
Type: Bug

Status: Resolved

Priority: Major

Resolution: Fixed

Affects Version/s: 0.8.0

Fix Version/s: 0.9.0

Labels:
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)
raises:
pyarrow.lib.ArrowInvalid: Decimal type with precision 2 does not fit into precision inferred from first array element: 1
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 pandasarrow 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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