Fokko commented on code in PR #1839:
URL: https://github.com/apache/iceberg-python/pull/1839#discussion_r2020991374
##########
pyiceberg/io/pyarrow.py:
##########
@@ -2350,8 +2351,19 @@ def data_file_statistics_from_parquet_metadata(
stats_col.iceberg_type, statistics.physical_type,
stats_col.mode.length
)
- col_aggs[field_id].update_min(statistics.min)
- col_aggs[field_id].update_max(statistics.max)
+ if isinstance(stats_col.iceberg_type, DecimalType) and
statistics.physical_type != "FIXED_LEN_BYTE_ARRAY":
+ precision = stats_col.iceberg_type.precision
+ scale = stats_col.iceberg_type.scale
+ decimal_type = pa.decimal128(precision, scale)
+ col_aggs[field_id].update_min(
+ pa.array([Decimal(statistics.min_raw) /
(10**scale)], decimal_type)[0].as_py()
+ )
+ col_aggs[field_id].update_max(
+ pa.array([Decimal(statistics.max_raw) /
(10**scale)], decimal_type)[0].as_py()
+ )
Review Comment:
I think we can simplify this into:
```suggestion
scale = stats_col.iceberg_type.scale
col_aggs[field_id].update_min(unscaled_to_decimal(statistics.min_raw, scale))
col_aggs[field_id].update_max(unscaled_to_decimal(statistics.max_raw, scale))
```
We already have the `unscale_to_decimal` that we can import from
`pyiceberg.utils.decimal`.
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