visualization is done

data_pipelines/historical_weather_data.py

@@ -4,7 +4,6 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import requests_cache
 from retry_requests import retry
-
 from compute_et0_adjusted import compute_et0
 
 
@@ -75,7 +74,7 @@ def aggregate_hourly_weather_data(
 		hourly_data: pd.DataFrame,
 ) -> pd.DataFrame:
 	resampled_data = hourly_data.resample("1ME").agg({
-		"temperature_2m": ["min", "max"],
+		"temperature_2m": ["min", "max", "mean"],
 		"relative_humidity_2m": ["min", "max"],
 		"wind_speed_10m": "mean",
 		"precipitation": "mean",
@@ -87,6 +86,7 @@ def aggregate_hourly_weather_data(
 		"day_of_year": resampled_data.index.dayofyear,
 		"air_temperature_min": resampled_data[("temperature_2m", "min")],
 		"air_temperature_max": resampled_data[("temperature_2m", "max")],
+		"air_temperature_mean": resampled_data[("temperature_2m", "mean")],
 		"relative_humidity_min": resampled_data[("relative_humidity_2m", "min")],
 		"relative_humidity_max": resampled_data[("relative_humidity_2m", "max")],
 		"precipitation": resampled_data[("precipitation", "mean")],