jorisvandenbossche commented on code in PR #39535:
URL: https://github.com/apache/arrow/pull/39535#discussion_r1446239096
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python/pyarrow/pandas_compat.py:
##########
@@ -789,9 +788,10 @@ def table_to_dataframe(
# Set of the string repr of all numpy dtypes that can be stored in a pandas
# dataframe (complex not included since not supported by Arrow)
_pandas_supported_numpy_types = {
- str(np.dtype(typ))
- for typ in (_np_sctypes['int'] + _np_sctypes['uint'] +
_np_sctypes['float'] +
- ['object', 'bool'])
+ "int8", "int16", "int32", "int64",
+ "uint8", "uint16", "uint32", "uint64",
+ "float16", "float32", "float64", "float128",
Review Comment:
On second thought: those types are not arrow types, but the numpy dtype
stored in the pandas metadata, i.e. the original dtype of the pandas DataFrame
column that was converted to a pyarrow.Table.
So in theory you can have a pandas DataFrame with a float128 columns, and
get that in the metadata (and then having that included in the list above is
fine). Now, this is currently also not possible, as we haven't implemented the
conversion of numpy float128 to a pyarrow float array, and thus the conversion
of such a DataFrame currently fails.
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