§ θ‘j³)γσ—ddlmZddlZddlmZddlmZddlm Z ddl m Z ej e ¦«5ddlmZddd¦«n #1swxYwYerddlmZddlmZddlmZdd „Zdd„Zdd„Zdd„Zddœdd„Zddœdd„Zdd„ZdS)ι)Ϊ annotationsN)Ϊ TYPE_CHECKING)Ϊparse_into_list_of_expressions©Ϊ wrap_expr)ΪIterable)ΪExpr)ΪIntoExprΪexprsϊIntoExpr | Iterable[IntoExpr]Ϊreturnr cσV—t|Ž}ttj|¦«¦«S)u  Compute the logical 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©r Ϊpyexprss ϊl/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/polars/functions/aggregation/horizontal.pyrrσ(€υR-¨eΠ4€Gέ •SΤ'¨Ρ0Τ0Ρ 1Τ 1Π1σcσV—t|Ž}ttj|¦«¦«S)u  Compute the logical 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”˜ )rrrΪany_horizontalrs rrrArrcσV—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”˜ )rrrΪmax_horizontalrs rrrnσ'€υ>-¨eΠ4€Gέ •SΤ'¨Ρ0Τ0Ρ 1Τ 1Π1rcσV—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”˜ )rrrΪmin_horizontalrs rrr‘rrT)Ϊ ignore_nullsrΪboolcσX—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”˜ )rrrΪsum_horizontal©rr rs rr r ΄s*€υH-¨eΠ4€Gέ •SΤ'¨°Ρ>Τ>Ρ ?Τ ?Π?rcσX—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”˜ )rrrΪmean_horizontalr!s rr#r#άs*€υH-¨eΠ4€Gέ •SΤ(¨°,Ρ?Τ?Ρ @Τ @Π@rc σ,—t|Ž}d„|D¦«}tjtjd¦« tjtjt|¦«¦«¦«¦«d„|¦« d¦«S)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”˜ cσ,—g|]}t|¦«‘ŒS©r)Ϊ.0Ϊes rϊ z&cum_sum_horizontal..$s€Π3Π3Π3 a•Y˜q‘\”\Π3Π3Π3rrcσ —||zS)Nr&)ΪaΪbs rϊz$cum_sum_horizontal..(s €Q˜‘U€rΪcum_sum) rΪFΪcum_foldΪlitΪcastΪdtype_ofr ΪlistΪalias)r rΪ exprs_wrappeds rΪcum_sum_horizontalr7s€υ>-¨eΠ4€GΨ3Π3¨7Π3Ρ3Τ3€Mε Œ:έ Œˆa‰Œ Š •a”j₯Τ!1΅$°u±+΄+Ρ!>Τ!>Ρ?Τ?Ρ@Τ@ΨΠΨρ τ χ ‚eˆIΡΤπ r)r r r r )r r rrr r )Ϊ __future__rΪ contextlibΪtypingrΪpolars.functionsΪ functionsr/Ϊpolars._utils.parserΪpolars._utils.wraprΪsuppressΪ ImportErrorΪ polars._plrΪ_plrrΪcollections.abcrΪpolarsr Ϊpolars._typingr rrrrr r#r7r&rrϊrFsοπΨ"Π"Π"Π"Π"Π"ΰΠΠΠΨ Π Π Π Π Π ΰΠΠΠΠΠΨ>Π>Π>Π>Π>Π>Ψ(Π(Π(Π(Π(Π(ΰ€ZΤ˜Ρ%Τ%ππΨΠΠΠΠΠπππρτππππππψψψππππππ(Ψ(Π(Π(Π(Π(Π(ΰΠΠΠΠΠΨ'Π'Π'Π'Π'Π'π*2π*2π*2π*2πZ*2π*2π*2π*2πZ 2π 2π 2π 2πF 2π 2π 2π 2πHAEπ%@π%@π%@π%@π%@π%@πRAEπ%Aπ%Aπ%Aπ%Aπ%Aπ%AπP&π&π&π&π&π&s³AΑA Α A