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airqo-platform / AirQo-api

This repo contains API definitions for the AirQo platform
MIT License
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Fix aggregate_data method to work with pandas 2.X.X #3236

Closed MartinKalema closed 1 week ago

MartinKalema commented 2 weeks ago

WHAT DOES THIS PR DO?

This PR fixes the aggregate_data method to ensure compatibility with pandas 2.0 and above. The method now correctly handles non-numeric columns by excluding them from the resampling operation, which resolves the TypeError encountered when trying to compute the mean of non-numeric data. It is also compatible with pandas 1.5.3 and runs error-free.

CHANGES MADE

WHY IS THIS PR NECESSARY?

In pandas 2.0 and above, the aggregate_data method fails with a TypeError due to the presence of non-numeric data in the DataFrame when attempting to compute the mean. This PR addresses this issue by ensuring that only numeric columns are included in the resampling operation, allowing the method to function correctly with the latest versions of pandas. It also ensures backward compatibility with pandas 1.5.3, avoiding any errors.

AIRFLOW LOGS

The production and staging environment seem to be using pandas 1.5.3, the following warning is logged:

[2024-06-21, 12:25:37 UTC] {logging_mixin.py:150} 
WARNING - /home/airflow/.local/lib/python3.10/site-packages/airqo_etl_utils/weather_data_utils.py:202 
FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. 
In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which 
should be valid for the function.

In pandas 2.0 and above, this results in a TypeError:

TypeError: Could not convert string 'TA00032TA00032TA00032TA00032TA00032TA00032
TA00032TA00032TA00032TA00032TA00032TA00032' to numeric

HOW TO TEST. Run this code in a jupyter notebook. 

from datetime import datetime, timedelta, timezone
from airqo_etl_utils.date import date_to_str_hours
from airqo_etl_utils.weather_data_utils import WeatherDataUtils

def extract_hourly_weather_data():

    hour_of_day = datetime.now(timezone.utc) - timedelta(hours=2)
    start_date_tahmo = date_to_str_hours(hour_of_day)
    end_date_tahmo = datetime.strftime(hour_of_day, "%Y-%m-%dT%H:59:59Z")

    return WeatherDataUtils.extract_hourly_data(
        start_date_time=start_date_tahmo, end_date_time=end_date_tahmo
    )

extract_hourly_weather_data()

Summary by CodeRabbit

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Commits Files that changed from the base of the PR and between 5a6a6ab1577c87b734157dbad5a500562e4f400f and 89ed2a4717b1514ca3a31180dddba29831b41b53.

Walkthrough

The aggregate_data function in weather_data_utils.py has been enhanced to improve data processing efficiency. Key changes include more precise handling of timestamps, refined averaging and summation, and clearer column naming conventions. These updates ensure more accurate and comprehensible weather data aggregation for improved analysis and reporting.

Changes

Files Change Summary
src/workflows/airqo_etl_utils/weather_data_utils.py The aggregate_data function now includes refined handling of timestamps, optimized index setting, selective column resampling for averaging and summing, and improved column renaming for better clarity.

Poem

In the code where data flows,
Improvements come and clarity shows.
Timestamps sync and columns rename,
For weather stations, never the same.
Data neat and errors few,
A smoother path for insights new.
🌦️🌟📊

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codecov[bot] commented 2 weeks ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 30.29%. Comparing base (474322b) to head (89ed2a4).

Additional details and impacted files [![Impacted file tree graph](https://app.codecov.io/gh/airqo-platform/AirQo-api/pull/3236/graphs/tree.svg?width=650&height=150&src=pr&token=HHq3qS3cL6&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=airqo-platform)](https://app.codecov.io/gh/airqo-platform/AirQo-api/pull/3236?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=airqo-platform) ```diff @@ Coverage Diff @@ ## staging #3236 +/- ## ======================================== Coverage 30.29% 30.29% ======================================== Files 184 184 Lines 24493 24493 Branches 3205 3205 ======================================== Hits 7421 7421 Misses 16958 16958 Partials 114 114 ```
Baalmart commented 2 weeks ago

Hi @MartinKalema , thanks again. In the background through phone calls, Slack private DMs and emails, please mobilise your colleagues to review your recent PRs. Thanks!