This API client provides access to weather data from Open-Meteo Weather API based on the Python library niquests and compatible with the requests library.
A key feature is its use of FlatBuffers instead of JSON for data transfer. FlatBuffers are particularly efficient when dealing with large volumes of time-series data. The library supports Zero-Copy data transfer, allowing you to seamlessly analyze data directly within numpy, pandas, or polars without performance overhead. Schema definitions are available on GitHub open-meteo/sdk.
This library is aimed at data scientists who need to quickly process and analyze weather data, including historical data from 1940 onward through the Open-Meteo Historical Weather API.
The following example gets an hourly forecast (temperature, wind speed, and precipitation) for Berlin, and also retrieves the current temperature and humidity. To improve efficiency, request only the necessary variables.
# pip install openmeteo-requests
import openmeteo_requests
openmeteo = openmeteo_requests.Client()
# 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://api.open-meteo.com/v1/forecast"
params = {
"latitude": 52.52,
"longitude": 13.41,
"hourly": ["temperature_2m", "precipitation", "wind_speed_10m"],
"current": ["temperature_2m", "relative_humidity_2m"],
}
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 difference to GMT+0: {response.UtcOffsetSeconds()}s")
# Process current data. The order of variables needs to be the same as requested.
current = response.Current()
current_temperature_2m = current.Variables(0).Value()
current_relative_humidity_2m = current.Variables(1).Value()
print(f"Current time: {current.Time()}")
print(f"Current temperature_2m: {current_temperature_2m}")
print(f"Current relative_humidity_2m: {current_relative_humidity_2m}")or the same but using async/wait:
# pip install openmeteo-requests
import openmeteo_requests
import asyncio
async def main():
openmeteo = openmeteo_requests.AsyncClient()
# 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://api.open-meteo.com/v1/forecast"
params = {
"latitude": 52.52,
"longitude": 13.41,
"hourly": ["temperature_2m", "precipitation", "wind_speed_10m"],
"current": ["temperature_2m", "relative_humidity_2m"],
}
responses = await 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 difference to GMT+0: {response.UtcOffsetSeconds()}s")
# Process current data. The order of variables needs to be the same as requested.
current = response.Current()
current_temperature_2m = current.Variables(0).Value()
current_relative_humidity_2m = current.Variables(1).Value()
print(f"Current time: {current.Time()}")
print(f"Current temperature_2m: {current_temperature_2m}")
print(f"Current relative_humidity_2m: {current_relative_humidity_2m}")
asyncio.run(main())Note 1: To retrieve data for multiple locations, you can provide a list of latitude and longitude coordinates. The API will return an array of results, one for each location. In the examples, we only demonstrate processing data from the first location response = responses[0] for brevity. See multiple locations & models for more information.
Note 2: Due to the FlatBuffers data format, accessing each attribute, like Latitude, requires a function call (e.g., Latitude()). This approach allows for efficient data access without the need for expensive parsing.
When using NumPy, hourly or daily data is readily available as a NumPy array of floats.
import numpy as np
hourly = response.Hourly()
hourly_time = range(hourly.Time(), hourly.TimeEnd(), hourly.Interval())
hourly_variables = list(map(lambda i: hourly.Variables(i), range(0, hourly.VariablesLength())))
hourly_temperature_2m = next(filter(lambda x: x.Variable() == Variable.temperature and x.Altitude() == 2, hourly_variables)).ValuesAsNumpy()
hourly_precipitation = next(filter(lambda x: x.Variable() == Variable.precipitation, hourly_variables)).ValuesAsNumpy()
hourly_wind_speed_10m = next(filter(lambda x: x.Variable() == Variable.wind_speed and x.Altitude() == 10, hourly_variables)).ValuesAsNumpy()After using NumPy to create arrays for hourly data, you can use Pandas to create a DataFrame from hourly data like follows:
import pandas as pd
hourly_data = {"date": pd.date_range(
start = pd.to_datetime(hourly.Time(), unit = "s"),
end = pd.to_datetime(hourly.TimeEnd(), unit = "s"),
freq = pd.Timedelta(seconds = hourly.Interval()),
inclusive = "left"
)}
hourly_data["temperature_2m"] = hourly_temperature_2m
hourly_data["precipitation"] = hourly_precipitation
hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
hourly_dataframe_pd = pd.DataFrame(data = hourly_data)
print(hourly_dataframe_pd)
# date temperature_2m precipitation wind_speed_10m
# 0 2024-06-21 00:00:00 17.437000 0.0 6.569383
# 1 2024-06-21 01:00:00 17.087000 0.0 6.151683
# 2 2024-06-21 02:00:00 16.786999 0.0 7.421590
# 3 2024-06-21 03:00:00 16.337000 0.0 5.154416Additionally, Polars can also be used to create a DataFrame from hourly data using the NumPy arrays created previously:
import polars as pl
from datetime import datetime, timedelta, timezone
start = datetime.fromtimestamp(hourly.Time(), timezone.utc)
end = datetime.fromtimestamp(hourly.TimeEnd(), timezone.utc)
freq = timedelta(seconds = hourly.Interval())
hourly_dataframe_pl = pl.select(
date = pl.datetime_range(start, end, freq, closed = "left"),
temperature_2m = hourly_temperature_2m,
precipitation = hourly_precipitation,
wind_speed_10m = hourly_wind_speed_10m
)
print(hourly_dataframe_pl)
# ┌─────────────────────────┬────────────────┬───────────────┬────────────────┐
# │ date ┆ temperature_2m ┆ precipitation ┆ wind_speed_10m │
# │ --- ┆ --- ┆ --- ┆ --- │
# │ datetime[μs, UTC] ┆ f32 ┆ f32 ┆ f32 │
# ╞═════════════════════════╪════════════════╪═══════════════╪════════════════╡
# │ 2024-06-21 00:00:00 UTC ┆ 17.437 ┆ 0.0 ┆ 6.569383 │
# │ 2024-06-21 01:00:00 UTC ┆ 17.087 ┆ 0.0 ┆ 6.151683 │
# │ 2024-06-21 02:00:00 UTC ┆ 16.786999 ┆ 0.0 ┆ 7.42159 │
# │ 2024-06-21 03:00:00 UTC ┆ 16.337 ┆ 0.0 ┆ 5.154416 │For improved development speed and efficiency when working with large datasets, consider using caching. You can integrate the requests-cache library by passing a cached session to the Open-Meteo API client.
A recommended configuration is to cache data for one hour (expire_after=3600), though indefinite caching (expire_after=-1) is also supported. Cached data is stored in a local SQLite database named .cache.sqlite. For more detailed configuration options, please refer to the requests-cache documentation.
To further enhance reliability, especially when dealing with network instability, the retry-requests library automatically retries failed API calls due to unexpected network or server errors.
# 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=3600)
retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
openmeteo = openmeteo_requests.Client(session=retry_session)
# Using the client object `openmeteo` will now cache all weather dataIf you are requesting data for multiple locations or models, you’ll receive an array of results. To access all of the data, replace response = responses[0] with a loop that iterates through the responses array, allowing you to process each location or model’s data.
...
params = {
"latitude": [52.52, 50.1155],
"longitude": [13.41, 8.6842],
"hourly": "temperature_2m",
"models": ["icon_global", "icon_eu"],
}
...
# Process 2 locations and 2 models
for response in responses:
print(f"\nCoordinates: {response.Latitude()}°N {response.Longitude()}°E")
print(f"Elevation: {response.Elevation()} m asl")
print(f"Timezone difference to GMT+0: {response.UtcOffsetSeconds()}s")
print(f"Model Nº: {response.Model()}")
...- Document FlatBuffers data structure
- Document time start/end/interval
- Document timezones behavior
- Document pressure level and upper level
- Document endpoints for air quality, etc
- Consider dedicated pandas library to convert responses quickly
MIT