semvec/pypi-packages huggingface dataset:
energyquantified 0.15.1
Energy Quantified Time series API client.
Requires Python: >=3.10

# Energy Quantified Python Client

![Apache License version 2.0](https://img.shields.io/github/license/energyquantified/eq-python-client?style=flat "Apache License version 2.0")
![Python 3.10+](https://img.shields.io/pypi/pyversions/energyquantified.svg?style=flat "Python versions")
![Wheel](https://img.shields.io/pypi/wheel/energyquantified "Wheel")

[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='<insert api key here>')

# 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).
apache-airflow-task-sdk 1.1.7
Python Task SDK for Apache Airflow DAG Authors
Requires Python: <3.14,>=3.10
Keywords: airflow, automation, dag, data, orchestration, pipelines, workflow

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# Apache Airflow Task SDK

[![PyPI version](https://badge.fury.io/py/apache-airflow-task-sdk.svg)](https://badge.fury.io/py/apache-airflow-task-sdk)
[![License](https://img.shields.io/:license-Apache%202-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0.txt)
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The Apache Airflow Task SDK includes interfaces for Dag authors and Task execution logic for Python.

## Installation

To install the Task SDK, use pip:

```bash
pip install apache-airflow-task-sdk
```
icu4py 1.0.1
Python bindings to the ICU (International Components for Unicode) library (ICU4C).
Requires Python: >=3.10
Keywords: i18n, icu, icu4c, internationalization, l10n, localization, unicode

======
icu4py
======

.. image:: https://img.shields.io/readthedocs/icu4py?style=for-the-badge
   :target: https://icu4py.readthedocs.io/en/latest/

.. image:: https://img.shields.io/github/actions/workflow/status/adamchainz/icu4py/main.yml.svg?branch=main&style=for-the-badge
   :target: https://github.com/adamchainz/icu4py/actions?workflow=CI

.. image:: https://img.shields.io/badge/Coverage-100%25-success?style=for-the-badge
   :target: https://github.com/adamchainz/icu4py/actions?workflow=CI

.. image:: https://img.shields.io/pypi/v/icu4py.svg?style=for-the-badge
   :target: https://pypi.org/project/icu4py/

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge
   :target: https://github.com/psf/black

.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=for-the-badge
   :target: https://github.com/pre-commit/pre-commit
   :alt: pre-commit

----

Python bindings to the ICU (International Components for Unicode) library (ICU4C).

Documentation
=============

Please see https://icu4py.readthedocs.io/.
apache-airflow-core 3.1.7
Core packages for Apache Airflow, schedule and API server
Requires Python: <3.14,>=3.10
Keywords: airflow, automation, dag, data, orchestration, pipelines, workflow

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This is a core airflow package that contains the functionality of Apache Airflow components:
scheduler, API server, dag file processor and triggerer.