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https://issues.apache.org/jira/browse/ARROW-17834?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17609607#comment-17609607
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Chang She commented on ARROW-17834:
-----------------------------------
One additional tricky thing here is what if the storage array also need
additional arguments.
Most open-source datasets in computer vision has a predetermined dictionary, so
often-times you'd want read in a CSV data dictionary and pass in the class
names in the right order to construct the storage DictionaryArray.
> [Python] Allow creating ExtensionArray through pa.array(..) constructor
> -----------------------------------------------------------------------
>
> Key: ARROW-17834
> URL: https://issues.apache.org/jira/browse/ARROW-17834
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Joris Van den Bossche
> Priority: Major
>
> Currently, creating an ExtensionArray from a python sequence (or numpy array,
> ..) requires the following:
> {code:python}
> from pyarrow.tests.test_extension_type import IntegerType
> storage_array = pa.array([1, 2, 3])
> ext_arr = pa.ExtensionArray.from_storage(IntegerType(), storage_array)
> {code}
> While doing this directly in {{pa.array(..)}} doesn't work:
> {code:python}
> >>> pa.array([1, 2, 3], type=IntegerType())
> ArrowNotImplementedError: extension
> {code}
> I think it should be possible to basically to the ExtensionArray.from_storage
> under the hood in {{pa.array(..)}} when the specified type is an extension
> type?
> I think this should also enable converting from a pandas DataFrame (with a
> column with matching storage values) to a Table with a specified schema that
> includes an extension type. Like:
> {code}
> df = pd.DataFrame({'a': [1, 2, 3]})
> pa.table(df, schema=pa.schema([('a', IntegerType())]))
> {code}
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