[GitHub] [arrow] honno opened a new issue, #35535: NaNs are interchanged as null

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

honno opened a new issue, #35535:
URL: https://github.com/apache/arrow/issues/35535

   ### Describe the bug, including details regarding any error messages, version, and platform.
   
   First of, this might not be a problem—let me know if that's the case! Hard for me to wrap my head around the consequences of Arrow's NA model with interchanging :sweat_smile:
   
   So, if one were to interchange a `pandas.DataFrame` containing NaNs to a `pyarrow.Table`, one gets nulls in place of NaNs.
   
   ```python
   >>> import pandas as pd
   >>> import numpy as np
   >>> df = pd.DataFrame({"foo": pd.Series([float("nan")], dtype=np.float64)})
   >>> from pyarrow.interchange import from_dataframe
   >>> from_dataframe(df)
   pyarrow.Table
   foo: double
   ----
   foo: [[null]    # expect NaN?
   ```
   
   We get the same with `modin`, which also adopts the interchange protocol.
   
   ```python
   >>> import modin
   >>> import ray
   >>> ray.init(local_mode=True)
   >>> from modin.config import Engine
   >>> Engine.put("ray")
   >>> from modin import pandas as mpd
   >>> df = mpd.DataFrame({"foo": mpd.Series([float("nan")], dtype=np.float64)})
   >>> from_dataframe(df)
   pyarrow.Table
   foo: double
   ----
   foo: [[null]]
   ```
   
   I see interchanging another `pa.Table` with NaNs works fine, asummedly because `from_dataframe()` short-circuits when it gets a `pa.Table`/`pa.RecordBatch`.
   
   ```python
   >>> table = pa.Table.from_pydict({"foo": pa.array([float("nan")], type=pa.float64())})
   >>> from_dataframe(table)
   pyarrow.Table
   foo: double
   ----
   foo: [[nan]]
   ```
   
   Using the arrow nightly from https://pypi.fury.io/arrow-nightlies/
   
   cc @AlenkaF
   
   
   
   ### Component(s)
   
   Python


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[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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