Antony Mayi created ARROW-2160:
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Summary: decimal precision inference
Key: ARROW-2160
URL: https://issues.apache.org/jira/browse/ARROW-2160
Project: Apache Arrow
Issue Type: Improvement
Components: C++, Python
Affects Versions: 0.8.0
Reporter: Antony Mayi
{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 it has 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 this logic be part of the
{{Table.from_pandas()}} directly to make this transparent?
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