[ https://issues.apache.org/jira/browse/ARROW-12150?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Antoine Pitrou updated ARROW-12150: ----------------------------------- Summary: [Python] Bad type inference of mixed-precision Decimals (was: [Python] Invalid data when Decimal is exported to parquet ) > [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} > -- This message was sent by Atlassian Jira (v8.3.4#803005)