[GitHub] [arrow] jorisvandenbossche commented on issue #35535: NaNs are interchanged as null

["jorisvandenbossche (via GitHub)" <gi...@apache.org>]

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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