data.fetch_data.load_day_ahead_price

data.fetch_data.load_day_ahead_price(
    data_home=None,
    on_missing='raise',
    column='Day-ahead Price',
)

Load the ENTSO-E day-ahead spot price (DE/LU) as an hourly series.

Reads column from interim/day_ahead_price.csv (written by spotforecast2_safe.downloader.entsoe.download_day_ahead_price) and converts the index to a UTC DatetimeIndex with hourly frequency. Missing values are rejected by default (fail-safe). The day-ahead auction price is published on D-1 and is leakage-clean at forecast time as long as the day-ahead value (not the realised price) is used.

Parameters

Name	Type	Description	Default
data_home	Optional[Union[str, Path]]	Root data directory. If `None`, resolved via `get_data_home()`.	`None`
on_missing	`OnMissing`	How to handle NaN rows. `'raise'` (default) fails fast; `'ffill_bfill'` forward/back-fills; `'passthrough'` returns raw NaN.	`'raise'`
column	str	Name of the price column to read. Defaults to `"Day-ahead Price"`.	`'Day-ahead Price'`

Returns

Name	Type	Description
	pd.Series	pd.Series: Hourly UTC-indexed day-ahead price series.

Raises

Name	Type	Description
	FileNotFoundError	If `interim/day_ahead_price.csv` is absent.
	KeyError	If column is not present in the file.
	ValueError	If `on_missing='raise'` and the series has NaNs.

Examples

import os
import shutil
import tempfile

import pandas as pd

from spotforecast2_safe.data.fetch_data import load_day_ahead_price

tmp = tempfile.mkdtemp()
os.environ["SPOTFORECAST2_DATA"] = tmp
interim = os.path.join(tmp, "interim")
os.makedirs(interim, exist_ok=True)

idx = pd.date_range("2023-01-01", periods=48, freq="h", tz="UTC")
pd.DataFrame(
    {"Day-ahead Price": 95.0}, index=idx
).rename_axis("Time (UTC)").to_csv(
    os.path.join(interim, "day_ahead_price.csv")
)

s = load_day_ahead_price()
print(isinstance(s, pd.Series), len(s))

shutil.rmtree(tmp)
del os.environ["SPOTFORECAST2_DATA"]

True 48