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 tous
-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}]