openmeteo-requests 1.0.0

Open-Meteo Python Library

Open-Meteo Python API Client

An API client to get weather data from the Open-Meteo Weather API based on the Python library requests.

Instead of using JSON, the API client uses FlatBuffers to transfer data. Encoding data in FlatBuffers is more efficient for long time-series data. Data can be transferred to numpy or pandas using Zero-Copy to analyze large amount of data quickly.

TODO:

• Document data structure
• Consider dedicated pandas library

Basic Usage

# pip install openmeteo-requests

import openmeteo_requests

om = openmeteo_requests.Client()
params = {
    "latitude": 52.54,
    "longitude": 13.41,
    "hourly": ["temperature_2m", "precipitation"],
    "current": ["temperature_2m"]
}

results = om.weather_api("https://api.open-meteo.com/v1/forecast", params=params)
result = results[0]

print(f"Coordinates {result.Latitude()}°E {result.Longitude()}°N {result.Elevation()} m asl")
print(f"Timezone {result.Timezone()} {result.TimezoneAbbreviation()} Offset={result.UtcOffsetSeconds()}s")

print(f"Current temperature is {result.Current().Temperature2m().Value() °C}")

# Accessing hourly forecasts as numpy arrays
hourly = result.Hourly()
temperature_2m = hourly.Temperature2m().ValuesAsNumpy()
precipitation = hourly.Temperature2m().ValuesAsNumpy()

# Usage with Pandas Dataframes
import pandas as pd
date = pd.date_range(
    start=pd.to_datetime(hourly.Time().Start(), unit="s"),
    end=pd.to_datetime(hourly.Time().End(), unit="s"),
    freq=pd.Timedelta(seconds=hourly.Time().Interval()),
    inclusive="left"
)
df = pd.DataFrame(
    data={
        "date": date,
        "temperature_2m": hourly.Temperature2m().ValuesAsNumpy(),
        "precipitation": hourly.Precipitation().ValuesAsNumpy()
    }
)
print(df)
#date  temperature_2m  precipitation
#0  2023-08-01 00:00:00       16.945999            1.7
#1  2023-08-01 01:00:00       16.996000            2.1
#2  2023-08-01 02:00:00       16.996000            1.0
#3  2023-08-01 03:00:00       16.846001            0.2

Caching Data

If you are working with large amounts of data, caching data can make it easier to develop. You can pass a cached session from the library requests-cache to the Open-Meteo API client.

The following example stores all data indefinitely (expire_after=-1) in a SQLite database called .cache.sqlite. For more options read the requests-cache documentation.

Additionally, retry-requests to automatically retry failed API calls in case there has been any unexpected network or server error.

# pip install openmeteo-requests
# pip install requests-cache retry-requests

import openmeteo_requests
import requests_cache
from retry_requests import retry

# Setup the Open-Meteo API client with a cache and retry mechanism
cache_session = requests_cache.CachedSession('.cache', expire_after=-1)
retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
om = openmeteo_requests.Client(session=retry_session)

# Using the client object `om` will now cache all weather data

License

MIT