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Update/calculate hourly airqualitydata using bigqdata #4355

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merged 3 commits into from
Feb 5, 2025
Merged

Update/calculate hourly airqualitydata using bigqdata #4355

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30f5f27
Default values only for na
NicholasTurner23 Feb 5, 2025
2c14672
Cleanup
NicholasTurner23 Feb 5, 2025
fd3ac86
Merge branch 'staging' into update/calculate_hourly_airqualitydata_us…
NicholasTurner23 Feb 5, 2025
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5 changes: 2 additions & 3 deletions src/workflows/airqo_etl_utils/airqo_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -606,9 +606,8 @@ def calibrate_data(data: pd.DataFrame) -> pd.DataFrame:
]

# TODO: Need to opt for a different approach eg forward fill, can't do here as df only has data of last 1 hour. Perhaps use raw data only?
default_values = {col: 0 for col in columns_to_fill}
data = data.assign(**default_values)
# data[columns_to_fill] = data[columns_to_fill].fillna(0)
# Fill nas for the specified fields.
data[columns_to_fill] = data[columns_to_fill].fillna(0)
Comment on lines +609 to +610
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🛠️ Refactor suggestion

Consider using forward fill instead of zero for missing values.

Filling missing sensor readings with zero can significantly skew the calibration results. A more appropriate approach would be to use forward fill (ffill) or interpolation, which better preserves the data trends and real-world conditions.

Here's a suggested implementation:

-# Fill nas for the specified fields.
-data[columns_to_fill] = data[columns_to_fill].fillna(0)
+# Forward fill missing values, then backward fill any remaining NAs at the start
+data[columns_to_fill] = data[columns_to_fill].fillna(method='ffill').fillna(method='bfill')

This approach:

  1. First attempts to forward fill using previous valid measurements
  2. Then backward fills any remaining NAs at the start of the series
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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.

Suggested change
# Fill nas for the specified fields.
data[columns_to_fill] = data[columns_to_fill].fillna(0)
# Forward fill missing values, then backward fill any remaining NAs at the start
data[columns_to_fill] = data[columns_to_fill].fillna(method='ffill').fillna(method='bfill')


# additional input columns for calibration
data["avg_pm2_5"] = data[["s1_pm2_5", "s2_pm2_5"]].mean(axis=1).round(2)
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