vortexasdk.endpoints.anywhere_freight_pricing_top_ports_origin
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AnywhereFreightPricingTopPortsOrigin
AnywhereFreightPricingTopPortsOrigin(self)
Anywhere Freight Pricing Top Ports Origin endpoint.
List top origin ports. A top origin port refers to the port with the greatest volume of outgoing voyages from vessels in a specified class.
Please note, a subscription to our Anywhere Freight Pricing module is required to access Anywhere Freight Pricing.
search
AnywhereFreightPricingTopPortsOrigin.search(
destination_id: str,
vessel_class:
typing_extensions.Literal['oil_coastal', 'oil_specialised', 'oil_handysize_mr1', 'oil_handymax_mr2', 'oil_panamax_lr1', 'oil_aframax_lr2', 'oil_suezmax_lr3', 'oil_vlcc'],
product: typing_extensions.Literal['clean', 'dirty', 'crude'],
unit:
typing_extensions.Literal['usd_per_tonne', 'usd_per_barrel'] = 'usd_per_tonne',
avoid_zone:
typing.Optional[typing.List[typing_extensions.Literal['Panama Canal', 'Suez Canal']]] = None
)
List top origin ports for a given destination.
A top origin port refers to the port with the greatest volume of outgoing voyages from vessels in a specified class.
Arguments
destination_id: Geographical ID of the destination port.
vessel_class: The vessel class for the route. Must be one of:
`'oil_coastal'`, `'oil_specialised'`, `'oil_handysize_mr1'`,
`'oil_handymax_mr2'`, `'oil_panamax_lr1'`, `'oil_aframax_lr2'`,
`'oil_suezmax_lr3'`, `'oil_vlcc'`.
product: The product type. Must be one of: `'clean'`, `'dirty'`, `'crude'`.
unit: The unit for pricing. Must be one of: `'usd_per_tonne'`, `'usd_per_barrel'`.
Defaults to `'usd_per_tonne'`.
avoid_zone: Routing zones to avoid. Options: `'Panama Canal'`, `'Suez Canal'`.
Returns
AnywhereFreightPricingResult
Example
Get top origin ports for clean products to Rotterdam using MR2 vessels.
>>> from vortexasdk import AnywhereFreightPricingTopPortsOrigin
>>> result = AnywhereFreightPricingTopPortsOrigin().search(
... destination_id="68faf65af1345067f11dc6723b8da32f00e304a6f33c000118fccd81947deb4e",
... vessel_class="oil_handymax_mr2",
... product="clean",
... unit="usd_per_tonne",
... )
>>> df = result.to_df()
Returns a DataFrame with columns including geography info, rates, lumpsums, and confidence values:
| geography_name | date | rate | lumpsum | confidence | |
|---|---|---|---|---|---|
| 0 | Houston [US] | 2024-01-01 | 63.55 | 2351511.83 | 2 |