Skip to main content
Fetch time-series data for any indicator. Results are cached locally as parquet files — subsequent requests only fetch missing date ranges.

​
Setup

from esios import ESIOSClient

client = ESIOSClient()

​
Basic query

Fetch spot price (indicator 600) for January 2025: 
handle = client.indicators.get(600)
df = handle.historical("2025-01-01", "2025-01-31")
df
Returns: 
                           Portugal  Francia  España  Alemania  Bélgica  Países Bajos
datetime
2025-01-01 00:00:00+01:00    134.49    12.36  134.49      2.16    10.62         13.62
...
[2976 rows × 6 columns]
Columns are named by geography (e.g. “España”, “Portugal”, “Francia”). The index is a timezone-aware DatetimeIndex.

​
Filter by geography

Use geo_ids to request only specific countries: 
spain_id = handle.resolve_geo("España")
print(f"España geo_id: {spain_id}")
Output: 
España geo_id: 3
Fetch only Spain: 
df_spain = handle.historical("2025-01-01", "2025-01-07", geo_ids=[spain_id])
df_spain
Returns a single-column DataFrame: 
                           España
datetime
2025-01-01 00:00:00+01:00  134.49
...
[672 rows × 1 columns]

​
Aggregation options

The API supports time and geography aggregation: 
# Daily average price
df_daily = handle.historical(
    "2025-01-01", "2025-01-31",
    time_trunc="day",
)
df_daily.head()

​
Export to CSV

df.to_csv("spot_prices_2025_01.csv")

​
Next steps

Build docs developers (and LLMs) love

Get started for freeTalk to us