semvec/pypi-packages huggingface dataset:
django_tabler_icons 0.6.0
Use tabler icons with django templates
Requires Python: >=3.7

> If you are upgrading from 0.4.0, note that the default download location for icons has changed. Icons are now downloaded to the directory specified by the `XDG_DATA_HOME` environment variable, defaulting to `<user_home_directory>/.local/share/django-tabler-icons/icons` if not set. This change aligns with the XDG Base Directory Specification. If you have `TABLER_ICONS_DIR` set in your Django settings, it will take precedence. The previous default location was `<user_home_directory>/.config/django-tabler-icons/icons`, which you may want to remove if no longer needed.

# Django tabler icons
Use [the tabler](https://github.com/tabler/tabler-icons) icon set in your Django project.

## Installation
Install the package from PyPI:
```bash
python -m pip install django-tabler-icons
```

Add `tabler_icons` to your `INSTALLED_APPS`:
```python
INSTALLED_APPS = [
    ...
    'tabler_icons',
    ...
]
```

Download the icon set:
```bash
python manage.py download_icons
```

Icons will be downloaded to `<user_home_directory>/.config/django-tabler-icons`. You can override this by setting the `TABLER_ICONS_DIR` setting, in project settings. This command requires to load Django.

### Alternative download method

Icons can also be downloaded without loading Django using:
```bash
django_tabler_icons download
```
which will download the icon set to `<user_home_directory>/.config/django-tabler-icons`. This requires user confirmation to proceed. By passing `--yes` or `-y` as an argument, the confirmation is skipped.

## Usage

To add an icon to your template, use the `tabler_icon` template tag:
```django
{% load tabler_icons %}

{% tabler_icon '<icon_name>' %}
```

If you need to add classes to the icon, use the second argument:
```django
{% tabler_icon '<icon_name>' '<class_name_1> <class_name_2>' %}
```

The icons come with some default classes which you can remove by passing `no` to the third argument (`keep_default_classes`)
```django
{% tabler_icon '<icon_name>' '<class_name_1> <class_name_2>' 'no' %}
```

> The default is to keep the default classes so you can omit the third argument if you want to keep them.

## Relevant projects

[Heroicons](https://github.com/adamchainz/heroicons) is a package for using [heroicons](https://heroicons.com) in Django projects, which is also the inspiration for this package.
operator-learning 0.1.0
Add your description here
Requires Python: >=3.10

# Operator Learning

## Installation instructions
The package is not yet available on `pypi` — that is, it is not yet installable with `pip`. It will be soon. To develop this package I'm using [`uv`](https://docs.astral.sh/uv/), which is a modern and *way faster* package manager than `conda`. To get up and running, just follow these three steps:

1. Install `uv` following the [installation instructions](https://docs.astral.sh/uv/getting-started/installation/)
2. Clone the repository
    ```bash
    git clone git@github.com:pietronvll/operator_learning.git
    ```
3. Open a terminal window in the `operator_learning` folder just cloned, and install the dependencies by running 
    ```bash
    uv sync --dev
    ```

## Development
I use [Visual Studio Code](https://code.visualstudio.com/), which should automatically prompt you to use the virtual environment found in the `operator_learning` folder.

### Adding a dependency
As of now, `operator_learning` only depends on `numpy`, `scipy`, and `torch`. If you implement a functionality needing an additional dependency `dep`, you can add it to the project by simply running
```bash
uv add dep
```
If you need a *development* dependency `dev_dep`, something that you use for testing but is not needed for the core functionality, you can add it in the `--dev` group by running
```bash
uv add --dev dev_dep
```
For example, in the current `--dev` group — which you can read off `pyproject.toml` — we have `scikit-learn`, `matplotlib`, and HuggingFace's `datasets`.
DatasetHandler-Group-2 0.2.0
Dataset handler of group 2
Requires Python: >=3.6

# DatasetHandler_Group2 Library

DatasetHandler_Group2 is a Python library designed to simplify data cleaning, preprocessing and visualization created by the PBL group 2 of the third year of the bachelor's degree in Engineering Physics Applied to Industry at the AsFabrik campus of Mondragon Unibertsitatea.

The library created by the group formed by Irati Azpilgain, Mikel Ballesteros, Ander Crespo, Estibaliz Cruz, Lucas Lafuente and Mikel Puerta offers an easy-to-use interface for common data science tasks such as handling missing values, removing outliers and creating graphs.

## Installation

You can install DatasetHandler_Group2 via pip:

```bash
pip install DatasetHandler_Group2
```

## Usage

Here's a basic example of how to use DatasetHandler_Group2:

```python
from DatasetHandler_Group2 import DatasetHandler_Group2

# Initialize with your dataset and start the menu
handler = DatasetHandler_Group2(data="sample.csv")
handler.initiate()
```

## Features

- Load datasets from CSV or Excel files.
- Clean data by dropping or imputing missing values.
- Detect and remove outliers.
- Generate descriptive plots such as histograms, scatter plots, and heatmaps.
- Save the cleaned dataset back to a file.

## Dependencies

The following Python libraries are required:

- pandas
- numpy
- seaborn
- matplotlib
- scikit-learn

You can install them via pip:

```bash
pip install pandas numpy seaborn matplotlib scikit-learn
```
## License

This project is licensed under the MIT License.

## Contact

For questions or issues, please contact mikel.puerta@alumni.mondragon.edu