chatExcel

from __future__ import annotations import contextlib import operator from typing import TYPE_CHECKING, Any, Callable, Literal, Sequence, cast from duckdb import CoalesceOperator, FunctionExpression, StarExpression from duckdb.typing import DuckDBPyType from narwhals._compliant import LazyExpr from narwhals._compliant.window import WindowInputs from narwhals._duckdb.expr_dt import DuckDBExprDateTimeNamespace from narwhals._duckdb.expr_list import DuckDBExprListNamespace from narwhals._duckdb.expr_str import DuckDBExprStringNamespace from narwhals._duckdb.expr_struct import DuckDBExprStructNamespace from narwhals._duckdb.utils import ( col, generate_order_by_sql, generate_partition_by_sql, lit, narwhals_to_native_dtype, when, ) from narwhals._expression_parsing import ExprKind from narwhals._utils import Implementation, not_implemented, requires if TYPE_CHECKING: from duckdb import Expression from typing_extensions import Self from narwhals._compliant.typing import ( AliasNames, EvalNames, EvalSeries, WindowFunction, ) from narwhals._duckdb.dataframe import DuckDBLazyFrame from narwhals._duckdb.namespace import DuckDBNamespace from narwhals._expression_parsing import ExprMetadata from narwhals._utils import Version, _FullContext from narwhals.dtypes import DType from narwhals.typing import ( FillNullStrategy, NonNestedLiteral, NumericLiteral, RankMethod, RollingInterpolationMethod, TemporalLiteral, ) DuckDBWindowFunction = WindowFunction[DuckDBLazyFrame, Expression] DuckDBWindowInputs = WindowInputs[Expression] with contextlib.suppress(ImportError): # requires duckdb>=1.3.0 from duckdb import SQLExpression class DuckDBExpr(LazyExpr["DuckDBLazyFrame", "Expression"]): _implementation = Implementation.DUCKDB def __init__( self, call: EvalSeries[DuckDBLazyFrame, Expression], window_function: DuckDBWindowFunction | None = None, *, evaluate_output_names: EvalNames[DuckDBLazyFrame], alias_output_names: AliasNames | None, backend_version: tuple[int, ...], version: Version, ) -> None: self._call = call self._evaluate_output_names = evaluate_output_names self._alias_output_names = alias_output_names self._backend_version = backend_version self._version = version self._metadata: ExprMetadata | None = None self._window_function: DuckDBWindowFunction | None = window_function @property def window_function(self) -> DuckDBWindowFunction: def default_window_func( df: DuckDBLazyFrame, window_inputs: DuckDBWindowInputs ) -> list[Expression]: assert not window_inputs.order_by # noqa: S101 partition_by_sql = generate_partition_by_sql(*window_inputs.partition_by) template = f"{{expr}} over ({partition_by_sql})" return [SQLExpression(template.format(expr=expr)) for expr in self(df)] return self._window_function or default_window_func def __call__(self, df: DuckDBLazyFrame) -> Sequence[Expression]: return self._call(df) def __narwhals_expr__(self) -> None: ... def __narwhals_namespace__(self) -> DuckDBNamespace: # pragma: no cover # Unused, just for compatibility with PandasLikeExpr from narwhals._duckdb.namespace import DuckDBNamespace return DuckDBNamespace( backend_version=self._backend_version, version=self._version ) def _cum_window_func( self, *, reverse: bool, func_name: Literal["sum", "max", "min", "count", "product"], ) -> DuckDBWindowFunction: def func(df: DuckDBLazyFrame, inputs: DuckDBWindowInputs) -> list[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=not reverse) partition_by_sql = generate_partition_by_sql(*inputs.partition_by) sql = ( f"{func_name} ({{expr}}) over ({partition_by_sql} {order_by_sql} " "rows between unbounded preceding and current row)" ) return [SQLExpression(sql.format(expr=expr)) for expr in self(df)] return func def _rolling_window_func( self, *, func_name: Literal["sum", "mean", "std", "var"], center: bool, window_size: int, min_samples: int, ddof: int | None = None, ) -> DuckDBWindowFunction: supported_funcs = ["sum", "mean", "std", "var"] if center: half = (window_size - 1) // 2 remainder = (window_size - 1) % 2 start = f"{half + remainder} preceding" end = f"{half} following" else: start = f"{window_size - 1} preceding" end = "current row" def func(df: DuckDBLazyFrame, inputs: DuckDBWindowInputs) -> list[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=True) partition_by_sql = generate_partition_by_sql(*inputs.partition_by) window = f"({partition_by_sql} {order_by_sql} rows between {start} and {end})" if func_name in {"sum", "mean"}: func_: str = func_name elif func_name == "var" and ddof == 0: func_ = "var_pop" elif func_name in "var" and ddof == 1: func_ = "var_samp" elif func_name == "std" and ddof == 0: func_ = "stddev_pop" elif func_name == "std" and ddof == 1: func_ = "stddev_samp" elif func_name in {"var", "std"}: # pragma: no cover msg = f"Only ddof=0 and ddof=1 are currently supported for rolling_{func_name}." raise ValueError(msg) else: # pragma: no cover msg = f"Only the following functions are supported: {supported_funcs}.\nGot: {func_name}." raise ValueError(msg) condition_sql = f"count({{expr}}) over {window} >= {min_samples}" value_sql = f"{func_}({{expr}}) over {window}" return [ when( SQLExpression(condition_sql.format(expr=expr)), SQLExpression(value_sql.format(expr=expr)), ) for expr in self(df) ] return func def broadcast(self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]) -> Self: if kind is ExprKind.LITERAL: return self if self._backend_version < (1, 3): msg = "At least version 1.3 of DuckDB is required for binary operations between aggregates and columns." raise NotImplementedError(msg) return self.over([lit(1)], []) @classmethod def from_column_names( cls, evaluate_column_names: EvalNames[DuckDBLazyFrame], /, *, context: _FullContext, ) -> Self: def func(df: DuckDBLazyFrame) -> list[Expression]: return [col(name) for name in evaluate_column_names(df)] return cls( func, evaluate_output_names=evaluate_column_names, alias_output_names=None, backend_version=context._backend_version, version=context._version, ) @classmethod def from_column_indices(cls, *column_indices: int, context: _FullContext) -> Self: def func(df: DuckDBLazyFrame) -> list[Expression]: columns = df.columns return [col(columns[i]) for i in column_indices] return cls( func, evaluate_output_names=cls._eval_names_indices(column_indices), alias_output_names=None, backend_version=context._backend_version, version=context._version, ) def _callable_to_eval_series( self, call: Callable[..., Expression], /, **expressifiable_args: Self | Any ) -> EvalSeries[DuckDBLazyFrame, Expression]: def func(df: DuckDBLazyFrame) -> list[Expression]: native_series_list = self(df) other_native_series = { key: df._evaluate_expr(value) if self._is_expr(value) else lit(value) for key, value in expressifiable_args.items() } return [ call(native_series, **other_native_series) for native_series in native_series_list ] return func def _push_down_window_function( self, call: Callable[..., Expression], /, **expressifiable_args: Self | Any ) -> DuckDBWindowFunction: def window_f( df: DuckDBLazyFrame, window_inputs: DuckDBWindowInputs ) -> list[Expression]: # If a function `f` is elementwise, and `g` is another function, then # - `f(g) over (window)` # - `f(g over (window)) # are equivalent. # Make sure to only use with if `call` is elementwise! native_series_list = self.window_function(df, window_inputs) other_native_series = { key: df._evaluate_window_expr(value, window_inputs) if self._is_expr(value) else lit(value) for key, value in expressifiable_args.items() } return [ call(native_series, **other_native_series) for native_series in native_series_list ] return window_f def _with_callable( self, call: Callable[..., Expression], /, **expressifiable_args: Self | Any ) -> Self: """Create expression from callable. Arguments: call: Callable from compliant DataFrame to native Expression expr_name: Expression name expressifiable_args: arguments pass to expression which should be parsed as expressions (e.g. in `nw.col('a').is_between('b', 'c')`) """ return self.__class__( self._callable_to_eval_series(call, **expressifiable_args), evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, version=self._version, ) def _with_elementwise( self, call: Callable[..., Expression], /, **expressifiable_args: Self | Any ) -> Self: return self.__class__( self._callable_to_eval_series(call, **expressifiable_args), self._push_down_window_function(call, **expressifiable_args), evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, version=self._version, ) def _with_binary(self, op: Callable[..., Expression], other: Self | Any) -> Self: return self.__class__( self._callable_to_eval_series(op, other=other), self._push_down_window_function(op, other=other), evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, version=self._version, ) def _with_alias_output_names(self, func: AliasNames | None, /) -> Self: return type(self)( self._call, self._window_function, evaluate_output_names=self._evaluate_output_names, alias_output_names=func, backend_version=self._backend_version, version=self._version, ) def _with_window_function(self, window_function: DuckDBWindowFunction) -> Self: return self.__class__( self._call, window_function, evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, version=self._version, ) @classmethod def _alias_native(cls, expr: Expression, name: str) -> Expression: return expr.alias(name) def __and__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr & other, other) def __or__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr | other, other) def __add__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr + other, other) def __truediv__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr / other, other) def __rtruediv__(self, other: DuckDBExpr) -> Self: return self._with_binary( lambda expr, other: other.__truediv__(expr), other ).alias("literal") def __floordiv__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr // other, other) def __rfloordiv__(self, other: DuckDBExpr) -> Self: return self._with_binary( lambda expr, other: other.__floordiv__(expr), other ).alias("literal") def __mod__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr % other, other) def __rmod__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: other.__mod__(expr), other).alias( "literal" ) def __sub__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr - other, other) def __rsub__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: other.__sub__(expr), other).alias( "literal" ) def __mul__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr * other, other) def __pow__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr**other, other) def __rpow__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: other.__pow__(expr), other).alias( "literal" ) def __lt__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr < other, other) def __gt__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr > other, other) def __le__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr <= other, other) def __ge__(self, other: DuckDBExpr) -> Self: return self._with_binary(lambda expr, other: expr >= other, other) def __eq__(self, other: DuckDBExpr) -> Self: # type: ignore[override] return self._with_binary(lambda expr, other: expr == other, other) def __ne__(self, other: DuckDBExpr) -> Self: # type: ignore[override] return self._with_binary(lambda expr, other: expr != other, other) def __invert__(self) -> Self: invert = cast("Callable[..., Expression]", operator.invert) return self._with_elementwise(invert) def abs(self) -> Self: return self._with_elementwise(lambda expr: FunctionExpression("abs", expr)) def mean(self) -> Self: return self._with_callable(lambda expr: FunctionExpression("mean", expr)) def skew(self) -> Self: def func(expr: Expression) -> Expression: count = FunctionExpression("count", expr) # Adjust population skewness by correction factor to get sample skewness sample_skewness = ( FunctionExpression("skewness", expr) * (count - lit(2)) / FunctionExpression("sqrt", count * (count - lit(1))) ) return when(count == lit(0), lit(None)).otherwise( when(count == lit(1), lit(float("nan"))).otherwise( when(count == lit(2), lit(0.0)).otherwise(sample_skewness) ) ) return self._with_callable(func) def median(self) -> Self: return self._with_callable(lambda expr: FunctionExpression("median", expr)) def all(self) -> Self: def f(expr: Expression) -> Expression: return CoalesceOperator(FunctionExpression("bool_and", expr), lit(True)) # noqa: FBT003 def window_f( df: DuckDBLazyFrame, window_inputs: DuckDBWindowInputs ) -> list[Expression]: pb = generate_partition_by_sql(*window_inputs.partition_by) return [ CoalesceOperator( SQLExpression(f"{FunctionExpression('bool_and', expr)} over ({pb})"), lit(True), # noqa: FBT003 ) for expr in self(df) ] return self._with_callable(f)._with_window_function(window_f) def any(self) -> Self: def f(expr: Expression) -> Expression: return CoalesceOperator(FunctionExpression("bool_or", expr), lit(False)) # noqa: FBT003 def window_f( df: DuckDBLazyFrame, window_inputs: DuckDBWindowInputs ) -> list[Expression]: pb = generate_partition_by_sql(*window_inputs.partition_by) return [ CoalesceOperator( SQLExpression(f"{FunctionExpression('bool_or', expr)} over ({pb})"), lit(False), # noqa: FBT003 ) for expr in self(df) ] return self._with_callable(f)._with_window_function(window_f) def quantile( self, quantile: float, interpolation: RollingInterpolationMethod ) -> Self: def func(expr: Expression) -> Expression: if interpolation == "linear": return FunctionExpression("quantile_cont", expr, lit(quantile)) msg = "Only linear interpolation methods are supported for DuckDB quantile." raise NotImplementedError(msg) return self._with_callable(func) def clip( self, lower_bound: Self | NumericLiteral | TemporalLiteral | None, upper_bound: Self | NumericLiteral | TemporalLiteral | None, ) -> Self: def _clip_lower(expr: Expression, lower_bound: Any) -> Expression: return FunctionExpression("greatest", expr, lower_bound) def _clip_upper(expr: Expression, upper_bound: Any) -> Expression: return FunctionExpression("least", expr, upper_bound) def _clip_both( expr: Expression, lower_bound: Any, upper_bound: Any ) -> Expression: return FunctionExpression( "greatest", FunctionExpression("least", expr, upper_bound), lower_bound ) if lower_bound is None: return self._with_elementwise(_clip_upper, upper_bound=upper_bound) if upper_bound is None: return self._with_elementwise(_clip_lower, lower_bound=lower_bound) return self._with_elementwise( _clip_both, lower_bound=lower_bound, upper_bound=upper_bound ) def sum(self) -> Self: def f(expr: Expression) -> Expression: return CoalesceOperator(FunctionExpression("sum", expr), lit(0)) def window_f( df: DuckDBLazyFrame, window_inputs: DuckDBWindowInputs ) -> list[Expression]: pb = generate_partition_by_sql(*window_inputs.partition_by) return [ CoalesceOperator( SQLExpression(f"{FunctionExpression('sum', expr)} over ({pb})"), lit(0), ) for expr in self(df) ] return self._with_callable(f)._with_window_function(window_f) def n_unique(self) -> Self: def func(expr: Expression) -> Expression: # https://stackoverflow.com/a/79338887/4451315 return FunctionExpression( "array_unique", FunctionExpression("array_agg", expr) ) + FunctionExpression( "max", when(expr.isnotnull(), lit(0)).otherwise(lit(1)) ) return self._with_callable(func) def count(self) -> Self: return self._with_callable(lambda expr: FunctionExpression("count", expr)) def len(self) -> Self: return self._with_callable(lambda _expr: FunctionExpression("count")) def std(self, ddof: int) -> Self: if ddof == 0: return self._with_callable( lambda expr: FunctionExpression("stddev_pop", expr) ) if ddof == 1: return self._with_callable( lambda expr: FunctionExpression("stddev_samp", expr) ) def _std(expr: Expression) -> Expression: n_samples = FunctionExpression("count", expr) return ( FunctionExpression("stddev_pop", expr) * FunctionExpression("sqrt", n_samples) / (FunctionExpression("sqrt", (n_samples - lit(ddof)))) ) return self._with_callable(_std) def var(self, ddof: int) -> Self: if ddof == 0: return self._with_callable(lambda expr: FunctionExpression("var_pop", expr)) if ddof == 1: return self._with_callable(lambda expr: FunctionExpression("var_samp", expr)) def _var(expr: Expression) -> Expression: n_samples = FunctionExpression("count", expr) return ( FunctionExpression("var_pop", expr) * n_samples / (n_samples - lit(ddof)) ) return self._with_callable(_var) def max(self) -> Self: return self._with_callable(lambda expr: FunctionExpression("max", expr)) def min(self) -> Self: return self._with_callable(lambda expr: FunctionExpression("min", expr)) def null_count(self) -> Self: return self._with_callable( lambda expr: FunctionExpression("sum", expr.isnull().cast("int")) ) @requires.backend_version((1, 3)) def over( self, partition_by: Sequence[str | Expression], order_by: Sequence[str] ) -> Self: def func(df: DuckDBLazyFrame) -> Sequence[Expression]: return self.window_function(df, WindowInputs(partition_by, order_by)) return self.__class__( func, evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, version=self._version, ) def is_null(self) -> Self: return self._with_elementwise(lambda expr: expr.isnull()) def is_nan(self) -> Self: return self._with_elementwise(lambda expr: FunctionExpression("isnan", expr)) def is_finite(self) -> Self: return self._with_elementwise(lambda expr: FunctionExpression("isfinite", expr)) def is_in(self, other: Sequence[Any]) -> Self: return self._with_elementwise( lambda expr: FunctionExpression("contains", lit(other), expr) ) def round(self, decimals: int) -> Self: return self._with_elementwise( lambda expr: FunctionExpression("round", expr, lit(decimals)) ) @requires.backend_version((1, 3)) def shift(self, n: int) -> Self: def func(df: DuckDBLazyFrame, inputs: DuckDBWindowInputs) -> Sequence[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=True) partition_by_sql = generate_partition_by_sql(*inputs.partition_by) sql = f"lag({{expr}}, {n}) over ({partition_by_sql} {order_by_sql})" return [SQLExpression(sql.format(expr=expr)) for expr in self(df)] return self._with_window_function(func) @requires.backend_version((1, 3)) def is_first_distinct(self) -> Self: def func(df: DuckDBLazyFrame, inputs: DuckDBWindowInputs) -> Sequence[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=True) if inputs.partition_by: partition_by_sql = ( generate_partition_by_sql(*inputs.partition_by) + ", {expr}" ) else: partition_by_sql = "partition by {expr}" sql = ( f"{FunctionExpression('row_number')} " f"over({partition_by_sql} {order_by_sql})" ) return [SQLExpression(sql.format(expr=expr)) == lit(1) for expr in self(df)] return self._with_window_function(func) @requires.backend_version((1, 3)) def is_last_distinct(self) -> Self: def func(df: DuckDBLazyFrame, inputs: DuckDBWindowInputs) -> Sequence[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=False) if inputs.partition_by: partition_by_sql = ( generate_partition_by_sql(*inputs.partition_by) + ", {expr}" ) else: partition_by_sql = "partition by {expr}" sql = ( f"{FunctionExpression('row_number')} " f"over({partition_by_sql} {order_by_sql})" ) return [SQLExpression(sql.format(expr=expr)) == lit(1) for expr in self(df)] return self._with_window_function(func) @requires.backend_version((1, 3)) def diff(self) -> Self: def func(df: DuckDBLazyFrame, inputs: DuckDBWindowInputs) -> list[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=True) partition_by_sql = generate_partition_by_sql(*inputs.partition_by) sql = f"lag({{expr}}) over ({partition_by_sql} {order_by_sql})" return [expr - SQLExpression(sql.format(expr=expr)) for expr in self(df)] return self._with_window_function(func) @requires.backend_version((1, 3)) def cum_sum(self, *, reverse: bool) -> Self: return self._with_window_function( self._cum_window_func(reverse=reverse, func_name="sum") ) @requires.backend_version((1, 3)) def cum_max(self, *, reverse: bool) -> Self: return self._with_window_function( self._cum_window_func(reverse=reverse, func_name="max") ) @requires.backend_version((1, 3)) def cum_min(self, *, reverse: bool) -> Self: return self._with_window_function( self._cum_window_func(reverse=reverse, func_name="min") ) @requires.backend_version((1, 3)) def cum_count(self, *, reverse: bool) -> Self: return self._with_window_function( self._cum_window_func(reverse=reverse, func_name="count") ) @requires.backend_version((1, 3)) def cum_prod(self, *, reverse: bool) -> Self: return self._with_window_function( self._cum_window_func(reverse=reverse, func_name="product") ) @requires.backend_version((1, 3)) def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self: return self._with_window_function( self._rolling_window_func( func_name="sum", center=center, window_size=window_size, min_samples=min_samples, ) ) @requires.backend_version((1, 3)) def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self: return self._with_window_function( self._rolling_window_func( func_name="mean", center=center, window_size=window_size, min_samples=min_samples, ) ) @requires.backend_version((1, 3)) def rolling_var( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: return self._with_window_function( self._rolling_window_func( func_name="var", center=center, window_size=window_size, min_samples=min_samples, ddof=ddof, ) ) @requires.backend_version((1, 3)) def rolling_std( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: return self._with_window_function( self._rolling_window_func( func_name="std", center=center, window_size=window_size, min_samples=min_samples, ddof=ddof, ) ) def fill_null( self, value: Self | NonNestedLiteral, strategy: FillNullStrategy | None, limit: int | None, ) -> Self: if strategy is not None: if self._backend_version < (1, 3): # pragma: no cover msg = f"`fill_null` with `strategy={strategy}` is only available in 'duckdb>=1.3.0'." raise NotImplementedError(msg) def _fill_with_strategy( df: DuckDBLazyFrame, inputs: DuckDBWindowInputs ) -> Sequence[Expression]: order_by_sql = generate_order_by_sql(*inputs.order_by, ascending=True) partition_by_sql = generate_partition_by_sql(*inputs.partition_by) fill_func = "last_value" if strategy == "forward" else "first_value" _limit = "unbounded" if limit is None else limit rows_between = ( f"{_limit} preceding and current row" if strategy == "forward" else f"current row and {_limit} following" ) sql = ( f"{fill_func}({{expr}} ignore nulls) over " f"({partition_by_sql} {order_by_sql} rows between {rows_between})" ) return [SQLExpression(sql.format(expr=expr)) for expr in self(df)] return self._with_window_function(_fill_with_strategy) def _fill_constant(expr: Expression, value: Any) -> Expression: return CoalesceOperator(expr, value) return self._with_elementwise(_fill_constant, value=value) def cast(self, dtype: DType | type[DType]) -> Self: def func(expr: Expression) -> Expression: native_dtype = narwhals_to_native_dtype(dtype, self._version) return expr.cast(DuckDBPyType(native_dtype)) return self._with_elementwise(func) @requires.backend_version((1, 3)) def is_unique(self) -> Self: def _is_unique(expr: Expression, *partition_by: str | Expression) -> Expression: pb = generate_partition_by_sql(expr, *partition_by) sql = f"{FunctionExpression('count', col('*'))} over ({pb})" return SQLExpression(sql) == lit(1) def _unpartitioned_is_unique(expr: Expression) -> Expression: return _is_unique(expr) def _partitioned_is_unique( df: DuckDBLazyFrame, inputs: DuckDBWindowInputs ) -> Sequence[Expression]: assert not inputs.order_by # noqa: S101 return [_is_unique(expr, *inputs.partition_by) for expr in self(df)] return self._with_callable(_unpartitioned_is_unique)._with_window_function( _partitioned_is_unique ) @requires.backend_version((1, 3)) def rank(self, method: RankMethod, *, descending: bool) -> Self: if method in {"min", "max", "average"}: func = FunctionExpression("rank") elif method == "dense": func = FunctionExpression("dense_rank") else: # method == "ordinal" func = FunctionExpression("row_number") def _rank( expr: Expression, *, descending: bool, partition_by: Sequence[str | Expression] | None = None, ) -> Expression: order_by_sql = ( f"order by {expr} desc nulls last" if descending else f"order by {expr} asc nulls last" ) count_expr = FunctionExpression("count", StarExpression()) if partition_by is not None: window = f"{generate_partition_by_sql(*partition_by)} {order_by_sql}" count_window = f"{generate_partition_by_sql(*partition_by, expr)}" else: window = order_by_sql count_window = generate_partition_by_sql(expr) if method == "max": rank_expr = ( SQLExpression(f"{func} OVER ({window})") + SQLExpression(f"{count_expr} over ({count_window})") - lit(1) ) elif method == "average": rank_expr = SQLExpression(f"{func} OVER ({window})") + ( SQLExpression(f"{count_expr} over ({count_window})") - lit(1) ) / lit(2.0) else: rank_expr = SQLExpression(f"{func} OVER ({window})") return when(expr.isnotnull(), rank_expr) def _unpartitioned_rank(expr: Expression) -> Expression: return _rank(expr, descending=descending) def _partitioned_rank( df: DuckDBLazyFrame, inputs: DuckDBWindowInputs ) -> Sequence[Expression]: assert not inputs.order_by # noqa: S101 return [ _rank(expr, descending=descending, partition_by=inputs.partition_by) for expr in self(df) ] return self._with_callable(_unpartitioned_rank)._with_window_function( _partitioned_rank ) def log(self, base: float) -> Self: def _log(expr: Expression) -> Expression: log = FunctionExpression("log", expr) return ( when(expr < lit(0), lit(float("nan"))) .when(expr == lit(0), lit(float("-inf"))) .otherwise(log / FunctionExpression("log", lit(base))) ) return self._with_elementwise(_log) @property def str(self) -> DuckDBExprStringNamespace: return DuckDBExprStringNamespace(self) @property def dt(self) -> DuckDBExprDateTimeNamespace: return DuckDBExprDateTimeNamespace(self) @property def list(self) -> DuckDBExprListNamespace: return DuckDBExprListNamespace(self) @property def struct(self) -> DuckDBExprStructNamespace: return DuckDBExprStructNamespace(self) drop_nulls = not_implemented() unique = not_implemented()