polars.DataFrame.rows#

DataFrame.rows( *, named: bool = False, ) → list[tuple[Any, ...]] | list[dict[str, Any]][source]#

Returns all data in the DataFrame as a list of rows of python-native values.

By default, each row is returned as a tuple of values given in the same order as the frame columns. Setting named=True will return rows of dictionaries instead.

Parameters:

named: Return dictionaries instead of tuples. The dictionaries are 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:

list of row value tuples (default), or list of dictionaries (if named=True).

Warning

Row-iteration is not optimal as the underlying data is stored in columnar form; where possible, prefer export via one of the dedicated export/output methods. You should also consider using iter_rows instead, to avoid materialising all the data at once; there is little performance difference between the two, but peak memory can be reduced if processing rows in batches.

See also

iter_rows: Row iterator over frame data (does not materialise all rows).
rows_by_key: Materialises frame data as a key-indexed dictionary.

Notes

If you have ns-precision temporal values you should be aware that Python natively only supports up to μs-precision; ns-precision values will be truncated to microseconds on conversion to Python. If this matters to your use-case you should export to a different format (such as Arrow or NumPy).

Examples

>>> df = pl.DataFrame(
...     {
...         "x": ["a", "b", "b", "a"],
...         "y": [1, 2, 3, 4],
...         "z": [0, 3, 6, 9],
...     }
... )
>>> df.rows()
[('a', 1, 0), ('b', 2, 3), ('b', 3, 6), ('a', 4, 9)]
>>> df.rows(named=True)
[{'x': 'a', 'y': 1, 'z': 0},
 {'x': 'b', 'y': 2, 'z': 3},
 {'x': 'b', 'y': 3, 'z': 6},
 {'x': 'a', 'y': 4, 'z': 9}]