Open-Meteo Python Library
Project description
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
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