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.effective_controller.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)