Λ θΛ€hε)γσ.—ddlmZddlZddlmZddlmZddlm Z ddl m Z ddl m Z eje«5ddlmZddd«erddlmZddlmZdd lmZdd „Zdd „Zdd „Zdd „Zddœ dd„Zddœ dd„Zdd„Zy#1swYŒJxYw)ι)Ϊ annotationsN)Ϊ TYPE_CHECKING)Ϊparse_into_list_of_expressions)Ϊ wrap_expr)ΪUInt32)ΪIterable)ΪExpr)ΪIntoExprcσN—t|Ž}ttj|««S)u  Compute the bitwise AND horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. Notes ----- `Kleene logic`_ is used to deal with nulls: if the column contains any null values and no `False` values, the output is null. .. _Kleene logic: https://en.wikipedia.org/wiki/Three-valued_logic Examples -------- >>> df = pl.DataFrame( ... { ... "a": [False, False, True, True, False, None], ... "b": [False, True, True, None, None, None], ... "c": ["u", "v", "w", "x", "y", "z"], ... } ... ) >>> df.with_columns(all=pl.all_horizontal("a", "b")) shape: (6, 4) β”Œβ”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ all β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ bool ┆ bool ┆ str ┆ bool β”‚ β•žβ•β•β•β•β•β•β•β•ͺ═══════β•ͺ═════β•ͺ═══════║ β”‚ false ┆ false ┆ u ┆ false β”‚ β”‚ false ┆ true ┆ v ┆ false β”‚ β”‚ true ┆ true ┆ w ┆ true β”‚ β”‚ true ┆ null ┆ x ┆ null β”‚ β”‚ false ┆ null ┆ y ┆ false β”‚ β”‚ null ┆ null ┆ z ┆ null β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”˜ )rrΪplrΪall_horizontal©ΪexprsΪpyexprss ϊƒ/var/www/html/wine-match-dev/backend/winematch-backend/venv/lib/python3.12/site-packages/polars/functions/aggregation/horizontal.pyr r σ%€τR-¨eΠ4€Gά ”SΧ'Ρ'¨Σ0Σ 1Π1σcσN—t|Ž}ttj|««S)u  Compute the bitwise OR horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. Notes ----- `Kleene logic`_ is used to deal with nulls: if the column contains any null values and no `True` values, the output is null. .. _Kleene logic: https://en.wikipedia.org/wiki/Three-valued_logic Examples -------- >>> df = pl.DataFrame( ... { ... "a": [False, False, True, True, False, None], ... "b": [False, True, True, None, None, None], ... "c": ["u", "v", "w", "x", "y", "z"], ... } ... ) >>> df.with_columns(any=pl.any_horizontal("a", "b")) shape: (6, 4) β”Œβ”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ any β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ bool ┆ bool ┆ str ┆ bool β”‚ β•žβ•β•β•β•β•β•β•β•ͺ═══════β•ͺ═════β•ͺ═══════║ β”‚ false ┆ false ┆ u ┆ false β”‚ β”‚ false ┆ true ┆ v ┆ true β”‚ β”‚ true ┆ true ┆ w ┆ true β”‚ β”‚ true ┆ null ┆ x ┆ true β”‚ β”‚ false ┆ null ┆ y ┆ null β”‚ β”‚ null ┆ null ┆ z ┆ null β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”˜ )rrr Ϊany_horizontalrs rrrBrrcσN—t|Ž}ttj|««S)u  Get the maximum value horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, None], ... "c": ["x", "y", "z"], ... } ... ) >>> df.with_columns(max=pl.max_horizontal("a", "b")) shape: (3, 4) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ max β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ i64 ┆ i64 ┆ str ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ══════β•ͺ═════β•ͺ═════║ β”‚ 1 ┆ 4 ┆ x ┆ 4 β”‚ β”‚ 8 ┆ 5 ┆ y ┆ 8 β”‚ β”‚ 3 ┆ null ┆ z ┆ 3 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ )rrr Ϊmax_horizontalrs rrroσ$€τ>-¨eΠ4€Gά ”SΧ'Ρ'¨Σ0Σ 1Π1rcσN—t|Ž}ttj|««S)u  Get the minimum value horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, None], ... "c": ["x", "y", "z"], ... } ... ) >>> df.with_columns(min=pl.min_horizontal("a", "b")) shape: (3, 4) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ min β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ i64 ┆ i64 ┆ str ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ══════β•ͺ═════β•ͺ═════║ β”‚ 1 ┆ 4 ┆ x ┆ 1 β”‚ β”‚ 8 ┆ 5 ┆ y ┆ 5 β”‚ β”‚ 3 ┆ null ┆ z ┆ 3 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ )rrr Ϊmin_horizontalrs rrr’rrT)Ϊ ignore_nullscσP—t|Ž}ttj||««S)u Sum all values horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. ignore_nulls Ignore null values (default). If set to `False`, any null value in the input will lead to a null output. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, None], ... "c": ["x", "y", "z"], ... } ... ) >>> df.with_columns(sum=pl.sum_horizontal("a", "b")) shape: (3, 4) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ sum β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ i64 ┆ i64 ┆ str ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ══════β•ͺ═════β•ͺ═════║ β”‚ 1 ┆ 4 ┆ x ┆ 5 β”‚ β”‚ 8 ┆ 5 ┆ y ┆ 13 β”‚ β”‚ 3 ┆ null ┆ z ┆ 3 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ )rrr Ϊsum_horizontal©rrrs rrr΅s'€τH-¨eΠ4€Gά ”SΧ'Ρ'¨°Σ>Σ ?Π?rcσP—t|Ž}ttj||««S)u° Compute the mean of all values horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. ignore_nulls Ignore null values (default). If set to `False`, any null value in the input will lead to a null output. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, None], ... "c": ["x", "y", "z"], ... } ... ) >>> df.with_columns(mean=pl.mean_horizontal("a", "b")) shape: (3, 4) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ mean β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ i64 ┆ i64 ┆ str ┆ f64 β”‚ β•žβ•β•β•β•β•β•ͺ══════β•ͺ═════β•ͺ══════║ β”‚ 1 ┆ 4 ┆ x ┆ 2.5 β”‚ β”‚ 8 ┆ 5 ┆ y ┆ 6.5 β”‚ β”‚ 3 ┆ null ┆ z ┆ 3.0 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”˜ )rrr Ϊmean_horizontalrs rr r έs'€τH-¨eΠ4€Gά ”SΧ(Ρ(¨°,Σ?Σ @Π@rcσζ—t|Ž}|Dcgc] }t|«‘Œ}}tjtjd«j t «d„|«jd«Scc}w)ul Cumulatively sum all values horizontally across columns. Parameters ---------- *exprs Column(s) to use in the aggregation. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals. Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, None], ... "c": ["x", "y", "z"], ... } ... ) >>> df.with_columns(pl.cum_sum_horizontal("a", "b")) shape: (3, 4) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c ┆ cum_sum β”‚ β”‚ --- ┆ --- ┆ --- ┆ --- β”‚ β”‚ i64 ┆ i64 ┆ str ┆ struct[2] β”‚ β•žβ•β•β•β•β•β•ͺ══════β•ͺ═════β•ͺ═══════════║ β”‚ 1 ┆ 4 ┆ x ┆ {1,5} β”‚ β”‚ 8 ┆ 5 ┆ y ┆ {8,13} β”‚ β”‚ 3 ┆ null ┆ z ┆ {3,null} β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ rcσ —||zS)N©)ΪaΪbs rϊz$cum_sum_horizontal..(s €Έ!ΈaΉ%€rΪcum_sum)rrΪFΪcum_foldΪlitΪcastrΪalias)rrΪeΪ exprs_wrappeds rΪcum_sum_horizontalr/sa€τ>-¨eΠ4€GΨ+2Φ3 a”Y˜q•\Π3€MΠ3τ :‰:”a—e‘e˜A“h—m‘m€FΣ+Ρ-?ΐΣ OΧ UΡ UΨσ πως4sA.)rϊIntoExpr | Iterable[IntoExpr]Ϊreturnr )rr0rΪboolr1r )Ϊ __future__rΪ contextlibΪtypingrΪpolars.functionsΪ functionsr(Ϊpolars._utils.parserΪpolars._utils.wraprΪpolars.datatypesrΪsuppressΪ ImportErrorΪ polars.polarsΪpolarsr Ϊcollections.abcrr Ϊpolars._typingr r rrrrr r/r#rrϊrAsΐπέ"γέ εέ>έ(έ#ΰ€ZΧΡ˜Σ%ρ έχ ρέ(εέ'σ*2σZ*2σZ 2σF 2πHAEρ%@Ψ )π%@Ψ9=π%@ΰ σ%@πRAEρ%AΨ )π%AΨ9=π%Aΰ σ%AτP%χu π ϊs »B Β B