NoaaGfsWeather Class

Represents the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System (GFS) dataset.

This dataset contains 15-day US hourly weather forecast data (example: temperature, precipitation, wind) produced by the Global Forecast System (GFS) from the National Oceanic and Atmospheric Administration (NOAA). For information about this dataset, including column descriptions, different ways to access the dataset, and examples, see NOAA Global Forecast System in the Microsoft Azure Open Datasets catalog.

Initialize filtering fields.

Inheritance

OpenDatasetBase

NoaaGfsWeather

Constructor

NoaaGfsWeather(start_date: datetime = datetime.datetime(2018, 1, 1, 0, 0), end_date: datetime = datetime.datetime(2018, 1, 1, 0, 0), cols: List[str] | None = None, limit: int | None = -1, enable_telemetry: bool = True)

Parameters

Name	Description
start_date	datetime The date at which to start loading data, inclusive. If None, the default_start_date is used. default value: 2018-01-01 00:00:00
end_date	datetime The date at which to end loading data, inclusive. If None, the default_end_date is used. default value: 2018-01-01 00:00:00
cols	list[str] A list of columns names to load from the dataset. If None, all columns are loaded. For information on the available columns in this dataset, see NOAA Global Forecast System. default value: None
limit	int A value indicating the number of days of data to load with to_pandas_dataframe(). If not specified, the default of -1 means no limit on days loaded. default value: -1
enable_telemetry	bool Whether to enable telemetry on this dataset. default value: True
start_date Required	datetime The start date you'd like to query inclusively.
end_date Required	datetime The end date you'd like to query inclusively.
cols Required	List[str] A list of column names you'd like to retrieve. None will get all columns.
limit Required	int to_pandas_dataframe() will load only "limit" days of data. -1 means no limit.
enable_telemetry Required	bool Indicates whether to send telemetry.

Remarks

The example below shows how to use access the dataset.

   from azureml.opendatasets import NoaaGfsWeather
   from datetime import datetime
   from dateutil.relativedelta import relativedelta


   end_date = datetime.today()
   start_date = datetime.today() - relativedelta(months=1)
   gfs = NoaaGfsWeather(start_date=start_date, end_date=end_date)
   gfs_df = gfs.to_pandas_dataframe()

Methods

filter

Filter time.

filter

Filter time.

filter(env: RuntimeEnv, min_date: datetime, max_date: datetime)

Parameters

Name	Description
env Required	<xref:azureml.opendatasets.RuntimeEnv> The runtime environment.
min_date Required	The min date.
max_date Required	datetime The max date.

Returns

Type	Description
	filtered data frame.

Attributes

default_end_date

default_end_date = datetime.datetime(2024, 5, 1, 0, 0)

default_start_date

default_start_date = datetime.datetime(2018, 1, 1, 0, 0)

id_column_name

id_column_name = 'ID'

latitude_column_name

latitude_column_name = 'latitude'

longitude_column_name

longitude_column_name = 'longitude'