polars.DataFrame.row#

DataFrame.row( index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[False] = False, ) → tuple[Any, ...][source]#

DataFrame.row( index: int | None = None, *, by_predicate: Expr | None = None, named: Literal[True], ) → dict[str, Any]

Get the values of a single row, either by index or by predicate.

Parameters:

index: Row index.
by_predicate: Select the row according to a given expression/predicate.
named: Return a dictionary instead of a tuple. The dictionary is a mapping of column name to row value. This is more expensive than returning a regular tuple, but allows for accessing values by column name.

Returns:

tuple (default) or dictionary of row values

Warning

You should NEVER use this method to iterate over a DataFrame; if you require row-iteration you should strongly prefer use of iter_rows() instead.

See also

iter_rows: Row iterator over frame data (does not materialise all rows).
rows: Materialise all frame data as a list of rows (potentially expensive).
item: Return dataframe element as a scalar.

Notes

The index and by_predicate params are mutually exclusive. Additionally, to ensure clarity, the by_predicate parameter must be supplied by keyword.

When using by_predicate it is an error condition if anything other than one row is returned; more than one row raises TooManyRowsReturnedError, and zero rows will raise NoRowsReturnedError (both inherit from RowsError).

Examples

Specify an index to return the row at the given index as a tuple.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.row(2)
(3, 8, 'c')

Specify named=True to get a dictionary instead with a mapping of column names to row values.

>>> df.row(2, named=True)
{'foo': 3, 'bar': 8, 'ham': 'c'}

Use by_predicate to return the row that matches the given predicate.

>>> df.row(by_predicate=(pl.col("ham") == "b"))
(2, 7, 'b')