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PERF: fix some of .clip() performance regression by using numpy arrays where possible #24735

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Merged
merged 1 commit into from
Jan 20, 2019
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PERF: fix some of .clip() performance regression by using numpy arrays where possible #24735

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8 changes: 5 additions & 3 deletions asv_bench/benchmarks/series_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,11 +140,13 @@ def time_map(self, mapper):


class Clip(object):
params = [50, 1000, 10**5]
param_names = ['n']

def setup(self):
self.s = Series(np.random.randn(50))
def setup(self, n):
self.s = Series(np.random.randn(n))

def time_clip(self):
def time_clip(self, n):
self.s.clip(0, 1)


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18 changes: 12 additions & 6 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7148,12 +7148,18 @@ def _clip_with_scalar(self, lower, upper, inplace=False):
raise ValueError("Cannot use an NA value as a clip threshold")

result = self
if upper is not None:
subset = self.le(upper, axis=None) | isna(result)
result = result.where(subset, upper, axis=None, inplace=False)
if lower is not None:
subset = self.ge(lower, axis=None) | isna(result)
result = result.where(subset, lower, axis=None, inplace=False)
mask = isna(self.values)

with np.errstate(all='ignore'):
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What's the point of this?

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This is simply reverting back to what this block used to do; it's needed in the event values <= upper would otherwise raise a type error.

if upper is not None:
subset = self.to_numpy() <= upper
result = result.where(subset, upper, axis=None, inplace=False)
if lower is not None:
subset = self.to_numpy() >= lower
result = result.where(subset, lower, axis=None, inplace=False)

if np.any(mask):
result[mask] = np.nan

if inplace:
self._update_inplace(result)
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