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import openmeteo_requests import pandas as pd import requests_cache from retry_requests import retry # Setup the Open-Meteo API client with cache and retry on error cache_session = requests_cache.CachedSession('.cache', expire_after = 3600) retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2) openmeteo = openmeteo_requests.Client(session = retry_session) # Make sure all required weather variables are listed here # The order of variables in hourly or daily is important to assign them correctly below url = "https://air-quality-api.open-meteo.com/v1/air-quality" params = { "latitude": 34.4611, "longitude": 45.8626, "hourly": ["pm10", "pm2_5", "dust"], "past_days": 1, "forecast_days": 7 } responses = openmeteo.weather_api(url, params=params) # Process first location. Add a for-loop for multiple locations or weather models response = responses[0] print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E") print(f"Elevation {response.Elevation()} m asl") print(f"Timezone {response.Timezone()}{response.TimezoneAbbreviation()}") print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s") # Process hourly data. The order of variables needs to be the same as requested. hourly = response.Hourly() hourly_pm10 = hourly.Variables(0).ValuesAsNumpy() hourly_pm2_5 = hourly.Variables(1).ValuesAsNumpy() hourly_dust = hourly.Variables(2).ValuesAsNumpy() hourly_data = {"date": pd.date_range( start = pd.to_datetime(hourly.Time(), unit = "s", utc = True), end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True), freq = pd.Timedelta(seconds = hourly.Interval()), inclusive = "left" )} hourly_data["pm10"] = hourly_pm10 hourly_data["pm2_5"] = hourly_pm2_5 hourly_data["dust"] = hourly_dust hourly_dataframe = pd.DataFrame(data = hourly_data) print(hourly_dataframe)