[jira] [Commented] (ARROW-7168) [Python] pa.array() doesn't respect provided dictionary type with all NaNs

["Joris Van den Bossche (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-7168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16974511#comment-16974511 ] 

Joris Van den Bossche commented on ARROW-7168:
----------------------------------------------

[~buhrmann] thanks for the report. When passing a type like that, I agree it should be honoured.

Some other observations:

Also when it's not all-NaN, the specified type gets ignored:

{code}
In [19]: cat = pd.Categorical(['a', 'b']) 

In [20]: typ = pa.dictionary(index_type=pa.int8(), value_type=pa.int64(), ordered=False)  

In [21]: pa.array(cat, type=typ) 
Out[21]: 
<pyarrow.lib.DictionaryArray object at 0x7ff87b6a50b8>

-- dictionary:
  [
    "a",
    "b"
  ]
-- indices:
  [
    0,
    1
  ]

In [22]: pa.array(cat, type=typ).type  
Out[22]: DictionaryType(dictionary<values=string, indices=int8, ordered=0>)
{code}

So I suppose it's a more general problem, not specifically related to this all-NaN case (it only appears for you in this case, as otherwise the specified type and the type from the data will probably match).

In the example I show here above, we should probably raise an error is the specified type is not compatible (string vs int categories).

> [Python] pa.array() doesn't respect provided dictionary type with all NaNs
> --------------------------------------------------------------------------
>
>                 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
>            Priority: Major
>
> 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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