jorisvandenbossche commented on issue #35535:
URL: https://github.com/apache/arrow/issues/35535#issuecomment-1543482341
> So, if one were to interchange a `pandas.DataFrame` containing NaNs to a
`pyarrow.Table`, one gets nulls in place of NaNs.
AFAIK this is expected, because pandas creates an interchange object
explicitly indicating that the NaN is used as the null value:
```
In [15]: df.__dataframe__().get_column_by_name("foo").describe_null
Out[15]: (<ColumnNullType.USE_NAN: 1>, None)
```
And thus pyarrow correctly converts this to null on the Arrow side.
> I see interchanging another `pa.Table` with NaNs works fine, asummedly
because `from_dataframe()` short-circuits when it gets a
`pa.Table`/`pa.RecordBatch`.
We are indeed short-cutting here. But even when not doing that (by
explicitly using the lower level function that doesn't have this
short-circuit), we preserve NaNs:
```
In [22]: from pyarrow.interchange.from_dataframe import _from_dataframe
In [23]: _from_dataframe(table.__dataframe__())
Out[23]:
pyarrow.Table
foo: double
----
foo: [[nan]]
```
That's because pyarrow does _not_ set that the NaN is the missing value
indicator, and thus NaNs in the input are preserved.
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