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Merged
merged 7 commits into from
Feb 9, 2022
Merged

PERF: faster constructors from ea scalars #45854

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f02b294
faster constructors from ea scalars
lukemanley Feb 7, 2022
8b45e67
handle categoricals
lukemanley Feb 7, 2022
9f58d27
whatsnew
lukemanley Feb 7, 2022
660ee13
faster categorical constructor
lukemanley Feb 7, 2022
71fa91e
cleanup
lukemanley Feb 7, 2022
8e3f506
use .repeat
lukemanley Feb 8, 2022
98c3406
fix failing tests
lukemanley Feb 8, 2022
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23 changes: 23 additions & 0 deletions asv_bench/benchmarks/frame_ctor.py
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Expand Up @@ -2,8 +2,10 @@

import pandas as pd
from pandas import (
NA,
Categorical,
DataFrame,
Float64Dtype,
MultiIndex,
Series,
Timestamp,
Expand Down Expand Up @@ -138,6 +140,27 @@ def time_frame_from_range(self):
self.df = DataFrame(self.data)


class FromScalar:
def setup(self):
self.nrows = 100_000

def time_frame_from_scalar_ea_float64(self):
DataFrame(
1.0,
index=range(self.nrows),
columns=list("abc"),
dtype=Float64Dtype(),
)

def time_frame_from_scalar_ea_float64_na(self):
DataFrame(
NA,
index=range(self.nrows),
columns=list("abc"),
dtype=Float64Dtype(),
)


class FromArrays:

goal_time = 0.2
Expand Down
1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.5.0.rst
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Expand Up @@ -206,6 +206,7 @@ Performance improvements
- Performance improvement in :meth:`DataFrame.duplicated` when subset consists of only one column (:issue:`45236`)
- Performance improvement in :meth:`.GroupBy.transform` when broadcasting values for user-defined functions (:issue:`45708`)
- Performance improvement in :meth:`.GroupBy.transform` for user-defined functions when only a single group exists (:issue:`44977`)
- Performance improvement in :class:`DataFrame` and :class:`Series` constructors for extension dtype scalars (:issue:`45854`)
-

.. ---------------------------------------------------------------------------
Expand Down
7 changes: 6 additions & 1 deletion pandas/core/dtypes/cast.py
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Expand Up @@ -1652,7 +1652,12 @@ def construct_1d_arraylike_from_scalar(

if isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
subarr = cls._from_sequence([value] * length, dtype=dtype)
if isinstance(dtype, CategoricalDtype):
subarr = cls._from_sequence([value] * length, dtype=dtype)
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what happens if you don't special case Categorical?

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Calling take on the empty categorical would error because the categories were not defined.

I just pushed another commit which first sets the categories. Categorical now has a similar improvement:

import pandas as pd

N = 1_000_000

%timeit pd.Series(1, index=range(N), dtype='category')
415 ms ± 91.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)     <- main
4.5 ms ± 1.57 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)  <- PR

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Calling take on the empty categorical would error because the categories were not defined.

Hmm i think this might be a problem for other not-fully-initialized dtype objects. IIRC IntervalDtype without 'closed' set is another one, not sure if we have a way to detect the general case

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latest update uses .repeat per @jreback's suggestion below.

else:
subarr = cls._from_sequence([], dtype=dtype)
taker = np.broadcast_to(np.intp(-1), length)
subarr = subarr.take(taker, allow_fill=True, fill_value=value)

else:

Expand Down