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345
lib/python3.11/site-packages/narwhals/_pandas_like/expr.py
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345
lib/python3.11/site-packages/narwhals/_pandas_like/expr.py
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from narwhals._compliant import EagerExpr
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from narwhals._expression_parsing import evaluate_output_names_and_aliases
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from narwhals._pandas_like.group_by import PandasLikeGroupBy
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from narwhals._pandas_like.series import PandasLikeSeries
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from narwhals._utils import generate_temporary_column_name
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from typing_extensions import Self
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from narwhals._compliant.typing import AliasNames, EvalNames, EvalSeries, ScalarKwargs
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from narwhals._expression_parsing import ExprMetadata
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from narwhals._pandas_like.dataframe import PandasLikeDataFrame
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from narwhals._pandas_like.namespace import PandasLikeNamespace
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from narwhals._utils import Implementation, Version, _LimitedContext
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from narwhals.typing import PythonLiteral
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WINDOW_FUNCTIONS_TO_PANDAS_EQUIVALENT = {
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"cum_sum": "cumsum",
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"cum_min": "cummin",
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"cum_max": "cummax",
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"cum_prod": "cumprod",
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# Pandas cumcount starts counting from 0 while Polars starts from 1
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# Pandas cumcount counts nulls while Polars does not
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# So, instead of using "cumcount" we use "cumsum" on notna() to get the same result
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"cum_count": "cumsum",
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"rolling_sum": "sum",
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"rolling_mean": "mean",
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"rolling_std": "std",
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"rolling_var": "var",
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"shift": "shift",
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"rank": "rank",
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"diff": "diff",
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"fill_null": "fillna",
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"quantile": "quantile",
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"ewm_mean": "mean",
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}
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def window_kwargs_to_pandas_equivalent(
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function_name: str, kwargs: ScalarKwargs
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) -> dict[str, PythonLiteral]:
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if function_name == "shift":
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assert "n" in kwargs # noqa: S101
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pandas_kwargs: dict[str, PythonLiteral] = {"periods": kwargs["n"]}
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elif function_name == "rank":
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assert "method" in kwargs # noqa: S101
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assert "descending" in kwargs # noqa: S101
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_method = kwargs["method"]
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pandas_kwargs = {
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"method": "first" if _method == "ordinal" else _method,
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"ascending": not kwargs["descending"],
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"na_option": "keep",
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"pct": False,
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}
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elif function_name.startswith("cum_"): # Cumulative operation
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pandas_kwargs = {"skipna": True}
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elif function_name.startswith("rolling_"): # Rolling operation
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assert "min_samples" in kwargs # noqa: S101
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assert "window_size" in kwargs # noqa: S101
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assert "center" in kwargs # noqa: S101
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pandas_kwargs = {
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"min_periods": kwargs["min_samples"],
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"window": kwargs["window_size"],
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"center": kwargs["center"],
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}
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elif function_name in {"std", "var"}:
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assert "ddof" in kwargs # noqa: S101
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pandas_kwargs = {"ddof": kwargs["ddof"]}
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elif function_name == "fill_null":
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assert "strategy" in kwargs # noqa: S101
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assert "limit" in kwargs # noqa: S101
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pandas_kwargs = {"strategy": kwargs["strategy"], "limit": kwargs["limit"]}
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elif function_name == "quantile":
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assert "quantile" in kwargs # noqa: S101
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assert "interpolation" in kwargs # noqa: S101
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pandas_kwargs = {
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"q": kwargs["quantile"],
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"interpolation": kwargs["interpolation"],
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}
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elif function_name.startswith("ewm_"):
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assert "com" in kwargs # noqa: S101
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assert "span" in kwargs # noqa: S101
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assert "half_life" in kwargs # noqa: S101
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assert "alpha" in kwargs # noqa: S101
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assert "adjust" in kwargs # noqa: S101
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assert "min_samples" in kwargs # noqa: S101
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assert "ignore_nulls" in kwargs # noqa: S101
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pandas_kwargs = {
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"com": kwargs["com"],
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"span": kwargs["span"],
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"halflife": kwargs["half_life"],
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"alpha": kwargs["alpha"],
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"adjust": kwargs["adjust"],
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"min_periods": kwargs["min_samples"],
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"ignore_na": kwargs["ignore_nulls"],
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}
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else: # sum, len, ...
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pandas_kwargs = {}
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return pandas_kwargs
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class PandasLikeExpr(EagerExpr["PandasLikeDataFrame", PandasLikeSeries]):
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def __init__(
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self,
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call: EvalSeries[PandasLikeDataFrame, PandasLikeSeries],
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*,
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depth: int,
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function_name: str,
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evaluate_output_names: EvalNames[PandasLikeDataFrame],
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alias_output_names: AliasNames | None,
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implementation: Implementation,
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version: Version,
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scalar_kwargs: ScalarKwargs | None = None,
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) -> None:
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self._call = call
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self._depth = depth
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self._function_name = function_name
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self._evaluate_output_names = evaluate_output_names
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self._alias_output_names = alias_output_names
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self._implementation = implementation
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self._version = version
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self._scalar_kwargs = scalar_kwargs or {}
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self._metadata: ExprMetadata | None = None
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def __narwhals_namespace__(self) -> PandasLikeNamespace:
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from narwhals._pandas_like.namespace import PandasLikeNamespace
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return PandasLikeNamespace(self._implementation, version=self._version)
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@classmethod
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def from_column_names(
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cls: type[Self],
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evaluate_column_names: EvalNames[PandasLikeDataFrame],
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/,
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*,
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context: _LimitedContext,
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function_name: str = "",
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) -> Self:
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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try:
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return [
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PandasLikeSeries(
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df._native_frame[column_name],
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implementation=df._implementation,
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version=df._version,
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)
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for column_name in evaluate_column_names(df)
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]
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except KeyError as e:
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if error := df._check_columns_exist(evaluate_column_names(df)):
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raise error from e
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raise
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return cls(
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func,
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depth=0,
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function_name=function_name,
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evaluate_output_names=evaluate_column_names,
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alias_output_names=None,
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implementation=context._implementation,
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version=context._version,
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)
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@classmethod
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def from_column_indices(cls, *column_indices: int, context: _LimitedContext) -> Self:
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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native = df.native
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return [
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PandasLikeSeries.from_native(native.iloc[:, i], context=df)
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for i in column_indices
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]
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return cls(
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func,
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depth=0,
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function_name="nth",
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evaluate_output_names=cls._eval_names_indices(column_indices),
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alias_output_names=None,
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implementation=context._implementation,
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version=context._version,
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)
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def ewm_mean(
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self,
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*,
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com: float | None,
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span: float | None,
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half_life: float | None,
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alpha: float | None,
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adjust: bool,
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min_samples: int,
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ignore_nulls: bool,
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) -> Self:
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return self._reuse_series(
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"ewm_mean",
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scalar_kwargs={
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"com": com,
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"span": span,
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"half_life": half_life,
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"alpha": alpha,
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"adjust": adjust,
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"min_samples": min_samples,
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"ignore_nulls": ignore_nulls,
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},
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)
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def over( # noqa: C901, PLR0915
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self, partition_by: Sequence[str], order_by: Sequence[str]
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) -> Self:
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if not partition_by:
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# e.g. `nw.col('a').cum_sum().order_by(key)`
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# We can always easily support this as it doesn't require grouping.
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assert order_by # noqa: S101
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def func(df: PandasLikeDataFrame) -> Sequence[PandasLikeSeries]:
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token = generate_temporary_column_name(8, df.columns)
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df = df.with_row_index(token, order_by=None).sort(
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*order_by, descending=False, nulls_last=False
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)
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results = self(df.drop([token], strict=True))
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sorting_indices = df.get_column(token)
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for s in results:
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s._scatter_in_place(sorting_indices, s)
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return results
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elif not self._is_elementary():
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msg = (
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"Only elementary expressions are supported for `.over` in pandas-like backends.\n\n"
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"Please see: "
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"https://narwhals-dev.github.io/narwhals/concepts/improve_group_by_operation/"
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)
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raise NotImplementedError(msg)
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else:
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function_name = PandasLikeGroupBy._leaf_name(self)
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pandas_function_name = WINDOW_FUNCTIONS_TO_PANDAS_EQUIVALENT.get(
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function_name, PandasLikeGroupBy._REMAP_AGGS.get(function_name)
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)
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if pandas_function_name is None:
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msg = (
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f"Unsupported function: {function_name} in `over` context.\n\n"
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f"Supported functions are {', '.join(WINDOW_FUNCTIONS_TO_PANDAS_EQUIVALENT)}\n"
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f"and {', '.join(PandasLikeGroupBy._REMAP_AGGS)}."
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)
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raise NotImplementedError(msg)
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pandas_kwargs = window_kwargs_to_pandas_equivalent(
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function_name, self._scalar_kwargs
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)
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def func(df: PandasLikeDataFrame) -> Sequence[PandasLikeSeries]: # noqa: C901, PLR0912, PLR0914, PLR0915
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output_names, aliases = evaluate_output_names_and_aliases(self, df, [])
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if function_name == "cum_count":
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plx = self.__narwhals_namespace__()
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df = df.with_columns(~plx.col(*output_names).is_null())
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if function_name.startswith("cum_"):
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assert "reverse" in self._scalar_kwargs # noqa: S101
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reverse = self._scalar_kwargs["reverse"]
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else:
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assert "reverse" not in self._scalar_kwargs # noqa: S101
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reverse = False
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if order_by:
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columns = list(set(partition_by).union(output_names).union(order_by))
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token = generate_temporary_column_name(8, columns)
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df = (
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df.simple_select(*columns)
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.with_row_index(token, order_by=None)
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.sort(*order_by, descending=reverse, nulls_last=reverse)
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)
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sorting_indices = df.get_column(token)
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elif reverse:
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columns = list(set(partition_by).union(output_names))
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df = df.simple_select(*columns)._gather_slice(slice(None, None, -1))
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grouped = df._native_frame.groupby(partition_by)
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if function_name.startswith("rolling"):
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rolling = grouped[list(output_names)].rolling(**pandas_kwargs)
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assert pandas_function_name is not None # help mypy # noqa: S101
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if pandas_function_name in {"std", "var"}:
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assert "ddof" in self._scalar_kwargs # noqa: S101
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res_native = getattr(rolling, pandas_function_name)(
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ddof=self._scalar_kwargs["ddof"]
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)
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else:
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res_native = getattr(rolling, pandas_function_name)()
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elif function_name.startswith("ewm"):
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if self._implementation.is_pandas() and (
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self._implementation._backend_version()
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) < (1, 2): # pragma: no cover
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msg = (
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"Exponentially weighted calculation is not available in over "
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f"context for pandas versions older than 1.2.0, found {self._implementation._backend_version()}."
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)
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raise NotImplementedError(msg)
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ewm = grouped[list(output_names)].ewm(**pandas_kwargs)
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assert pandas_function_name is not None # help mypy # noqa: S101
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res_native = getattr(ewm, pandas_function_name)()
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elif function_name == "fill_null":
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assert "strategy" in self._scalar_kwargs # noqa: S101
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assert "limit" in self._scalar_kwargs # noqa: S101
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df_grouped = grouped[list(output_names)]
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if self._scalar_kwargs["strategy"] == "forward":
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res_native = df_grouped.ffill(limit=self._scalar_kwargs["limit"])
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elif self._scalar_kwargs["strategy"] == "backward":
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res_native = df_grouped.bfill(limit=self._scalar_kwargs["limit"])
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else: # pragma: no cover
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# This is deprecated in pandas. Indeed, `nw.col('a').fill_null(3).over('b')`
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# does not seem very useful, and DuckDB doesn't support it either.
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msg = "`fill_null` with `over` without `strategy` specified is not supported."
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raise NotImplementedError(msg)
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elif function_name == "len":
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if len(output_names) != 1: # pragma: no cover
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msg = "Safety check failed, please report a bug."
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raise AssertionError(msg)
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res_native = grouped.transform("size").to_frame(aliases[0])
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else:
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res_native = grouped[list(output_names)].transform(
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pandas_function_name, **pandas_kwargs
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)
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result_frame = df._with_native(res_native).rename(
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dict(zip(output_names, aliases))
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)
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results = [result_frame.get_column(name) for name in aliases]
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if order_by:
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for s in results:
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s._scatter_in_place(sorting_indices, s)
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return results
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if reverse:
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return [s._gather_slice(slice(None, None, -1)) for s in results]
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return results
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return self.__class__(
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func,
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depth=self._depth + 1,
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function_name=self._function_name + "->over",
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evaluate_output_names=self._evaluate_output_names,
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alias_output_names=self._alias_output_names,
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implementation=self._implementation,
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version=self._version,
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)
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