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[SPARK-49890][PYTHON] Extract the preparation of df.sample to parent class #48365

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[SPARK-49890][PYTHON] Extract the preparation of df.sample to parent class #48365

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40 changes: 2 additions & 38 deletions python/pyspark/sql/classic/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -601,44 +601,8 @@ def sample( # type: ignore[misc]
fraction: Optional[Union[int, float]] = None,
seed: Optional[int] = None,
) -> ParentDataFrame:
# For the cases below:
# sample(True, 0.5 [, seed])
# sample(True, fraction=0.5 [, seed])
# sample(withReplacement=False, fraction=0.5 [, seed])
is_withReplacement_set = type(withReplacement) == bool and isinstance(fraction, float)

# For the case below:
# sample(faction=0.5 [, seed])
is_withReplacement_omitted_kwargs = withReplacement is None and isinstance(fraction, float)

# For the case below:
# sample(0.5 [, seed])
is_withReplacement_omitted_args = isinstance(withReplacement, float)

if not (
is_withReplacement_set
or is_withReplacement_omitted_kwargs
or is_withReplacement_omitted_args
):
argtypes = [type(arg).__name__ for arg in [withReplacement, fraction, seed]]
raise PySparkTypeError(
errorClass="NOT_BOOL_OR_FLOAT_OR_INT",
messageParameters={
"arg_name": "withReplacement (optional), "
+ "fraction (required) and seed (optional)",
"arg_type": ", ".join(argtypes),
},
)

if is_withReplacement_omitted_args:
if fraction is not None:
seed = cast(int, fraction)
fraction = withReplacement
withReplacement = None

seed = int(seed) if seed is not None else None
args = [arg for arg in [withReplacement, fraction, seed] if arg is not None]
jdf = self._jdf.sample(*args)
_w, _f, _s = self._preapare_args_for_sample(withReplacement, fraction, seed)
jdf = self._jdf.sample(*[_w, _f, _s])
return DataFrame(jdf, self.sparkSession)

def sampleBy(
Expand Down
47 changes: 4 additions & 43 deletions python/pyspark/sql/connect/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -781,53 +781,14 @@ def sample(
fraction: Optional[Union[int, float]] = None,
seed: Optional[int] = None,
) -> ParentDataFrame:
# For the cases below:
# sample(True, 0.5 [, seed])
# sample(True, fraction=0.5 [, seed])
# sample(withReplacement=False, fraction=0.5 [, seed])
is_withReplacement_set = type(withReplacement) == bool and isinstance(fraction, float)

# For the case below:
# sample(faction=0.5 [, seed])
is_withReplacement_omitted_kwargs = withReplacement is None and isinstance(fraction, float)

# For the case below:
# sample(0.5 [, seed])
is_withReplacement_omitted_args = isinstance(withReplacement, float)

if not (
is_withReplacement_set
or is_withReplacement_omitted_kwargs
or is_withReplacement_omitted_args
):
argtypes = [type(arg).__name__ for arg in [withReplacement, fraction, seed]]
raise PySparkTypeError(
errorClass="NOT_BOOL_OR_FLOAT_OR_INT",
messageParameters={
"arg_name": "withReplacement (optional), "
+ "fraction (required) and seed (optional)",
"arg_type": ", ".join(argtypes),
},
)

if is_withReplacement_omitted_args:
if fraction is not None:
seed = cast(int, fraction)
fraction = withReplacement
withReplacement = None

if withReplacement is None:
withReplacement = False

seed = int(seed) if seed is not None else random.randint(0, sys.maxsize)

_w, _f, _s = self._preapare_args_for_sample(withReplacement, fraction, seed)
res = DataFrame(
plan.Sample(
child=self._plan,
lower_bound=0.0,
upper_bound=fraction, # type: ignore[arg-type]
with_replacement=withReplacement, # type: ignore[arg-type]
seed=seed,
upper_bound=_f,
with_replacement=_w,
seed=_s,
),
session=self._session,
)
Expand Down
42 changes: 42 additions & 0 deletions python/pyspark/sql/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,8 @@

# mypy: disable-error-code="empty-body"

import sys
import random
from typing import (
Any,
Callable,
Expand Down Expand Up @@ -2040,6 +2042,46 @@ def sample(
"""
...

def _preapare_args_for_sample(
self,
withReplacement: Optional[Union[float, bool]] = None,
fraction: Optional[Union[int, float]] = None,
seed: Optional[int] = None,
) -> Tuple[bool, float, int]:
from pyspark.errors import PySparkTypeError

if isinstance(withReplacement, bool) and isinstance(fraction, float):
# For the cases below:
# sample(True, 0.5 [, seed])
# sample(True, fraction=0.5 [, seed])
# sample(withReplacement=False, fraction=0.5 [, seed])
_seed = int(seed) if seed is not None else random.randint(0, sys.maxsize)
return withReplacement, fraction, _seed

elif withReplacement is None and isinstance(fraction, float):
# For the case below:
# sample(faction=0.5 [, seed])
_seed = int(seed) if seed is not None else random.randint(0, sys.maxsize)
return False, fraction, _seed

elif isinstance(withReplacement, float):
# For the case below:
# sample(0.5 [, seed])
_seed = int(fraction) if fraction is not None else random.randint(0, sys.maxsize)
_fraction = float(withReplacement)
return False, _fraction, _seed

else:
argtypes = [type(arg).__name__ for arg in [withReplacement, fraction, seed]]
raise PySparkTypeError(
errorClass="NOT_BOOL_OR_FLOAT_OR_INT",
messageParameters={
"arg_name": "withReplacement (optional), "
+ "fraction (required) and seed (optional)",
"arg_type": ", ".join(argtypes),
},
)

@dispatch_df_method
def sampleBy(
self, col: "ColumnOrName", fractions: Dict[Any, float], seed: Optional[int] = None
Expand Down