Weather Data API

Examples

This tutorial shows you how to get started using Weather API in Python. The snippets of code on this page can be put together to make your own script for extracting data.

Installing requests

Installing pandas

Retrieve data from weather data API

The next block of code retrieves data from weather data API using the requests.get function. Its first argument is the endpoint we are going to get data from, in this case the weather endpoint. The next argument is a dictionary containing the parameters that we need to define in order to retrieve data from this endpoint: timeStart, timeEnd, interval, longitude, latitude, parameters

Make sure requests and pandas libraries are installed.

Libraries needed (pandas is not standard and must be installed in Python)

pip install requests

pip install pandas

import requests

import pandas as pd

# Define endpoint and parameters
endpoint = 'https://irh.nibio.no/weather/amalgamation/amalgamate'

parameters = {
    'timeStart': '2024-02-19T07:00:00Z',
    'timeEnd': '2024-02-20T12:00:00Z',
    'interval': '3600',
    'latitude': '-14.159339',
    'longitude': '33.776864',
    'parameters': '1001,3001'
}

# Issue an HTTP GET request
response = requests.get(endpoint, parameters)

if response.status_code == 200:
    # Extract JSON data
    json = response.json()
    location_weather_data = json['locationWeatherData'][0]
    data = location_weather_data['data']

    df = pd.DataFrame(data, columns=['Instantaneous temperature at 2m', 'Instantaneous RH at 2m (%)'])
    print (df)

else:
    print('Error! Returned status code %s' % response.status_code)

If the above block returned an error, then the rest cannot be run. If it printed out a success, then we are all set.

We will use the pandas library to insert the weather data into a table format. This is useful for doing all kinds of analysis on the returned data. If you only want to print or save the data, then pandas won't be necessary, and you can simply loop over the elements in the data variable.

Result from the weather data API

Using pandas, we have formatted the data into a table for easy understanding of the data.

    Instantaneous temperature at 2m  Instantaneous RH at 2m (%)
0                               32.6                   55.700000
1                               33.4                   52.700000
2                               34.4                   48.700000
3                               34.8                   46.900000
4                               34.9                   46.500000
..                               ...                         ...
216                             29.3                   79.366667
217                             29.5                   79.000000
218                             29.7                   78.633333
219                             29.9                   78.266667
220                             30.1                   77.900000

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