvandenbossche Apr 3, 2020
d93e811
fix test
jorisvandenbossche Apr 3, 2020
899913d
add whatsnew
jorisvandenbossche Apr 3, 2020
9d496eb
also add for boolean
jorisvandenbossche Apr 3, 2020
cd1e291
Update pandas/core/arrays/integer.py
jorisvandenbossche Apr 3, 2020
cb992a2
different nesting to be mypy friendlier
jorisvandenbossche Apr 3, 2020
eb00ad0
update docstring
jorisvandenbossche Apr 3, 2020
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different nesting to be mypy friendlier
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@jorisvandenbossche
jorisvandenbossche committed Apr 3, 2020
commit cb992a22982d2dcdff1b33b283030d638338c764
59 changes: 30 additions & 29 deletions pandas/core/array_algos/masked_reductions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,40 +47,41 @@ def sum(
return np.sum(values, where=~mask)


def _minmax(func):
def reduction(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
"""
Reduction for 1D masked array.
def _minmax(func, values: np.ndarray, mask: np.ndarray, skipna: bool = True):
"""
Reduction for 1D masked array.

Parameters
----------
values : np.ndarray
Numpy array with the values (can be of any dtype that support the
operation).
mask : np.ndarray
Boolean numpy array (True values indicate missing values).
skipna : bool, default True
Whether to skip NA.
"""
if not skipna:
if mask.any():
return libmissing.NA
else:
if values.size:
return func(values)
else:
# min/max with empty array raise in numpy, pandas returns NA
return libmissing.NA
Parameters
----------
values : np.ndarray
Numpy array with the values (can be of any dtype that support the
operation).
mask : np.ndarray
Boolean numpy array (True values indicate missing values).
skipna : bool, default True
Whether to skip NA.
"""
if not skipna:
if mask.any():
return libmissing.NA
else:
subset = values[~mask]
if subset.size:
return func(values[~mask])
if values.size:
return func(values)
else:
# min/max with empty array raise in numpy, pandas returns NA
return libmissing.NA
else:
subset = values[~mask]
if subset.size:
return func(values[~mask])
else:
# min/max with empty array raise in numpy, pandas returns NA
return libmissing.NA


return reduction
def min(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
return _minmax(np.min, values=values, mask=mask, skipna=skipna)


min = _minmax(np.min)
max = _minmax(np.max)
def max(values: np.ndarray, mask: np.ndarray, skipna: bool = True):
return _minmax(np.max, values=values, mask=mask, skipna=skipna)
0