polars.max#
- polars.max(exprs: Series) PythonLiteral | None [source]#
- polars.max(exprs: IntoExpr | Iterable[IntoExpr], *more_exprs: IntoExpr) Expr
Get the maximum value.
If a single column is passed, get the maximum value of that column (vertical). If multiple columns are passed, get the maximum value of each row (horizontal).
- 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.
- *more_exprs
Additional columns to use in the aggregation, specified as positional arguments.
Examples
Get the maximum value by columns with a string column name.
>>> 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 │ ╞═════╡ │ 8 │ └─────┘
Get the maximum value by row with a list of columns/expressions.
>>> df.select(pl.max(["a", "b"])) shape: (3, 1) ┌─────┐ │ max │ │ --- │ │ i64 │ ╞═════╡ │ 4 │ │ 8 │ │ 3 │ └─────┘
To aggregate maximums for more than one column/expression use
pl.col(list).max()
or a regular expression selector likepl.sum(regex)
:>>> df.select(pl.col(["a", "b"]).max()) shape: (1, 2) ┌─────┬─────┐ │ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ ╞═════╪═════╡ │ 8 ┆ 5 │ └─────┴─────┘
>>> df.select(pl.max("^.*[ab]$")) shape: (1, 2) ┌─────┬─────┐ │ a ┆ b │ │ --- ┆ --- │ │ i64 ┆ i64 │ ╞═════╪═════╡ │ 8 ┆ 5 │ └─────┴─────┘