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https://issues.apache.org/jira/browse/ARROW-2135?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wes McKinney updated ARROW-2135:
--------------------------------
    Fix Version/s: 0.9.0

> [Python] NaN values silently casted to int64 when passing explicit schema for 
> conversion in Table.from_pandas
> -------------------------------------------------------------------------------------------------------------
>
>                 Key: ARROW-2135
>                 URL: https://issues.apache.org/jira/browse/ARROW-2135
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 0.8.0
>            Reporter: Matthew Gilbert
>            Priority: Major
>             Fix For: 0.9.0
>
>
> If you create a {{Table}} from a {{DataFrame}} of ints with a NaN value the 
> NaN is improperly cast. Since pandas casts these to floats, when converted to 
> a table the NaN is interpreted as an integer. This seems like a bug since a 
> known limitation in pandas (the inability to have null valued integers data) 
> is taking precedence over arrow's functionality to store these as an IntArray 
> with nulls.
>  
> {code}
> import pyarrow as pa
> import pandas as pd
> df = pd.DataFrame({"a":[1, 2, pd.np.NaN]})
> schema = pa.schema([pa.field("a", pa.int64(), nullable=True)])
> table = pa.Table.from_pandas(df, schema=schema)
> table[0]
> <pyarrow.lib.Column object at 0x7f2151d19c90>
> chunk 0: <pyarrow.lib.Int64Array object at 0x7f213bf356d8>
> [
>   1,
>   2,
>   -9223372036854775808
> ]{code}
>  



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