semvec/pypi-packages huggingface dataset:
yafin 0.1.1
Unofficial Yahoo Finance API client
Requires Python: >=3.11
Keywords: yahoo finance, finance, api client

# Yafin

Unofficial [Yahoo!Ⓡ finance](https://finance.yahoo.com) Python API client.

- Not affiliated with Yahoo, Inc.
- Open source library that uses publicly available APIs.
- Intended for research, educational purposes and personal use only.
- Synchronous and asynchronous.
- Not returning pandas dataframes (because why?).
- Uses caching and utilizes singleton pattern in symbol class to save resources.
- Minimal and build on [curl-cffi](https://github.com/lexiforest/curl_cffi)
- Approx. 2x faster, than other Yahoo finance clients. Run the tests yourself - `make test-perf` (All tests running synchronously, returning pandas DataFrame and http responses are mocked.)

![test-perf](docs/test-perf.png)

**Installation**: `pip install yafin` for more details, see the [Documentation](https://lukinkratas.github.io/yafin/)

**Documentation and Examples**: [https://lukinkratas.github.io/yafin/](https://lukinkratas.github.io/yafin/)

## Quick Examples

### Symbol

```python
from yafin import Symbol

with Symbol('META') as meta:
    meta_1y_chart = meta.get_chart(interval='1d', period_range='1y')
```

### AsyncSymbol


```python
import asyncio
from yafin import AsyncSymbol

async def main() -> None:

    async with AsyncSymbol('META') as meta:
        meta_1y_chart = await meta.get_chart(interval='1d', period_range='1y')

if __name__ == '__main__':
    asyncio.run(main())
```

### Symbols

```python
from yafin import Symbols

with Symbols('META,AAPL') as meta_aapl:
    meta_aapl_1y_chart = meta_aapl.get_chart(interval='1d', period_range='1y')
```

For more details, see the [Examples](https://lukinkratas.github.io/yafin/examples/symbol/) section in documentation.
pymixology 0.1.0
A cocktail package
Requires Python: >=3.7

# data533_group6_step3

A cocktail inventory and recommendation package.

## Installation

You can install `pymixology` from PyPI:

```bash
pip install pymixology
```

View the project on PyPI: [https://pypi.org/project/pymixology/](https://pypi.org/project/pymixology/)
ml-dataviz 0.1.0
Data Visualization and Insight Library
Requires Python: >=3.10

# DataViz

A lightweight Python library for interactive data exploration and automatic insights.

## Features
- 🔍 Data quality analysis
- 💡 Automatic insight generation
- 🔧 Code Generation (`generate_code()`)
- ⚡ Code Optimizer (`optimize()`)
- 📊 Chart Builder (`suggest_charts()`)
- 💼 Generate analysis (`export_analysis_code()`)
- 📈 Smart visualization


## Installation

```bash
pip install -e .
```

## Quick Start

```python
import pandas as pd
from dataviz import enhanced

# Load your data
df = pd.read_csv('your_data.csv')

# Create DataViz object
dv = analyze(df)

# View first N rows with interactive table
dv.show(n=20)  # default is 20

# Get automatic insights
dv.insights()  # Basic insights (5 by default)
dv.insights(max_insights=10)  # More insights
dv.insights(detailed=True, max_insights=15)  # Comprehensive analysis

# Get optimization suggestions
dv.optimize()

# Control number of charts and visualization recommendations
dv.visualize(max_charts=2)      # Show only 2 charts
dv.visualize(max_charts=6)      # Show 6 charts
dv.visualize(max_charts=10)     # Show 10 charts
dv.visualize(max_charts='all')  # Show all available charts (default)

# Individual plots
dv.plot_distribution('age', kind='hist')  # or 'kde', 'box'
dv.plot_categorical('category', top_n=15)
dv.plot_scatter('x_col', 'y_col', hue='category')
dv.plot_correlation(method='pearson')  # or 'spearman', 'kendall'
dv.plot_pairplot(columns=['col1', 'col2', 'col3'], hue='category')

# Perform operations (these get tracked)
dv.filter(df['col'] > value, "df['col'] > value")
dv.sort_values('col', ascending=False)
dv.drop_missing(['col'])

# Generate code for your operations
dv.generate_code(df_name='my_df', include_load=True)

# Or export complete analysis code
dv.export_analysis_code('my_analysis.py')

# Suggest charts with code snippets
dv.suggest_charts(max_charts=6)  # Get code for 6 charts
dv.suggest_charts(max_charts='all')  # Get all suggestions

# Reset to original DataFrame
dv.reset()

```
omotes-simulator-core 0.0.28
Core library of the OMOTES simulator

# OMOTES-simulator-core

This repository is part of the 'Nieuwe Warmte Nu Design Toolkit' project. Omotes-simulator-core is the core library for the thermo-hydraulic numerical simulations within the NWN Design toolkit. The simulator requires a network schematization in ESDL format, simulation start/end time and a timestep-duration as input. The simulator will return a pandas dataframe that contains the all the output time series.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install omotes-simulator-core.

```bash
pip install omotes-simulator-core
```

## Usage

```python
from omotes_simulator_core.entities.esdl_object import EsdlObject
from omotes_simulator_core.entities.simulation_configuration import SimulationConfiguration
from omotes_simulator_core.infrastructure.simulation_manager import SimulationManager
from omotes_simulator_core.infrastructure.utils import pyesdl_from_file

# Create a callable that prints the progress messages
def progressLogger(progress: float, message: str) -> None:
    """Function to report progress to logging/stdout."""
    logger.info(f"({progress*100:.2f}%) {message}")

#  Create simulation config parameters
config = SimulationConfiguration(
        simulation_id=uuid.uuid1(),  #creates a new uuid for this simulation
        name="test run",             # user defined name
        timestep=3600,               # timestep, start and stop datetime (simulation period)
        start=datetime.strptime("2019-01-01T00:00:00", "%Y-%m-%dT%H:%M:%S"),
        stop=datetime.strptime("2019-01-01T01:00:00", "%Y-%m-%dT%H:%M:%S"),
    )
esdl_file_path = "path/to/whatever/file.esdl"

# Run the simulation
sim = SimulationManager(EsdlObject(pyesdl_from_file(esdl_file_path)), config)
result = sim.execute(progressLogger)
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

For more information, please see [CONTRIBUTING.md]

## License

[GPLv3](https://choosealicense.com/licenses/gpl-3.0/), see [LICENSE]
plex-letterboxd 0.5
Export Plex watch history to Letterboxd-compatible CSV format
Requires Python: >=3.9

## Plex to Letterboxd Exporter

Exports Plex watch history and ratings to a Letterboxd‑compatible CSV file using TMDB IDs for reliable matching.

### Install

From [PyPI](https://pypi.org/project/plex-letterboxd/):

```bash
pip install plex-letterboxd
```

Or with [uv](https://docs.astral.sh/uv/):

```bash
uv pip install plex-letterboxd
```

From source:

```bash
git clone https://github.com/brege/plex-letterboxd.git
cd plex-letterboxd
pip install .
```

Or with uv:

```bash
git clone https://github.com/brege/plex-letterboxd.git
cd plex-letterboxd
uv pip install .
```

### Configure

There are two ways to include your Plex token.

**Option 1:** Set your Plex token in `config.yaml`:
```yaml
plex:
  url: http://your-plex-server:32400
  token: PLEX_TOKEN
  timeout: 60
```

**Option 2:** Kometa users may source Kometa's config in this project's `config.yaml`:
```yaml
kometa:
  config_path: ./path/to/Kometa/config.yml
```

Exporter options are in `config.yaml`.
- export: output, after, before, user, library
- csv: rating, review, max\_rows, genres, tags, rewatch, mark\_rewatch

See [`config.example.yaml`](config.example.yaml) for available options.

### Usage

- List users
```bash
plex-letterboxd --list-users
```

- Export for a specific user
```bash
plex-letterboxd --user USERNAME --output plex-export.csv
```

- Export a date range
```bash
plex-letterboxd \
    --user USERNAME \
    --after 2024-01-01 \
    --before 2024-12-31 \
    --output plex-export-2024.csv
```

Import at https://letterboxd.com/import/

See `plex-letterboxd --help` for CLI options.

### Output CSV Columns

| Field         | Description                           |
|:------------- |:------------------------------------- |
| `tmdbID`      | TMDB ID for precise matching          |
| `Title`       | Movie title                           |
| `Year`        | Release year                          |
| `Directors`   | Director names                        |
| `WatchedDate` | When you watched it (YYYY‑MM‑DD)      |
| `Rating`      | Your rating (0.5–5.0), if enabled     |
| `Tags`        | Genres and/or custom tags, if enabled |
| `Rewatch`     | Whether it's a rewatch                |

---

## Automated Exports

Set up a [systemd timer](https://www.freedesktop.org/software/systemd/man/systemd.timer.html) for automated monthly exports with CSV checkpointing.

### Setup Timer

Monthly
```bash
bash <(curl -s https://raw.githubusercontent.com/brege/plex-letterboxd/refs/heads/main/systemd/install.sh)
```

Or weekly
```bash
bash <(curl -s https://raw.githubusercontent.com/brege/plex-letterboxd/refs/heads/main/systemd/install.sh) weekly
```

The timer will run the exporter on your chosen schedule, producing CSV files in `~/.config/plex-letterboxd/data/`. Configure the output data directory via `config.yaml` to change.

I suggest running this on the same machine Plex runs on, since it's typically always online.

## License

[GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html)
pytest-xdocker 0.2.7
Pytest fixture to run docker across test runs.
Requires Python: <4.0,>=3.10

pytest-xdocker
==============

`Pytest <http://pytest.org>`_  fixture to run docker across test runs.

.. image:: https://img.shields.io/badge/license-MIT-blue.svg
   :target: https://github.com/cr3/pytest-xdocker/blob/master/LICENSE
   :alt: License
.. image:: https://img.shields.io/pypi/v/pytest-xdocker.svg
   :target: https://pypi.python.org/pypi/pytest-xdocker/
   :alt: PyPI
.. image:: https://img.shields.io/github/issues-raw/cr3/pytest-xdocker.svg
   :target: https://github.com/cr3/pytest-xdocker/issues
   :alt: Issues

Requirements
------------

You will need the following prerequisites to use pytest-xdocker:

- Python 3.9, 3.10, 3.11, 3.12, 3.13

Installation
------------

To install pytest-xdocker:

.. code-block:: bash

  $ pip install pytest-xdocker

Usage
-----

TODO

Resources
---------

- `Documentation <https://cr3.github.io/pytest-xdocker/>`_
- `Release Notes <http://github.com/cr3/pytest-xdocker/blob/master/CHANGES.rst>`_
- `Issue Tracker <http://github.com/cr3/pytest-xdocker/issues>`_
- `Source Code <http://github.com/cr3/pytest-xdocker/>`_
- `PyPi <https://pypi.org/project/pytest-xdocker/>`_