Vessels API Use Case - Explore Vessels¶

Run this example in Colab¶

Setup

In [ ]:

pip install seaborn

pip install seaborn

Install the Signal Ocean SDK:

In [ ]:

pip install signal-ocean

pip install signal-ocean

Import required libraries / APIs / packages:

Import Vessels API and some additional libraries

In [ ]:

from signal_ocean.vessels import VesselsAPI
import pandas as pd
import seaborn as sns
from datetime import datetime
from signal_ocean import Connection

from signal_ocean.vessels import VesselsAPI import pandas as pd import seaborn as sns from datetime import datetime from signal_ocean import Connection

Fill in your personal SignalOcean API subscription key acquired here and open a connection:

In [1]:

signal_ocean_api_key = '' #replace with your subscription key

signal_ocean_api_key = '' #replace with your subscription key

Create a connection

In [2]:

connection = Connection(signal_ocean_api_key)

connection = Connection(signal_ocean_api_key)

Call the vessels API¶

The Vessels API retrieves vessel information.

In [2]:

api = VesselsAPI(connection)

api = VesselsAPI(connection)

Get details for all vessels¶

In [5]:

vessels = api.get_vessels()

vessels = api.get_vessels()

Extract the returned information into a dataframe

In [6]:

df = pd.DataFrame([x.__dict__ for x in vessels])

df = pd.DataFrame([x.__dict__ for x in vessels])

Visualize fleet age by vessel class for VLCC, Suezmax and Aframax Tankers¶

In [7]:

df['vessel_age'] = df.apply(lambda r: datetime.now().year - r['year_built'], axis=1)
data = df[(pd.isnull(df['scrapped_date']))&(df['vessel_class'].isin(['VLCC', 'Suezmax', 'Aframax']))]

sns.set(rc={'figure.figsize':(9,6)})
sns.kdeplot(data=data, x='vessel_age', hue='vessel_class', multiple="stack", palette="crest",
   alpha=.7, linewidth=0.1);

df['vessel_age'] = df.apply(lambda r: datetime.now().year - r['year_built'], axis=1) data = df[(pd.isnull(df['scrapped_date']))&(df['vessel_class'].isin(['VLCC', 'Suezmax', 'Aframax']))] sns.set(rc={'figure.figsize':(9,6)}) sns.kdeplot(data=data, x='vessel_age', hue='vessel_class', multiple="stack", palette="crest", alpha=.7, linewidth=0.1);

Visualize pairwise relationships for deadweight, length_overall and breadth_extreme for VLCC, Suezmax and Aframax Tankers¶

In [8]:

data = df[(pd.isnull(df['scrapped_date']))&(df['vessel_class'].isin(['VLCC', 'Suezmax', 'Aframax']))]

sns.pairplot(data, kind='kde', hue='vessel_class', vars=['deadweight', 'length_overall', 'breadth_extreme']);

data = df[(pd.isnull(df['scrapped_date']))&(df['vessel_class'].isin(['VLCC', 'Suezmax', 'Aframax']))] sns.pairplot(data, kind='kde', hue='vessel_class', vars=['deadweight', 'length_overall', 'breadth_extreme']);