spgci_qa 0.0.3
SPGCI is an API Client for the S&P Commodity Insights REST API
License: Apache-2.0
Requires Python: <4.0.0,>=3.9.0

# spgci_qa

Python Client for the [S&P Global Commodity Insights API](https://developer.platts.com).

Looking for more examples? Check out our [Notebook Gallery](https://developer.spglobal.com/commodityinsights/tools/notebooks).

## Installation

Requires Python >= 3.7.0.

```bash
pip install spgci_qa
```

## Getting Started

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/achristie/images/blob/master/readme.ipynb)

```python
    import spgci_qa-qa as ci

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

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

## ![spgci](https://raw.githubusercontent.com/achristie/images/master/getting_started.gif)

---

Alternatively, you can set your credentials via Environment Variables and _omit_ the `set_credentials` call:
| Environment Variable | Description |
| :------------------- | :----------------|
| spgci_qa_USERNAME | Your Username |
| spgci_qa_PASSWORD | Your Password |
| spgci_qa_APPKEY | Your AppKey |

## 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

```python
import spgci_qa as ci

mdd = ci.MarketData()

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

mdd.get_mdcs(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".
```

### Forward Curves

```python
import spgci_qa 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

```python
import spgci_qa 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

```python
import spgci_qa 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

```python
import spgci_qa 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

```python
import spgci_qa 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

```python
import spgci_qa as ci

ni = ci.Insights()

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.
```

### Global Oil Demand

```python
import spgci_qa as ci

od = ci.GlobalOilDemand()

od.get_demand(country="Cambodia", product=["Naphtha", "Ethane"])
# DataFrame of forecast monthly demand for ("Naphtha", "Ethane") for Cambodia.

products = od.get_reference_data(type=od.RefTypes.Products)
# DataFrame of all "products" covered by Global Oil Demand dataset.

od.get_demand(product=products["productName"][:3], year_gte=2023)
# DataFrame of forecast monthly demand for the first 3 products in the previous DataFrame and the year >= 2023.

od.get_demand_archive(scenario_id=150, country="Norway")
# DataFrame of an archived (March 2023) forecast of monthly oil demand for Norway.
```

### North America Natural Gas Analytics

```python
import spgci_qa as ci
from datetime import date

ng = ci.NANaturalGasAnalytics()

ng.get_pipelines(state="NJ", facility_type="Interconnect")
# DataFrame of pipelines in "NJ" with facility type "Interconnect"

ng.get_pipelines(pipeline_name="Algonquin")
# DataFrame of pipelines with name "Algonquin"

ng.get_pipeline_flows(pipeline_id=32)
# DataFrame of flows for pipeline_id 32 (Algonquin) for last 2 days.

d = date(2023, 7, 24)
ng.get_pipeline_flows(nomination_cycle="I2", gasdate=d)
# DataFrame of all pipeline flows during the I2 nomination cycle on gas date 2023-07-24

```

### Global Integrated Energy Model

```python
import spgci_qa 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 Oil Demand dataset.

```

### Refining Margins & Crude Arbitrage

```python
import spgci_qa 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

```python
import spgci_qa 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_reference_data(type=lng.RefTypes.LiquefactionProjects)
# DataFrame of liquefaction projects.

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

```

### Crude Analytics

```python
import spgci_qa 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.

```

## Disclaimer

Please note that by using this Alpha offering, you agree that you are participating in a prototype to develop these software development kits (“Alpha SDKs”) and that your participation and use of the Alpha SDKs is at your own risk. You will be liable for all activities by anyone using your application in connection with the Alpha SDKs.

If you are dissatisfied with the Alpha SDKs, your sole remedy is to stop using it, and S&P Global Commodity Insights (“spgci_qa”) will not pay you any damages (direct, indirect, special, or consequential) in connection with your use of the Alpha SDKs. Commodity Insights spgci_qa disclaims any and all representations and warranties, express or implied, including but not limited to any warranties of merchantability or fitness for a particular purpose or use as to the Alpha SDKs, and does not guarantee the adequacy, accuracy, timeliness or completeness of the Alpha SDKs or any component of them.