Update fix/clean up #4292
Update fix/clean up #4292
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Updates from airqo staging
Updates from airqo staging
📝 WalkthroughWalkthroughThe pull request introduces refinements to exception handling, method signatures, and Airflow DAG configurations across multiple files. The changes focus on improving error logging, input validation, and task execution robustness in the AirQo ETL (Extract, Transform, Load) workflows. Key modifications include updating the Changes
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Actionable comments posted: 1
🧹 Nitpick comments (2)
src/workflows/airqo_etl_utils/airqo_utils.py (2)
512-512: Address the TODO comment regarding raw data quality checks.The comment indicates a need for a more structured approach to raw data quality checks.
Would you like me to help design a more robust implementation for the raw data quality checks?
513-520: Consider adding error handling to the pattern matching block.While the pattern matching implementation is clean, it would benefit from error handling to gracefully handle unexpected device categories.
match device_category: case DeviceCategory.LOW_COST_GAS: - AirQoGxExpectations.from_pandas().gaseous_low_cost_sensor_raw_data_check( - data - ) + try: + AirQoGxExpectations.from_pandas().gaseous_low_cost_sensor_raw_data_check( + data + ) + except Exception as e: + logger.warning(f"Raw data quality check failed for LOW_COST_GAS: {e}") case DeviceCategory.LOW_COST: - AirQoGxExpectations.from_pandas().pm2_5_low_cost_sensor_raw_data(data) + try: + AirQoGxExpectations.from_pandas().pm2_5_low_cost_sensor_raw_data(data) + except Exception as e: + logger.warning(f"Raw data quality check failed for LOW_COST: {e}") + case _: + logger.warning(f"Unexpected device category: {device_category}")
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📒 Files selected for processing (1)
src/workflows/airqo_etl_utils/airqo_utils.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
src/workflows/airqo_etl_utils/airqo_utils.py
525-525: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
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- GitHub Check: Analyze (python)
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🔇 Additional comments (1)
src/workflows/airqo_etl_utils/airqo_utils.py (1)
488-507: Well-documented function with clear steps!The expanded docstring provides excellent clarity about the function's purpose and process. The step-by-step breakdown and proper type hints make it very maintainable.
| try: | ||
| data.dropna(subset=["timestamp"], inplace=True) | ||
| data["timestamp"] = pd.to_datetime(data["timestamp"]) | ||
| data.dropna(subset=["timestamp"], inplace=True) | ||
| except Exception as e: | ||
| logger.exception(f"There is an issue with the timestamp column: {e}") | ||
| raise KeyError(f"An error has occurred with the 'timestamp' column: {e}") |
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🛠️ Refactor suggestion
Improve exception handling by using raise from.
The current exception handling could be improved to better preserve the exception chain.
try:
data.dropna(subset=["timestamp"], inplace=True)
data["timestamp"] = pd.to_datetime(data["timestamp"])
except Exception as e:
logger.exception(f"There is an issue with the timestamp column: {e}")
- raise KeyError(f"An error has occurred with the 'timestamp' column: {e}")
+ raise KeyError(f"An error has occurred with the 'timestamp' column: {e}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try: | |
| data.dropna(subset=["timestamp"], inplace=True) | |
| data["timestamp"] = pd.to_datetime(data["timestamp"]) | |
| data.dropna(subset=["timestamp"], inplace=True) | |
| except Exception as e: | |
| logger.exception(f"There is an issue with the timestamp column: {e}") | |
| raise KeyError(f"An error has occurred with the 'timestamp' column: {e}") | |
| try: | |
| data.dropna(subset=["timestamp"], inplace=True) | |
| data["timestamp"] = pd.to_datetime(data["timestamp"]) | |
| except Exception as e: | |
| logger.exception(f"There is an issue with the timestamp column: {e}") | |
| raise KeyError(f"An error has occurred with the 'timestamp' column: {e}") from e |
🧰 Tools
🪛 Ruff (0.8.2)
525-525: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
Description
Enable task retries for weather data.
Summary by CodeRabbit
Release Notes
Bug Fixes
Refactor
Documentation
The changes focus on improving code reliability, error handling, and workflow consistency across various data processing components.