[jira] [Created] (ARROW-5655) [Python] Table.from_pydict/from_arrays not using types in specified schema correctly

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

Joris Van den Bossche created ARROW-5655:
--------------------------------------------

             Summary: [Python] Table.from_pydict/from_arrays not using types in specified schema correctly 
                 Key: ARROW-5655
                 URL: https://issues.apache.org/jira/browse/ARROW-5655
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
            Reporter: Joris Van den Bossche


Example with {{from_pydict}} (from https://github.com/apache/arrow/pull/4601#issuecomment-503676534):

{code:python}
In [15]: table = pa.Table.from_pydict(
    ...:     {'a': [1, 2, 3], 'b': [3, 4, 5]},
    ...:     schema=pa.schema([('a', pa.int64()), ('c', pa.int32())]))

In [16]: table
Out[16]: 
pyarrow.Table
a: int64
c: int32

In [17]: table.to_pandas()
Out[17]: 
   a  c
0  1  3
1  2  0
2  3  4
{code}

Note that the specified schema has 1) different column names and 2) has a non-default type (int32 vs int64) which leads to corrupted values.

This is partly due to {{Table.from_pydict}} not using the type information in the schema to convert the dictionary items to pyarrow arrays. But then it is also {{Table.from_arrays}} that is not correctly casting the arrays to another dtype if the schema specifies as such.

Additional question for {{Table.pydict}} is whether it actually should override the 'b' key from the dictionary as column 'c' as defined in the schema (this behaviour depends on the order of the dictionary, which is not guaranteed below python 3.6).




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