join_var#
- SingleCell.join_var(other, /, *, on=None, left_on=None, right_on=None, suffix='_right', validate='m:m', nulls_equal=False, coalesce=True)[source]#
Left-join var with another DataFrame.
Uses the same logic as
df.join().- Parameters:
other: DataFrame
a polars DataFrame to join var with
on: str | Expr | Sequence[str | Expr] | None
the name(s) of the join column(s) in both DataFrames
left_on: str | Expr | Sequence[str | Expr] | None
the name(s) of the join column(s) in var
right_on: str | Expr | Sequence[str | Expr] | None
the name(s) of the join column(s) in other
suffix: str
a suffix to append to columns with a duplicate name
validate: Literal['m:m', 'm:1', '1:m', '1:1']
checks whether the join is of the specified type. Can be:
’m:m’ (many-to-many): the default, no checks performed.
’1:1’ (one-to-one): check that none of the values in the join column(s) appear more than once in var or more than once in other.
’1:m’ (one-to-many): check that none of the values in the join column(s) appear more than once in var.
’m:1’ (many-to-one): check that none of the values in the join column(s) appear more than once in other.
nulls_equal: bool
whether to include null as a valid value to join on. By default, null values will never produce matches.
coalesce: bool
if True, coalesce each of the pairs of join columns (the columns in on or left_on/right_on) from var and other into a single column, filling missing values from one with the corresponding values from the other. If False, include both as separate columns, adding suffix to the join columns from other.
- Returns:
A new SingleCell dataset with the columns from other joined to var.
- Return type: