Welcome to Yr to Pandas’s documentation!

This module provides a low-level client for the YR weather API.

yr_to_pandas.yr_client.cached_yr_request(cache_filename, url, headers, params=None, **kwargs)[source]

Lowest level function, calls the yr api and returns a dictionary with the results.

Ensures Expires and If-Modified-Since yr headers are handled correctly to reduce load on server https://developer.yr.no/doc/GettingStarted/

Note: it is up to the application to ensure cache_filename is unique per unique request (i.e. different depending on url and location). From https://api.met.no/doc/TermsOfService: ensure you don’t request a new location for every meter!

Parameters:

cache_filename (str) – Uses the given filename to cache the data.
url (str) – Page url to visit
headers (dict) – header sent to requests.get, ensure this has a custom ‘user-agent’ and ‘Accept’: ‘application/json’
params (dict) – params sent to requests.get, will typically contain at least ‘lat’ and ‘lon’, depends on url.
**kwargs – are passed to requests.get

Returns:

Dictionary with results, depends on url, but will always contain ‘Expires’, ‘Last-Modified’ and ‘Cached’. The result is also stored in cache_filename as json and will be returned directly if this function is called before the data has expired.

Return type:

dict

Example

>>> lon = 10.83576
>>> lat = 59.71949
>>> cache_filename = 'tmp.json'
>>> server = 'https://api.met.no/weatherapi/'
>>> headers = {'user-agent': 'python-yr/ob@cakebox.net'}
>>> url = server + 'locationforecast/2.0/compact'
>>> payload = {'lat': lat, 'lon': lon}
>>> df = cached_yr_request(cache_filename, url, headers, params=payload)
>>> df.keys()
dict_keys(['type', 'geometry', 'properties', 'Expires', 'Last-Modified'])
>>> df['properties'].keys()
dict_keys(['meta', 'timeseries'])
>>> entry0 = df['properties']['timeseries'][0] # look at first entry
>>> entry0.keys()
dict_keys(['time', 'data'])
>>> entry0['data'].keys()
dict_keys(['instant', 'next_12_hours', 'next_1_hours', 'next_6_hours'])

Examples

Provides basic examples to use the yr_to_pandas library.

Intended use-case is to adapt yr_examples to your needs. Using as is will:

fetch data for the specified latitude and longitude.
save the data in a local .parquet cache.

yr_to_pandas.yr_examples.get_airquality(lat, lon, areaclass='grunnkrets')[source]

Call airqualityforecast/0.1 to get hourly forecast for a given lat/lon.

More information https://api.met.no/weatherapi/airqualityforecast/0.1/documentation

Uses the scripts directory as the cache directory both to cache the previous request (in a .json file) and to keep history of previous results (in a .parquet file).

Parameters:

lat (int) – latitude, will be rounded to 4 digits.
lon (int) – longitude, will be rounded to 4 digits.
arealclass= (str) – Size of the area, one of grunnkrets, fylke, kommune or delomrade

Returns:

returns a dataframe

Return type:

pandas.DataFrame

Examples

>>> df = get_airquality(lat = 59.71949, lon = 10.83576)
>>> df.keys()
Index(['time', 'AQI', 'no2_concentration [ug/m3]', 'AQI_no2',
       'no2_nonlocal_fraction [%]', 'no2_nonlocal_fraction_seasalt [%]',
       'no2_local_fraction_traffic_exhaust [%]',
       'no2_local_fraction_traffic_nonexhaust [%]',
       'no2_local_fraction_shipping [%]', 'no2_local_fraction_heating [%]',
       'no2_local_fraction_industry [%]', 'pm10_concentration [ug/m3]',
       'AQI_pm10', 'pm10_nonlocal_fraction [%]',
       'pm10_nonlocal_fraction_seasalt [%]',
       'pm10_local_fraction_traffic_exhaust [%]',
       'pm10_local_fraction_traffic_nonexhaust [%]',
       'pm10_local_fraction_shipping [%]', 'pm10_local_fraction_heating [%]',
       'pm10_local_fraction_industry [%]', 'pm25_concentration [ug/m3]',
       'AQI_pm25', 'pm25_nonlocal_fraction [%]',
       'pm25_nonlocal_fraction_seasalt [%]',
       'pm25_local_fraction_traffic_exhaust [%]',
       'pm25_local_fraction_traffic_nonexhaust [%]',
       'pm25_local_fraction_shipping [%]', 'pm25_local_fraction_heating [%]',
       'pm25_local_fraction_industry [%]', 'o3_concentration [ug/m3]',
       'AQI_o3', 'o3_nonlocal_fraction [%]',
       'o3_nonlocal_fraction_seasalt [%]',
       'o3_local_fraction_traffic_exhaust [%]',
       'o3_local_fraction_traffic_nonexhaust [%]',
       'o3_local_fraction_shipping [%]', 'o3_local_fraction_heating [%]',
       'o3_local_fraction_industry [%]'],
      dtype='object')

yr_to_pandas.yr_examples.get_hourly_forecast_compact(lat, lon)[source]

Call locationforecast/2.0/compact to get hourly forecast for a given lat/lon.

Uses the scripts directory as the cache directory both to cache the previous request (in a .json file) and to keep history of previous results (in a .parquet file).

Parameters:

lat (int) – latitude, will be rounded to 4 digits.
lon (int) – longitude, will be rounded to 4 digits.

Returns:

returns a dataframe

Return type:

pandas.DataFrame

Examples

>>> df = get_hourly_forecast_compact(lat = 59.71949, lon = 10.83576)
>>> df.keys()
Index(['air_pressure_at_sea_level', 'air_temperature', 'cloud_area_fraction',
       'relative_humidity', 'wind_from_direction', 'wind_speed', 'time',
       'next_1_hours_precipitation_amount', 'next_1_hours_symbol_code',
       'next_6_hours_precipitation_amount', 'next_6_hours_symbol_code',
       'next_12_hours_symbol_code'],
      dtype='object')

yr_to_pandas.yr_examples.get_nowcast(lat, lon)[source]

Call nowcast/2.0/compact to get hourly forecast for a given lat/lon.

More information https://api.met.no/weatherapi/nowcast/2.0/documentation

Uses the scripts directory as the cache directory both to cache the previous request (in a .json file) and to keep history of previous results (in a .parquet file).

Parameters:

lat (int) – latitude, will be rounded to 4 digits.
lon (int) – longitude, will be rounded to 4 digits.

Returns:

returns a dataframe

Return type:

pandas.DataFrame

Examples

>>> df = get_nowcast(lat = 59.71949, lon = 10.83576)
>>> sorted(df.keys())
['air_temperature', 'precipitation_rate', 'relative_humidity', 'time', 'wind_from_direction', 'wind_speed', 'wind_speed_of_gust']

Parsers

This module provides functions to parse YR weather API responses into pandas DataFrames.

yr_to_pandas.yr_parser.parse_airquality(res)[source]: Convert a dictionary from airqualityforecast/0.1 to a flat pandas DataFrame.

yr_to_pandas.yr_parser.parse_hourly_forecast_compact(res)[source]

Convert a dictionary from locationforecast/2.0/compact to a flat pandas DataFrame.

See get_hourly_forecast_compact(...) for example usage.

yr_to_pandas.yr_parser.parse_nowcast(res)[source]

Convert a dictionary from nowcast/2.0/compact to a flat pandas DataFrame.

See get_nowcast(...) for example usage.

Welcome to Yr to Pandas’s documentation!

Examples

Parsers

Indices and tables