Λ θΛ€hθγσt—ddlmZddlmZddlmZerddlmZddœd d„Z ddœdd„Z dd „Z dd „Z dd „Z dd „Zy)ι)Ϊ annotations)Ϊ TYPE_CHECKINGN)ΪExprT©Ϊ ignore_nullscσt—|stjd«Stj|Žj|¬«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 * If set to `True` (default), null values are ignored. If there are no non-null values, the output is `True`. * 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) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b β”‚ β”‚ --- ┆ --- β”‚ β”‚ u32 ┆ u32 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 2 ┆ 0 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ Evaluate bitwise AND for a column. >>> df.select(pl.all("a")) shape: (1, 1) β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ bool β”‚ β•žβ•β•β•β•β•β•β•β•‘ β”‚ false β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”˜ Ϊ*r)ΪFΪcolΪall©rΪnamess ϊ/var/www/html/wine-match-dev/backend/winematch-backend/venv/lib/python3.12/site-packages/polars/functions/aggregation/vertical.pyr r s2€ρr ܏u‰uS‹zΠδ 5‰5%ˆ=Χ Ρ ¨,Π Σ 7Π7σcσF—tj|Žj|¬«S)uτ 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 * If set to `True` (default), null values are ignored. If there are no non-null values, the output is `False`. * 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) β”Œβ”€β”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ bool β”‚ β•žβ•β•β•β•β•β•β•‘ β”‚ true β”‚ β””β”€β”€β”€β”€β”€β”€β”˜ r)r r Ϊanyr s rrrJs!€τT 5‰5%ˆ=Χ Ρ ¨,Π Σ 7Π7rcσB—tj|Žj«S)uς 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) β”Œβ”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ i64 β”‚ β•žβ•β•β•β•β•β•‘ β”‚ 8 β”‚ β””β”€β”€β”€β”€β”€β”˜ Get the maximum value of multiple columns. >>> df.select(pl.max("^a|b$")) shape: (1, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 8 ┆ 5 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ >>> df.select(pl.max("a", "b")) shape: (1, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 8 ┆ 5 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ )r r Ϊmax©rs rrrwσ€τr 5‰5%ˆ=Χ Ρ Σ ΠrcσB—tj|Žj«S)uς Get the minimum value. Syntactic sugar for `col(names).min()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. See Also -------- min_horizontal Examples -------- Get the minimum value of a column. >>> df = pl.DataFrame( ... { ... "a": [1, 8, 3], ... "b": [4, 5, 2], ... "c": ["foo", "bar", "foo"], ... } ... ) >>> df.select(pl.min("a")) shape: (1, 1) β”Œβ”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ i64 β”‚ β•žβ•β•β•β•β•β•‘ β”‚ 1 β”‚ β””β”€β”€β”€β”€β”€β”˜ Get the minimum value of multiple columns. >>> df.select(pl.min("^a|b$")) shape: (1, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 1 ┆ 2 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ >>> df.select(pl.min("a", "b")) shape: (1, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ b β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 1 ┆ 2 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ )r r Ϊminrs rrr³rrcσB—tj|Žj«S)u™ Sum all values. Syntactic sugar for `col(name).sum()`. Parameters ---------- *names Name(s) of the columns to use in the aggregation. Notes ----- If there are no non-null values, then the output is `0`. If you would prefer empty sums to return `None`, you can use `pl.when(pl.col(name).count()>0).then(pl.sum(name))` instead of `pl.sum(name)`. 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) β”Œβ”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ i64 β”‚ β•žβ•β•β•β•β•β•‘ β”‚ 3 β”‚ β””β”€β”€β”€β”€β”€β”˜ Sum multiple columns. >>> df.select(pl.sum("a", "c")) shape: (1, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ c β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 3 ┆ 11 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ >>> df.select(pl.sum("^.*[bc]$")) shape: (1, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ b ┆ c β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ i64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 7 ┆ 11 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ )r r Ϊsumrs rrrοs€τ@ 5‰5%ˆ=Χ Ρ Σ ΠrcσB—tj|Žj«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) β”Œβ”€β”€β”€β”€β”€β” β”‚ a β”‚ β”‚ --- β”‚ β”‚ i64 β”‚ β•žβ•β•β•β•β•β•‘ β”‚ 1 β”‚ β”‚ 3 β”‚ β”‚ 6 β”‚ β””β”€β”€β”€β”€β”€β”˜ )r r Ϊcum_sumrs rrr2s€τF 5‰5%ˆ=Χ Ρ Σ "Π"r)rΪstrrΪboolΪreturnr)rrrrrzExpr | bool | None)rrrr)Ϊ __future__rΪtypingrΪpolars.functionsΪ functionsr Ϊpolarsrr rrrrr©rrϊr&sBπέ"ε εαέπ+/υ<8π~+/υ*8σZ9σx9σx@τF##r