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https://issues.apache.org/jira/browse/ARROW-1156?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16063524#comment-16063524
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Wenchen Fan commented on ARROW-1156:
------------------------------------
yea that's true, but its functionality is quite limited, a lot of data types
are not supported, e.g.
{code}
>>> pa.array([None], type=pa.int32())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pyarrow/array.pxi", line 1051, in pyarrow.lib.array
(/Users/travis/build/xhochy/pyarrow-macos-wheels/arrow/python/build/temp.macosx-10.6-intel-2.7/lib.cxx:19136)
File "pyarrow/error.pxi", line 66, in pyarrow.lib.check_status
(/Users/travis/build/xhochy/pyarrow-macos-wheels/arrow/python/build/temp.macosx-10.6-intel-2.7/lib.cxx:7124)
pyarrow.lib.ArrowNotImplementedError: NotImplemented: No type converter
implemented for int32
{code}
BTW if I already have a numpy array, it's less efficient if I have to convert
numpy array to python list and call {{pyarrow.array}}
> pyarrow.Array.from_pandas should take a type parameter
> ------------------------------------------------------
>
> Key: ARROW-1156
> URL: https://issues.apache.org/jira/browse/ARROW-1156
> Project: Apache Arrow
> Issue Type: New Feature
> Affects Versions: 0.4.0
> Reporter: Wenchen Fan
>
> It's convenient to infer the data type so that users can just write
> {{pyarrow.Array.from_pandas(arr)}}, however, sometimes users want
> fine-grained control, e.g., if we have an object type numpy array, whose
> values are all null. When we convert it to an arrow column vector, we may
> need a specific type instead of NullType.
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