polars.DataFrame.join

DataFrame.join(other: DataFrame, left_on: str | pli.Expr | Sequence[str | pli.Expr] | None = None, right_on: str | pli.Expr | Sequence[str | pli.Expr] | None = None, on: str | pli.Expr | Sequence[str | pli.Expr] | None = None, how: JoinStrategy = 'inner', suffix: str = '_right') → DataFrame

Join in SQL-like fashion.

Parameters:

other

DataFrame to join with.

left_on

Name(s) of the left join column(s).

right_on

Name(s) of the right join column(s).

on

Name(s) of the join columns in both DataFrames.

how{‘inner’, ‘left’, ‘outer’, ‘semi’, ‘anti’, ‘cross’}

Join strategy.

suffix

Suffix to append to columns with a duplicate name.

Returns:

Joined DataFrame

See also

join_asof

Notes

For joining on columns with categorical data, see pl.StringCache().

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> other_df = pl.DataFrame(
...     {
...         "apple": ["x", "y", "z"],
...         "ham": ["a", "b", "d"],
...     }
... )
>>> df.join(other_df, on="ham")
shape: (2, 4)
┌─────┬─────┬─────┬───────┐
│ foo ┆ bar ┆ ham ┆ apple │
│ --- ┆ --- ┆ --- ┆ ---   │
│ i64 ┆ f64 ┆ str ┆ str   │
╞═════╪═════╪═════╪═══════╡
│ 1   ┆ 6.0 ┆ a   ┆ x     │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2   ┆ 7.0 ┆ b   ┆ y     │
└─────┴─────┴─────┴───────┘

>>> df.join(other_df, on="ham", how="outer")
shape: (4, 4)
┌──────┬──────┬─────┬───────┐
│ foo  ┆ bar  ┆ ham ┆ apple │
│ ---  ┆ ---  ┆ --- ┆ ---   │
│ i64  ┆ f64  ┆ str ┆ str   │
╞══════╪══════╪═════╪═══════╡
│ 1    ┆ 6.0  ┆ a   ┆ x     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 2    ┆ 7.0  ┆ b   ┆ y     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ null ┆ null ┆ d   ┆ z     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 3    ┆ 8.0  ┆ c   ┆ null  │
└──────┴──────┴─────┴───────┘