§ q-Ph<ãóv—ddlmZddlmZddlmZerddlmZddœdd „Z ddœdd„Z dd„Z dd„Z dd„Z dd„ZdS)é)Ú annotations)Ú TYPE_CHECKINGN)ÚExprT©Ú ignore_nullsÚnamesÚstrrÚboolÚreturnrcór—|stjd¦«Stj|Ž |¬¦«S)u¥ Either return an expression representing all columns, or evaluate a bitwise AND operation. If no arguments are passed, this function is syntactic sugar for `col("*")`. Otherwise, this function is syntactic sugar for `col(names).all()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. ignore_nulls Ignore null values (default). If set to `False`, `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 See Also -------- all_horizontal Examples -------- Selecting all columns. >>> df = pl.DataFrame( ... { ... "a": [True, False, True], ... "b": [False, False, False], ... } ... ) >>> df.select(pl.all().sum()) shape: (1, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ u32 ┆ u32 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 2 ┆ 0 │ └─────┴─────┘ Evaluate bitwise AND for a column. >>> df.select(pl.all("a")) shape: (1, 1) ┌───────┠│ a │ │ --- │ │ bool │ â•žâ•â•â•â•â•â•â•â•¡ │ false │ └───────┘ Ú*r)ÚFÚcolÚall©rrs úe/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/polars/functions/aggregation/vertical.pyrr s8€ðp ðÝŒuS‰zŒzÐå Œ5%ˆ=× Ò ¨,Ð Ñ 7Ô 7Ð7óúExpr | bool | NonecóF—tj|Ž |¬¦«S)u“ Evaluate a bitwise OR operation. Syntactic sugar for `col(names).any()`. See Also -------- any_horizontal Parameters ---------- *names Name(s) of the columns to use in the aggregation. ignore_nulls Ignore null values (default). If set to `False`, `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": [True, False, True], ... "b": [False, False, False], ... } ... ) >>> df.select(pl.any("a")) shape: (1, 1) ┌──────┠│ a │ │ --- │ │ bool │ â•žâ•â•â•â•â•â•â•¡ │ true │ └──────┘ r)rrÚanyrs rrrIs"€õR Œ5%ˆ=× Ò ¨,Ð Ñ 7Ô 7Ð7rcóB—tj|Ž ¦«S)uò Get the maximum value. Syntactic sugar for `col(names).max()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. See Also -------- max_horizontal Examples -------- Get the maximum value of a column. >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.max("a")) shape: (1, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 8 │ └─────┘ Get the maximum value of multiple columns. >>> df.select(pl.max("^a|b$")) shape: (1, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 8 ┆ 5 │ └─────┴─────┘ >>> df.select(pl.max("a", "b")) shape: (1, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 8 ┆ 5 │ └─────┴─────┘ )rrÚmax©rs rrruó€õr Œ5%ˆ=× Ò Ñ Ô ÐrcóB—tj|Ž ¦«S)uò Get the minimum value. Syntactic sugar for `col(names).min()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. See Also -------- min_horizontal Examples -------- Get the minimum value of a column. >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.min("a")) shape: (1, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 1 │ └─────┘ Get the minimum value of multiple columns. >>> df.select(pl.min("^a|b$")) shape: (1, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 2 │ └─────┴─────┘ >>> df.select(pl.min("a", "b")) shape: (1, 2) ┌─────┬─────┠│ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 1 ┆ 2 │ └─────┴─────┘ )rrÚminrs rrr±rrcóB—tj|Ž ¦«S)u® Sum all values. Syntactic sugar for `col(name).sum()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. See Also -------- sum_horizontal Examples -------- Sum a column. >>> df = pl.DataFrame( ... { ... "a": [1, 2], ... "b": [3, 4], ... "c": [5, 6], ... } ... ) >>> df.select(pl.sum("a")) shape: (1, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 3 │ └─────┘ Sum multiple columns. >>> df.select(pl.sum("a", "c")) shape: (1, 2) ┌─────┬─────┠│ a ┆ c │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 3 ┆ 11 │ └─────┴─────┘ >>> df.select(pl.sum("^.*[bc]$")) shape: (1, 2) ┌─────┬─────┠│ b ┆ c │ │ --- ┆ --- │ │ i64 ┆ i64 │ â•žâ•â•â•â•â•â•ªâ•â•â•â•â•â•¡ │ 7 ┆ 11 │ └─────┴─────┘ )rrÚsumrs rrrírrcóB—tj|Ž ¦«S)uk Cumulatively sum all values. Syntactic sugar for `col(names).cum_sum()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. See Also -------- cumsum_horizontal Examples -------- >>> df = pl.DataFrame( ... { ... "a": [1, 2, 3], ... "b": [4, 5, 6], ... } ... ) >>> df.select(pl.cum_sum("a")) shape: (3, 1) ┌─────┠│ a │ │ --- │ │ i64 │ â•žâ•â•â•â•â•â•¡ │ 1 │ │ 3 │ │ 6 │ └─────┘ )rrÚcum_sumrs rr r )s€õF Œ5%ˆ=× Ò Ñ "Ô "Ð"r)rr rr r r)rr rr r r)rr r r)Ú __future__rÚtypingrÚpolars.functionsÚ functionsrÚpolarsrrrrrrr ©rrúr'súðØ"Ð"Ð"Ð"Ð"Ð"à Ð Ð Ð Ð Ð àÐÐÐÐÐàðØÐÐÐÐÐð+/ð;8ð;8ð;8ð;8ð;8ð;8ð|+/ð)8ð)8ð)8ð)8ð)8ð)8ðX9ð9ð9ð9ðx9ð9ð9ð9ðx9ð9ð9ð9ðx##ð##ð##ð##ð##ð##r