python-entsoe 0.1.1

Python client for the ENTSO-E Transparency Platform API

python-entsoe

Python client for the ENTSO-E Transparency Platform API.

Typed, namespace-organized access to European electricity market data — load, prices, generation, transmission, and balancing.

pip install python-entsoe

Quick Start

from entsoe import Client
import pandas as pd

client = Client()  # reads ENTSOE_API_KEY from environment

start = pd.Timestamp("2024-06-01", tz="Europe/Paris")
end = pd.Timestamp("2024-06-08", tz="Europe/Paris")

df = client.load.actual(start, end, country="FR")

Every method returns a pandas.DataFrame with a timestamp column (UTC) and a value column.

Authentication

Get a free API key at https://transparency.entsoe.eu/ (register, then request a token via email).

Set it as an environment variable:

export ENTSOE_API_KEY=your-token-here

Or pass it directly:

client = Client(api_key="your-token-here")

API Reference

client.load

Method	Description	Parameters
actual(start, end, country)	Actual total system load	country: ISO code (e.g., "FR")
forecast(start, end, country)	Day-ahead load forecast	country: ISO code

df = client.load.actual(start, end, country="FR")
df = client.load.forecast(start, end, country="FR")

client.prices

Method	Description	Parameters
day_ahead(start, end, country)	Day-ahead market prices (EUR/MWh)	country: ISO code

df = client.prices.day_ahead(start, end, country="FR")
# Returns: timestamp, value, currency, price_unit

client.generation

Method	Description	Parameters
actual(start, end, country, psr_type=None)	Actual generation per type	psr_type: filter by fuel (optional)
forecast(start, end, country, psr_type=None)	Wind & solar forecast	psr_type: filter by fuel (optional)
installed_capacity(start, end, country, psr_type=None)	Installed capacity per type	psr_type: filter by fuel (optional)
per_plant(start, end, country, psr_type=None)	Generation per production unit	psr_type: filter by fuel (optional)

# All generation types
df = client.generation.actual(start, end, country="FR")

# Solar only
df = client.generation.actual(start, end, country="FR", psr_type="B16")

# Installed capacity
df = client.generation.installed_capacity(start, end, country="FR")

client.transmission

Method	Description	Parameters
crossborder_flows(start, end, country_from, country_to)	Physical cross-border flows	Two country codes
scheduled_exchanges(start, end, country_from, country_to)	Scheduled commercial exchanges	Two country codes
net_transfer_capacity(start, end, country_from, country_to)	Day-ahead NTC	Two country codes

df = client.transmission.crossborder_flows(
    start, end, country_from="FR", country_to="ES"
)

client.balancing

Method	Description	Parameters
imbalance_prices(start, end, country)	System imbalance prices	country: ISO code
imbalance_volumes(start, end, country)	System imbalance volumes	country: ISO code

df = client.balancing.imbalance_prices(start, end, country="FR")

Country Codes

Use standard ISO codes. Some bidding zones have specific codes:

Code	Area
FR	France
DE_LU	Germany/Luxembourg (bidding zone)
ES	Spain
NL	Netherlands
BE	Belgium
IT	Italy
IT_NORTH	Italy North
NO_1 .. NO_5	Norway zones
SE_1 .. SE_4	Sweden zones
DK_1, DK_2	Denmark zones

Note: For day-ahead prices and balancing data, use DE_LU instead of DE. See data availability notes for details.

Full list of 60+ supported areas in _mappings.py.

PSR Types (Fuel Types)

Use PSR codes to filter generation by fuel type:

Code	Fuel Type
B01	Biomass
B04	Fossil Gas
B05	Fossil Hard coal
B06	Fossil Oil
B10	Hydro Pumped Storage
B11	Hydro Run-of-river
B12	Hydro Water Reservoir
B14	Nuclear
B16	Solar
B18	Wind Offshore
B19	Wind Onshore

Full list in _mappings.py. Human-readable names available via:

from entsoe._mappings import PSR_TYPES
print(PSR_TYPES["B16"])  # "Solar"

Timestamps

All start and end parameters must be timezone-aware pd.Timestamp objects:

# Correct
start = pd.Timestamp("2024-01-01", tz="Europe/Paris")
start = pd.Timestamp("2024-01-01", tz="UTC")

# Wrong — will raise InvalidParameterError
start = pd.Timestamp("2024-01-01")

Returned timestamps are always in UTC.

Features

• Autocomplete-friendly — type client. and see all domains, then drill into methods
• Automatic year-splitting — requests spanning more than 1 year are split transparently
• ZIP handling — endpoints returning compressed responses are decompressed automatically
• Retry with backoff — rate-limited requests (HTTP 429) are retried with exponential backoff
• Clear errors — NoDataError, InvalidParameterError, RateLimitError with descriptive messages

Error Handling

from entsoe import Client, NoDataError, InvalidParameterError

client = Client()

try:
    df = client.prices.day_ahead(start, end, country="FR")
except NoDataError:
    print("No data available for this period")
except InvalidParameterError as e:
    print(f"Bad parameters: {e}")

Examples

See the examples/ directory for Jupyter notebooks with plotly visualizations:

• load.ipynb — actual load, forecast comparison, multi-country profiles
• prices.ipynb — day-ahead prices, distributions, hourly heatmap
• generation.ipynb — generation mix, solar vs wind, installed capacity
• transmission.ipynb — cross-border flows, bidirectional charts, NTC
• balancing.ipynb — imbalance prices, multi-country, distribution

Development

git clone https://github.com/jsulopzs/python-entsoe.git
cd python-entsoe
uv sync

# Run tests (requires ENTSOE_API_KEY in .env)
uv run pytest tests/ -v

# Regenerate example notebooks
uv run python scripts/generate_notebooks.py

License

MIT