https://github.com/energyquantified/eq-python-client
Python library for Montel EQ's Time Series API.
https://github.com/energyquantified/eq-python-client
api-client data-analysis energy-data energy-market integration pandas power-market python time-series
Last synced: 2 months ago
JSON representation
Python library for Montel EQ's Time Series API.
- Host: GitHub
- URL: https://github.com/energyquantified/eq-python-client
- Owner: energyquantified
- License: apache-2.0
- Created: 2020-07-21T17:43:49.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2026-02-04T11:40:37.000Z (4 months ago)
- Last Synced: 2026-02-21T13:49:02.801Z (4 months ago)
- Topics: api-client, data-analysis, energy-data, energy-market, integration, pandas, power-market, python, time-series
- Language: Python
- Homepage: https://energyquantified-python.readthedocs.io/
- Size: 619 KB
- Stars: 30
- Watchers: 5
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE
Awesome Lists containing this project
README
# Energy Quantified Python Client
[Documentation](https://energyquantified-python.readthedocs.io) |
[Python package](https://pypi.org/project/energyquantified/) |
[GitHub repository](https://github.com/energyquantified/eq-python-client)
The Python library for [Energy Quantified](https://www.energyquantified.com)'s
Time Series API. It allows you to access thousands of data series directly from
Energy Quantified's time series database. It integrates with the popular
[pandas](https://pandas.pydata.org/docs/) library and the [polars](https://docs.pola.rs/)
libarary for high-performance data analysis and manipulation.
Developed for **Python 3.10+**.
```python
from datetime import date, timedelta
from energyquantified import EnergyQuantified
# Initialize client
eq = EnergyQuantified(api_key='')
# Freetext search (filtering on attributes is also supported)
curves = eq.metadata.curves(q='de wind production actual')
# Load time series data
curve = curves[0]
timeseries = eq.timeseries.load(
curve,
begin=date.today() - timedelta(days=10),
end=date.today()
)
# Convert to Pandas data frame
pd_df = timeseries.to_pandas_dataframe()
# Convert to Polars data frame
pl_df = timeseries.to_polars_dataframe()
```
Full [documentation](https://energyquantified-python.readthedocs.io) available
at Read the Docs.
## Features
- Simple authentication
- Metadata caching
- Rate-limiting and automatic retries on network errors
- Full-text search and keyword search for curves and powerplants
- Forecasts- and time series data
- Period-based data
- OHLC data with SRMC calculations
- Shows your subscription for each data series
- Support for timezones, resolutions, aggregations and unit conversions
- Easy-to-use filters for issue dates and forecast types
- Push feed for live updates on data modifications
- Integrates with pandas and polars
**Note:** A user account with an API key is required to use this library.
Create an account on [Energy Quantified](https://www.energyquantified.com)'s
home page. Trial users get access to 30 days of history.
## Installation
Install with **pip**:
```bash
# Install
pip install energyquantified
# Upgrade
pip install --upgrade energyquantified
```
## Documentation
Find the [documentation](https://energyquantified-python.readthedocs.io) at
Read the Docs.
## License
The Energy Quantified Python client is licensed under the
[Apache License version 2.0](https://opensource.org/licenses/Apache-2.0).