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https://issues.apache.org/jira/browse/ARROW-9817?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Andrew Wieteska updated ARROW-9817:
-----------------------------------
Description:
pa.array raises with mixed scalars for np.float16 and a numpy int:
{code:java}
In [1]: import numpy as np
...: import pyarrow as pa
...: pa.array([np.float16(1.5), np.int64(5)])
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-2-b1e9d890f325> in <module>
1 import numpy as np
2 import pyarrow as pa
----> 3 pa.array([np.float16(1.5), np.int64(5)])
~/git_repo/arrow/python/pyarrow/array.pxi in pyarrow.lib.array()
269 else:
270 # ConvertPySequence does strict conversion if type is explicitly passed
--> 271 return _sequence_to_array(obj, mask, size, type, pool, c_from_pandas)
272
273
~/git_repo/arrow/python/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
38
39 with nogil:
---> 40 check_status(ConvertPySequence(sequence, mask, options, &out))
41
42 if out.get().num_chunks() == 1:
~/git_repo/arrow/python/pyarrow/error.pxi in pyarrow.lib.check_status()
82
83 if status.IsInvalid():
---> 84 raise ArrowInvalid(message)
85 elif status.IsIOError():
86 # Note: OSError constructor is
ArrowInvalid: Cannot mix NumPy dtypes float16 and int64
{code}
On master I get the same behaviour with all numpy ints (signed and unsigned)
was:
pa.array raises with mixed scalars for np.float16 and a numpy int:
{code:java}
In [1]: import numpy as np
...: import pyarrow as pa
...: pa.array([np.float16(1.5), np.int64(5)])
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-2-b1e9d890f325> in <module>
1 import numpy as np
2 import pyarrow as pa
----> 3 pa.array([np.float16(1.5), np.int64(5)])
~/git_repo/arrow/python/pyarrow/array.pxi in pyarrow.lib.array()
269 else:
270 # ConvertPySequence does strict conversion if type is explicitly passed
--> 271 return _sequence_to_array(obj, mask, size, type, pool, c_from_pandas)
272
273
~/git_repo/arrow/python/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
38
39 with nogil:
---> 40 check_status(ConvertPySequence(sequence, mask, options, &out))
41
42 if out.get().num_chunks() == 1:
~/git_repo/arrow/python/pyarrow/error.pxi in pyarrow.lib.check_status()
82
83 if status.IsInvalid():
---> 84 raise ArrowInvalid(message)
85 elif status.IsIOError():
86 # Note: OSError constructor is
ArrowInvalid: Cannot mix NumPy dtypes float16 and int64
{code}
Do we expect this to work?
> [Python] pa.array raises for mixed scalar types (float16 + int)
> ---------------------------------------------------------------
>
> Key: ARROW-9817
> URL: https://issues.apache.org/jira/browse/ARROW-9817
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 1.0.0
> Reporter: Andrew Wieteska
> Priority: Major
>
> pa.array raises with mixed scalars for np.float16 and a numpy int:
> {code:java}
> In [1]: import numpy as np
> ...: import pyarrow as pa
> ...: pa.array([np.float16(1.5), np.int64(5)])
> ---------------------------------------------------------------------------
> ArrowInvalid Traceback (most recent call last)
> <ipython-input-2-b1e9d890f325> in <module>
> 1 import numpy as np
> 2 import pyarrow as pa
> ----> 3 pa.array([np.float16(1.5), np.int64(5)])
> ~/git_repo/arrow/python/pyarrow/array.pxi in pyarrow.lib.array()
> 269 else:
> 270 # ConvertPySequence does strict conversion if type is explicitly passed
> --> 271 return _sequence_to_array(obj, mask, size, type, pool, c_from_pandas)
> 272
> 273
> ~/git_repo/arrow/python/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
> 38
> 39 with nogil:
> ---> 40 check_status(ConvertPySequence(sequence, mask, options, &out))
> 41
> 42 if out.get().num_chunks() == 1:
> ~/git_repo/arrow/python/pyarrow/error.pxi in pyarrow.lib.check_status()
> 82
> 83 if status.IsInvalid():
> ---> 84 raise ArrowInvalid(message)
> 85 elif status.IsIOError():
> 86 # Note: OSError constructor is
> ArrowInvalid: Cannot mix NumPy dtypes float16 and int64
> {code}
> On master I get the same behaviour with all numpy ints (signed and unsigned)
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