from __future__ import annotations from typing import TYPE_CHECKING from typing import Any from narwhals._pandas_like.utils import select_columns_by_name from narwhals.dependencies import get_pandas from narwhals.dependencies import get_pyarrow from narwhals.exceptions import InvalidIntoExprError from narwhals.utils import Implementation from narwhals.utils import import_dtypes_module from narwhals.utils import isinstance_or_issubclass from narwhals.utils import parse_version if TYPE_CHECKING: import dask.dataframe as dd import dask_expr from narwhals._dask.dataframe import DaskLazyFrame from narwhals._dask.expr import DaskExpr from narwhals.dtypes import DType from narwhals.utils import Version def maybe_evaluate(df: DaskLazyFrame, obj: Any) -> Any: from narwhals._dask.expr import DaskExpr if isinstance(obj, DaskExpr): results = obj._call(df) if len(results) != 1: # pragma: no cover msg = "Multi-output expressions (e.g. `nw.all()` or `nw.col('a', 'b')`) not supported in this context" raise NotImplementedError(msg) result = results[0] if not obj._returns_scalar: validate_comparand(df._native_frame, result) if obj._returns_scalar: # Return scalar, let Dask do its broadcasting return result[0] return result return obj def parse_exprs_and_named_exprs( df: DaskLazyFrame, *exprs: Any, **named_exprs: Any ) -> dict[str, dask_expr.Series]: results = {} for expr in exprs: if hasattr(expr, "__narwhals_expr__"): _results = expr._call(df) elif isinstance(expr, str): _results = [df._native_frame[expr]] else: raise InvalidIntoExprError.from_invalid_type(type(expr)) return_scalar = getattr(expr, "_returns_scalar", False) for _result in _results: results[_result.name] = _result[0] if return_scalar else _result for name, value in named_exprs.items(): _results = value._call(df) if len(_results) != 1: # pragma: no cover msg = "Named expressions must return a single column" raise AssertionError(msg) return_scalar = getattr(value, "_returns_scalar", False) for _result in _results: results[name] = _result[0] if return_scalar else _result return results def add_row_index( frame: dd.DataFrame, name: str, backend_version: tuple[int, ...], implementation: Implementation, ) -> dd.DataFrame: original_cols = frame.columns frame = frame.assign(**{name: 1}) return select_columns_by_name( frame.assign(**{name: frame[name].cumsum(method="blelloch") - 1}), [name, *original_cols], backend_version, implementation, ) def validate_comparand(lhs: dask_expr.Series, rhs: dask_expr.Series) -> None: import dask_expr if not dask_expr._expr.are_co_aligned(lhs._expr, rhs._expr): # pragma: no cover # are_co_aligned is a method which cheaply checks if two Dask expressions # have the same index, and therefore don't require index alignment. # If someone only operates on a Dask DataFrame via expressions, then this # should always be the case: expression outputs (by definition) all come from the # same input dataframe, and Dask Series does not have any operations which # change the index. Nonetheless, we perform this safety check anyway. # However, we still need to carefully vet which methods we support for Dask, to # avoid issues where `are_co_aligned` doesn't do what we want it to do: # https://github.com/dask/dask-expr/issues/1112. msg = "Objects are not co-aligned, so this operation is not supported for Dask backend" raise RuntimeError(msg) def narwhals_to_native_dtype(dtype: DType | type[DType], version: Version) -> Any: dtypes = import_dtypes_module(version) if isinstance_or_issubclass(dtype, dtypes.Float64): return "float64" if isinstance_or_issubclass(dtype, dtypes.Float32): return "float32" if isinstance_or_issubclass(dtype, dtypes.Int64): return "int64" if isinstance_or_issubclass(dtype, dtypes.Int32): return "int32" if isinstance_or_issubclass(dtype, dtypes.Int16): return "int16" if isinstance_or_issubclass(dtype, dtypes.Int8): return "int8" if isinstance_or_issubclass(dtype, dtypes.UInt64): return "uint64" if isinstance_or_issubclass(dtype, dtypes.UInt32): return "uint32" if isinstance_or_issubclass(dtype, dtypes.UInt16): return "uint16" if isinstance_or_issubclass(dtype, dtypes.UInt8): return "uint8" if isinstance_or_issubclass(dtype, dtypes.String): if (pd := get_pandas()) is not None and parse_version( pd.__version__ ) >= parse_version("2.0.0"): if get_pyarrow() is not None: return "string[pyarrow]" return "string[python]" # pragma: no cover return "object" # pragma: no cover if isinstance_or_issubclass(dtype, dtypes.Boolean): return "bool" if isinstance_or_issubclass(dtype, dtypes.Categorical): return "category" if isinstance_or_issubclass(dtype, dtypes.Datetime): return "datetime64[us]" if isinstance_or_issubclass(dtype, dtypes.Date): return "date32[day][pyarrow]" if isinstance_or_issubclass(dtype, dtypes.Duration): return "timedelta64[ns]" if isinstance_or_issubclass(dtype, dtypes.List): # pragma: no cover msg = "Converting to List dtype is not supported yet" return NotImplementedError(msg) if isinstance_or_issubclass(dtype, dtypes.Struct): # pragma: no cover msg = "Converting to Struct dtype is not supported yet" return NotImplementedError(msg) if isinstance_or_issubclass(dtype, dtypes.Array): # pragma: no cover msg = "Converting to Array dtype is not supported yet" return NotImplementedError(msg) msg = f"Unknown dtype: {dtype}" # pragma: no cover raise AssertionError(msg) def name_preserving_sum(s1: dask_expr.Series, s2: dask_expr.Series) -> dask_expr.Series: return (s1 + s2).rename(s1.name) def name_preserving_div(s1: dask_expr.Series, s2: dask_expr.Series) -> dask_expr.Series: return (s1 / s2).rename(s1.name) def binary_operation_returns_scalar(lhs: DaskExpr, rhs: DaskExpr | Any) -> bool: # If `rhs` is a DaskExpr, we look at `_returns_scalar`. If it isn't, # it means that it was a scalar (e.g. nw.col('a') + 1), and so we default # to `True`. return lhs._returns_scalar and getattr(rhs, "_returns_scalar", True)