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https://issues.apache.org/jira/browse/ARROW-7168?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Antoine Pitrou reassigned ARROW-7168:
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Assignee: Joris Van den Bossche
> [Python] pa.array() doesn't respect specified dictionary type
> -------------------------------------------------------------
>
> Key: ARROW-7168
> URL: https://issues.apache.org/jira/browse/ARROW-7168
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
> Affects Versions: 0.15.1
> Reporter: Thomas Buhrmann
> Assignee: Joris Van den Bossche
> Priority: Major
> Labels: pull-request-available
> Fix For: 1.0.0
>
> Time Spent: 1h 20m
> Remaining Estimate: 0h
>
> This might be related to ARROW-6548 and others dealing with all NaN columns.
> When creating a dictionary array, even when fully specifying the desired
> type, this type is not respected when the data contains only NaNs:
> {code:python}
> # This may look a little artificial but easily occurs when processing
> categorial data in batches and a particular batch containing only NaNs
> ser = pd.Series([None, None]).astype('object').astype('category')
> typ = pa.dictionary(index_type=pa.int8(), value_type=pa.string(),
> ordered=False)
> pa.array(ser, type=typ).type
> {code}
> results in
> {noformat}
> >> DictionaryType(dictionary<values=null, indices=int8, ordered=0>)
> {noformat}
> which means that one cannot e.g. serialize batches of categoricals if the
> possibility of all-NaN batches exists, even when trying to enforce that each
> batch has the same schema (because the schema is not respected).
> I understand that inferring the type in this case would be difficult, but I'd
> imagine that a fully specified type should be respected in this case?
> In the meantime, is there a workaround to manually create a dictionary array
> of the desired type containing only NaNs?
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