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[SPARK-40744][PS] Make _reduce_for_stat_function in groupby accept min_count #38201

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[SPARK-40744][PS] Make _reduce_for_stat_function in groupby accept min_count #38201

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zhengruifeng Oct 11, 2022
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zhengruifeng Oct 11, 2022
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zhengruifeng Oct 11, 2022
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108 changes: 26 additions & 82 deletions python/pyspark/pandas/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -468,21 +468,10 @@ def first(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> Fr
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")

if min_count > 0:

def first(col: Column) -> Column:
return F.when(
F.count(F.when(~F.isnull(col), F.lit(0))) < min_count, F.lit(None)
).otherwise(F.first(col, ignorenulls=True))

else:

def first(col: Column) -> Column:
return F.first(col, ignorenulls=True)

return self._reduce_for_stat_function(
first,
lambda col: F.first(col, ignorenulls=True),
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)

def last(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> FrameLike:
Expand Down Expand Up @@ -549,21 +538,10 @@ def last(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> Fra
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")

if min_count > 0:

def last(col: Column) -> Column:
return F.when(
F.count(F.when(~F.isnull(col), F.lit(0))) < min_count, F.lit(None)
).otherwise(F.last(col, ignorenulls=True))

else:

def last(col: Column) -> Column:
return F.last(col, ignorenulls=True)

return self._reduce_for_stat_function(
last,
lambda col: F.last(col, ignorenulls=True),
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)

def max(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> FrameLike:
Expand Down Expand Up @@ -624,20 +602,10 @@ def max(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> Fram
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")

if min_count > 0:

def max(col: Column) -> Column:
return F.when(
F.count(F.when(~F.isnull(col), F.lit(0))) < min_count, F.lit(None)
).otherwise(F.max(col))

else:

def max(col: Column) -> Column:
return F.max(col)

return self._reduce_for_stat_function(
max, accepted_spark_types=(NumericType, BooleanType) if numeric_only else None
F.max,
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)

def mean(self, numeric_only: Optional[bool] = True) -> FrameLike:
Expand Down Expand Up @@ -802,20 +770,10 @@ def min(self, numeric_only: Optional[bool] = False, min_count: int = -1) -> Fram
if not isinstance(min_count, int):
raise TypeError("min_count must be integer")

if min_count > 0:

def min(col: Column) -> Column:
return F.when(
F.count(F.when(~F.isnull(col), F.lit(0))) < min_count, F.lit(None)
).otherwise(F.min(col))

else:

def min(col: Column) -> Column:
return F.min(col)

return self._reduce_for_stat_function(
min, accepted_spark_types=(NumericType, BooleanType) if numeric_only else None
F.min,
accepted_spark_types=(NumericType, BooleanType) if numeric_only else None,
min_count=min_count,
)

# TODO: sync the doc.
Expand Down Expand Up @@ -944,20 +902,11 @@ def sum(self, numeric_only: Optional[bool] = True, min_count: int = 0) -> FrameL
f"numeric_only=False, skip unsupported columns: {unsupported}"
)

if min_count > 0:

def sum(col: Column) -> Column:
return F.when(
F.count(F.when(~F.isnull(col), F.lit(0))) < min_count, F.lit(None)
).otherwise(F.sum(col))

else:

def sum(col: Column) -> Column:
return F.sum(col)

return self._reduce_for_stat_function(
sum, accepted_spark_types=(NumericType,), bool_to_numeric=True
F.sum,
accepted_spark_types=(NumericType, BooleanType),
bool_to_numeric=True,
min_count=min_count,
)

# TODO: sync the doc.
Expand Down Expand Up @@ -1324,22 +1273,11 @@ def prod(self, numeric_only: Optional[bool] = True, min_count: int = 0) -> Frame

self._validate_agg_columns(numeric_only=numeric_only, function_name="prod")

if min_count > 0:

def prod(col: Column) -> Column:
return F.when(
F.count(F.when(~F.isnull(col), F.lit(0))) < min_count, F.lit(None)
).otherwise(SF.product(col, True))

else:

def prod(col: Column) -> Column:
return SF.product(col, True)

return self._reduce_for_stat_function(
prod,
lambda col: SF.product(col, True),
accepted_spark_types=(NumericType, BooleanType),
bool_to_numeric=True,
min_count=min_count,
)

def all(self, skipna: bool = True) -> FrameLike:
Expand Down Expand Up @@ -3596,6 +3534,7 @@ def _reduce_for_stat_function(
sfun: Callable[[Column], Column],
accepted_spark_types: Optional[Tuple[Type[DataType], ...]] = None,
bool_to_numeric: bool = False,
**kwargs: Any,
) -> FrameLike:
"""Apply an aggregate function `sfun` per column and reduce to a FrameLike.

Expand All @@ -3615,14 +3554,19 @@ def _reduce_for_stat_function(
psdf: DataFrame = DataFrame(internal)

if len(psdf._internal.column_labels) > 0:
min_count = kwargs.get("min_count", 0)
stat_exprs = []
for label in psdf._internal.column_labels:
psser = psdf._psser_for(label)
stat_exprs.append(
sfun(psser._dtype_op.nan_to_null(psser).spark.column).alias(
psser._internal.data_spark_column_names[0]
input_scol = psser._dtype_op.nan_to_null(psser).spark.column
output_scol = sfun(input_scol)

if min_count > 0:
output_scol = F.when(
F.count(F.when(~F.isnull(input_scol), F.lit(0))) >= min_count, output_scol
)
)

stat_exprs.append(output_scol.alias(psser._internal.data_spark_column_names[0]))
sdf = sdf.groupby(*groupkey_names).agg(*stat_exprs)
else:
sdf = sdf.select(*groupkey_names).distinct()
Expand Down