Drop null values causing pipeline-task(clean_data_raw_data) failure

Walkthrough

## Walkthrough In the `clean_low_cost_sensor_data` method of the `airqo_utils.py` file, a line has been added to drop NaN values from the dataset before converting a specific column to datetime format. This improvement ensures that missing values are handled early in the data cleaning process, thus enhancing the robustness and accuracy of subsequent data transformations. ## Changes | File Path | Change Summary | |--------------------------------------------|-------------------------------------------------------------------------------| | `src/workflows/airqo_etl_utils/airqo_utils.py` | Added a line to drop NaN values before converting a column to datetime format in the `clean_low_cost_sensor_data` method. | ## Sequence Diagram(s) ```mermaid sequenceDiagram participant User participant clean_low_cost_sensor_data participant DataFrame User->>clean_low_cost_sensor_data: call method clean_low_cost_sensor_data->>DataFrame: drop NaN values clean_low_cost_sensor_data->>DataFrame: convert column to datetime clean_low_cost_sensor_data-->>User: return cleaned data ``` ## Poem > In the data's gentle flow, > We drop the NaNs and let them go, > Converting columns, date and time, > Ensuring accuracy, simply prime. > With cleaner data, insights grow, > In AirQo's realm, progress will show.

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airqo-platform / AirQo-api

Drop null values causing pipeline-task(clean_data_raw_data) failure #3287

Summary by CodeRabbit