myeia is a simple Python wrapper for the U.S. Energy Information Administration (EIA) APIv2. It is designed to be simple to use and to provide a consistent interface for accessing EIA data.
pip install myeia- backoff
- pandas
- python-dateutil
- python-dotenv
- requests
To obtain an API Key you need to register on the EIA website.
To find all EIA Datasets visit API Dashboard.
from myeia.api import API
eia = API()# Create your the .env file in your projects root directory
touch .envBy Default the EIA class will look for your API EIA_TOKEN.
If you have registered for an API key you can set it in your .env file.
EIA_TOKEN=YOUR_TOKEN_HERELets look at an example of how to get the EIA Natural Gas Futures.
You can use the simpler v1 API method where you only need to pass the series_id or you can use the newer v2 API method where you need to pass the route, series, and frequency.
df = eia.get_series(series_id="NG.RNGC1.D")
df = eia.get_series_via_route(
route="natural-gas/pri/fut",
series="RNGC1",
frequency="daily",
)
df.head()Output Example:
Natural Gas Futures Contract 1 (Dollars per Million Btu)
Date
2022-09-13 8.284
2022-09-12 8.249
2022-09-09 7.996
2022-09-08 7.915
2022-09-07 7.842
... ...Lets look at another example the Total OPEC Petroleum Supply where the facet is available as seriesId. By Default it is set as series but we can define the facet as seriesId.
df = eia.get_series(series_id="STEO.PAPR_OPEC.M")
df = eia.get_series_via_route(
route="steo",
series="PAPR_OPEC",
frequency="monthly",
facet="seriesId",
)
df.head()Output Example:
Total OPEC Petroleum Supply
Date
2023-12-01 34.517314
2023-11-01 34.440397
2023-10-01 34.376971
2023-09-01 34.416242
2023-08-01 34.451823
... ...You can also filter by multiple facets. Lets look at the UAE Crude oil, NGPL, and other liquids where the facets we choose are countryRegionId and productId.
The difference here is that both facet columns are present in the dataframe, unlike the previous examples where only one facet was present.
df = eia.get_series_via_route(
route="international",
series=["ARE", 55],
frequency="monthly",
facet=["countryRegionId", "productId"],
)
df.head()Output Example:
countryRegionId productId Crude oil, NGPL, and other liquids
Date
2024-03-01 ARE 55 4132.394334
2024-02-01 ARE 55 4132.394334
2024-01-01 ARE 55 4142.394334
2023-12-01 ARE 55 4082.394334
2023-11-01 ARE 55 4082.394334
... ... ... ...For multiple series you have to loop through the series and append the data to a list.
data = []
for item in ["RNGC1", "RNGC2"]:
df = eia.get_series_via_route(
route="natural-gas/pri/fut",
series=item,
frequency="daily",
facet="series",
)
data.append(df)
df = pd.concat(data, axis=1)
df.head()Output Example:
Natural Gas Futures Contract 1 (Dollars per Million Btu) Natural Gas Futures Contract 2 (Dollars per Million Btu)
Date
2023-08-29 2.556 2.662
2023-08-28 2.579 2.665
2023-08-25 2.540 2.657
2023-08-24 2.519 2.636
2023-08-23 2.497 2.592
... ... ...You can define a start and end date for your query.
df = eia.get_series(
series_id="NG.RNGC1.D",
start_date="2021-01-01",
end_date="2021-01-31",
)
df.head()Output Example:
Natural Gas Futures Contract 1 (Dollars per Million Btu)
Date
2021-01-29 2.564
2021-01-28 2.664
2021-01-27 2.760
2021-01-26 2.656
2021-01-25 2.602
... ... This also works for the get_series_via_route method.
df = eia.get_series_via_route(
route="natural-gas/pri/fut",
series="RNGC1",
frequency="daily",
start_date="2021-01-01",
end_date="2021-01-31",
)
df.head()Output Example:
Natural Gas Futures Contract 1 (Dollars per Million Btu)
Date
2021-01-29 2.564
2021-01-28 2.664
2021-01-27 2.760
2021-01-26 2.656
2021-01-25 2.602
... ...
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