SPGCI Python Library

Python Client for the S&P Global Commodity Insights API.

Looking for more examples? Check out our Notebook Gallery.

Installation

Requires Python >= 3.9.0.

pip install spgci

Getting Started

    import spgci as ci

    ci.set_credentials(<username>, <password>)
    mdd = ci.MarketData()

    symbols = ["PCAAS00", "PCAAT00"]
    mdd.get_assessments_by_symbol_current(symbol=symbols)

Alternatively, you can set your credentials via Environment Variables and omit the set_credentials call:

Features

• Automatically generates token prior to making request.
• Returns data as a pandas DataFrame (set raw=False to get the raw request.response object).
• Can auto-paginate response and concatenates into a single DataFrame (set paginate=True to enable).
• Sets datatype for date and datetime fields in DataFrame.
• Composes nicely with native python/pandas types. Arguments support lists and pd.Series which are automatically converted into filter expressions.

Datasets Supported

Market Data

import spgci as ci

mdd = ci.MarketData()

mdd.get_symbols(commodity="Crude oil")
# DataFrame of symbols with commodity = "Crude oil".

mdd.get_symbol(subscribed only=True)
# DataFrame of all Market Data Categories you are subscribed to.

mdd.get_assessments_by_mdc_current(mdc="ET")
# DataFrame of current assessments for all symbols in the Market Data Category "ET".

mdd.get_assessments_by_mdc_historical(mdc="ET")
# DataFrame of historical assessments for all symbols in the Market Data Category "ET"

mdd.get_mdcs()
# DataFrame for Market Data Categories (MDCs) available

mdd.get_assessments_by_symbol_current(symbol="PMABQ00")
# DataFrame for symbol assements for the current latest date

mdd.get_assessments_by_symbol_historical(symbol="PMABQ00")
# DataFrame for symbol assements for historical data from the earliest date possible

Forward Curves

import spgci as ci

fc = ci.ForwardCurves()

fc.get_curves(
    commodity=["Benzene", "Crude oil"],
    derivative_maturity_frequency="Month"
    )
# DataFrame of all curves with commodity in ("Benzene", "Crude Oil") and have a Monthly frequency.

fc.get_assessments(curve_code=["CN003", "CN006"])
# DataFrame of the latest assessments for all symbols in the curves ("CN003", "CN006").

Energy Price Forecast

import spgci as ci

epf = ci.EnergyPriceForecast()

epf.get_prices_shortterm(symbol="PCAAS00", month=[10, 11, 12])
# DataFrame of monthly forecasts for the symbol "PCAAS00" in the last 3 months of the year.

epf.get_prices_longterm(year=[2020, 2021], sector="Energy Transition", delivery_region="Europe")
# DataFrame of the annual forecasts for the years in ("2020", "2021"), where the sector is "Energy Transition" and the delivery region is "Europe".

EWindow Market Data

import spgci as ci
from datetime import date

ewmd = ci.EWindowMarketData()

ewmd.get_markets()
# DataFrame of Markets.

d = date(2023,2,13)
ewmd.get_botes(market=["EU BFOE", "US MidWest"], order_time=d)
# DataFrame of all BOTes in the markets ("EU BFOE", "US MidWest") on Feb 13, 2023.

World Oil Supply

import spgci as ci

wos = ci.WorldOilSupply()

countries = wos.get_reference_data(type=wos.RefTypes.Countries)
# DataFrame of all countries.

wos.get_ownership(country=countries['countryName'][:3], year=2040)
# DataFrame of Ownership for the first three countries from the countries endpoint and year "2040".

World Refinery Database

import spgci as ci

wrd = ci.WorldRefineryData()

wrd.get_yields(year=2020, owner="BP")
# DataFrame of yields for the year "2020" where "BP" is the refinery owner.

ref = wrd.get_reference_data(type=wrd.RefTypes.Refineries)
# DataFrame of all refineries.

az = ref[ref['Name'].str.contains("Al-Zour")]
wrd.get_runs(refinery_id=az["Id"])
# DataFrame of runs for the refineries with "Al-Zour" in the name.

wrd.get_outages(refinery_id=245)
# DataFrame of outages for refineryId 245.

Insights

import spgci as ci
import datetime

ni = ci.Insights()
two_weeks_ago = today - datetime.timedelta(days=14)

ni.get_stories(q="Suez", content_type=ni.ContentType.MarketCommentary)
# DataFrame of articles related to "Suez" where the content type is "Market Commentary".

ni.get_subscriber_notes(q="Naptha")
# DataFrame of all subscriber notes related to "Naptha".

ni.get_heards(q="Steel", content_type=ni.HeardsContentType.Heard, geography=['Europe', 'Middle East'], strip_html=True)
# DataFrame of all Heards related to "Steel" where the geography is in ("Europe", "Middle East") with HTML Tags removed from the headline and body.

ni.get_content(id="169b5f45-04e4-43f2-b83b-ecc16f96be55")
# DataFrame to see more content details in an article ID

ni.get_spotlights(updated_date_gte=two_weeks_ago)
# DataFrame for spotlights articles from two weeks ago to today

ni.get_spotlights(updated_date_gte=two_weeks_ago,strip_html=True)
# DataFrame for spotlights articles from two weeks ago to today, with HTML tags stripped

ni.get_content(id="02ed2748-1262-45e1-ad97-9629e29e0274",download=True)
# Download file from get_content endpoint, e.g. pdf file

ni.get_content(id="02ed2748-1262-45e1-ad97-9629e29e0274", download="my_report.pdf")
# Report name can be specified when downloading, otherwise the file will be named using the content ID

Global Integrated Energy Model (Deprecated)

Deprecated: GlobalIntegratedEnergyModel is deprecated and will be removed in a future version.

import spgci as ci

giem = ci.GlobalIntegratedEnergyModel()

giem.get_demand(country="Cambodia", product=["Naphtha", "Ethane"])
# DataFrame of energy demand for ("Naphtha", "Ethane") for Cambodia.

giem.get_demand_archive(scenario_id=559, country="Cambodia", product=["Naphtha", "Ethane"])
# DataFrame of an archived demand data of giem for Cambodia.

giem.get_reference_data(type=giem.RefTypes.Products)
# DataFrame of all "products" covered by Global Integrated Energy Model dataset.

Refining Margins & Crude Arbitrage

import spgci as ci

af = ci.Arbflow()

af.get_margins_catalog(location_id = 34, crude_symbol="AAQZB00")
# DataFrame of refining margins catalog for ("AAQZB00") for Location Id 34.

af.get_margins_data(margin_id=229, margin_date='2023-08-16')
# DataFrame of refining margins data of arbflow for '2023-08-16'.

af.get_arbitrage(margin_id=[220,330], base_margin_id=1514, frequency_id=2)
# DataFrame of arbitrage data with frequencyId = 2 (Monthly).

af.get_reference_data(type=af.RefTypes.Locations)
# DataFrame of all "locations" covered by Refining Margins & Crude Arbitrage dataset.

LNG Global Analytics

import spgci as ci

lng = ci.LNGGlobalAnalytics()

lng.get_tenders(country_name="United States", paginate=True)
# DataFrame of tenders with country = 'United States'.

lng.get_tenders(contract_type="FOB", contract_option="Sell")
# DataFrame of tenders with ContractType = "FOB" and ContractOption = "Sell".

lng.get_liquefaction_projects()
# DataFrame of liquefaction projects.

lng.get_outages(liquefaction_project_name="ADNOC LNG")
# DataFrame of all LNG outages tied to the liquefaction project "ADNOC LNG".

lng.get_netbacks(date_gt="2024-01-01", import_geography="Brazil")
# DataFrame of all LNG Netbacks where import geography is 'Brazil' since Jan 1, 2024.

lng.get_supply_forecast_current(export_market="Indonesia")
# DataFrame of the current supply forecast for Indonesia

lng.get_demand_forecast_history(import_market="Japan", month="2024-12-01")
# DataFrame of all historical forecasts (one per month) of the LNG demand for Japan in December 2024.

lng.get_cargo_trips(trade_route="Suez Canal", date_loaded_gte="2024-01-01")
# DataFrame of trips through the Suez Canaal that loaded since 2024.

lng.get_cargo_events_partial_reexport()
# DataFrame of partial re-exports (a fraction of the delivered LNG is re-exported during transportation)

lng.get_assets_contracts_offtake_contracts(assumed_destination="Japan")
# DataFrame of offtake contracts headed to 'Japan'

lng.get_assets_contracts_liquefaction_trains(train_status=["Existing","Under Construction"])
# DataFrame of liquefaction trains which are either 'Existing' or 'Under Construction'

lng.get_gas_demand(market=["United States", "Mexico"], period_type="Monthly")
# DataFrame of gas demand for the different categories in the U.S. and Mexico on a monthly basis.

lng.get_power_generation(
    market=["India", "Pakistan", "Bangladesh"],
    source="Power Generation By Fuel",
    period_type="Monthly",
    paginate=True,
)
# DataFrame of power generation for India, Pakistan, Bangladesh by fuel type on a monthly basis.

lng.get_demand_forecast_short_term_current(import_market="France",month_gte="2025-01-01")
# DataFrame of short term demand forecast for import market of France starting January 1st 2025

lng.get_supply_forecast_short_term_current(export_market="Canada",month_gte="2025-01-01")
# DataFrame of short term supply forecast for export market of Canada starting January 1st 2025

lng.get_events_trade_route(trade_route="Cape of Good Hope", vessel_direction="Westbound")
# DataFrame for events recording key information, e.g Westbound vessels passing throught the Cape of Good Hope

lng.get_events_diversion(diversion_location="Indian Ocean")
# DataFrame for events of diversion, e.g. in the Indian Ocean

Crude Analytics

import spgci as ci

ca = ci.CrudeAnalytics()

ca.get_country_scores(status="Current")
# DataFrame of latest scores for all countries.

ca.get_country_scores(country="United States")
# DataFrame of all (historical and current) scores for country = "United States".

ca.get_country_total_scores()
# DataFrame of aggregated scores, supply and capacity per date.

Weather

import spgci as ci

w = ci.Weather()

w.get_forecast(city="Boston")
# DataFrame of forecasts for Boston

w.get_forecast(market="United States", weather_date_gte="2024-01-01", weather_date_lte="2024-01-31")
# DateFrame of forecasts in the United States in January 2024.

w.get_actual(market="Hong Kong", paginate=True)
# DataFrame of actual weather in Hong Kong, paginate=True to get full history.

Structured Heards

import spgci as ci

sh = ci.StructuredHeards()

sh.get_markets()
# DataFrame of the list of markets that have structured heards.

sh.get_heards(market="Americas crude oil", heard_type="trade")
# DateFrame of heards in the Americas crude oil market that are of type 'trade'.

Chemicals Analysis and Forecasts

import spgci as ci

chem = ci.Chemicals()

chem.get_unique_values(dataset="production", columns=["commodity"])
# See the unique values for each field per dataset. Useful for finding out the exact values to fitler on for fields like commodity, region, etc..

chem.get_capacity_events(
    event_begin_date_gt="2023-01-01", country="China", event_type=["expand", "startup"]
)
# DataFrame of capacity events in plants in China, that are either "expand" or "startup" since 2023-01-01

chem.get_capacity_to_consume(is_active=[True, False], commodity="Polybutadiene rubber")
# DateFrame of capacities to consume. Setting `is_active` to [True, False] allows you to include records that were corrected or removed which is useful for Point-In-Time analysis.

chem.get_short_term_prices(commodity="Acetone", delivery_region="US Gulf Coast")
# DataFrame of short-term (monthly) price forecasts for Acetone delivered to the US Gulf Coast.

chem.get_outages(
    start_date_gte="2023-01-01",
    capacity_gte=500,
    alert_status=['Confirmed']
)
# DataFrame of chemical plant outages since Jan 1, 2023 where the capacity impacted is >= 500 and the status is `Confirmed`.

European Gas Analytics

import spgci as ci

egp = ci.EUGasAnalytics()

egp.get_daily_flow_point_selection(
    from_country="Norway",
    gas_day_gte="2024-10-01",
    gas_day_lte="2024-10-31",
    uom="MCM",
    direction="Net",
)
# DataFrame of all net flows out of Norway in October 2024 in Million Cubic Meters.


df = egp.get_overview_hub_balance(
    gas_day_gte="2024-10-01",
    gas_day_lte="2024-10-31",
    average_type="Daily value",
    hub="PEG",
    uom="MCM",
)
df.pivot_table(
    index=["hubFlowType", "hubSubFlowType"],
    columns="dayMonthOrdinal",
    values="quantity",
)
# DataFrame of hub balances for October 2024 for PEG in Million Cubic Meters
# Pivot Table shows this grouped by FlowType (demand, supply, storage, etc...) and +/- N Days.

egp.get_daily_country_overview(country="Netherlands", uom="MCM", gas_day_gte="2025-05-01", gas_day_lte="2025-05-31")
# DataFrame of the daily supply and demand of a specified country

Oil and NGL Analytics

import spgci as ci

rp = ci.OilNGLAnalytics()

rp.get_arbflow_arbitrage(from_region="Persian Gulf", to_region="Japan")
# DataFrame of arbitrage opportunities for commodities from the Persian Gulf to Japan by concept, commodity and date.

rp.get_arbflow_arbitrage(commodity="Jet Fuel", concept="incentive")
# DataFrame of arbitrage opportunities for Jet Fuel using the `incentive` concept.

rp.get_refinery_runs_latest(outlook_horizon="Short-Term", region="Latin America", report_for_date='2026-04-01')
# DataFrame of forecast monthly runs per country in Latin America for April 2026 for the most recent forecast.

rp.get_oil_inventory(
    commodity="Naphtha",
    outlook_horizon="Short-Term",
    geography="Amsterdam, Rotterdam and Antwerp",
    frequency='Monthly',
)
# DataFrame of oil inventory for ARA for Naptha, including the latest and historical forecasts.

Integrated Energy Scenarios

import spgci as ci

ies = ci.IntegratedEnergyScenarios()

ies.get_gdp(country="United States", scenario="Inflections 2024", year=2030)
# DataFrame of GDP forecasts for 2030, using the Inflections 2024 scenario, in both Nominal and Real and a PPP basis and not.

ies.get_unique_values(dataset="gdp", columns="scenario")
# DataFrame of unique values for `scenario` in the `get_gdp` method.

Agri and Food

import spgci as ci

agri = ci.AgriAndFood()

agri.get_cost_of_production(commodity="wheat",geography="India", year=2025)
# DataFrame of the cost of production of wheat in India for the year 2025.

agri.get_unique_values(dataset="cost-of-production", columns="commodity")
# DataFrame of unique values for `commodity` in the `get_cost_of_production` method.

agri.get_price_purchase_forecast(commodity="corn", reporting_region="United States", currency="USD",report_for_date_gte="2023-01-01", report_for_date_lte="2024-12-31")
# DataFrame of the price purchase forecast for corn in the United States in USD for the period from January 1, 2023 to December 31, 2024.

Americas Gas (Gaslytics)

import spgci as ci

ag = ci.AmericasGas()

ag.get_pipeline_flows(date_frequency="Monthly", subregion="New England", meter_type_primary="Power")
# DataFrame of monthly pipeline flows for Power as a primary meter type in New England

ag.get_modeled_demand_actual(flow_date="2025-07-07")
# DataFrame of actual modeled demand for a specific date

ag.get_outlook_marketbalances_prices(
    date_frequency="Summer",
    domain="Canada",
    category="LNG exports",
    vintage_type="Long Term Outlook",
    vintage="2025.01"
)
# DataFrame of market balances and prices outlook for Canadian LNG exports for the 2025 Long Term Outlook

ag.get_unique_values("outlook-marketbalances-prices", "region")
# DataFrame the unique values in a dataset and column, e.g. the regions available in outlook market balances prices dataset

ag.get_tariff_rate_data(pipeline_name="Northern Border",rate_frequency="Unit Rate",rate_name="Firm Transporta")
# DataFrame for tariff rate data in Northern Border, specifying for unit rate frequency and Firm Transporta

ag.get_pipeline_profiles_data(pipeline_id=18)
# DataFrame for pipeline profiles data filtered by ID number

ag.get_reference_data_geography(state="Alberta")
# DataFrame for reference data by on geography/state

ag.get_market_balances_data(flow_date_gte="2025-10-14")
# DataFrame for market balances data on flow date

Gas Long Term Supply and Demand

import spgci as ci

gas = ci.GasLongTermSupplyAndDemand()

gas.get_europe(subject_area="Demand", geography="Germany", year=2030)
# DataFrame of the long term gas demand outlook for Germany in 2030.

gas.get_unique_values(dataset="europe", columns="subjectArea")
# DataFrame of unique values for `subject_area` in the Europe dataset.

EU Power

import spgci as ci

eup = ci.EUPower()

eup.get_power_assets(country="Germany", plant_technology="Solar")
# DataFrame of power assets in Germany with the technology "Solar".

eup.get_unique_values("power-assets", "country")
# DataFrame of unique values for `country` in the power assets dataset.

Smart Heards

import spgci as ci

sm = ci.SmartHeards()

sm.get_markets()
# DataFrame of the markets that have smart heards.

sm.get_heards(market="Americas crude oil", strip_html=True)
# DataFrame of smart heards in the Americas crude oil market with HTML tags removed.

Scenario Manager

import spgci as ci
from spgci.scenario_manager import FuelBlend, FuelConsumption

sm = ci.ScenarioManager()

sm.calculate_eu_compliance_penalty(
    origin_port="Rotterdam, Europe",
    destination_port="New York, Americas",
    fuel_blends=[
        FuelBlend(
            fuels=[
                FuelConsumption(fuel="VLSFO", consumption=50),
                FuelConsumption(fuel="MGO", consumption=50),
            ]
        )
    ],
    reporting_period="2025-2029",
)
# DataFrame of the FuelEU Maritime compliance penalty for a voyage from Rotterdam to New York.

sm.calculate_ondemand_price(
    start_date="2026-02-15",
    end_date="2026-02-17",
    commodity="Gasoline",
    product_grade="Gasoline Unl 87 USGC Prompt Pipeline",
)
# DataFrame of on-demand delivered gasoline prices between the two dates.

sm.get_reference_data_pipelines(pipeline_name="Colonial", origin_state="TX")
# DataFrame of pipelines named "Colonial" originating in Texas.

Road Fuel

import spgci as ci
from spgci.roadfuel import BiofuelBlend

rf = ci.RoadFuel()

rf.calculate_price(
    region="Netherlands",
    transport_sector="Maritime",
    obligation_year=["2026", "2028"],
    reference_biofuel_blending=BiofuelBlend("RD-A", 1000, emission_factors=14),
    fuels_used={"mgo": 10, "vlsfo": 20, "hsfo": 30},
)
# DataFrame of RED III blending economics for 2026 and 2028 using the biofuel "RD-A".

rf.get_obligation_data(region="Netherlands", transport_sector="Maritime", year_gte=2026)
# DataFrame of blending obligations for the Netherlands maritime sector from 2026.

Freight Rate Forecast

import spgci as ci

frf = ci.FreightRateForecast()

frf.get_freight_rate_forecast_latest(symbol="AALPP00")
# DataFrame of the latest freight rate forecasts for AALPP00 (USAC Clean Caribbean)

frf.get_freight_rate_forecast_drivers_latest(commodity="Clean Freight", report_for_date="2027-11-01")
# DataFrame of the latest drivers behind the 2027-11-01 clean freight rate forecasts (per symbol).

Metals

import spgci as ci

m = ci.Metals()

m.get_market_outlook(commodity="Steel", frequency="Quarterly")
# DataFrame of quarterly market outlook data for steel.

m.get_unique_values(dataset="market-outlook", columns="commodity")
# DataFrame of unique values for `commodity` in the market outlook dataset.

Global EAC Analytics

import spgci as ci

eac = ci.GlobalEacAnalytics()

eac.get_outlooks(country="United States", certificate_name="RECs")
# DataFrame of environmental attribute certificate (EAC) outlooks for the United States.

Clean Energy Technology (CET)

import spgci as ci

# Economic Outlooks
eco = ci.CetEconomicOutlooks()
eco.get_levelized_cost(geography="United States")
# DataFrame of levelized cost of electricity for the United States.

eco.get_solarpv_capex(vintage_rank=1)
# DataFrame of solar PV capex from the latest vintage.

# Market Outlooks
mkt = ci.CetMarketOutlooks()
mkt.get_wind_installs(vintage_rank=1)
# DataFrame of wind installations from the latest vintage.

# Policies
pol = ci.CetPolicies()
pol.get_targets(geography="Germany")
# DataFrame of clean energy targets in Germany.

# PPAs
ppa = ci.CetPpa()
ppa.get_low_carbon_electricity(technology="Onshore wind power technology")
# DataFrame of low carbon electricity PPAs for onshore wind.

# Supply Chain
sc = ci.CetSupplyChain()
sc.get_battery_orders(year_announced=2024)
# DataFrame of battery orders announced in 2024.