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https://issues.apache.org/jira/browse/ARROW-2160?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16365056#comment-16365056
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Julian Hyde commented on ARROW-2160:
------------------------------------

As the resident SQL curmudgeon let me point out that the SQL world has a 
different definition of precision, apparently, than the Python world. In SQL, 
precision is the total number of digits (left and right of the point). The 
number 1234.56 has precision 6 and scale 2.

I'm not claiming that SQL's terminology is "right", but since the terminologies 
are at odds, let's be clear when we talk about precision.

> [C++/Python]  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
>             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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