polars.DataFrame.iter_columns#

DataFrame.iter_columns() → Iterator[Series][source]#

Returns an iterator over the columns of this DataFrame.

Yields:

Series

Notes

Consider whether you can use all() instead. If you can, it will be more efficient.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> [s.name for s in df.iter_columns()]
['a', 'b']

If you’re using this to modify a dataframe’s columns, e.g.

>>> # Do NOT do this
>>> pl.DataFrame(column * 2 for column in df.iter_columns())
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2   ┆ 4   │
│ 6   ┆ 8   │
│ 10  ┆ 12  │
└─────┴─────┘

then consider whether you can use all() instead:

>>> df.select(pl.all() * 2)
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2   ┆ 4   │
│ 6   ┆ 8   │
│ 10  ┆ 12  │
└─────┴─────┘