### Install entsoe-py

Source: https://context7.com/energieid/entsoe-py/llms.txt

Install the entsoe-py library using pip.

```bash
python3 -m pip install entsoe-py
```

--------------------------------

### Initialize EntsoePandasClient and Query Data

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Initializes the EntsoePandasClient with an API key and demonstrates various query methods for different types of energy market data. Requires pandas Timestamps with timezone information for start and end parameters.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key=<YOUR API KEY>)

start = pd.Timestamp('20171201', tz='Europe/Brussels')
end = pd.Timestamp('20180101', tz='Europe/Brussels')
country_code = 'BE'  # Belgium
country_code_from = 'FR'  # France
country_code_to = 'DE_LU' # Germany-Luxembourg
type_marketagreement_type = 'A01'
contract_marketagreement_type = "A01"
process_type = 'A51'

# methods that return Pandas Series
client.query_day_ahead_prices(country_code, start=start, end=end)
client.query_aggregated_bids(country_code, start=start, end=end, process_type)
client.query_net_position(country_code, start=start, end=end, dayahead=True)
client.query_crossborder_flows(country_code_from, country_code_to, start=start, end=end)
client.query_scheduled_exchanges(country_code_from, country_code_to, start=start, end=end, dayahead=False)
client.query_net_transfer_capacity_dayahead(country_code_from, country_code_to, start=start, end=end)
client.query_net_transfer_capacity_weekahead(country_code_from, country_code_to, start=start, end=end)
client.query_net_transfer_capacity_monthahead(country_code_from, country_code_to, start=start, end=end)
client.query_net_transfer_capacity_yearahead(country_code_from, country_code_to, start=start, end=end)
client.query_intraday_offered_capacity(country_code_from, country_code_to, start=start, end=end, implicit=True)
client.query_offered_capacity(country_code_from, country_code_to, contract_marketagreement_type, start=start, end=end, implicit=True)
client.query_aggregate_water_reservoirs_and_hydro_storage(country_code, start=start, end=end)

# methods that return Pandas DataFrames
client.query_load(country_code, start=start, end=end)
client.query_load_forecast(country_code, start=start, end=end)
client.query_load_and_forecast(country_code, start=start, end=end)
client.query_generation_forecast(country_code, start=start, end=end)
client.query_wind_and_solar_forecast(country_code, start=start, end=end, psr_type=None)
client.query_intraday_wind_and_solar_forecast(country_code, start=start, end=end, psr_type=None)
client.query_generation(country_code, start=start, end=end, psr_type=None)
client.query_generation_per_plant(country_code, start=start, end=end, psr_type=None, include_eic=False)
client.query_installed_generation_capacity(country_code, start=start, end=end, psr_type=None)
client.query_installed_generation_capacity_per_unit(country_code, start=start, end=end, psr_type=None)
client.query_activated_balancing_energy_prices(country_code, start=start, end=end, process_type='A16', psr_type=None, business_type=None, standard_market_product=None, original_market_product=None)
client.query_activated_balancing_energy(country_code, start=start, end=end, business_type, psr_type=None)
client.query_imbalance_prices(country_code, start=start, end=end, psr_type=None)
client.query_imbalance_volumes(country_code, start=start, end=end, psr_type=None)
client.query_contracted_reserve_prices(country_code, type_marketagreement_type, start=start, end=end, psr_type=None)
client.query_contracted_reserve_amount(country_code, type_marketagreement_type, start=start, end=end, psr_type=None)
client.query_unavailability_of_generation_units(country_code, start=start, end=end, docstatus=None, periodstartupdate=None, periodendupdate=None)
client.query_unavailability_of_production_units(country_code, start, end, docstatus=None, periodstartupdate=None, periodendupdate=None)
client.query_unavailability_transmission(country_code_from, country_code_to, start=start, end=end, docstatus=None, periodstartupdate=None, periodendupdate=None)
client.query_withdrawn_unavailability_of_generation_units(country_code, start, end)
client.query_unavailability_of_offshore_grid(area_code, start, end)
client.query_physical_crossborder_allborders(country_code, start, end, export=True)
client.query_import(country_code, start, end)
client.query_generation_import(country_code, start, end)
client.query_procured_balancing_capacity(country_code, process_type, start=start, end=end, type_marketagreement_type=None)
```

--------------------------------

### EntsoePandasClient Usage

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

This section provides examples of how to use the EntsoePandasClient to query various types of energy data. It covers methods returning Pandas Series and DataFrames, along with necessary setup.

```APIDOC
## EntsoePandasClient Initialization and Basic Usage

### Description
Initialize the EntsoePandasClient with your API key and set up time parameters and country codes for data queries.

### Method
Initialization and method calls

### Endpoint
N/A (Client-side library)

### Parameters
- **api_key** (string) - Required - Your ENTSO-E API key.
- **start** (pandas.Timestamp) - Required - The start of the time period (must include timezone).
- **end** (pandas.Timestamp) - Required - The end of the time period (must include timezone).
- **country_code** (string) - Required - The country code for the query.
- **country_code_from** (string) - Required - The source country code for cross-border data.
- **country_code_to** (string) - Required - The destination country code for cross-border data.
- **process_type** (string) - Optional - The process type for certain queries (e.g., 'A51').
- **dayahead** (boolean) - Optional - Flag to indicate day-ahead data.
- **implicit** (boolean) - Optional - Flag for implicit capacity.
- **psr_type** (string or list) - Optional - Type of physical or non-physical resource.
- **business_type** (string) - Optional - Business type for balancing energy.
- **standard_market_product** (string) - Optional - Standard market product type.
- **original_market_product** (string) - Optional - Original market product type.
- **docstatus** (string) - Optional - Document status for unavailability data.
- **periodstartupdate** (string) - Optional - Period start update for unavailability data.
- **periodendupdate** (string) - Optional - Period end update for unavailability data.
- **include_eic** (boolean) - Optional - Whether to include EIC codes.
- **area_code** (string) - Required - The area code for offshore grid unavailability.
- **export** (boolean) - Optional - Flag to indicate export data.
- **type_marketagreement_type** (string) - Required - Type of market agreement.

### Request Example
```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='YOUR_API_KEY')

start = pd.Timestamp('20171201', tz='Europe/Brussels')
end = pd.Timestamp('20180101', tz='Europe/Brussels')
country_code = 'BE'
country_code_from = 'FR'
country_code_to = 'DE_LU'
process_type = 'A51'
contract_marketagreement_type = 'A01'

# Example query for day-ahead prices
prices = client.query_day_ahead_prices(country_code, start=start, end=end)
```

### Response
Responses vary depending on the method called. Methods typically return Pandas Series or DataFrames containing the requested energy data.

#### Success Response (200)
- **Data** (Pandas Series or DataFrame) - Contains the queried energy data.

#### Response Example
```python
# Example response for query_day_ahead_prices (Pandas Series)
# Output will be a time series of prices
```

## Specific Data Query Methods

### Description
This section lists various methods available in EntsoePandasClient to query specific types of energy data, categorized by whether they return a Pandas Series or DataFrame.

### Methods Returning Pandas Series

- **query_day_ahead_prices**: Retrieves day-ahead electricity prices.
- **query_aggregated_bids**: Retrieves aggregated bids.
- **query_net_position**: Retrieves net position data.
- **query_crossborder_flows**: Retrieves cross-border electricity flow data.
- **query_scheduled_exchanges**: Retrieves scheduled cross-border electricity exchanges.
- **query_net_transfer_capacity_dayahead**: Retrieves day-ahead net transfer capacity.
- **query_net_transfer_capacity_weekahead**: Retrieves week-ahead net transfer capacity.
- **query_net_transfer_capacity_monthahead**: Retrieves month-ahead net transfer capacity.
- **query_net_transfer_capacity_yearahead**: Retrieves year-ahead net transfer capacity.
- **query_intraday_offered_capacity**: Retrieves intraday offered capacity.
- **query_offered_capacity**: Retrieves offered capacity.
- **query_aggregate_water_reservoirs_and_hydro_storage**: Retrieves aggregate water reservoirs and hydro storage data.

### Methods Returning Pandas DataFrames

- **query_load**: Retrieves load data.
- **query_load_forecast**: Retrieves load forecast data.
- **query_load_and_forecast**: Retrieves both load and load forecast data.
- **query_generation_forecast**: Retrieves generation forecast data.
- **query_wind_and_solar_forecast**: Retrieves wind and solar generation forecast data.
- **query_intraday_wind_and_solar_forecast**: Retrieves intraday wind and solar generation forecast data.
- **query_generation**: Retrieves generation data.
- **query_generation_per_plant**: Retrieves generation data per plant.
- **query_installed_generation_capacity**: Retrieves installed generation capacity.
- **query_installed_generation_capacity_per_unit**: Retrieves installed generation capacity per unit.
- **query_activated_balancing_energy_prices**: Retrieves activated balancing energy prices.
- **query_activated_balancing_energy**: Retrieves activated balancing energy data.
- **query_imbalance_prices**: Retrieves imbalance prices.
- **query_imbalance_volumes**: Retrieves imbalance volumes.
- **query_contracted_reserve_prices**: Retrieves contracted reserve prices.
- **query_contracted_reserve_amount**: Retrieves contracted reserve amounts.
- **query_unavailability_of_generation_units**: Retrieves unavailability of generation units.
- **query_unavailability_of_production_units**: Retrieves unavailability of production units.
- **query_unavailability_transmission**: Retrieves transmission unavailability data.
- **query_withdrawn_unavailability_of_generation_units**: Retrieves withdrawn unavailability of generation units.
- **query_unavailability_of_offshore_grid**: Retrieves unavailability of offshore grid data.
- **query_physical_crossborder_allborders**: Retrieves physical cross-border data for all borders.
- **query_import**: Retrieves import data.
- **query_generation_import**: Retrieves generation import data.
- **query_procured_balancing_capacity**: Retrieves procured balancing capacity.

### Request Example (DataFrame)
```python
# Example query for load data
load_data = client.query_load(country_code, start=start, end=end)
```

### Response Example (DataFrame)
```python
# Example response for query_load (Pandas DataFrame)
# Output will be a DataFrame with load information
```

## Dumping Results to File

### Description
Demonstrates how to save the queried data to a CSV file using the `.to_csv()` method available on Pandas Series and DataFrames.

### Method
`to_csv()` method on Pandas objects.

### Endpoint
N/A (Client-side operation)

### Parameters
- **path_or_buf** (string) - Required - The file path or buffer to write to.

### Request Example
```python
ts = client.query_day_ahead_prices(country_code, start=start, end=end)
ts.to_csv('outfile.csv')
```

### Response
No direct API response; the data is saved to a local file.

#### Success Response
- **File Output**: Data successfully written to the specified CSV file.

#### Response Example
```
# A file named 'outfile.csv' will be created in the current directory
# containing the queried day-ahead prices.
```
```

--------------------------------

### Query Installed Generation Capacity

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves installed generation capacity by fuel type for a specified country and time range. You can also query capacity per generation unit.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20230101', tz='Europe/Brussels')
end = pd.Timestamp('20231231', tz='Europe/Brussels')

# Query installed capacity for Belgium
capacity = client.query_installed_generation_capacity('BE', start=start, end=end)

print(capacity.columns.tolist())
# ['Biomass', 'Fossil Gas', 'Hydro Pumped Storage', 'Nuclear',
#  'Solar', 'Wind Offshore', 'Wind Onshore', ...]

# Query per generation unit
capacity_per_unit = client.query_installed_generation_capacity_per_unit(
    'BE', start=start, end=end
)
```

--------------------------------

### Query Physical Cross-Border Flows

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves actual physical electricity flows between two bidding zones. Positive values indicate export from the origin country. Use `query_import` to get all imports to a country.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Paris')
end = pd.Timestamp('20231202', tz='Europe/Paris')

# Query physical flows from France to Germany-Luxembourg
flows_fr_de = client.query_crossborder_flows(
    country_code_from='FR',
    country_code_to='DE_LU',
    start=start,
    end=end
)

# Query flows in opposite direction
flows_de_fr = client.query_crossborder_flows(
    country_code_from='DE_LU',
    country_code_to='FR',
    start=start,
    end=end
)

# Get all imports to a country from all neighbors
all_imports = client.query_import('FR', start=start, end=end)
print(all_imports.columns)  # ['BE', 'CH', 'DE_LU', 'ES', 'GB', 'IT_NORD', 'sum']
```

--------------------------------

### Load Bidding Zones and Plot Chloropleth Map

Source: https://github.com/energieid/entsoe-py/blob/master/entsoe/geo/README.MD

Loads bidding zone geometries for specified zones and a given date, assigns a value, and plots a chloropleth map using Plotly. Ensure geopandas, plotly, and pandas are installed.

```python
from utils import load_zones
import plotly.express as px
import pandas as pd

zones = ['some', 'ISO2', 'codes']

geo_df = load_zones(zones, pd.Timestamp('somedate'))
geo_df['value'] = range(1,len(geo_df)+1)

fig = px.choropleth(geo_df,
                   geojson=geo_df.geometry,
                   locations=geo_df.index,
                   color="value",
                   projection="mercator",
                   color_continuous_scale='rainbow')
fig.update_geos(fitbounds="locations", visible=False)
fig.show()
```

--------------------------------

### Installed Generation Capacity

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves installed generation capacity by fuel type.

```APIDOC
## query_installed_generation_capacity - Installed Capacity

### Description
Retrieves installed generation capacity by fuel type.

### Methods
- `query_installed_generation_capacity`
- `query_installed_generation_capacity_per_unit`

### Parameters
#### Query Parameters (for `query_installed_generation_capacity`)
- **country_code** (string) - Required - The country code for which to retrieve installed capacity.
- **start** (datetime) - Required - The start of the period.
- **end** (datetime) - Required - The end of the period.

#### Query Parameters (for `query_installed_generation_capacity_per_unit`)
- **country_code** (string) - Required - The country code for which to retrieve installed capacity.
- **start** (datetime) - Required - The start of the period.
- **end** (datetime) - Required - The end of the period.

### Request Example (Installed capacity by fuel type)
```python
capacity = client.query_installed_generation_capacity('BE', start=start, end=end)
print(capacity.columns.tolist())
```

### Request Example (Installed capacity per unit)
```python
capacity_per_unit = client.query_installed_generation_capacity_per_unit(
    'BE', start=start, end=end
)
```

### Response
#### Success Response (200)
- **capacity** (DataFrame) - A pandas DataFrame containing installed generation capacity. Columns represent fuel types (e.g., 'Biomass', 'Fossil Gas', 'Nuclear', 'Solar', 'Wind Offshore', 'Wind Onshore').
- **capacity_per_unit** (DataFrame) - A pandas DataFrame containing installed generation capacity per unit.
```

--------------------------------

### Get Neighboring Bidding Zones

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieve a list of neighboring bidding zones for a given area code. Uses a predefined mapping for quick lookups.

```python
# Get neighboring bidding zones
print(NEIGHBOURS['BE'])  # ['NL', 'DE_AT_LU', 'FR', 'GB', 'DE_LU']
```

--------------------------------

### Initialize EntsoeFileClient

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Instantiate the EntsoeFileClient with your ENTSO-E username and password to access the file library.

```python
from entsoe.files import EntsoeFileClient

client = EntsoeFileClient(username=<YOUR ENTSOE USERNAME>, pwd=<YOUR ENTSOE PASSWORD>)
```

--------------------------------

### Initialize and Use EntsoeRawClient

Source: https://context7.com/energieid/entsoe-py/llms.txt

Initialize the raw client with an API key and query for day-ahead prices or unavailability data. Raw responses are returned as XML strings or ZIP bytes.

```python
from entsoe import EntsoeRawClient
import pandas as pd

# Initialize client with API key
client = EntsoeRawClient(api_key='your-api-key')

# Define time range with timezone-aware timestamps
start = pd.Timestamp('20231201', tz='Europe/Brussels')
end = pd.Timestamp('20231202', tz='Europe/Brussels')

# Query day-ahead prices - returns XML string
xml_response = client.query_day_ahead_prices('BE', start, end)

# Save XML response to file
with open('prices.xml', 'w') as f:
    f.write(xml_response)

# Query unavailability data - returns ZIP bytes
zip_bytes = client.query_unavailability_of_generation_units('DE_LU', start, end)

# Save ZIP response to file
with open('unavailability.zip', 'wb') as f:
    f.write(zip_bytes)
```

--------------------------------

### Initialize EntsoeRawClient and Query Data

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Instantiate the EntsoeRawClient with an API key and demonstrate various methods for querying different types of energy market data, such as day-ahead prices, bids, load, and cross-border flows. Some methods return XML strings, while others return ZIP archives.

```python
from entsoe import EntsoeRawClient
import pandas as pd

client = EntsoeRawClient(api_key=<YOUR API KEY>)

start = pd.Timestamp('20171201', tz='Europe/Brussels')
end = pd.Timestamp('20180101', tz='Europe/Brussels')
country_code = 'BE'  # Belgium
country_code_from = 'FR'  # France
country_code_to = 'DE_LU' # Germany-Luxembourg
type_marketagreement_type = 'A01'
contract_marketagreement_type = 'A01'
process_type = 'A51'

# methods that return XML
client.query_day_ahead_prices(country_code, start, end)
client.query_aggregated_bids(country_code, start, end, process_type)
client.query_net_position(country_code, start, end, dayahead=True)
client.query_load(country_code, start, end)
client.query_load_forecast(country_code, start, end)
client.query_wind_and_solar_forecast(country_code, start, end, psr_type=None)
client.query_intraday_wind_and_solar_forecast(country_code, start, end, psr_type=None)
client.query_generation_forecast(country_code, start, end)
client.query_generation(country_code, start, end, psr_type=None)
client.query_generation_per_plant(country_code, start, end, psr_type=None)
client.query_installed_generation_capacity(country_code, start, end, psr_type=None)
client.query_installed_generation_capacity_per_unit(country_code, start, end, psr_type=None)
client.query_crossborder_flows(country_code_from, country_code_to, start, end)
client.query_scheduled_exchanges(country_code_from, country_code_to, start, end, dayahead=False)
client.query_net_transfer_capacity_dayahead(country_code_from, country_code_to, start, end)
client.query_net_transfer_capacity_weekahead(country_code_from, country_code_to, start, end)
client.query_net_transfer_capacity_monthahead(country_code_from, country_code_to, start, end)
client.query_net_transfer_capacity_yearahead(country_code_from, country_code_to, start, end)
client.query_intraday_offered_capacity(country_code_from, country_code_to, start, end, implicit=True)
client.query_offered_capacity(country_code_from, country_code_to, start, end, contract_marketagreement_type, implicit=True)
client.query_contracted_reserve_prices(country_code, start, end, type_marketagreement_type, psr_type=None)
client.query_contracted_reserve_prices_procured_capacity(country_code, start, end, process_type type_marketagreement_type, psr_type=None)
client.query_contracted_reserve_amount(country_code, start, end, type_marketagreement_type, psr_type=None)
client.query_procured_balancing_capacity(country_code, start, end, process_type, type_marketagreement_type=None)
client.query_aggregate_water_reservoirs_and_hydro_storage(country_code, start, end)
client.query_activated_balancing_energy_prices(country_code, start, end, process_type='A16', psr_type=None, business_type=None, standard_market_product=None, original_market_product=None)
client.query_activated_balancing_energy(country_code, start, end, business_type, psr_type=None)

# methods that return ZIP (bytes)
client.query_imbalance_prices(country_code, start, end, psr_type=None)
client.query_imbalance_volumes(country_code, start=start, end=end, psr_type=None)
client.query_unavailability_of_generation_units(country_code, start, end, docstatus=None, periodstartupdate=None, periodendupdate=None)
client.query_unavailability_of_production_units(country_code, start, end, docstatus=None, periodstartupdate=None, periodendupdate=None)
client.query_unavailability_transmission(country_code_from, country_code_to, start, end, docstatus=None, periodstartupdate=None, periodendupdate=None)
client.query_unavailability_of_offshore_grid(area_code, start, end)
client.query_withdrawn_unavailability_of_generation_units(country_code, start, end)
```

--------------------------------

### Dump Raw XML or ZIP Data to File

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Demonstrates how to save the raw XML string or ZIP bytes returned by specific client methods to local files. Ensure the file is opened in the correct mode ('w' for text, 'wb' for binary).

```python
xml_string = client.query_day_ahead_prices(country_code, start, end)
with open('outfile.xml', 'w') as f:
    f.write(xml_string)
```

```python
zip_bytes = client.query_unavailability_of_generation_units(country_code, start, end)
with open('outfile.zip', 'wb') as f:
    f.write(zip_bytes)
```

--------------------------------

### Configure ENTSO-E Client with Environment Variables

Source: https://context7.com/energieid/entsoe-py/llms.txt

Configure the ENTSO-E client by setting environment variables for API key, username, password, and custom endpoint URL. The client automatically picks up these variables on initialization.

```python
import os

# Set API key via environment variable
os.environ['ENTSOE_API_KEY'] = 'your-api-key'

# Set custom endpoint URL (optional)
os.environ['ENTSOE_ENDPOINT_URL'] = 'https://custom-endpoint.example.com/api'

# For file client
os.environ['ENTSOE_USERNAME'] = 'your-username'
os.environ['ENTSOE_PWD'] = 'your-password'

# Client will automatically use environment variables
from entsoe import EntsoePandasClient
client = EntsoePandasClient()  # No api_key parameter needed
```

--------------------------------

### Query Actual Total Load with EntsoePandasClient

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieve actual total electricity load (consumption) data for a specified bidding zone. Returns a DataFrame with load values.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Paris')
end = pd.Timestamp('20231202', tz='Europe/Paris')

# Query actual load for France
load_df = client.query_load('FR', start=start, end=end)

print(load_df.head())
#                            Actual Load
# 2023-12-01 00:00:00+01:00      58234.0
# 2023-12-01 00:15:00+01:00      57891.0
# ...
```

--------------------------------

### Query Day-Ahead Load Forecast with EntsoePandasClient

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieve forecasted electricity load for a specified bidding zone. This snippet also shows how to query actual load and combine both using a utility function.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231210', tz='Europe/Berlin')
end = pd.Timestamp('20231211', tz='Europe/Berlin')

# Query day-ahead load forecast for Germany-Luxembourg
forecast = client.query_load_forecast('DE_LU', start=start, end=end)

# Compare with actual load
actual = client.query_load('DE_LU', start=start, end=end)

# Combine using utility function
combined = client.query_load_and_forecast('DE_LU', start=start, end=end)
print(combined.columns)  # ['Forecasted Load', 'Actual Load']
```

--------------------------------

### List Files in Folder

Source: https://context7.com/energieid/entsoe-py/llms.txt

List available files within a specified folder in the ENTSO-E File Library. Returns a dictionary mapping filenames to unique IDs.

```python
# List available files in a folder
file_list = client.list_folder('AcceptedAggregatedOffers_17.1.D')
print(f"Found {len(file_list)} files")
# Returns: {'filename1.csv': 'unique-id-1', 'filename2.csv': 'unique-id-2', ...}
```

--------------------------------

### Download Single File from ENTSO-E

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Download a single file from the ENTSO-E file library by specifying the folder and filename. Requires the file_list to be previously obtained.

```python
# either download one file by name:
df = client.download_single_file(folder='AcceptedAggregatedOffers_17.1.D', filename=list(file_list.keys())[0])
```

--------------------------------

### Initialize and Use EntsoePandasClient

Source: https://context7.com/energieid/entsoe-py/llms.txt

Initialize the Pandas client with an API key. This client automatically parses API responses into Pandas Series and DataFrames, handling time zones and multi-year queries.

```python
from entsoe import EntsoePandasClient
import pandas as pd

# Initialize client
client = EntsoePandasClient(api_key='your-api-key')

# Define time range (timezone-aware timestamps required)
start = pd.Timestamp('20231201', tz='Europe/Brussels')
end = pd.Timestamp('20231208', tz='Europe/Brussels')
country_code = 'BE'  # Belgium

# Query day-ahead prices - returns pd.Series
prices = client.query_day_ahead_prices(country_code, start=start, end=end)
print(prices.head())
# 2023-12-01 00:00:00+01:00    85.32
# 2023-12-01 01:00:00+01:00    82.15
# ...

# Export to CSV
prices.to_csv('day_ahead_prices.csv')
```

--------------------------------

### Query Day-Ahead Market Prices with EntsoePandasClient

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieve day-ahead electricity prices for a specified bidding zone. The result is a timezone-aware Pandas Series. Prices are hourly before October 2025 and 15-minute intervals thereafter.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231215', tz='Europe/Amsterdam')
end = pd.Timestamp('20231216', tz='Europe/Amsterdam')

# Query for Netherlands
prices = client.query_day_ahead_prices('NL', start=start, end=end)

# Query for Germany-Luxembourg bidding zone
prices_de = client.query_day_ahead_prices('DE_LU', start=start, end=end)

# Result is a timezone-aware Pandas Series
print(f"Average price: {prices.mean():.2f} EUR/MWh")
print(f"Max price: {prices.max():.2f} EUR/MWh")
print(f"Min price: {prices.min():.2f} EUR/MWh")
```

--------------------------------

### Download Multiple Files from ENTSO-E

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Download multiple files from the ENTSO-E file library by providing a list of their unique IDs. This is an efficient way to retrieve several files at once.

```python
# or download multiple by unique_id:
df = client.download_multiple_files(['a1a82b3f-c453-4181-8d20-ad39c948d4b0', '64e47e15-bac6-4212-b2dd-9667bdf33b5d'])
```

--------------------------------

### Save Query Result to CSV

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Demonstrates how to save the results of a data query (e.g., day-ahead prices) to a CSV file using the pandas `to_csv` method. This is useful for local storage and further analysis.

```python
ts = client.query_day_ahead_prices(country_code, start=start, end=end)
ts.to_csv('outfile.csv')
```

--------------------------------

### query_load - Actual Total Load

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves actual total electricity load (consumption) data for a specified bidding zone. Returns a DataFrame with load values.

```APIDOC
## POST /api/query_load

### Description
Retrieves actual total electricity load (consumption) data for a specified bidding zone. Returns a DataFrame with load values.

### Method
POST (or GET, depending on client implementation)

### Endpoint
N/A (Client-side abstraction of ENTSO-E REST API)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **country_code** (string) - Required - The bidding zone code (e.g., 'FR').
- **start** (pd.Timestamp) - Required - The start of the time range (timezone-aware).
- **end** (pd.Timestamp) - Required - The end of the time range (timezone-aware).

### Request Example
```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Paris')
end = pd.Timestamp('20231202', tz='Europe/Paris')

# Query actual load for France
load_df = client.query_load('FR', start=start, end=end)

print(load_df.head())
#                            Actual Load
# 2023-12-01 00:00:00+01:00      58234.0
# 2023-12-01 00:15:00+01:00      57891.0
# ...
```

### Response
#### Success Response (200)
- **Pandas DataFrame** - DataFrame with 'Actual Load' column, indexed by timestamp.
```

--------------------------------

### Download Raw ZIP Bytes

Source: https://context7.com/energieid/entsoe-py/llms.txt

Download the raw ZIP bytes of a single file for custom processing. Requires folder and filename.

```python
# Download raw ZIP bytes for custom processing
raw_bytes = client.download_single_file_raw(
    folder='AcceptedAggregatedOffers_17.1.D',
    filename=list(file_list.keys())[0]
)
```

--------------------------------

### Access PSR Type Mappings

Source: https://context7.com/energieid/entsoe-py/llms.txt

Access mappings for Production System Resource (PSR) types, such as Solar, Wind Onshore, and Nuclear. Uses predefined keys for lookup.

```python
# PSR Type mappings for generation types
print(PSRTYPE_MAPPINGS['B16'])  # Solar
print(PSRTYPE_MAPPINGS['B19'])  # Wind Onshore
print(PSRTYPE_MAPPINGS['B14'])  # Nuclear
```

--------------------------------

### Query Generation by Fuel Type

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves actual electricity generation aggregated by production type. Use `psr_type` to filter for specific fuel sources and `nett=True` for net generation.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Brussels')
end = pd.Timestamp('20231202', tz='Europe/Brussels')

# Query all generation types for Belgium
generation = client.query_generation('BE', start=start, end=end)

print(generation.columns.tolist())
# ['Biomass', 'Fossil Gas', 'Nuclear', 'Solar', 'Wind Onshore', ...]

# Filter for specific PSR type (e.g., B16 for Solar)
solar_gen = client.query_generation('BE', start=start, end=end, psr_type='B16')

# Get net generation (generation minus consumption for storage)
net_gen = client.query_generation('BE', start=start, end=end, nett=True)
```

--------------------------------

### Download Single File

Source: https://context7.com/energieid/entsoe-py/llms.txt

Download a single file by its folder and filename from the ENTSO-E File Library. Returns a pandas DataFrame.

```python
# Download a single file by name - returns DataFrame
df = client.download_single_file(
    folder='AcceptedAggregatedOffers_17.1.D',
    filename=list(file_list.keys())[0]
)
```

--------------------------------

### Download Multiple Files

Source: https://context7.com/energieid/entsoe-py/llms.txt

Download multiple files by their unique IDs from the ENTSO-E File Library. Returns a concatenated pandas DataFrame.

```python
# Download multiple files by unique IDs - returns concatenated DataFrame
file_ids = list(file_list.values())[:5]  # First 5 files
df_combined = client.download_multiple_files(file_ids)
```

--------------------------------

### Custom API Request with Parameters

Source: https://context7.com/energieid/entsoe-py/llms.txt

Make custom API requests using the `_base_request` method for unsupported endpoints. Requires specifying parameters like document type and domain.

```python
from entsoe import EntsoeRawClient
import pandas as pd

client = EntsoeRawClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Brussels')
end = pd.Timestamp('20231202', tz='Europe/Brussels')

# Custom request with document type and domain parameters
params = {
    'documentType': 'A44',  # Price document
    'in_Domain': '10YBE----------2',  # Belgium EIC code
    'out_Domain': '10YBE----------2'
}
response = client._base_request(params=params, start=start, end=end)
print(response.text)
```

--------------------------------

### Imbalance Volumes

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves system imbalance volumes.

```APIDOC
## query_imbalance_volumes - Imbalance Volumes

### Description
Retrieves system imbalance volumes.

### Method
`query_imbalance_volumes`

### Endpoint
`/query_imbalance_volumes`

### Parameters
#### Query Parameters
- **country_code** (string) - Required - The country code for which to retrieve imbalance volumes (e.g., 'DE_LU').
- **start** (datetime) - Required - The start of the period.
- **end** (datetime) - Required - The end of the period.
- **include_resolution** (boolean) - Optional - Whether to include resolution information.

### Request Example
```python
imbalance_vol = client.query_imbalance_volumes('DE_LU', start=start, end=end)
imbalance_vol_res = client.query_imbalance_volumes(
    'DE_LU', start=start, end=end, include_resolution=True
)
```

### Response
#### Success Response (200)
- **imbalance_volumes** (DataFrame) - A pandas DataFrame containing system imbalance volumes. Can include resolution information if requested.
```

--------------------------------

### Query Generation by Power Plant

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves actual generation data for individual power plants. Use `include_eic=True` to include EIC codes and `psr_type` to filter by generation type.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231215', tz='Europe/Brussels')
end = pd.Timestamp('20231216', tz='Europe/Brussels')

# Query per-plant generation for Belgium
plant_gen = client.query_generation_per_plant('BE', start=start, end=end)

# Include EIC codes in output
plant_gen_eic = client.query_generation_per_plant(
    'BE', start=start, end=end, include_eic=True
)

# Filter by PSR type (e.g., B14 for Nuclear)
nuclear_plants = client.query_generation_per_plant(
    'BE', start=start, end=end, psr_type='B14'
)
```

--------------------------------

### List Files in ENTSO-E Folder

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Use the list_folder method to retrieve a dictionary of filenames and their corresponding unique IDs from a specified folder in the ENTSO-E file library.

```python
# this returns a dict of {filename: unique_id}:
file_list = client.list_folder('AcceptedAggregatedOffers_17.1.D')
```

--------------------------------

### Make Custom API Requests

Source: https://github.com/energieid/entsoe-py/blob/master/README.md

Allows making custom requests to the ENTSO-E API by defining parameters manually. This is useful for accessing data not covered by the client's predefined methods. The response text can be accessed directly.

```python
params = {
    'documentType': 'A44',
    'in_Domain': '10YBE----------2',
    'out_Domain': '10YBE----------2'
}
response = client._base_request(params=params, start=start, end=end)
print(response.text)
```

--------------------------------

### query_load_forecast - Day-Ahead Load Forecast

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves forecasted electricity load for a specified bidding zone. This method can be used in conjunction with `query_load` to compare forecasts with actual consumption.

```APIDOC
## POST /api/query_load_forecast

### Description
Retrieves forecasted electricity load for a specified bidding zone. Returns a DataFrame with forecasted load values.

### Method
POST (or GET, depending on client implementation)

### Endpoint
N/A (Client-side abstraction of ENTSO-E REST API)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **country_code** (string) - Required - The bidding zone code (e.g., 'DE_LU').
- **start** (pd.Timestamp) - Required - The start of the time range (timezone-aware).
- **end** (pd.Timestamp) - Required - The end of the time range (timezone-aware).

### Request Example
```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231210', tz='Europe/Berlin')
end = pd.Timestamp('20231211', tz='Europe/Berlin')

# Query day-ahead load forecast for Germany-Luxembourg
forecast = client.query_load_forecast('DE_LU', start=start, end=end)

# Compare with actual load
actual = client.query_load('DE_LU', start=start, end=end)

# Combine using utility function
combined = client.query_load_and_forecast('DE_LU', start=start, end=end)
print(combined.columns)  # ['Forecasted Load', 'Actual Load']
```

### Response
#### Success Response (200)
- **Pandas DataFrame** - DataFrame with 'Forecasted Load' column, indexed by timestamp.
```

--------------------------------

### Query Imbalance Volumes

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves system imbalance volumes for a specified country or region and time range. An option to include resolution information is available.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Berlin')
end = pd.Timestamp('20231202', tz='Europe/Berlin')

# Query imbalance volumes for Germany-Luxembourg
imbalance_vol = client.query_imbalance_volumes('DE_LU', start=start, end=end)

# Include resolution information
imbalance_vol_res = client.query_imbalance_volumes(
    'DE_LU', start=start, end=end, include_resolution=True
)
```

--------------------------------

### Query Hydro Reservoir Levels

Source: https://context7.com/energieid/entsoe-py/llms.txt

Query aggregate water reservoirs and hydro storage levels for specific countries. Supports countries with significant hydro power generation.

```python
hydro_storage = client.query_aggregate_water_reservoirs_and_hydro_storage(
    'NO_1', start=start, end=end
)
```

```python
ch_hydro = client.query_aggregate_water_reservoirs_and_hydro_storage(
    'CH', start=start, end=end
)
```

--------------------------------

### Query Imbalance Settlement Prices

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves imbalance prices used for settlement in the balancing market for a specified country and time range. The output typically includes columns for positive and negative imbalance prices.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231201', tz='Europe/Brussels')
end = pd.Timestamp('20231202', tz='Europe/Brussels')

# Query imbalance prices for Belgium
imbalance_prices = client.query_imbalance_prices('BE', start=start, end=end)

print(imbalance_prices.columns.tolist())
```

--------------------------------

### Net Transfer Capacity (Week-ahead, Month-ahead, Year-ahead)

Source: https://context7.com/energieid/entsoe-py/llms.txt

Queries for Net Transfer Capacity (NTC) for different time horizons.

```APIDOC
## Net Transfer Capacity (NTC)

### Description
Retrieves Net Transfer Capacity data for different time horizons (week-ahead, month-ahead, year-ahead) between two countries.

### Methods
- `query_net_transfer_capacity_weekahead`
- `query_net_transfer_capacity_monthahead`
- `query_net_transfer_capacity_yearahead`

### Parameters
- **country_code_from** (string) - Required - The code of the exporting country.
- **country_code_to** (string) - Required - The code of the importing country.
- **start** (datetime) - Required - The start of the period.
- **end** (datetime) - Required - The end of the period.

### Request Example (Week-ahead)
```python
ntc_wa = client.query_net_transfer_capacity_weekahead(
    country_code_from='BE',
    country_code_to='NL',
    start=start,
    end=end
)
```

### Request Example (Month-ahead)
```python
ntc_ma = client.query_net_transfer_capacity_monthahead('BE', 'NL', start, end)
```

### Request Example (Year-ahead)
```python
ntc_ya = client.query_net_transfer_capacity_yearahead('BE', 'NL', start, end)
```
```

--------------------------------

### Query Day-Ahead Scheduled Exchanges

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves commercial scheduled exchanges between bidding zones. Set `dayahead=True` for day-ahead schedules and `dayahead=False` for total scheduled exchanges including intraday adjustments.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231210', tz='Europe/Amsterdam')
end = pd.Timestamp('20231211', tz='Europe/Amsterdam')

# Day-ahead scheduled exchanges from Netherlands to Belgium
scheduled = client.query_scheduled_exchanges(
    country_code_from='NL',
    country_code_to='BE',
    start=start,
    end=end,
    dayahead=True
)

# Total scheduled exchanges (includes intraday adjustments)
total_scheduled = client.query_scheduled_exchanges(
    country_code_from='NL',
    country_code_to='BE',
    start=start,
    end=end,
    dayahead=False
)
```

--------------------------------

### query_day_ahead_prices - Day-Ahead Market Prices

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves Single Day-Ahead Coupling (SDAC) electricity prices for a specified bidding zone. The data is returned as a timezone-aware Pandas Series.

```APIDOC
## POST /api/query_day_ahead_prices

### Description
Retrieves SDAC (Single Day-Ahead Coupling) electricity prices for a specified bidding zone. Returns hourly prices before October 2025, and 15-minute prices after. The result is a timezone-aware Pandas Series.

### Method
POST (or GET, depending on client implementation)

### Endpoint
N/A (Client-side abstraction of ENTSO-E REST API)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **country_code** (string) - Required - The bidding zone code (e.g., 'BE', 'DE_LU').
- **start** (pd.Timestamp) - Required - The start of the time range (timezone-aware).
- **end** (pd.Timestamp) - Required - The end of the time range (timezone-aware).
- **process_type** (string) - Optional - The type of process (e.g., 'A65').
- **market_type** (string) - Optional - The type of market (e.g., 'A65').

### Request Example
```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20231215', tz='Europe/Amsterdam')
end = pd.Timestamp('20231216', tz='Europe/Amsterdam')

# Query for Netherlands
prices = client.query_day_ahead_prices('NL', start=start, end=end)

# Query for Germany-Luxembourg bidding zone
prices_de = client.query_day_ahead_prices('DE_LU', start=start, end=end)

# Result is a timezone-aware Pandas Series
print(f"Average price: {prices.mean():.2f} EUR/MWh")
print(f"Max price: {prices.max():.2f} EUR/MWh")
print(f"Min price: {prices.min():.2f} EUR/MWh")
```

### Response
#### Success Response (200)
- **Pandas Series** - Timezone-aware Series with prices (EUR/MWh) indexed by timestamp.
```

--------------------------------

### Query Hydro Storage Data

Source: https://context7.com/energieid/entsoe-py/llms.txt

Retrieves aggregate filling rate of water reservoirs and hydro storage for a specified country and time range.

```python
from entsoe import EntsoePandasClient
import pandas as pd

client = EntsoePandasClient(api_key='your-api-key')

start = pd.Timestamp('20230101', tz='Europe/Oslo')
end = pd.Timestamp('20231231', tz='Europe/Oslo')


```