polars.DataFrame.rows#

DataFrame.rows(named: Literal[False] = False) → list[tuple[Any, ...]][source]#

DataFrame.rows(named: Literal[True]) → list[dict[str, Any]]

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

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:

A list of tuples (default) or dictionaries of row values.

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.

See also

iter_rows: Row iterator over frame data (does not materialise all rows).

Notes

If you have ns-precision temporal values you should be aware that python natively only supports up to us-precision; if this matters you should export to a different format.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> df.rows()
[(1, 2), (3, 4), (5, 6)]
>>> df.rows(named=True)
[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}, {'a': 5, 'b': 6}]