Update fix/clean up #4177
Update fix/clean up #4177
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Actionable comments posted: 1
🧹 Nitpick comments (1)
src/workflows/airqo_etl_utils/data_validator.py (1)
84-92: Consider optimizing the integer conversion process.The current implementation involves multiple type conversions that could be simplified while improving error handling and traceability:
- The NaN ↔ empty string conversions could be eliminated
- Consider adding logging for values that fail conversion
- The sentinel value (-1) might need to be configurable per column
Here's a suggested improvement:
- .fillna("") # Replace NaN with empty strings - .astype(str) # Convert to string - .str.strip() # Remove leading/trailing whitespace - .replace("", np.nan) # Replace empty strings with NaN for clarity - .apply( - lambda x: pd.to_numeric(x, errors="coerce") - ) # Convert to numeric - .fillna(-1) # Replace NaN with -1 for invalid/missing values - .astype(np.int64) # Convert to integer type + .apply(pd.to_numeric, errors='coerce') # Direct numeric conversion + .apply(lambda x: logger.debug(f"Invalid value in {col}: {x}") or x if pd.isna(x) else x) # Log invalid values + .fillna(-1) # Replace NaN with sentinel value + .astype(np.int64)
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src/workflows/airqo_etl_utils/data_validator.py(2 hunks)
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🔇 Additional comments (1)
src/workflows/airqo_etl_utils/data_validator.py (1)
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4-4: Consider adding unit tests for integer handling.Given the critical nature of data validation in the ETL process, it would be beneficial to add comprehensive unit tests covering:
- Edge cases in integer conversion
- Handling of various invalid inputs
- Verification of sentinel value (-1) handling
Would you like me to help create unit tests for these scenarios? Additionally, consider adding data quality monitoring to track:
- Frequency of invalid integer values
- Distribution of sentinel values across columns
| data["device_number"] = ( | ||
| data["device_number"] | ||
| .fillna("") | ||
| .astype(str) | ||
| .str.strip() | ||
| .replace("", np.nan) | ||
| .apply(lambda x: pd.to_numeric(x, errors="coerce")) | ||
| .fillna(-1) | ||
| .astype(np.int64) | ||
| ) |
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🛠️ Refactor suggestion
Eliminate code duplication in device_number processing.
The device_number processing duplicates the integer conversion logic from format_data_types. This violates the DRY principle and could lead to maintenance issues.
Consider these improvements:
- Utilize the existing
format_data_typesmethod:
- data["device_number"] = (
- data["device_number"]
- .fillna("")
- .astype(str)
- .str.strip()
- .replace("", np.nan)
- .apply(lambda x: pd.to_numeric(x, errors="coerce"))
- .fillna(-1)
- .astype(np.int64)
- )
+ data = DataValidationUtils.format_data_types(
+ data=data,
+ integers=["device_number"]
+ )- If device_number requires special handling, document the reasoning:
# Add above the device_number processing
# Special handling for device_number to ensure consistent integer representation
# across the API response. Device numbers are processed separately from other
# integer columns because [explain the reason].
Description
[Provide a brief description of the changes made in this PR]
Improve data clean up for missing integer values.
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