§ q-Phå)ãó—ddlmZddlZddlmZddlmZddlm Z ddl m Z ddl m Z eje¦«5ddlmZddd¦«n #1swxYwYerddlmZddlmZdd lmZdd„Zdd„Zdd„Zdd„Zddœdd„Zddœdd„Zdd„ZdS)é)Ú annotationsN)Ú TYPE_CHECKING)Úparse_into_list_of_expressions©Ú wrap_expr)ÚUInt32)ÚIterable)ÚExpr)ÚIntoExprÚexprsúIntoExpr | Iterable[IntoExpr]Úreturnr cóV—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) ┌───────┬───────┬─────┬───────┠│ a ┆ b ┆ c ┆ all │ │ --- ┆ --- ┆ --- ┆ --- │ │ bool ┆ bool ┆ str ┆ bool │ â•žâ•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•¡ │ false ┆ false ┆ u ┆ false │ │ false ┆ true ┆ v ┆ false │ │ true ┆ true ┆ w ┆ true │ │ true ┆ null ┆ x ┆ null │ │ false ┆ null ┆ y ┆ false │ │ null ┆ null ┆ z ┆ null │ └───────┴───────┴─────┴───────┘ )rrÚplrÚall_horizontal©r Úpyexprss úg/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/polars/functions/aggregation/horizontal.pyrró(€õR-¨eÐ4€GÝ •SÔ'¨Ñ0Ô0Ñ 1Ô 1Ð1ócóV—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) ┌───────┬───────┬─────┬───────┠│ a ┆ b ┆ c ┆ any │ │ --- ┆ --- ┆ --- ┆ --- │ │ bool ┆ bool ┆ str ┆ bool │ â•žâ•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•¡ │ false ┆ false ┆ u ┆ false │ │ false ┆ true ┆ v ┆ true │ │ true ┆ true ┆ w ┆ true │ │ true ┆ null ┆ x ┆ true │ │ false ┆ null ┆ y ┆ null │ │ null ┆ null ┆ z ┆ null │ └───────┴───────┴─────┴───────┘ )rrrÚany_horizontalrs rrrBrrcóV—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) ┌─────┬──────┬─────┬─────┠│ a ┆ b ┆ c ┆ max │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 4 ┆ x ┆ 4 │ │ 8 ┆ 5 ┆ y ┆ 8 │ │ 3 ┆ null ┆ z ┆ 3 │ └─────┴──────┴─────┴─────┘ )rrrÚmax_horizontalrs rrroó'€õ>-¨eÐ4€GÝ •SÔ'¨Ñ0Ô0Ñ 1Ô 1Ð1rcóV—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) ┌─────┬──────┬─────┬─────┠│ a ┆ b ┆ c ┆ min │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 4 ┆ x ┆ 1 │ │ 8 ┆ 5 ┆ y ┆ 5 │ │ 3 ┆ null ┆ z ┆ 3 │ └─────┴──────┴─────┴─────┘ )rrrÚmin_horizontalrs rrr’rrT)Ú ignore_nullsrÚboolcóX—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) ┌─────┬──────┬─────┬─────┠│ a ┆ b ┆ c ┆ sum │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 4 ┆ x ┆ 5 │ │ 8 ┆ 5 ┆ y ┆ 13 │ │ 3 ┆ null ┆ z ┆ 3 │ └─────┴──────┴─────┴─────┘ )rrrÚsum_horizontal©rr rs rr!r!µs*€õH-¨eÐ4€GÝ •SÔ'¨°Ñ>Ô>Ñ ?Ô ?Ð?rcóX—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) ┌─────┬──────┬─────┬──────┠│ a ┆ b ┆ c ┆ mean │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str ┆ f64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•¡ │ 1 ┆ 4 ┆ x ┆ 2.5 │ │ 8 ┆ 5 ┆ y ┆ 6.5 │ │ 3 ┆ null ┆ z ┆ 3.0 │ └─────┴──────┴─────┴──────┘ )rrrÚmean_horizontalr"s rr$r$Ýs*€õH-¨eÐ4€GÝ •SÔ(¨°,Ñ?Ô?Ñ @Ô @Ð@rcóÔ—t|Ž}d„|D¦«}tjtjd¦« t ¦«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) ┌─────┬──────┬─────┬───────────┠│ a ┆ b ┆ c ┆ cum_sum │ │ --- ┆ --- ┆ --- ┆ --- │ │ i64 ┆ i64 ┆ str ┆ struct[2] │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•ªâ•â•â•â•â•â•ªâ•â•â•â•â•â•â•â•â•â•â•â•¡ │ 1 ┆ 4 ┆ x ┆ {1,5} │ │ 8 ┆ 5 ┆ y ┆ {8,13} │ │ 3 ┆ null ┆ z ┆ {3,null} │ └─────┴──────┴─────┴───────────┘ có,—g|]}t|¦«‘ŒS©r)Ú.0Úes rú z&cum_sum_horizontal..%s€Ð3Ð3Ð3 a•Y˜q‘\”\Ð3Ð3Ð3rrcó —||zS)Nr')ÚaÚbs rúz$cum_sum_horizontal..(s €¸!¸a¹%€rÚcum_sum)rÚFÚcum_foldÚlitÚcastrÚalias)r rÚ exprs_wrappeds rÚcum_sum_horizontalr6sd€õ>-¨eÐ4€GØ3Ð3¨7Ð3Ñ3Ô3€Mõ Œ:•a”e˜A‘h”h—m’m¥FÑ+Ô+Ð-?Ð-?ÀÑ OÔ O× UÒ UØñ ô ðr)r r rr )r r rrrr )Ú __future__rÚ contextlibÚtypingrÚpolars.functionsÚ functionsr0Úpolars._utils.parserÚpolars._utils.wraprÚpolars.datatypesrÚsuppressÚ ImportErrorÚ polars.polarsÚpolarsrÚcollections.abcr r Úpolars._typingr rrrrr!r$r6r'rrúrEsðØ"Ð"Ð"Ð"Ð"Ð"àÐÐÐØ Ð Ð Ð Ð Ð àÐÐÐÐÐØ>Ð>Ð>Ð>Ð>Ð>Ø(Ð(Ð(Ð(Ð(Ð(Ø#Ð#Ð#Ð#Ð#Ð#à€ZÔ˜Ñ%Ô%ð ð ØÐÐÐÐÐð ð ð ñ ô ð ð ð ð ð ð øøøð ð ð ð ðð(Ø(Ð(Ð(Ð(Ð(Ð(àÐÐÐÐÐØ'Ð'Ð'Ð'Ð'Ð'ð*2ð*2ð*2ð*2ðZ*2ð*2ð*2ð*2ðZ 2ð 2ð 2ð 2ðF 2ð 2ð 2ð 2ðHAEð%@ð%@ð%@ð%@ð%@ð%@ðRAEð%Að%Að%Að%Að%Að%AðP%ð%ð%ð%ð%ð%s¹A Á AÁA