polars.DataFrame.to_pandas

DataFrame.to_pandas(
    *,
    use_pyarrow_extension_array: bool = False,
    **kwargs: Any,
) → DataFrame

Convert this DataFrame to a pandas DataFrame.

This operation copies data if use_pyarrow_extension_array is not enabled.

Parameters:

use_pyarrow_extension_array

Use PyArrow-backed extension arrays instead of NumPy arrays for the columns of the pandas DataFrame. This allows zero copy operations and preservation of null values. Subsequent operations on the resulting pandas DataFrame may trigger conversion to NumPy if those operations are not supported by PyArrow compute functions.

**kwargs

Additional keyword arguments to be passed to pyarrow.Table.to_pandas().

Returns:

pandas.DataFrame

Notes

This operation requires that both pandas and pyarrow are installed.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.to_pandas()
   foo  bar ham
0    1  6.0   a
1    2  7.0   b
2    3  8.0   c

Null values in numeric columns are converted to NaN.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, None],
...         "bar": [6.0, None, 8.0],
...         "ham": [None, "b", "c"],
...     }
... )
>>> df.to_pandas()
   foo  bar  ham
0  1.0  6.0  NaN
1  2.0  NaN    b
2  NaN  8.0    c

Pass use_pyarrow_extension_array=True to get a pandas DataFrame with columns backed by PyArrow extension arrays. This will preserve null values.

>>> df.to_pandas(use_pyarrow_extension_array=True)
    foo   bar   ham
0     1   6.0  <NA>
1     2  <NA>     b
2  <NA>   8.0     c
>>> _.dtypes
foo           int64[pyarrow]
bar          double[pyarrow]
ham    large_string[pyarrow]
dtype: object