Supply trend with market rates¶

This is an example combining functionality from our Voyages, Market Rates and Tonnage List APIs. The aim of the example is to plot a supply trend with superimposed, predicted market rates, in order to get a hollistic view of market behaviour during a specific period, for a specific load port, and a specific vessel class.

Run this example in Colab¶

Setup¶

Install the Signal Ocean SDK:

In [28]:

!pip install signal-ocean

!pip install signal-ocean

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[notice] A new release of pip available: 22.2 -> 22.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip

Import required dependencies:

In [38]:

from datetime import date, timedelta

import pandas as pd
import matplotlib.pyplot as plt

from signal_ocean import Connection
from signal_ocean.voyages import VoyagesAPI
from signal_ocean.market_rates import MarketRatesAPI, CargoId
from signal_ocean.tonnage_list import (
    TonnageListAPI,
    VesselClassFilter,
    PortFilter,
    VesselFilter,
    PushType,
    MarketDeployment,
    CommercialStatus,
    VesselSubclass,
    IndexLevel,
    DateRange,
)

from datetime import date, timedelta import pandas as pd import matplotlib.pyplot as plt from signal_ocean import Connection from signal_ocean.voyages import VoyagesAPI from signal_ocean.market_rates import MarketRatesAPI, CargoId from signal_ocean.tonnage_list import ( TonnageListAPI, VesselClassFilter, PortFilter, VesselFilter, PushType, MarketDeployment, CommercialStatus, VesselSubclass, IndexLevel, DateRange, )

Get your personal Signal Ocean API subscription key (acquired here) and use it to create a Connection:

In [39]:

signal_ocean_api_key = ""  # replace with your subscription key
connection = Connection(signal_ocean_api_key)

signal_ocean_api_key = "" # replace with your subscription key connection = Connection(signal_ocean_api_key)

Create instances of APIs used throughout this notebook:

In [40]:

voyage_api = VoyagesAPI(connection)
tonnage_list_api = TonnageListAPI(connection)
market_rate_api = MarketRatesAPI(connection)

voyage_api = VoyagesAPI(connection) tonnage_list_api = TonnageListAPI(connection) market_rate_api = MarketRatesAPI(connection)

Parametrization¶

In order to plot a supply trend with the predicted market rates, we need to specify the following parameters for our queries:

1. vessel_class - the class of queried vessels.
2. load_port - the loading port.
3. days_back - the number of days the supply trend should reach back, starting from the current date.
4. laycan_end_in_days - the maximum estimated time of arrival for available vessels to reach the loading port.
5. push_type - the vessels' push status, if available from the emails in your account.
6. market_deployment - the vessels' market deployment.
7. commercial_status - commercial status of the vessels at each point in time.
8. vessel_subclass - the type of oil product the vessel is classified to carry.
9. ais_since - how many days since the vessel has transmitted its AIS data.
10. route_description - the route of interest with available market rates.

In [41]:

vessel_class_filter = VesselClassFilter(name_like="Aframax")
load_port_filter = PortFilter(name_like="Ceyhan")
days_back = 90
laycan_end_in_days = 10

vessel_filter = VesselFilter(
    push_types=[PushType.PUSHED_POSS, PushType.PUSHED],
    market_deployments=[MarketDeployment.RELET, MarketDeployment.SPOT],
    commercial_statuses=[
        CommercialStatus.AVAILABLE,
        CommercialStatus.ON_SUBS,
        CommercialStatus.FAILED,
        CommercialStatus.CANCELLED,
    ],
    vessel_subclass=VesselSubclass.DIRTY,
    latest_ais_since=5,
)

route_description = "Afra - Med/Med"

today = date.today()
start_date = today - timedelta(days=days_back)
end_date = today

vessel_class_filter = VesselClassFilter(name_like="Aframax") load_port_filter = PortFilter(name_like="Ceyhan") days_back = 90 laycan_end_in_days = 10 vessel_filter = VesselFilter( push_types=[PushType.PUSHED_POSS, PushType.PUSHED], market_deployments=[MarketDeployment.RELET, MarketDeployment.SPOT], commercial_statuses=[ CommercialStatus.AVAILABLE, CommercialStatus.ON_SUBS, CommercialStatus.FAILED, CommercialStatus.CANCELLED, ], vessel_subclass=VesselSubclass.DIRTY, latest_ais_since=5, ) route_description = "Afra - Med/Med" today = date.today() start_date = today - timedelta(days=days_back) end_date = today

For more information, see the Tonnage List and Market Rate API sections.

Retrieve the historical tonnage list¶

In [42]:

vessel_class = tonnage_list_api.get_vessel_classes(vessel_class_filter)[0]
load_port = tonnage_list_api.get_ports(load_port_filter)[0]

htl = tonnage_list_api.get_historical_tonnage_list(
    load_port,
    vessel_class,
    laycan_end_in_days,
    DateRange(start_date, end_date),
    vessel_filter,
)

htl_for_supply_trend = htl.to_data_frame()
htl_for_supply_trend

vessel_class = tonnage_list_api.get_vessel_classes(vessel_class_filter)[0] load_port = tonnage_list_api.get_ports(load_port_filter)[0] htl = tonnage_list_api.get_historical_tonnage_list( load_port, vessel_class, laycan_end_in_days, DateRange(start_date, end_date), vessel_filter, ) htl_for_supply_trend = htl.to_data_frame() htl_for_supply_trend

Out[42]:

		name	vessel_class	ice_class	year_built	deadweight	length_overall	breadth_extreme	subclass	market_deployment_point_in_time	push_type_point_in_time	...	open_prediction_accuracy_point_in_time	open_country_point_in_time	open_narrow_area_point_in_time	open_wide_area_point_in_time	availability_port_type_point_in_time	availability_date_type_point_in_time	fixture_type_point_in_time	current_vessel_sub_type_id_point_in_time	current_vessel_sub_type_point_in_time	willing_to_switch_current_vessel_sub_type_point_in_time
date	imo
2023-09-06 12:00:00+00:00	9262924	Linda I	Aframax	NaN	2002	109189	241.00	42	Dirty	Relet	Pushed	...	Narrow Area	Russian Federation	Black Sea	Black Sea / Sea Of Marmara	Source	Source	NaN	1	Source	False
	9293155	Wonder Vega	Aframax	NaN	2005	106062	244.00	42	Dirty	Spot	Pushed	...	Narrow Area	Greece	East Mediterranean	Mediterranean	Source	Source	NaN	1	Source	False
	9407457	Matilda	Aframax	NaN	2009	112935	249.96	44	Dirty	Spot	Pushed	...	Narrow Area	Turkey	Sea of Marmara	Black Sea / Sea Of Marmara	Source	Source	Scraped	1	Source	False
	9308443	Royall	Aframax	1D	2006	110531	244.50	42	Dirty	Relet	Pushed	...	Port	Egypt	Red Sea	Red Sea	Source	Source	Scraped	1	Source	False
	9276573	Minerva Eleonora	Aframax	1C	2004	103622	244.00	42	Dirty	Spot	Pushed	...	Narrow Area	Egypt	Red Sea	Red Sea	Source	Source	NaN	1	Source	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-06-09 12:00:00+00:00	9334739	Olib	Aframax	1C	2009	108932	246.00	48	Dirty	Spot	Pushed	...	Narrow Area	Spain	Iberian Atlantic Coast	UK Continent	Source	Source	Scraped	1	Source	False
	9285835	Sea Fidelity	Aframax	NaN	2005	115341	249.87	44	Dirty	Spot	Pushed POSS	...	Port	Italy	Central Mediterranean	Mediterranean	Source	Prediction	NaN	1	Prediction	False
	9261619	Sea Luck III	Aframax	NaN	2003	105869	243.96	43	Dirty	Spot	Pushed	...	Narrow Area	Saudi Arabia	Red Sea	Red Sea	Prediction	Prediction	NaN	1	Prediction	False
	9329409	Kriti Samaria	Aframax	NaN	2007	105865	241.03	42	Dirty	Relet	Pushed	...	Port	Italy	Central Mediterranean	Mediterranean	Source	Prediction	NaN	1	Prediction	False
	9645401	Affinity V	Aframax	NaN	2016	114070	252.00	45	Dirty	Spot	Pushed POSS	...	Port	Italy	Central Mediterranean	Mediterranean	Source	Prediction	NaN	1	Prediction	False

3029 rows × 29 columns

Get daily market rates for the route¶

We'll need a helper function to find our desired route for the market rates:

In [43]:

def return_selected_route(vessel_class, route_description):
    vessel_routes = market_rate_api.get_routes(vessel_class_id=vessel_class.id)
    for i, v in enumerate(vessel_routes):
        if vessel_routes[i].description == route_description:
            break
    return vessel_routes[i]

def return_selected_route(vessel_class, route_description): vessel_routes = market_rate_api.get_routes(vessel_class_id=vessel_class.id) for i, v in enumerate(vessel_routes): if vessel_routes[i].description == route_description: break return vessel_routes[i]

Which we can use to find the route object returned from the Market Rates API:

In [44]:

market_rate_route = return_selected_route(vessel_class, route_description)

market_rate_route = return_selected_route(vessel_class, route_description)

And use it to query for market rates:

In [45]:

market_rates = market_rate_api.get_market_rates(
    start_date,
    route_id=market_rate_route.id,
    vessel_class_id=vessel_class.id,
    end_date=end_date,
    cargo_id=CargoId(market_rate_route.cargo_id)
)

market_rates = pd.DataFrame([vars(vr) for vr in market_rates])
market_rates["rate_date"] = pd.to_datetime(market_rates["rate_date"])
market_rates.set_index("rate_date", inplace=True)
market_rates

market_rates = market_rate_api.get_market_rates( start_date, route_id=market_rate_route.id, vessel_class_id=vessel_class.id, end_date=end_date, cargo_id=CargoId(market_rate_route.cargo_id) ) market_rates = pd.DataFrame([vars(vr) for vr in market_rates]) market_rates["rate_date"] = pd.to_datetime(market_rates["rate_date"]) market_rates.set_index("rate_date", inplace=True) market_rates

Out[45]:

	route_id	rate_value	unit	vessel_class_id	deprecated_to
rate_date
2023-06-09	R1	147.5	WS	86	None
2023-06-10	R1	147.5	WS	86	None
2023-06-11	R1	147.5	WS	86	None
2023-06-12	R1	142.5	WS	86	None
2023-06-13	R1	142.5	WS	86	None
...	...	...	...	...	...
2023-09-03	R1	101.5	WS	86	None
2023-09-04	R1	101.5	WS	86	None
2023-09-05	R1	96.5	WS	86	None
2023-09-06	R1	95.5	WS	86	None
2023-09-07	R1	95.5	WS	86	None

91 rows × 5 columns

Plot the daily supply trend with market rates¶

In [46]:

plt.style.use("seaborn")
fig, axs = plt.subplots(figsize=(15, 6))

supply_trend = htl_for_supply_trend.groupby(IndexLevel.DATE, sort=True).size()
supply_trend.index = supply_trend.index.strftime("%d %b %y")

# Left axis (Supply plot)
supply_plot = supply_trend.plot(ax=axs, x="date", color="#3086EF", marker="o")
supply_plot.set_ylabel(
    "Vessel count", color=plt.gca().lines[-1].get_color(), fontsize=14
)
supply_plot.set_ylim(ymin=0)

# Right axis (Market rate plot)
market_rates.index = market_rates.index.strftime("%d %b %y")
mr_plot = market_rates.plot(
    ax=axs, y="rate_value", secondary_y=True, color="#F06C6E", marker="d"
)
mr_plot.set_ylabel(
    "Market Rates (WS)", color=plt.gca().lines[-1].get_color(), fontsize=14
)

axs.set_xlim(0, len(supply_trend) - 1)
axs.set_xlabel("")
axs.get_legend().remove()

plt.style.use("seaborn") fig, axs = plt.subplots(figsize=(15, 6)) supply_trend = htl_for_supply_trend.groupby(IndexLevel.DATE, sort=True).size() supply_trend.index = supply_trend.index.strftime("%d %b %y") # Left axis (Supply plot) supply_plot = supply_trend.plot(ax=axs, x="date", color="#3086EF", marker="o") supply_plot.set_ylabel( "Vessel count", color=plt.gca().lines[-1].get_color(), fontsize=14 ) supply_plot.set_ylim(ymin=0) # Right axis (Market rate plot) market_rates.index = market_rates.index.strftime("%d %b %y") mr_plot = market_rates.plot( ax=axs, y="rate_value", secondary_y=True, color="#F06C6E", marker="d" ) mr_plot.set_ylabel( "Market Rates (WS)", color=plt.gca().lines[-1].get_color(), fontsize=14 ) axs.set_xlim(0, len(supply_trend) - 1) axs.set_xlabel("") axs.get_legend().remove()

C:\Users\Kimon\AppData\Local\Temp\ipykernel_18828\1394344043.py:1: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
  plt.style.use("seaborn")

Perform a point-to-point comparison¶

Here's an example of a workflow to compare two different points in time.

Specify the dates to compare:

In [47]:

point_a = today - timedelta(days=1)
point_b = today - timedelta(days=5)

point_a = today - timedelta(days=1) point_b = today - timedelta(days=5)

In [48]:

dates = htl_for_supply_trend.index.get_level_values("date").date

if not any(dates == point_a):
    print("Point A is not within imported date values.")
elif not any(dates == point_b):
    print("Point B is not within imported date values.")

vessels_in_a = htl_for_supply_trend.loc[(dates == point_a)]
vessels_in_b = htl_for_supply_trend.loc[(dates == point_b)]

dates = htl_for_supply_trend.index.get_level_values("date").date if not any(dates == point_a): print("Point A is not within imported date values.") elif not any(dates == point_b): print("Point B is not within imported date values.") vessels_in_a = htl_for_supply_trend.loc[(dates == point_a)] vessels_in_b = htl_for_supply_trend.loc[(dates == point_b)]

Intersection of Vessel IMOs¶

In [49]:

intersection = pd.merge(
    vessels_in_a.add_suffix("_IN_A"),
    vessels_in_b.add_suffix("_IN_B"),
    how="inner",
    on="imo",
)

intersection

intersection = pd.merge( vessels_in_a.add_suffix("_IN_A"), vessels_in_b.add_suffix("_IN_B"), how="inner", on="imo", ) intersection

Out[49]:

	name_IN_A	vessel_class_IN_A	ice_class_IN_A	year_built_IN_A	deadweight_IN_A	length_overall_IN_A	breadth_extreme_IN_A	subclass_IN_A	market_deployment_point_in_time_IN_A	push_type_point_in_time_IN_A	...	open_prediction_accuracy_point_in_time_IN_B	open_country_point_in_time_IN_B	open_narrow_area_point_in_time_IN_B	open_wide_area_point_in_time_IN_B	availability_port_type_point_in_time_IN_B	availability_date_type_point_in_time_IN_B	fixture_type_point_in_time_IN_B	current_vessel_sub_type_id_point_in_time_IN_B	current_vessel_sub_type_point_in_time_IN_B	willing_to_switch_current_vessel_sub_type_point_in_time_IN_B
imo
9262924	Linda I	Aframax	NaN	2002	109189	241.00	42	Dirty	Relet	Pushed	...	Narrow Area	Russian Federation	Black Sea	Black Sea / Sea Of Marmara	Source	Source	NaN	1	Source	False
9407457	Matilda	Aframax	NaN	2009	112935	249.96	44	Dirty	Spot	Pushed	...	Port	Turkey	Sea of Marmara	Black Sea / Sea Of Marmara	Source	Prediction	NaN	1	Prediction	False
9276573	Minerva Eleonora	Aframax	1C	2004	103622	244.00	42	Dirty	Spot	Pushed	...	Port	Saudi Arabia	Red Sea	Red Sea	Source	Prediction	NaN	1	Prediction	False
9930105	Ghat	Aframax	NaN	2022	115519	249.90	44	Dirty	Spot	Pushed	...	Port	Italy	Central Mediterranean	Mediterranean	Source	Prediction	Scraped	1	Prediction	False
9815616	Levantine Sea	Aframax	1C	2018	114218	249.80	44	Dirty	Spot	Pushed	...	Port	Turkey	Sea of Marmara	Black Sea / Sea Of Marmara	Source	Prediction	NaN	1	Prediction	False
9801524	Ambelos	Aframax	NaN	2017	114674	250.00	44	Dirty	Spot	Pushed	...	Narrow Area	Oman	Arabian Gulf	Arabian Gulf	Source	Source	NaN	1	Source	False
9592290	Nissos Paros	Aframax	NaN	2012	115723	248.97	44	Dirty	Spot	Pushed	...	Narrow Area	Oman	Arabian Gulf	Arabian Gulf	Source	Source	NaN	1	Source	False
9259197	Thalia III	Aframax	NaN	2003	107127	246.78	42	Dirty	Relet	Pushed	...	Port	Turkey	Sea of Marmara	Black Sea / Sea Of Marmara	Source	Source	NaN	1	Source	False
9417452	Chrysanthemum	Aframax	NaN	2009	105187	243.97	42	Dirty	Spot	Pushed	...	Narrow Area	Spain	Iberian Atlantic Coast	UK Continent	Source	Source	NaN	1	Source	False

9 rows × 58 columns

Difference between vessel IMOs in relation to point A¶

In [50]:

vessels_in_a_but_not_in_b = (
    vessels_in_a.add_suffix("_IN_A")
    .merge(
        vessels_in_b.add_suffix("_IN_B"), indicator=True, how="left", on="imo"
    )
    .loc[lambda x: x["_merge"] != "both"]
)

vessels_in_a_but_not_in_b

vessels_in_a_but_not_in_b = ( vessels_in_a.add_suffix("_IN_A") .merge( vessels_in_b.add_suffix("_IN_B"), indicator=True, how="left", on="imo" ) .loc[lambda x: x["_merge"] != "both"] ) vessels_in_a_but_not_in_b

Out[50]:

	name_IN_A	vessel_class_IN_A	ice_class_IN_A	year_built_IN_A	deadweight_IN_A	length_overall_IN_A	breadth_extreme_IN_A	subclass_IN_A	market_deployment_point_in_time_IN_A	push_type_point_in_time_IN_A	...	open_country_point_in_time_IN_B	open_narrow_area_point_in_time_IN_B	open_wide_area_point_in_time_IN_B	availability_port_type_point_in_time_IN_B	availability_date_type_point_in_time_IN_B	fixture_type_point_in_time_IN_B	current_vessel_sub_type_id_point_in_time_IN_B	current_vessel_sub_type_point_in_time_IN_B	willing_to_switch_current_vessel_sub_type_point_in_time_IN_B	_merge
imo
9293155	Wonder Vega	Aframax	NaN	2005	106062	244.00	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9308443	Royall	Aframax	1D	2006	110531	244.50	42	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9913511	Nafsika	Aframax	NaN	2022	112100	237.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9773753	Seacalm	Aframax	NaN	2017	112119	238.30	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9337418	Patmos Warrior	Aframax	NaN	2007	105572	239.00	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9527855	Alicante	Aframax	NaN	2013	115708	248.97	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9779941	Shusha	Aframax	1C	2017	113838	250.00	44	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9292515	Erik Spirit	Aframax	NaN	2005	115525	249.90	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9592252	Minerva Kythnos	Aframax	NaN	2011	115674	248.97	44	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9436020	Crude Centurion	Aframax	NaN	2010	112863	249.96	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9309423	Minerva Nounou	Aframax	1A	2006	114850	253.59	44	Dirty	Spot	Pushed POSS	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9857468	Seatribute	Aframax	NaN	2020	111890	237.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9384992	Sousta	Aframax	NaN	2007	106045	240.50	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9787170	Minerva Karteria	Aframax	NaN	2018	114780	250.01	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9290361	Seaoath	Aframax	1C	2005	105472	243.98	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9439670	Captain A. Stellatos	Aframax	NaN	2010	105849	243.97	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9402495	Dubai Charm	Aframax	NaN	2010	115514	250.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only

17 rows × 59 columns

Difference between vessel IMOs in relation to point B¶

In [51]:

vessels_in_b_but_not_in_a = (
    vessels_in_b.add_suffix("_IN_B")
    .merge(
        vessels_in_a.add_suffix("_IN_A"), indicator=True, how="left", on="imo"
    )
    .loc[lambda x: x["_merge"] != "both"]
)

vessels_in_b_but_not_in_a

vessels_in_b_but_not_in_a = ( vessels_in_b.add_suffix("_IN_B") .merge( vessels_in_a.add_suffix("_IN_A"), indicator=True, how="left", on="imo" ) .loc[lambda x: x["_merge"] != "both"] ) vessels_in_b_but_not_in_a

Out[51]:

	name_IN_B	vessel_class_IN_B	ice_class_IN_B	year_built_IN_B	deadweight_IN_B	length_overall_IN_B	breadth_extreme_IN_B	subclass_IN_B	market_deployment_point_in_time_IN_B	push_type_point_in_time_IN_B	...	open_country_point_in_time_IN_A	open_narrow_area_point_in_time_IN_A	open_wide_area_point_in_time_IN_A	availability_port_type_point_in_time_IN_A	availability_date_type_point_in_time_IN_A	fixture_type_point_in_time_IN_A	current_vessel_sub_type_id_point_in_time_IN_A	current_vessel_sub_type_point_in_time_IN_A	willing_to_switch_current_vessel_sub_type_point_in_time_IN_A	_merge
imo
9821706	Elandra Sound	Aframax	NaN	2018	115711	249.90	44	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9309435	Minerva Alice	Aframax	1A	2006	114850	254.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9420617	Silver	Aframax	NaN	2010	107507	243.80	43	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9810513	Karabakh	Aframax	NaN	2018	114667	250.00	44	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9334739	Olib	Aframax	1C	2009	108932	246.00	48	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9356438	Alyarmouk	Aframax	NaN	2008	116038	248.96	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9384564	Red Sun	Aframax	NaN	2008	115325	243.80	42	Dirty	Spot	Pushed POSS	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9373656	Seastar	Aframax	NaN	2008	116050	249.97	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9777931	Ryman	Aframax	1A	2017	112870	250.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9458016	Delta Star	Aframax	NaN	2013	115618	249.97	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9696773	Alfa Baltica	Aframax	NaN	2015	106373	228.60	42	Dirty	Spot	Pushed POSS	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9276585	Minerva Roxanne	Aframax	1C	2004	103560	243.56	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9802176	Sea Puma	Aframax	NaN	2018	114609	250.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9285861	Minerva Iris	Aframax	1A	2004	103124	243.57	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9787168	Minerva Eleftheria	Aframax	NaN	2018	114696	250.00	44	Dirty	Spot	Pushed POSS	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9728241	Minerva Baltica	Aframax	1B	2018	113172	250.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9398266	Kriti Legend	Aframax	NaN	2009	107518	243.80	42	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9317951	Minerva Libra	Aframax	1A	2007	116779	249.88	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9927196	Green Admire	Aframax	NaN	2022	114377	249.99	44	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9446427	Altai	Aframax	NaN	2011	115952	248.96	44	Dirty	Spot	Pushed POSS	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9338917	Aegean Harmony	Aframax	NaN	2007	115824	248.93	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9893046	Pacific Diamond	Aframax	1C	2021	113306	249.90	44	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9787194	Minerva Olympia	Aframax	NaN	2019	114780	250.00	44	Dirty	Spot	Pushed POSS	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9329409	Kriti Samaria	Aframax	NaN	2007	105865	241.03	42	Dirty	Relet	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9288734	Seabravery	Aframax	1C	2005	105042	243.96	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9334727	Dugi Otok	Aframax	1C	2008	108932	246.00	48	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9439383	PS Genova	Aframax	NaN	2010	108983	243.00	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9801976	Prometheus Light	Aframax	NaN	2019	114700	249.99	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9304368	Seasenator	Aframax	NaN	2007	105715	241.03	42	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only
9796743	Rava	Aframax	1C	2017	114385	250.00	44	Dirty	Spot	Pushed	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	left_only

30 rows × 59 columns

In [ ]: