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from __future__ import annotations
import re
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Iterable,
Iterator,
Literal,
Mapping,
Sequence,
TypeVar,
)
from narwhals._compliant.typing import (
CompliantDataFrameAny,
CompliantDataFrameT,
CompliantDataFrameT_co,
CompliantExprT_contra,
CompliantFrameT,
CompliantFrameT_co,
CompliantLazyFrameAny,
CompliantLazyFrameT,
DepthTrackingExprAny,
DepthTrackingExprT_contra,
EagerExprT_contra,
LazyExprT_contra,
NativeExprT_co,
)
from narwhals._typing_compat import Protocol38
from narwhals._utils import is_sequence_of
if TYPE_CHECKING:
from typing_extensions import TypeAlias
_SameFrameT = TypeVar("_SameFrameT", CompliantDataFrameAny, CompliantLazyFrameAny)
__all__ = [
"CompliantGroupBy",
"DepthTrackingGroupBy",
"EagerGroupBy",
"LazyGroupBy",
"NarwhalsAggregation",
]
NativeAggregationT_co = TypeVar(
"NativeAggregationT_co", bound="str | Callable[..., Any]", covariant=True
)
NarwhalsAggregation: TypeAlias = Literal[
"sum", "mean", "median", "max", "min", "std", "var", "len", "n_unique", "count"
]
_RE_LEAF_NAME: re.Pattern[str] = re.compile(r"(\w+->)")
class CompliantGroupBy(Protocol38[CompliantFrameT_co, CompliantExprT_contra]):
_compliant_frame: Any
@property
def compliant(self) -> CompliantFrameT_co:
return self._compliant_frame # type: ignore[no-any-return]
def __init__(
self,
compliant_frame: CompliantFrameT_co,
keys: Sequence[CompliantExprT_contra] | Sequence[str],
/,
*,
drop_null_keys: bool,
) -> None: ...
def agg(self, *exprs: CompliantExprT_contra) -> CompliantFrameT_co: ...
class DataFrameGroupBy(
CompliantGroupBy[CompliantDataFrameT_co, CompliantExprT_contra],
Protocol38[CompliantDataFrameT_co, CompliantExprT_contra],
):
def __iter__(self) -> Iterator[tuple[Any, CompliantDataFrameT_co]]: ...
class ParseKeysGroupBy(
CompliantGroupBy[CompliantFrameT, CompliantExprT_contra],
Protocol38[CompliantFrameT, CompliantExprT_contra],
):
def _parse_keys(
self,
compliant_frame: CompliantFrameT,
keys: Sequence[CompliantExprT_contra] | Sequence[str],
) -> tuple[CompliantFrameT, list[str], list[str]]:
if is_sequence_of(keys, str):
keys_str = list(keys)
return compliant_frame, keys_str, keys_str.copy()
else:
return self._parse_expr_keys(compliant_frame, keys=keys)
@staticmethod
def _parse_expr_keys(
compliant_frame: _SameFrameT, keys: Sequence[CompliantExprT_contra]
) -> tuple[_SameFrameT, list[str], list[str]]:
"""Parses key expressions to set up `.agg` operation with correct information.
Since keys are expressions, it's possible to alias any such key to match
other dataframe column names.
In order to match polars behavior and not overwrite columns when evaluating keys:
- We evaluate what the output key names should be, in order to remap temporary column
names to the expected ones, and to exclude those from unnamed expressions in
`.agg(...)` context (see https://github.com/narwhals-dev/narwhals/pull/2325#issuecomment-2800004520)
- Create temporary names for evaluated key expressions that are guaranteed to have
no overlap with any existing column name.
- Add these temporary columns to the compliant dataframe.
"""
tmp_name_length = max(len(str(c)) for c in compliant_frame.columns) + 1
def _temporary_name(key: str) -> str:
# 5 is the length of `__tmp`
key_str = str(key) # pandas allows non-string column names :sob:
return f"_{key_str}_tmp{'_' * (tmp_name_length - len(key_str) - 5)}"
output_names = compliant_frame._evaluate_aliases(*keys)
safe_keys = [
# multi-output expression cannot have duplicate names, hence it's safe to suffix
key.name.map(_temporary_name)
if (metadata := key._metadata) and metadata.expansion_kind.is_multi_output()
# otherwise it's single named and we can use Expr.alias
else key.alias(_temporary_name(new_name))
for key, new_name in zip(keys, output_names)
]
return (
compliant_frame.with_columns(*safe_keys),
compliant_frame._evaluate_aliases(*safe_keys),
output_names,
)
class DepthTrackingGroupBy(
ParseKeysGroupBy[CompliantFrameT, DepthTrackingExprT_contra],
Protocol38[CompliantFrameT, DepthTrackingExprT_contra, NativeAggregationT_co],
):
"""`CompliantGroupBy` variant, deals with `Eager` and other backends that utilize `CompliantExpr._depth`."""
_REMAP_AGGS: ClassVar[Mapping[NarwhalsAggregation, Any]]
"""Mapping from `narwhals` to native representation.
Note:
- `Dask` *may* return a `Callable` instead of a `str` referring to one.
"""
def _ensure_all_simple(self, exprs: Sequence[DepthTrackingExprT_contra]) -> None:
for expr in exprs:
if not self._is_simple(expr):
name = self.compliant._implementation.name.lower()
msg = (
f"Non-trivial complex aggregation found.\n\n"
f"Hint: you were probably trying to apply a non-elementary aggregation with a"
f"{name!r} table.\n"
"Please rewrite your query such that group-by aggregations "
"are elementary. For example, instead of:\n\n"
" df.group_by('a').agg(nw.col('b').round(2).mean())\n\n"
"use:\n\n"
" df.with_columns(nw.col('b').round(2)).group_by('a').agg(nw.col('b').mean())\n\n"
)
raise ValueError(msg)
@classmethod
def _is_simple(cls, expr: DepthTrackingExprAny, /) -> bool:
"""Return `True` is we can efficiently use `expr` in a native `group_by` context."""
return expr._is_elementary() and cls._leaf_name(expr) in cls._REMAP_AGGS
@classmethod
def _remap_expr_name(
cls, name: NarwhalsAggregation | Any, /
) -> NativeAggregationT_co:
"""Replace `name`, with some native representation.
Arguments:
name: Name of a `nw.Expr` aggregation method.
Returns:
A native compatible representation.
"""
return cls._REMAP_AGGS.get(name, name)
@classmethod
def _leaf_name(cls, expr: DepthTrackingExprAny, /) -> NarwhalsAggregation | Any:
"""Return the last function name in the chain defined by `expr`."""
return _RE_LEAF_NAME.sub("", expr._function_name)
class EagerGroupBy(
DepthTrackingGroupBy[CompliantDataFrameT, EagerExprT_contra, NativeAggregationT_co],
DataFrameGroupBy[CompliantDataFrameT, EagerExprT_contra],
Protocol38[CompliantDataFrameT, EagerExprT_contra, NativeAggregationT_co],
): ...
class LazyGroupBy(
ParseKeysGroupBy[CompliantLazyFrameT, LazyExprT_contra],
CompliantGroupBy[CompliantLazyFrameT, LazyExprT_contra],
Protocol38[CompliantLazyFrameT, LazyExprT_contra, NativeExprT_co],
):
_keys: list[str]
_output_key_names: list[str]
def _evaluate_expr(self, expr: LazyExprT_contra, /) -> Iterator[NativeExprT_co]:
output_names = expr._evaluate_output_names(self.compliant)
aliases = (
expr._alias_output_names(output_names)
if expr._alias_output_names
else output_names
)
native_exprs = expr(self.compliant)
if expr._is_multi_output_unnamed():
exclude = {*self._keys, *self._output_key_names}
for native_expr, name, alias in zip(native_exprs, output_names, aliases):
if name not in exclude:
yield expr._alias_native(native_expr, alias)
else:
for native_expr, alias in zip(native_exprs, aliases):
yield expr._alias_native(native_expr, alias)
def _evaluate_exprs(
self, exprs: Iterable[LazyExprT_contra], /
) -> Iterator[NativeExprT_co]:
for expr in exprs:
yield from self._evaluate_expr(expr)
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