# Parquet decimal precision overflow when reading into pandas — values truncated or converted to NaN

- **ID:** `data/parquet-decimal-precision-overflow-pandas`
- **Domain:** data
- **Category:** type_error
- **Verification:** ai_generated
- **Fix Rate:** 85%

## Root Cause

Parquet files store decimals with high precision (e.g., DECIMAL(38,10)) but pandas uses Python's float64 or int64, which cannot represent such high precision, causing overflow or silent truncation.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| pandas 2.2.0 | active | — | — |
| pyarrow 15.0.0 | active | — | — |
| Apache Parquet 1.13.0 | active | — | — |

## Workarounds

1. **Read the Parquet file with pyarrow directly and convert to pandas using `pq.read_table(path).to_pandas(timestamp_as_object=True)` which converts decimals to Python Decimal objects. Example: `import pyarrow.parquet as pq; table = pq.read_table('data.parquet'); df = table.to_pandas(timestamp_as_object=True, date_as_object=True)`** (90% success)
   ```
   Read the Parquet file with pyarrow directly and convert to pandas using `pq.read_table(path).to_pandas(timestamp_as_object=True)` which converts decimals to Python Decimal objects. Example: `import pyarrow.parquet as pq; table = pq.read_table('data.parquet'); df = table.to_pandas(timestamp_as_object=True, date_as_object=True)`
   ```
2. **Use `pd.read_parquet(path, engine='pyarrow', dtype_backend='numpy_nullable')` and then manually convert decimal columns to `pd.StringDtype()` to preserve precision: `df['dec_col'] = df['dec_col'].astype('string')`** (85% success)
   ```
   Use `pd.read_parquet(path, engine='pyarrow', dtype_backend='numpy_nullable')` and then manually convert decimal columns to `pd.StringDtype()` to preserve precision: `df['dec_col'] = df['dec_col'].astype('string')`
   ```
3. **If the data fits within 38 digits, use `pd.read_parquet(path, engine='pyarrow', use_nullable_dtypes=True)` which uses `pd.ArrowDtype` for decimals, preserving precision as Python Decimal objects** (80% success)
   ```
   If the data fits within 38 digits, use `pd.read_parquet(path, engine='pyarrow', use_nullable_dtypes=True)` which uses `pd.ArrowDtype` for decimals, preserving precision as Python Decimal objects
   ```

## Dead Ends

- **** — This uses pyarrow for conversion but still may overflow if the decimal precision exceeds 38 digits or if pyarrow's default decimal type cannot map to pandas. (70% fail)
- **** — Float64 can only represent ~15-17 significant digits; high-precision decimals will be truncated or rounded, losing data. (90% fail)
- **** — Fastparquet has similar limitations and may silently convert decimals to float64, causing the same overflow. (75% fail)