550 lines
20 KiB
Python
Executable File
550 lines
20 KiB
Python
Executable File
from __future__ import annotations
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from functools import reduce
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from typing import TYPE_CHECKING
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from typing import Any
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from typing import Callable
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from typing import Iterable
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from typing import Literal
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from typing import cast
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from narwhals._expression_parsing import combine_root_names
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from narwhals._expression_parsing import parse_into_exprs
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from narwhals._expression_parsing import reduce_output_names
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from narwhals._pandas_like.dataframe import PandasLikeDataFrame
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from narwhals._pandas_like.expr import PandasLikeExpr
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from narwhals._pandas_like.selectors import PandasSelectorNamespace
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from narwhals._pandas_like.series import PandasLikeSeries
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from narwhals._pandas_like.utils import create_native_series
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from narwhals._pandas_like.utils import horizontal_concat
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from narwhals._pandas_like.utils import vertical_concat
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if TYPE_CHECKING:
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from narwhals._pandas_like.typing import IntoPandasLikeExpr
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from narwhals.dtypes import DType
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from narwhals.typing import DTypes
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from narwhals.utils import Implementation
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class PandasLikeNamespace:
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@property
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def selectors(self) -> PandasSelectorNamespace:
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return PandasSelectorNamespace(
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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# --- not in spec ---
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def __init__(
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self,
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implementation: Implementation,
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backend_version: tuple[int, ...],
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dtypes: DTypes,
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) -> None:
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self._implementation = implementation
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self._backend_version = backend_version
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self._dtypes = dtypes
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def _create_expr_from_callable(
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self,
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func: Callable[[PandasLikeDataFrame], list[PandasLikeSeries]],
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*,
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depth: int,
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function_name: str,
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root_names: list[str] | None,
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output_names: list[str] | None,
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) -> PandasLikeExpr:
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return PandasLikeExpr(
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func,
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depth=depth,
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function_name=function_name,
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root_names=root_names,
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output_names=output_names,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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def _create_series_from_scalar(
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self, value: Any, series: PandasLikeSeries
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) -> PandasLikeSeries:
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return PandasLikeSeries._from_iterable(
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[value],
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name=series._native_series.name,
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index=series._native_series.index[0:1],
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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def _create_expr_from_series(self, series: PandasLikeSeries) -> PandasLikeExpr:
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return PandasLikeExpr(
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lambda _df: [series],
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depth=0,
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function_name="series",
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root_names=None,
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output_names=None,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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def _create_compliant_series(self, value: Any) -> PandasLikeSeries:
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return create_native_series(
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value,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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# --- selection ---
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def col(self, *column_names: str) -> PandasLikeExpr:
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return PandasLikeExpr.from_column_names(
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*column_names,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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def nth(self, *column_indices: int) -> PandasLikeExpr:
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return PandasLikeExpr.from_column_indices(
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*column_indices,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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def all(self) -> PandasLikeExpr:
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return PandasLikeExpr(
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lambda df: [
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PandasLikeSeries(
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df._native_frame[column_name],
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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for column_name in df.columns
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],
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depth=0,
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function_name="all",
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root_names=None,
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output_names=None,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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def lit(self, value: Any, dtype: DType | None) -> PandasLikeExpr:
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def _lit_pandas_series(df: PandasLikeDataFrame) -> PandasLikeSeries:
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pandas_series = PandasLikeSeries._from_iterable(
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data=[value],
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name="lit",
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index=df._native_frame.index[0:1],
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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if dtype:
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return pandas_series.cast(dtype)
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return pandas_series
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return PandasLikeExpr(
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lambda df: [_lit_pandas_series(df)],
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depth=0,
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function_name="lit",
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root_names=None,
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output_names=["lit"],
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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# --- reduction ---
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def sum(self, *column_names: str) -> PandasLikeExpr:
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return PandasLikeExpr.from_column_names(
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*column_names,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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).sum()
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def mean(self, *column_names: str) -> PandasLikeExpr:
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return PandasLikeExpr.from_column_names(
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*column_names,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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).mean()
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def max(self, *column_names: str) -> PandasLikeExpr:
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return PandasLikeExpr.from_column_names(
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*column_names,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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).max()
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def min(self, *column_names: str) -> PandasLikeExpr:
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return PandasLikeExpr.from_column_names(
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*column_names,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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).min()
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def len(self) -> PandasLikeExpr:
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return PandasLikeExpr(
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lambda df: [
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PandasLikeSeries._from_iterable(
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[len(df._native_frame)],
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name="len",
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index=[0],
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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],
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depth=0,
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function_name="len",
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root_names=None,
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output_names=["len"],
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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# --- horizontal ---
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def sum_horizontal(self, *exprs: IntoPandasLikeExpr) -> PandasLikeExpr:
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parsed_exprs = parse_into_exprs(*exprs, namespace=self)
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = (s.fill_null(0) for _expr in parsed_exprs for s in _expr._call(df))
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return [reduce(lambda x, y: x + y, series)]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="sum_horizontal",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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def all_horizontal(self, *exprs: IntoPandasLikeExpr) -> PandasLikeExpr:
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parsed_exprs = parse_into_exprs(*exprs, namespace=self)
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = (s for _expr in parsed_exprs for s in _expr._call(df))
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return [reduce(lambda x, y: x & y, series)]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="all_horizontal",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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def any_horizontal(self, *exprs: IntoPandasLikeExpr) -> PandasLikeExpr:
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parsed_exprs = parse_into_exprs(*exprs, namespace=self)
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = (s for _expr in parsed_exprs for s in _expr._call(df))
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return [reduce(lambda x, y: x | y, series)]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="any_horizontal",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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def mean_horizontal(self, *exprs: IntoPandasLikeExpr) -> PandasLikeExpr:
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parsed_exprs = parse_into_exprs(*exprs, namespace=self)
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = (s.fill_null(0) for _expr in parsed_exprs for s in _expr._call(df))
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non_na = (1 - s.is_null() for _expr in parsed_exprs for s in _expr._call(df))
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return [
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reduce(lambda x, y: x + y, series) / reduce(lambda x, y: x + y, non_na)
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]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="mean_horizontal",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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def min_horizontal(self, *exprs: IntoPandasLikeExpr) -> PandasLikeExpr:
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parsed_exprs = parse_into_exprs(*exprs, namespace=self)
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = [s for _expr in parsed_exprs for s in _expr._call(df)]
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return [
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PandasLikeSeries(
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native_series=self.concat(
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(s.to_frame() for s in series), how="horizontal"
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)
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._native_frame.min(axis=1)
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.rename(series[0].name, copy=False),
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="min_horizontal",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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def max_horizontal(self, *exprs: IntoPandasLikeExpr) -> PandasLikeExpr:
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parsed_exprs = parse_into_exprs(*exprs, namespace=self)
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = [s for _expr in parsed_exprs for s in _expr._call(df)]
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return [
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PandasLikeSeries(
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native_series=self.concat(
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(s.to_frame() for s in series), how="horizontal"
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)
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._native_frame.max(axis=1)
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.rename(series[0].name, copy=False),
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="max_horizontal",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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def concat(
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self,
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items: Iterable[PandasLikeDataFrame],
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*,
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how: Literal["horizontal", "vertical"],
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) -> PandasLikeDataFrame:
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dfs: list[Any] = [item._native_frame for item in items]
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if how == "horizontal":
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return PandasLikeDataFrame(
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horizontal_concat(
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dfs,
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implementation=self._implementation,
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backend_version=self._backend_version,
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),
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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if how == "vertical":
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return PandasLikeDataFrame(
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vertical_concat(
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dfs,
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implementation=self._implementation,
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backend_version=self._backend_version,
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),
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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raise NotImplementedError
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def when(
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self,
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*predicates: IntoPandasLikeExpr,
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) -> PandasWhen:
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plx = self.__class__(
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self._implementation, self._backend_version, dtypes=self._dtypes
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)
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if predicates:
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condition = plx.all_horizontal(*predicates)
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else:
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msg = "at least one predicate needs to be provided"
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raise TypeError(msg)
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return PandasWhen(
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condition, self._implementation, self._backend_version, dtypes=self._dtypes
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)
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def concat_str(
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self,
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exprs: Iterable[IntoPandasLikeExpr],
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*more_exprs: IntoPandasLikeExpr,
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separator: str = "",
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ignore_nulls: bool = False,
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) -> PandasLikeExpr:
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parsed_exprs: list[PandasLikeExpr] = [
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*parse_into_exprs(*exprs, namespace=self),
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*parse_into_exprs(*more_exprs, namespace=self),
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]
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def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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series = (
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s
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for _expr in parsed_exprs
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for s in _expr.cast(self._dtypes.String())._call(df)
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)
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null_mask = [s for _expr in parsed_exprs for s in _expr.is_null()._call(df)]
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if not ignore_nulls:
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null_mask_result = reduce(lambda x, y: x | y, null_mask)
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result = reduce(lambda x, y: x + separator + y, series).zip_with(
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~null_mask_result, None
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)
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else:
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init_value, *values = [
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s.zip_with(~nm, "") for s, nm in zip(series, null_mask)
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]
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sep_array = init_value.__class__._from_iterable(
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data=[separator] * len(init_value),
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name="sep",
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index=init_value._native_series.index,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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separators = (sep_array.zip_with(~nm, "") for nm in null_mask[:-1])
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result = reduce(
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lambda x, y: x + y,
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(s + v for s, v in zip(separators, values)),
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init_value,
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)
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return [result]
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return self._create_expr_from_callable(
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func=func,
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depth=max(x._depth for x in parsed_exprs) + 1,
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function_name="concat_str",
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root_names=combine_root_names(parsed_exprs),
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output_names=reduce_output_names(parsed_exprs),
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)
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class PandasWhen:
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def __init__(
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self,
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condition: PandasLikeExpr,
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implementation: Implementation,
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backend_version: tuple[int, ...],
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then_value: Any = None,
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otherwise_value: Any = None,
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*,
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dtypes: DTypes,
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) -> None:
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self._implementation = implementation
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self._backend_version = backend_version
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self._condition = condition
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self._then_value = then_value
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self._otherwise_value = otherwise_value
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self._dtypes = dtypes
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def __call__(self, df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
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from narwhals._expression_parsing import parse_into_expr
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from narwhals._pandas_like.namespace import PandasLikeNamespace
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from narwhals._pandas_like.utils import validate_column_comparand
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plx = PandasLikeNamespace(
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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condition = parse_into_expr(self._condition, namespace=plx)._call(df)[0] # type: ignore[arg-type]
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try:
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value_series = parse_into_expr(self._then_value, namespace=plx)._call(df)[0] # type: ignore[arg-type]
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except TypeError:
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# `self._otherwise_value` is a scalar and can't be converted to an expression
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value_series = condition.__class__._from_iterable( # type: ignore[call-arg]
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[self._then_value] * len(condition),
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name="literal",
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index=condition._native_series.index,
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implementation=self._implementation,
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backend_version=self._backend_version,
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dtypes=self._dtypes,
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)
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value_series = cast(PandasLikeSeries, value_series)
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value_series_native = value_series._native_series
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condition_native = validate_column_comparand(value_series_native.index, condition)
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if self._otherwise_value is None:
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return [
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value_series._from_native_series(
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value_series_native.where(condition_native)
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)
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]
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try:
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otherwise_series = parse_into_expr(
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self._otherwise_value, namespace=plx
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)._call(df)[0] # type: ignore[arg-type]
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except TypeError:
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# `self._otherwise_value` is a scalar and can't be converted to an expression
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return [
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value_series._from_native_series(
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value_series_native.where(condition_native, self._otherwise_value)
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)
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]
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else:
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return [value_series.zip_with(condition, otherwise_series)]
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def then(self, value: PandasLikeExpr | PandasLikeSeries | Any) -> PandasThen:
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self._then_value = value
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|
|
return PandasThen(
|
|
self,
|
|
depth=0,
|
|
function_name="whenthen",
|
|
root_names=None,
|
|
output_names=None,
|
|
implementation=self._implementation,
|
|
backend_version=self._backend_version,
|
|
dtypes=self._dtypes,
|
|
)
|
|
|
|
|
|
class PandasThen(PandasLikeExpr):
|
|
def __init__(
|
|
self,
|
|
call: PandasWhen,
|
|
*,
|
|
depth: int,
|
|
function_name: str,
|
|
root_names: list[str] | None,
|
|
output_names: list[str] | None,
|
|
implementation: Implementation,
|
|
backend_version: tuple[int, ...],
|
|
dtypes: DTypes,
|
|
) -> None:
|
|
self._implementation = implementation
|
|
self._backend_version = backend_version
|
|
self._dtypes = dtypes
|
|
self._call = call
|
|
self._depth = depth
|
|
self._function_name = function_name
|
|
self._root_names = root_names
|
|
self._output_names = output_names
|
|
|
|
def otherwise(self, value: PandasLikeExpr | PandasLikeSeries | Any) -> PandasLikeExpr:
|
|
# type ignore because we are setting the `_call` attribute to a
|
|
# callable object of type `PandasWhen`, base class has the attribute as
|
|
# only a `Callable`
|
|
self._call._otherwise_value = value # type: ignore[attr-defined]
|
|
self._function_name = "whenotherwise"
|
|
return self
|