Utils

Utils#

`convert_bool_to_emoji`	If value is boolean, convert to Green Checkmark or Red X. Otherwise, leave be.
`download_csvs_from_zip_url`
`filter_lmp_locations`	Filters DataFrame by locations, which can be a list, "ALL" or None
`format_interconnection_df`	Format interconnection queue data
`get_interconnection_queues`	Get interconnection queue data for all ISOs
`get_iso`	Get an ISO by its id
`get_response_blob`
`get_zip_file`
`get_zip_folder`
`is_dst_end`
`is_today`
`is_within_last_days`	Returns whether date is within N days
`list_isos`	List available ISOs
`load_folder`	Load a single DataFrame for same schema csv files in a folder
`make_availability_df`
`make_availability_table`
`make_lmp_availability_df`
`make_lmp_availability_table`
`move_cols_to_front`	Move columns to front of DataFrame

gridstatus.utils.convert_bool_to_emoji(value)[source]#: If value is boolean, convert to Green Checkmark or Red X. Otherwise, leave be.

gridstatus.utils.download_csvs_from_zip_url(url, process_csv=None, verbose=False)[source]#

gridstatus.utils.filter_lmp_locations(df, locations=None, location_type=None)[source]#

Filters DataFrame by locations, which can be a list, “ALL” or None

Parameters:

gridstatus.utils.format_interconnection_df(queue, rename, extra=None, missing=None)[source]#: Format interconnection queue data

gridstatus.utils.get_interconnection_queues()[source]#: Get interconnection queue data for all ISOs

gridstatus.utils.get_response_blob(resp: requests.Response) → io.BytesIO[source]#

gridstatus.utils.is_within_last_days(date, days, tz)[source]#: Returns whether date is within N days

gridstatus.utils.load_folder(path, time_zone=None, verbose=True)[source]#

Load a single DataFrame for same schema csv files in a folder

Parameters:

path (str) – path to folder
time_zone (str) – time zone to localize to timestamps. By default returns as UTC
verbose (bool, optional) – print verbose output. Defaults to True.

Returns:

A DataFrame of all files

Return type:

pandas.DataFrame

gridstatus.utils.move_cols_to_front(df, cols_to_move)[source]#: Move columns to front of DataFrame