Curves & Data Access

Data in the API is stored in curves. A curve is a collection of metadata describing one or more time series. There are 4 main curve types:

TIME_SERIES
TAGGED
INSTANCES
TAGGED_INSTANCES

Additionally, there is a specialized curve type:

PAIRED_LIST — for data structures with two related values per point.

Searching for curves

Each curve can have the following metadata attributes:

commodity, categories, area, border_source, station
sources, scenarios, unit, time_zone
version, frequency, data_type

The standard way of finding curves is by searching using a combination of these attributes. You can search via the API web interface (see the documentation) or within Python using session.search():

curves = session.search(category='WND', area=['EE', 'LT'], frequency='H')

The valid values for each attribute can be accessed using the corresponding session.get_ATTRIBUTE() function.

Getting a curve object

To fetch data, first get a curve object. You can get one by searching (which returns a list of curve objects), or by name using session.get_curve():

curve = session.get_curve(name='pro ee wnd intraday mwh/h cet h a')

Each curve has the following attributes:

id — curve ID
name — curve name
curve_state — Normal availability, Beta release, or Scheduled for removal
curve_type — one of TIME_SERIES, TAGGED, INSTANCES, TAGGED_INSTANCES, or PAIRED_LIST

Access attribute values with curve.attribute_name:

>>> curve = session.get_curve(name='pro ee wnd intraday mwh/h cet h a')
>>> curve.name
'pro ee wnd intraday mwh/h cet h a'
>>> curve.curve_type
'TIME_SERIES'

Getting data from a curve

Each curve type has its own methods for fetching data. Check the type with curve.curve_type.

TIME_SERIES

A Time Series curve holds a single time series (actual values, backcasts, normals, etc.). Use get_data():

curve = session.get_curve(name='pro ee wnd intraday mwh/h cet h a')
ts = curve.get_data(data_from='2018-01-01T14:00Z', data_to='2018-02-01T14:00Z')

The method returns a TS object. You can also process data server-side (aggregate to daily/weekly/monthly/yearly) by passing additional parameters to get_data() — see the API Reference and Examples.

TAGGED

A Tagged curve holds a set of related time series, each identified by a tag (commonly used for ensemble weather data).

# Get available tags
tags = curve.get_tags()

# Get data for all tags
ts_list = curve.get_data(data_from='2018-01-01', data_to='2018-02-01')

# Get data for a single tag
ts = curve.get_data(data_from='2018-01-01', data_to='2018-02-01', tag='Avg')

# Get data for multiple tags
ts_list = curve.get_data(data_from='2018-01-01', data_to='2018-02-01',
                         tag=['Avg', '01', '12'])

INSTANCES

An Instance curve contains a time series for each issue date (typically forecasts). You can:

# Fetch a single instance by issue date
ts = curve.get_instance(issue_date='2018-01-01T00:00')

# Search for multiple instances in a date range
ts_list = curve.search_instances(
    issue_date_from='2018-07-01Z00:00',
    issue_date_to='2018-07-04Z00:00',
    with_data=True
)

# Fetch the latest available instance
ts = curve.get_latest()

TAGGED_INSTANCES

Tagged Instance curves combine Tagged and Instance curves — each time series is defined by a unique combination of issue date and tag. Ensemble forecasts are a typical use case.

# Get available tags
tags = curve.get_tags()

# Get data for the default tag at a given issue date
ts_list = curve.get_instance(issue_date='2018-07-01T00:00')

# Get data for a single tag
ts = curve.get_instance(issue_date='2018-07-01T00:00', tag='Avg')

# Get data for multiple tags
ts_list = curve.get_instance(issue_date='2018-07-01T00:00',
                             tag=['Avg', '02', '05'])

# Search for multiple instances
ts_list = curve.search_instances(
    issue_date_from='2018-07-01Z00:00',
    issue_date_to='2018-07-04Z00:00',
    with_data=True,
    tags=['Avg', '11']
)

# Get the latest instance for a specific tag
ts = curve.get_latest(tags='03')

PAIRED_LIST

A PairedList curve contains paired values where each data point has two related values (e.g. price and volume).

# Get available tags
tags = curve.get_tags()

# Fetch paired data
paired_data = curve.get_data(
    data_from='2025-01-01',
    data_to='2025-01-31',
    with_data=True
)

# Convert to DataFrame
df = paired_data[0].to_pandas()

Working with TS objects

All data-fetching methods return a TS object. The most important method is to_pandas(), which returns a pandas.Series with a date index:

>>> curve = session.get_curve(name='pro ee wnd intraday mwh/h cet h a')
>>> ts = curve.get_data(data_from="2018-01-01", data_to="2018-01-05",
...                     frequency="D", function="SUM")
>>> ts.to_pandas()
2018-01-01 00:00:00+01:00    2169.0
2018-01-02 00:00:00+01:00    3948.0
2018-01-03 00:00:00+01:00    1489.0
2018-01-04 00:00:00+01:00    1860.0
Freq: D, Name: pro ee wnd intraday mwh/h cet h a, dtype: float64

The TS class also provides simple aggregation methods: ts.sum(), ts.mean(), and ts.median().

See the Examples and pandas documentation for more on working with Series and DataFrame objects.

Working with PairedTS objects

PAIRED_LIST curves return PairedTS objects instead of TS objects. The to_pandas() method returns a pandas.DataFrame where each tag becomes a column:

>>> curve = session.get_curve(name='paired_list curve name')
>>> paired_data = curve.get_data(data_from="2025-12-18",
...                              data_to="2025-12-19", with_data=True)
>>> df = paired_data[0].to_pandas()
>>> df.head()
     price  volume
0  -581.93     5.0
1  -572.00     5.0
2  -562.97     5.0
3  -544.84     5.0
4  -536.21     5.0

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