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Many indicators return data for multiple geographies (countries, regions). The v2 client pivots these automatically into wide-format DataFrames.

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Setup

from esios import ESIOSClient

client = ESIOSClient()

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Spot price across countries

Indicator 600 (spot price) returns data for 8 European countries: 
handle = client.indicators.get(600)
handle.geos_dataframe()
Returns: 
   geo_id      geo_name
0       1      Portugal
1       2       Francia
2       3        España
3    8826      Alemania
4    8827       Bélgica
5    8828  Países Bajos
Fetch all geographies: 
df = handle.historical("2025-01-01", "2025-01-07")
df
Returns a wide DataFrame with one column per country: 
                           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
...
[672 rows × 6 columns]
Each column is a country name. This makes plotting and analysis straightforward.

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Compare two countries

df[["España", "Portugal"]].plot(
    figsize=(12, 4),
    title="Spain vs Portugal Spot Price (€/MWh)",
)

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Correlation matrix

df.corr().round(2)
Shows how prices correlate across countries: 
              Portugal  Francia  España  Alemania  Bélgica  Países Bajos
Portugal          1.00     0.66    0.99      0.42     0.64          0.60
Francia           0.66     1.00    0.67      0.89     0.99          0.87
España            0.99     0.67    1.00      0.44     0.65          0.61
...

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Select specific geographies

Use geo_ids to fetch only the countries you need, reducing API calls: 
spain_id = handle.resolve_geo("España")
france_id = handle.resolve_geo("Francia")

df_subset = handle.historical(
    "2025-01-01", "2025-01-07",
    geo_ids=[spain_id, france_id],
)
df_subset
Returns only the selected columns: 
                           España  Francia
datetime
2025-01-01 00:00:00+01:00  134.49    12.36
...
[672 rows × 2 columns]

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Next steps

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