[
https://issues.apache.org/jira/browse/ARROW-2135?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Antoine Pitrou reassigned ARROW-2135:
-------------------------------------
Assignee: Antoine Pitrou
> [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
> Assignee: Antoine Pitrou
> 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}
>
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
