The OfferIndicatorsManager provides access to electricity market offer indicators with automatic chunking and parquet caching.
OfferIndicatorsManager
Access via client.offer_indicators.
list()
List all available offer indicators as a DataFrame.
df = client.offer_indicators.list()
print(df[["name", "description"]].head())
pd.DataFrame with columns:
id — Offer indicator IDname — Full indicator namedescription — HTML-stripped description- Additional metadata fields
Note: Unlike regular indicators, offer indicators do not support catalog caching.
get(indicator_id)
Get a specific offer indicator by ID and return a handle for data retrieval.
Unique offer indicator identifier
handle = client.offer_indicators.get(25)
OfferIndicatorHandle instance with metadata and data retrieval methods.
OfferIndicatorHandle
Returned by OfferIndicatorsManager.get(). Provides metadata access and historical data retrieval for a specific offer indicator.
Attributes
Raw API metadata (excluding values)
Parsed offer indicator model
historical(start, end, *, locale=“es”)
Fetch historical values as a DataFrame with timezone-aware DatetimeIndex.
Start date (YYYY-MM-DD format)
End date (YYYY-MM-DD format)
Response language ("es" or "en")
handle = client.offer_indicators.get(25)
df = handle.historical(
start="2024-01-01",
end="2024-01-07",
)
pd.DataFrame with:
- DatetimeIndex (timezone-aware, Europe/Madrid)
value column with offer indicator values- Additional columns may include
geo_id, geo_name if applicable
Features:
- Automatically chunks requests in 3-day windows (shorter than regular indicators)
- Uses local parquet cache when enabled
- Only fetches missing date ranges from API
- Handles duplicates by keeping latest values
Note: Offer indicators use shorter chunk sizes (3 days) compared to regular indicators (~3 weeks) due to larger response sizes.
Example Usage
List offer indicators
import esios
client = esios.ESIOSClient(token="your-token")
# List all offer indicators
df = client.offer_indicators.list()
print(df.head())
Get single offer indicator data
# Get offer indicator handle
handle = client.offer_indicators.get(25)
print(f"Name: {handle.name}")
# Fetch historical data
df = handle.historical(
start="2024-01-01",
end="2024-01-07",
)
print(df.head())
print(f"Shape: {df.shape}")
Week of data
handle = client.offer_indicators.get(25)
# Fetch a week (will make 3 requests: day 1-3, 4-6, 7)
df = handle.historical(
start="2024-01-01",
end="2024-01-07",
)
print(df.describe())
Month of data with caching
# First request fetches from API and caches
handle = client.offer_indicators.get(25)
df = handle.historical(
start="2024-01-01",
end="2024-01-31",
)
# Second request uses cache
df_cached = handle.historical(
start="2024-01-01",
end="2024-01-31",
)
# Partial overlap: only fetches missing days
df_extended = handle.historical(
start="2024-01-15",
end="2024-02-15",
)
handle = client.offer_indicators.get(25)
print(f"ID: {handle.id}")
print(f"Name: {handle.name}")
print(f"Metadata: {handle.metadata}")
Compare offer indicators
# Manually fetch and merge multiple offer indicators
import pandas as pd
handles = [
client.offer_indicators.get(25),
client.offer_indicators.get(26),
]
dfs = []
for handle in handles:
df = handle.historical("2024-01-01", "2024-01-07")
df = df.rename(columns={"value": handle.name})
dfs.append(df)
result = pd.concat(dfs, axis=1)
print(result.head())
Differences from Regular Indicators
Offer indicators differ from regular indicators in several ways:
| Feature | Regular Indicators | Offer Indicators |
|---|
| Chunk size | ~21 days | 3 days |
| Geography support | Yes (multi-geo pivoting) | Limited |
| Catalog caching | Yes | No |
| Metadata caching | Yes | No |
| Aggregation support | Yes (time_agg, geo_agg) | No |
| Compare method | Yes | No |
