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https://issues.apache.org/jira/browse/ARROW-12150?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17311541#comment-17311541
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Antoine Pitrou commented on ARROW-12150:
----------------------------------------
Actually the problem is that converting from Python Decimals with mixed
precision produces the wrong Arrow decimal type:
{code:python}
>>> arr = pa.array([Decimal('1.234'), Decimal('456.7')])
>>> arr
<pyarrow.lib.Decimal128Array object at 0x7fd86ef69590>
[
1.234,
456.700
]
>>> arr.type
Decimal128Type(decimal128(4, 3))
# BUG: 456.7 doesn't fit in decimal128(4, 3)!
{code}
You can workaround the issue by specifying the column types explicitly when
creating your table, for example:
{code:python}
decs_from_values = pa.array([Decimal(v) for v in values_floats],
type=pa.decimal256(54, 51))
{code}
As a sidenote, it is a bad practice to instantiate decimals from floating-point
numbers, because floating-point numbers can't exactly represent all decimal
numbers, which can lead to excessive digits in the results, e.g.:
{code:python}
>>> Decimal(579.119995117188)
Decimal('579.11999511718795474735088646411895751953125')
>>> Decimal("579.119995117188")
Decimal('579.119995117188')
{code}
> [Python] Bad type inference of mixed-precision Decimals
> -------------------------------------------------------
>
> Key: ARROW-12150
> URL: https://issues.apache.org/jira/browse/ARROW-12150
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 3.0.0
> Environment: - macOS Big Sur 11.2.1
> - python 3.8.2
> Reporter: abdel alfahham
> Priority: Major
>
> Exporting _pyarrow.table_ that contains mixed-precision _Decimals_ using
> _parquet.write_table_ creates a parquet that contains invalid data/values.
> In the example below the first value of _data_decimal_ is turned from
> Decimal('579.11999511718795474735088646411895751953125000000000') in the
> pyarrow table to
> Decimal('-378.68971792399258172661600550482428224218070136475136') in the
> parquet.
>
> {code:java}
> import pyarrow
> from decimal import Decimal
> values_floats = [579.119995117188, 6.40999984741211, 2.0] # floats
> decs_from_values = [Decimal(v) for v in values_floats] # Decimal
> decs_from_float = [Decimal.from_float(v) for v in values_floats]
> decs_str = [Decimal(str(v)) for v in values_floats] # Decimal
> data_dict = {"data_decimal": decs_from_values, # python Decimal
> "data_decimal_from_float": decs_from_float,
> "data_float":values_floats, # python floats
> "data_dec_str": decs_str}
> table = pyarrow.table(data=data_dict)
> print(table.to_pydict()) # before saving
> pyarrow.parquet.write_table(table, "./pyarrow_decimal.parquet") # saving
> print(pyarrow.parquet.read_table("./pyarrow_decimal.parquet").to_pydict()) #
> after saving
> {code}
>
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