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from __future__ import annotations
import operator
from functools import reduce
from typing import TYPE_CHECKING, Callable, Iterable, Sequence
from narwhals._compliant import LazyNamespace, LazyThen, LazyWhen
from narwhals._expression_parsing import (
combine_alias_output_names,
combine_evaluate_output_names,
)
from narwhals._spark_like.dataframe import SparkLikeLazyFrame
from narwhals._spark_like.expr import SparkLikeExpr
from narwhals._spark_like.selectors import SparkLikeSelectorNamespace
from narwhals._spark_like.utils import (
import_functions,
import_native_dtypes,
narwhals_to_native_dtype,
)
if TYPE_CHECKING:
from sqlframe.base.column import Column
from narwhals._spark_like.dataframe import SQLFrameDataFrame # noqa: F401
from narwhals._spark_like.expr import SparkWindowInputs
from narwhals._utils import Implementation, Version
from narwhals.typing import ConcatMethod, IntoDType, NonNestedLiteral
class SparkLikeNamespace(
LazyNamespace[SparkLikeLazyFrame, SparkLikeExpr, "SQLFrameDataFrame"]
):
def __init__(
self,
*,
backend_version: tuple[int, ...],
version: Version,
implementation: Implementation,
) -> None:
self._backend_version = backend_version
self._version = version
self._implementation = implementation
@property
def selectors(self) -> SparkLikeSelectorNamespace:
return SparkLikeSelectorNamespace.from_namespace(self)
@property
def _expr(self) -> type[SparkLikeExpr]:
return SparkLikeExpr
@property
def _lazyframe(self) -> type[SparkLikeLazyFrame]:
return SparkLikeLazyFrame
@property
def _F(self): # type: ignore[no-untyped-def] # noqa: ANN202, N802
if TYPE_CHECKING:
from sqlframe.base import functions
return functions
else:
return import_functions(self._implementation)
@property
def _native_dtypes(self): # type: ignore[no-untyped-def] # noqa: ANN202
if TYPE_CHECKING:
from sqlframe.base import types
return types
else:
return import_native_dtypes(self._implementation)
def _with_elementwise(
self, func: Callable[[Iterable[Column]], Column], *exprs: SparkLikeExpr
) -> SparkLikeExpr:
def call(df: SparkLikeLazyFrame) -> list[Column]:
cols = (col for _expr in exprs for col in _expr(df))
return [func(cols)]
def window_function(
df: SparkLikeLazyFrame, window_inputs: SparkWindowInputs
) -> list[Column]:
cols = (
col for _expr in exprs for col in _expr.window_function(df, window_inputs)
)
return [func(cols)]
return self._expr(
call=call,
window_function=window_function,
evaluate_output_names=combine_evaluate_output_names(*exprs),
alias_output_names=combine_alias_output_names(*exprs),
backend_version=self._backend_version,
version=self._version,
implementation=self._implementation,
)
def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> SparkLikeExpr:
def _lit(df: SparkLikeLazyFrame) -> list[Column]:
column = df._F.lit(value)
if dtype:
native_dtype = narwhals_to_native_dtype(
dtype, version=self._version, spark_types=df._native_dtypes
)
column = column.cast(native_dtype)
return [column]
return self._expr(
call=_lit,
evaluate_output_names=lambda _df: ["literal"],
alias_output_names=None,
backend_version=self._backend_version,
version=self._version,
implementation=self._implementation,
)
def len(self) -> SparkLikeExpr:
def func(df: SparkLikeLazyFrame) -> list[Column]:
return [df._F.count("*")]
return self._expr(
func,
evaluate_output_names=lambda _df: ["len"],
alias_output_names=None,
backend_version=self._backend_version,
version=self._version,
implementation=self._implementation,
)
def all_horizontal(self, *exprs: SparkLikeExpr) -> SparkLikeExpr:
def func(cols: Iterable[Column]) -> Column:
return reduce(operator.and_, cols)
return self._with_elementwise(func, *exprs)
def any_horizontal(self, *exprs: SparkLikeExpr) -> SparkLikeExpr:
def func(cols: Iterable[Column]) -> Column:
return reduce(operator.or_, cols)
return self._with_elementwise(func, *exprs)
def max_horizontal(self, *exprs: SparkLikeExpr) -> SparkLikeExpr:
def func(cols: Iterable[Column]) -> Column:
return self._F.greatest(*cols)
return self._with_elementwise(func, *exprs)
def min_horizontal(self, *exprs: SparkLikeExpr) -> SparkLikeExpr:
def func(cols: Iterable[Column]) -> Column:
return self._F.least(*cols)
return self._with_elementwise(func, *exprs)
def sum_horizontal(self, *exprs: SparkLikeExpr) -> SparkLikeExpr:
def func(cols: Iterable[Column]) -> Column:
return reduce(
operator.add, (self._F.coalesce(col, self._F.lit(0)) for col in cols)
)
return self._with_elementwise(func, *exprs)
def mean_horizontal(self, *exprs: SparkLikeExpr) -> SparkLikeExpr:
def func(cols: Iterable[Column]) -> Column:
cols = list(cols)
F = exprs[0]._F # noqa: N806
# PySpark before 3.5 doesn't have `try_divide`, SQLFrame doesn't have it.
divide = getattr(F, "try_divide", operator.truediv)
return divide(
reduce(
operator.add, (self._F.coalesce(col, self._F.lit(0)) for col in cols)
),
reduce(
operator.add,
(
col.isNotNull().cast(self._native_dtypes.IntegerType())
for col in cols
),
),
)
return self._with_elementwise(func, *exprs)
def concat(
self, items: Iterable[SparkLikeLazyFrame], *, how: ConcatMethod
) -> SparkLikeLazyFrame:
dfs = [item._native_frame for item in items]
if how == "vertical":
cols_0 = dfs[0].columns
for i, df in enumerate(dfs[1:], start=1):
cols_current = df.columns
if not ((len(cols_current) == len(cols_0)) and (cols_current == cols_0)):
msg = (
"unable to vstack, column names don't match:\n"
f" - dataframe 0: {cols_0}\n"
f" - dataframe {i}: {cols_current}\n"
)
raise TypeError(msg)
return SparkLikeLazyFrame(
native_dataframe=reduce(lambda x, y: x.union(y), dfs),
backend_version=self._backend_version,
version=self._version,
implementation=self._implementation,
)
if how == "diagonal":
return SparkLikeLazyFrame(
native_dataframe=reduce(
lambda x, y: x.unionByName(y, allowMissingColumns=True), dfs
),
backend_version=self._backend_version,
version=self._version,
implementation=self._implementation,
)
raise NotImplementedError
def concat_str(
self, *exprs: SparkLikeExpr, separator: str, ignore_nulls: bool
) -> SparkLikeExpr:
def func(df: SparkLikeLazyFrame) -> list[Column]:
cols = [s for _expr in exprs for s in _expr(df)]
cols_casted = [s.cast(df._native_dtypes.StringType()) for s in cols]
null_mask = [df._F.isnull(s) for s in cols]
if not ignore_nulls:
null_mask_result = reduce(operator.or_, null_mask)
result = df._F.when(
~null_mask_result,
reduce(
lambda x, y: df._F.format_string(f"%s{separator}%s", x, y),
cols_casted,
),
).otherwise(df._F.lit(None))
else:
init_value, *values = [
df._F.when(~nm, col).otherwise(df._F.lit(""))
for col, nm in zip(cols_casted, null_mask)
]
separators = (
df._F.when(nm, df._F.lit("")).otherwise(df._F.lit(separator))
for nm in null_mask[:-1]
)
result = reduce(
lambda x, y: df._F.format_string("%s%s", x, y),
(
df._F.format_string("%s%s", s, v)
for s, v in zip(separators, values)
),
init_value,
)
return [result]
return self._expr(
call=func,
evaluate_output_names=combine_evaluate_output_names(*exprs),
alias_output_names=combine_alias_output_names(*exprs),
backend_version=self._backend_version,
version=self._version,
implementation=self._implementation,
)
def when(self, predicate: SparkLikeExpr) -> SparkLikeWhen:
return SparkLikeWhen.from_expr(predicate, context=self)
class SparkLikeWhen(LazyWhen[SparkLikeLazyFrame, "Column", SparkLikeExpr]):
@property
def _then(self) -> type[SparkLikeThen]:
return SparkLikeThen
def __call__(self, df: SparkLikeLazyFrame) -> Sequence[Column]:
self.when = df._F.when
self.lit = df._F.lit
return super().__call__(df)
def _window_function(
self, df: SparkLikeLazyFrame, window_inputs: SparkWindowInputs
) -> Sequence[Column]:
self.when = df._F.when
self.lit = df._F.lit
return super()._window_function(df, window_inputs)
class SparkLikeThen(
LazyThen[SparkLikeLazyFrame, "Column", SparkLikeExpr], SparkLikeExpr
): ...
|
