162 lines
6.4 KiB
Python
162 lines
6.4 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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from typing import Any
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from typing import Sequence
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from narwhals._expression_parsing import evaluate_output_names_and_aliases
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from narwhals._pandas_like.utils import select_columns_by_name
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from narwhals.dependencies import get_pandas
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from narwhals.dependencies import get_pyarrow
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from narwhals.utils import Implementation
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from narwhals.utils import import_dtypes_module
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from narwhals.utils import isinstance_or_issubclass
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from narwhals.utils import parse_version
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try:
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import dask.dataframe.dask_expr as dx
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except ModuleNotFoundError: # pragma: no cover
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import dask_expr as dx
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if TYPE_CHECKING:
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import dask.dataframe as dd
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from narwhals._dask.dataframe import DaskLazyFrame
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from narwhals._dask.expr import DaskExpr
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from narwhals.dtypes import DType
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from narwhals.utils import Version
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def maybe_evaluate_expr(df: DaskLazyFrame, obj: DaskExpr | object) -> dx.Series | object:
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from narwhals._dask.expr import DaskExpr
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if isinstance(obj, DaskExpr):
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results = obj._call(df)
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assert len(results) == 1 # debug assertion # noqa: S101
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return results[0]
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return obj
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def evaluate_exprs(df: DaskLazyFrame, /, *exprs: DaskExpr) -> list[tuple[str, dx.Series]]:
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native_results: list[tuple[str, dx.Series]] = []
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for expr in exprs:
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native_series_list = expr._call(df)
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_, aliases = evaluate_output_names_and_aliases(expr, df, [])
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if len(aliases) != len(native_series_list): # pragma: no cover
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msg = f"Internal error: got aliases {aliases}, but only got {len(native_series_list)} results"
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raise AssertionError(msg)
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native_results.extend(zip(aliases, native_series_list))
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return native_results
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def align_series_full_broadcast(
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df: DaskLazyFrame, *series: dx.Series | object
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) -> Sequence[dx.Series]:
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return [
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s if isinstance(s, dx.Series) else df._native_frame.assign(_tmp=s)["_tmp"]
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for s in series
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] # pyright: ignore[reportReturnType]
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def add_row_index(
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frame: dd.DataFrame,
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name: str,
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backend_version: tuple[int, ...],
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implementation: Implementation,
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) -> dd.DataFrame:
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original_cols = frame.columns
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frame = frame.assign(**{name: 1})
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return select_columns_by_name(
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frame.assign(**{name: frame[name].cumsum(method="blelloch") - 1}),
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[name, *original_cols],
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backend_version,
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implementation,
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)
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def validate_comparand(lhs: dx.Series, rhs: dx.Series) -> None:
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try:
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import dask.dataframe.dask_expr as dx
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except ModuleNotFoundError: # pragma: no cover
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import dask_expr as dx
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if not dx.expr.are_co_aligned(lhs._expr, rhs._expr): # pragma: no cover
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# are_co_aligned is a method which cheaply checks if two Dask expressions
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# have the same index, and therefore don't require index alignment.
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# If someone only operates on a Dask DataFrame via expressions, then this
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# should always be the case: expression outputs (by definition) all come from the
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# same input dataframe, and Dask Series does not have any operations which
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# change the index. Nonetheless, we perform this safety check anyway.
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# However, we still need to carefully vet which methods we support for Dask, to
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# avoid issues where `are_co_aligned` doesn't do what we want it to do:
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# https://github.com/dask/dask-expr/issues/1112.
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msg = "Objects are not co-aligned, so this operation is not supported for Dask backend"
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raise RuntimeError(msg)
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def narwhals_to_native_dtype(dtype: DType | type[DType], version: Version) -> Any:
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dtypes = import_dtypes_module(version)
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if isinstance_or_issubclass(dtype, dtypes.Float64):
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return "float64"
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if isinstance_or_issubclass(dtype, dtypes.Float32):
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return "float32"
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if isinstance_or_issubclass(dtype, dtypes.Int64):
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return "int64"
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if isinstance_or_issubclass(dtype, dtypes.Int32):
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return "int32"
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if isinstance_or_issubclass(dtype, dtypes.Int16):
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return "int16"
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if isinstance_or_issubclass(dtype, dtypes.Int8):
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return "int8"
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if isinstance_or_issubclass(dtype, dtypes.UInt64):
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return "uint64"
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if isinstance_or_issubclass(dtype, dtypes.UInt32):
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return "uint32"
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if isinstance_or_issubclass(dtype, dtypes.UInt16):
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return "uint16"
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if isinstance_or_issubclass(dtype, dtypes.UInt8):
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return "uint8"
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if isinstance_or_issubclass(dtype, dtypes.String):
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if (pd := get_pandas()) is not None and parse_version(pd) >= (2, 0, 0):
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if get_pyarrow() is not None:
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return "string[pyarrow]"
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return "string[python]" # pragma: no cover
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return "object" # pragma: no cover
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if isinstance_or_issubclass(dtype, dtypes.Boolean):
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return "bool"
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if isinstance_or_issubclass(dtype, dtypes.Categorical):
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return "category"
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if isinstance_or_issubclass(dtype, dtypes.Datetime):
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return "datetime64[us]"
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if isinstance_or_issubclass(dtype, dtypes.Date):
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return "date32[day][pyarrow]"
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if isinstance_or_issubclass(dtype, dtypes.Duration):
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return "timedelta64[ns]"
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if isinstance_or_issubclass(dtype, dtypes.List): # pragma: no cover
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msg = "Converting to List dtype is not supported yet"
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return NotImplementedError(msg)
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if isinstance_or_issubclass(dtype, dtypes.Struct): # pragma: no cover
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msg = "Converting to Struct dtype is not supported yet"
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return NotImplementedError(msg)
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if isinstance_or_issubclass(dtype, dtypes.Array): # pragma: no cover
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msg = "Converting to Array dtype is not supported yet"
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return NotImplementedError(msg)
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if isinstance_or_issubclass(dtype, dtypes.Time): # pragma: no cover
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msg = "Converting to Time dtype is not supported yet"
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return NotImplementedError(msg)
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if isinstance_or_issubclass(dtype, dtypes.Binary): # pragma: no cover
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msg = "Converting to Binary dtype is not supported yet"
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return NotImplementedError(msg)
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msg = f"Unknown dtype: {dtype}" # pragma: no cover
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raise AssertionError(msg)
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def name_preserving_sum(s1: dx.Series, s2: dx.Series) -> dx.Series:
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return (s1 + s2).rename(s1.name) # pyright: ignore[reportOperatorIssue]
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def name_preserving_div(s1: dx.Series, s2: dx.Series) -> dx.Series:
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return (s1 / s2).rename(s1.name) # pyright: ignore[reportOperatorIssue]
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