Here we find all vessel movements chartered by specific corporations stored in a excel sheet.

First we we ingest an Excel spreadsheet (which can be viewed here) containing a custom list of charterers. We then convert these charter names to IDs, and search the VesselMovements endpoint to find all recent vessel movements charterered by these firms.

The below script returns:

vessel.name vessel.imo vessel.mmsi vessel.cubic_capacity vessel.dwt vessel.vessel_class origin.location.port.label destination.location.port.label destination.location.sts_zone.label origin.start_timestamp destination.end_timestamp cargoes.0.product.group.label vessel.corporate_entities.charterer.label vessel.corporate_entities.time_charterer.label vessel.corporate_entities.commercial_owner.label
0 OLKA G LUCK 9235469 248575000 89803 50199 handymax Amsterdam [NL] Tianjin [CN] nan 2020-05-22T15:00:34+0000 nan Clean Petroleum Products KOLMAR nan BoatBr S Shipping
1 ALEXTA YLOR 9245611 335608000 89123 53000 handymax Houston, TX [US] Veracruz [MX] nan 2020-06-06T21:10:16+0000 2020-06-08T09:19:40+0000 Clean Petroleum Products KOCH nan KOCH

(The above data has been anonymised in this example)

from datetime import datetime, timedelta
from typing import List

import pandas as pd

from vortexasdk import Corporations, VesselMovements
from vortexasdk.api import ID


def convert_to_corporation_ids(corporation_name: str) -> List[ID]:
    # The search returns all corporations with exactly the same name.
    return [c.id for c in Corporations().search(corporation_name, exact_term_match=True).to_list()]


if __name__ == "__main__":
    # Read our excel sheet of charterers into a dataframe
    charterers_df = pd.read_excel("./docs/examples/resources/my_charterers.xlsx")

    # Convert the charterer names into ids
    charterers_list_of_lists = charterers_df['charterers'].apply(convert_to_corporation_ids).to_list()
    charterers = [item for sublist in charterers_list_of_lists for item in sublist]

    # Query API
    df = VesselMovements().search(
        filter_charterers=charterers,
        filter_time_min=datetime.now() - timedelta(weeks=1),
        filter_time_max=datetime.now(),
    ).to_df()

    print(df)