ASF GitHub Bot commented on ARROW-2160:

cpcloud commented on a change in pull request #1618: ARROW-2160: [C++/Python]  
Fix decimal precision inference
URL: https://github.com/apache/arrow/pull/1618#discussion_r169129512

 File path: cpp/src/arrow/python/helpers.cc
 @@ -99,7 +99,8 @@ Status InferDecimalPrecisionAndScale(PyObject* 
python_decimal, int32_t* precisio
   DCHECK_NE(precision, NULLPTR);
-  OwnedRef as_tuple(PyObject_CallMethod(python_decimal, "as_tuple", "()"));
+  // TODO(phillipc): Make sure we perform PyDecimal_Check(python_decimal) as a 
 Review comment:
   I have a couple more tests to add here

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> [C++/Python]  Fix decimal precision inference
> ---------------------------------------------
>                 Key: ARROW-2160
>                 URL: https://issues.apache.org/jira/browse/ARROW-2160
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: C++, Python
>    Affects Versions: 0.8.0
>            Reporter: Antony Mayi
>            Assignee: Phillip Cloud
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 0.9.0
> {code}
> import pyarrow as pa
> import pandas as pd
> import decimal
> df = pd.DataFrame({'a': [decimal.Decimal('0.1'), decimal.Decimal('0.01')]})
> pa.Table.from_pandas(df)
> {code}
> raises:
> {code}
> pyarrow.lib.ArrowInvalid: Decimal type with precision 2 does not fit into 
> precision inferred from first array element: 1
> {code}
> Looks arrow is inferring the highest precision for given column based on the 
> first cell and expecting the rest fits in. I understand this is by design but 
> from the point of view of pandas-arrow compatibility this is quite painful as 
> pandas is more flexible (as demonstrated).
> What this means is that user trying to pass pandas {{DataFrame}} with 
> {{Decimal}} column(s) to arrow {{Table}} would always have to first:
> # Find the highest precision used in (each of) that column(s)
> # Adjust the first cell of (each of) that column(s) so that it explicitly 
> uses the highest precision of that column(s)
> # Only then pass such {{DataFrame}} to {{Table.from_pandas()}}
> So given this unavoidable procedure (and assuming arrow needs to be strict 
> about the highest precision for a column) - shouldn't some similar logic be 
> part of the {{Table.from_pandas()}} directly to make this transparent?

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