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https://issues.apache.org/jira/browse/ARROW-1156?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16063518#comment-16063518
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Wes McKinney commented on ARROW-1156:
-------------------------------------
Note that this functionality already exists in the {{pyarrow.array} function
{code}
In [8]: arr
Out[8]: array([None, None, None, None, None, None, None, None, None, None],
dtype=object)
In [9]: pa.array(arr)
Out[9]:
<pyarrow.lib.NullArray object at 0x7f10bd026548>
[
NA,
NA,
NA,
NA,
NA,
NA,
NA,
NA,
NA,
NA
]
In [10]: pa.array(arr, type=pa.float64())
Out[10]:
<pyarrow.lib.DoubleArray object at 0x7f10bd026688>
[
NA,
NA,
NA,
NA,
NA,
NA,
NA,
NA,
NA,
NA
]
{code}
> 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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