Update fix/clean up #4062
Update fix/clean up #4062
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📝 WalkthroughWalkthroughThe changes in this pull request involve modifications to several methods across different classes, primarily focusing on data handling and processing. Key alterations include updates to the Changes
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (1)
src/workflows/airqo_etl_utils/daily_data_utils.py (1)
33-51: Optimize data averaging by eliminating the loopYou can improve the efficiency of the
average_datamethod by removing the loop and performing grouping and resampling directly on the DataFrame. This will enhance performance, especially with large datasets.Here's how you might refactor the code:
data["timestamp"] = pd.to_datetime(data["timestamp"]) averaged_data = ( data.set_index("timestamp") .groupby(["network", "device_id", "site_id", "device_number"]) .resample("1D") .mean(numeric_only=True) .reset_index() )This approach simplifies the code by eliminating the need to concatenate individual DataFrames and reduces memory usage.
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📒 Files selected for processing (3)
src/analytics/api/models/events.py(1 hunks)src/workflows/airqo_etl_utils/bigquery_api.py(1 hunks)src/workflows/airqo_etl_utils/daily_data_utils.py(2 hunks)
🔇 Additional comments (2)
src/workflows/airqo_etl_utils/bigquery_api.py (1)
607-652: Confirm consistent handling of the 'network' parameter
The reload_data method now includes the network parameter with a default value of "all". Please verify that:
- All calls to
reload_dataprovide appropriatenetworkvalues. - The
compose_querymethod correctly handles"all"as a valid value for thenetworkparameter. - This change aligns with the overall design and does not introduce unintended side effects.
src/analytics/api/models/events.py (1)
592-592: Verify the impact of dropping 'pm2_5_raw_value' when all values are zero or NaN
The updated condition in simple_data_cleaning now drops the 'pm2_5_raw_value' column when all its values are zero or NaN. Please ensure that this change does not adversely affect any downstream processes or analyses that rely on this column, even if it contains only zeros or missing values.
| """ | ||
| Averages data in a pandas DataFrame on a daily basis for each device, | ||
| grouped by network and device ID. The function resamples data | ||
| to compute daily averages for numerical columns. | ||
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| Args: | ||
| data (pd.DataFrame): A pandas DataFrame containing the following columns: | ||
| - "timestamp": Timestamps of the data. | ||
| - "network": The network the data belongs to. | ||
| - "device_id": Unique identifier for the device. | ||
| - "site_id": Unique identifier for the site associated with the device. | ||
| - "device_number": Device number. | ||
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| Returns: | ||
| pd.DataFrame: A DataFrame containing daily averages for each device, | ||
| including metadata columns such as "tenant", "device_id", "site_id", | ||
| and "device_number". | ||
| """ |
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🛠️ Refactor suggestion
Update docstring to replace 'tenant' with 'network' for consistency
In the Returns section of the docstring for average_data, it mentions metadata columns such as "tenant", "device_id", "site_id", and "device_number". However, the code uses "network" instead of "tenant". Please update the docstring to reflect "network" to maintain consistency and avoid confusion.
| dataframe.copy() | ||
| ) # Not sure why this dataframe is being copied. # Memory wastage? |
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🛠️ Refactor suggestion
Clarify the purpose of copying the DataFrame or remove the unnecessary copy
The comment # Not sure why this dataframe is being copied. # Memory wastage? suggests uncertainty about the necessity of copying the DataFrame. If the copy is not required, consider removing data = dataframe.copy() to conserve memory. If it is necessary, please provide an explanation to clarify its purpose for future reference.
Description
[Provide a brief description of the changes made in this PR]
This PR cleans the daily measurements tasks
Related Issues
Summary by CodeRabbit
New Features
pm2_5_raw_valuecolumn if it contains only zeros or missing values.Bug Fixes
Documentation