The Kpler Python SDK provides access to Kpler data.
It can be easily integrated into notebooks, scripts, and applications.
It is available to all Kpler API clients (credentials required)
Documentation : https://python-sdk.dev.kpler.com
from kpler.sdk import Platform
from kpler.sdk.configuration import Configuration
from kpler.sdk.resources.trades import Trades
from datetime import date, timedelta
# Create configuration object
configuration = Configuration(Platform.Liquids, "<your email>", "<your password>")
# To change the platform use _change_platform method of Configuration class
configuration._change_platform(Platform.Liquids)
# Connect to one of the Kpler's client using your configuration object, ie: Trades
trades_client = Trades(configuration)
# Get all possible columns returned by the get_trades query
trades_columns = trades_client.get_columns()
# Do a get trades with default columns and with a maximum size of 5 for the dataframe
trades_df = trades_client.get(size=5)
# Get US imports over last week
us_imports = trades_client.get(
to_zones=["United States"],
products=["crude"],
with_forecast=False,
with_intra_country=True,
start_date=date.today() - timedelta(days=7),
columns=[
"vessel_name",
"closest_ancestor_product",
"closest_ancestor_grade",
"start",
"end",
"origin_location_name",
"destination_location_name",
]
)
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