vortexasdk.endpoints.fleet_utilisation_origin_breakdown

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FleetUtilisationOriginBreakdown

FleetUtilisationOriginBreakdown(self)

Please note: you will require a subscription to our Freight module to access this endpoint.

search

FleetUtilisationOriginBreakdown.search(self, breakdown_unit: str = None, breakdown_size: int = None, breakdown_geography: str = None, filter_time_min: datetime.datetime = datetime.datetime(2019, 10, 1, 0, 0), filter_time_max: datetime.datetime = datetime.datetime(2019, 10, 1, 1, 0), unit: str = 'b', filter_activity: str = None, filter_charterers: Union[str, List[str]] = None, filter_destinations: Union[str, List[str]] = None, filter_origins: Union[str, List[str]] = None, filter_owners: Union[str, List[str]] = None, filter_products: Union[str, List[str]] = None, filter_vessels: Union[str, List[str]] = None, filter_vessel_classes: Union[str, List[str]] = None, filter_vessel_status: str = None, filter_ship_to_ship: bool = None, filter_charterer_exists: bool = None, filter_vessel_age_min: int = None, filter_vessel_age_max: int = None, filter_vessel_dwt_min: int = None, filter_vessel_dwt_max: int = None, filter_vessel_scrubbers: str = 'disabled', filter_vessel_flags: Union[str, List[str]] = None, filter_vessel_ice_class: Union[str, List[str]] = None, filter_vessel_propulsion: Union[str, List[str]] = None, exclude_origins: Union[str, List[str]] = None, exclude_destinations: Union[str, List[str]] = None, exclude_products: Union[str, List[str]] = None, exclude_vessels: Union[str, List[str]] = None, exclude_vessel_classes: Union[str, List[str]] = None, exclude_charterers: Union[str, List[str]] = None, exclude_owners: Union[str, List[str]] = None, exclude_vessel_flags: Union[str, List[str]] = None, exclude_vessel_ice_class: Union[str, List[str]] = None, exclude_vessel_propulsion: Union[str, List[str]] = None) -> vortexasdk.endpoints.reference_breakdown_result.ReferenceBreakdownResult

Number of unique vessels by origin.

Arguments

  • breakdown_unit: Units to aggregate upon. Must be one of the following: 'b', 't', 'cbm', 'bpd', 'tpd', 'mpd'.

  • breakdown_size: Number of top geographies to return.

  • breakdown_geography: Geography hierarchy of the origin to aggregate upon. Must be one of the following: 'berth', 'terminal', 'port','country', 'shipping_region', 'region','trading_block','trading_region','trading_subregion','sts_zone','waypoint'.

  • filter_activity: Movement activity on which to base the time filter. Must be one of: 'loading_state', 'loading_start', 'loading_end', 'identified_for_loading_state', 'unloading_state', 'unloading_start', 'unloading_end', 'unloaded_state', 'storing_state', 'storing_start', 'storing_end', 'transiting_state', 'any_activity'.

  • filter_time_min: The UTC start date of the time filter.

  • filter_time_max: The UTC end date of the time filter.

  • unit: Unit of measurement. Enter 'b' for barrels or 't' for tonnes.

  • filter_charterers: A charterer ID, or list of charterer IDs to filter on.

  • filter_destinations: A geography ID, or list of geography IDs to filter on.

  • filter_origins: A geography ID, or list of geography IDs to filter on.

  • filter_owners: An corporation ID, or list of corporation IDs to filter on.

  • filter_products: A product ID, or list of product IDs to filter on.

  • filter_vessels: A vessel ID, or list of vessel IDs to filter on.

  • filter_vessel_classes: A vessel class, or list of vessel classes to filter on.

  • filter_vessel_status: The vessel status on which to base the filter. Enter 'vessel_status_ballast' for ballast vessels, 'vessel_status_laden_known' for laden vessels with known cargo (i.e. a type of cargo that Vortexa currently tracks) or 'vessel_status_laden_unknown' for laden vessels with unknown cargo (i.e. a type of cargo that Vortexa currently does not track).

  • filter_vessel_age_min: A number between 1 and 100 (representing years).

  • filter_vessel_age_max: A number between 1 and 100 (representing years).

  • filter_vessel_dwt_min: A number representing minimum deadweight tonnage of a vessel.

  • filter_vessel_dwt_max: A number representing maximum deadweight tonnage of a vessel.

  • filter_vessel_scrubbers: Either inactive 'disabled', or included 'inc' or excluded 'exc'.

  • filter_vessel_flags: A geography ID, or list of geography IDs to filter on.

  • filter_vessel_ice_class: An attribute ID, or list of attribute IDs to filter on.

  • filter_vessel_propulsion: An attribute ID, or list of attribute IDs to filter on.

  • filter_charterer_exists: A boolean to include or exclude the records to those that have a charterer.

  • filter_ship_to_ship: A boolean to include or exclude the records to those that are involved in an STS.

  • exclude_origins: A geography ID, or list of geography IDs to exclude.

  • exclude_destinations: A geography ID, or list of geography IDs to exclude.

  • exclude_products: A product ID, or list of product IDs to exclude.

  • exclude_vessels: A vessel ID, or list of vessel IDs to exclude.

  • exclude_vessel_classes: A vessel class, or list of vessel classes to exclude.

  • exclude_charterers: A charterer ID, or list of charterer IDs to exclude.

  • exclude_owners: An owner ID, or list of owner IDs to exclude.

  • exclude_vessel_flags: A geography ID, or list of geography IDs to exclude.

  • exclude_vessel_ice_class: An attribute ID, or list of attribute IDs to exclude.

  • exclude_vessel_propulsion: An attribute ID, or list of attribute IDs to exclude.

Returns

ReferenceBreakdownResult

Example

Top 5 countries by number of unique vessels by origin country breakdown, in the last quarter.

>>> from vortexasdk import FleetUtilisationOriginBreakdown, Vessels
>>> from datetime import datetime
>>> search_result = FleetUtilisationOriginBreakdown().search(
...    breakdown_geography='country',
...    breakdown_size='5',
...    filter_time_min=datetime(2020, 10, 18),
...    filter_time_max=datetime(2021, 1, 18))
>>> df = search_result.to_df()

returns

key label value count
0 934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2 China 3414 16439
1 50182d9d05051a6c8d24f0514d4ee828da6eaa29eacbb11cfe368f51526328ce Japan 1252 10670
2 2d92cc08f22524dba59f6a7e340f132a9da0ce9573cca968eb8e3752ef17a963 United States 1991 6048
3 bcd7dddaf951959ebf6076a3a594b426a246d3bffe13339b10d04e45f222e011 South Korea 1528 4893
4 3267ef2a83a749052c87e981f1bb12c6396acf590b4b1cd3316cf8f8c5aeb7bc Malaysia 1607 4597