A free historical weather API using Python and Open-Meteo.com

readme.md

In this gist you can see how to obtain historical weather data from Open Meteo api.

weather_api.py

import openmeteo_requests

import requests_cache
import pandas as pd
from retry_requests import retry

def get_weather_data(latitude, longitude, start_date, end_date, id = None):
    """
    Fetches historical weather data for a given location and date range using the Open-Meteo API.

    Args:
        latitude (float): Latitude of the location.
        longitude (float): Longitude of the location.
        start_date (str): Start date in 'YYYY-MM-DD' format.
        end_date (str): End date in 'YYYY-MM-DD' format.

    Returns:
        pd.DataFrame: DataFrame containing hourly weather data including temperature, humidity, precipitation, rain, snowfall, snow depth, and weather code.
    """

    # Setup the Open-Meteo API client with cache and retry on error
    cache_session = requests_cache.CachedSession('.cache', expire_after = -1)
    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://archive-api.open-meteo.com/v1/archive"
    params = {
        "latitude": latitude,
        "longitude": longitude,
        "start_date": start_date,
        "end_date": end_date,
        "hourly": ["temperature_2m", "relative_humidity_2m", "precipitation", "rain", "snowfall", "snow_depth", "weather_code"]
    }
    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_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
    hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy()
    hourly_precipitation = hourly.Variables(2).ValuesAsNumpy()
    hourly_rain = hourly.Variables(3).ValuesAsNumpy()
    hourly_snowfall = hourly.Variables(4).ValuesAsNumpy()
    hourly_snow_depth = hourly.Variables(5).ValuesAsNumpy()
    hourly_weather_code = hourly.Variables(6).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["temperature_2m"] = hourly_temperature_2m
    hourly_data["relative_humidity_2m"] = hourly_relative_humidity_2m
    hourly_data["precipitation"] = hourly_precipitation
    hourly_data["rain"] = hourly_rain
    hourly_data["snowfall"] = hourly_snowfall
    hourly_data["snow_depth"] = hourly_snow_depth
    hourly_data["weather_code"] = hourly_weather_code

    hourly_dataframe = pd.DataFrame(data = hourly_data)

    hourly_dataframe['latitude'] = latitude
    hourly_dataframe['longitude'] = longitude

    if id is not None:
        hourly_dataframe['id'] = id

    return hourly_dataframe

temp = get_weather_data(34.021586439393936, -118.299255,'2025-01-21','2025-01-21')
temp

wmo_weather_codes.csv