semvec/pypi-packages huggingface dataset:
zoombot 0.1.2
Python wrapper for Zoom Chatbot API
License: MIT
Requires Python: >=3.7,<4.0
Keywords: zoom,chat,bot

# zoombot

[![PyPI](https://img.shields.io/pypi/v/zoombot)](https://pypi.org/project/zoombot/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/zoombot)](https://pypi.org/project/zoombot/)
[![PyPI License](https://img.shields.io/pypi/l/zoombot)](https://pypi.org/project/zoombot/)
[![Code Style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black/)

Python wrapper for Zoom Chatbot API

### Usage
```python
from zoombot.core import Bot, Message


class MyBot(Bot):
    async def on_message(self, message: Message):
        await message.reply("Hello", f"You sent {message.content}")


if __name__ == "__main__":
    bot = MyBot(
        client_id="CLIENT_ID",
        client_secret="CLIENT_SECRET",
        bot_jid="BOT_JID",
        verification_token="VERIFICATION_TOKEN",
    )

    bot.run()
```
django-fflag 0.2.0
Lightweight feature flags for django
License: MIT
Keywords: feature-flags,django

# django-fflag

[![Build Status](https://travis-ci.org/mixkorshun/django-fflag.svg?branch=master)](https://travis-ci.org/mixkorshun/django-fflag)
[![codecov](https://codecov.io/gh/mixkorshun/django-fflag/branch/master/graph/badge.svg)](https://codecov.io/gh/mixkorshun/django-fflag)

Lightweight feature flag for django.

Feature flag is useful technique to develop your software. When you implement new functionality in you project,
you probably want to enable it gradually for some users.

django-fflags is realization of feature flag mechanism with following benefits:
 - flags can be enabled to some part of your users or concrete users list
 - flags part will be different and depends on your flag name
 - it will works with any users count

Learn more about feature flags: https://www.martinfowler.com/articles/feature-toggles.html

## Installation

Install package:
```bash
pip install django-fflag
```

Add following configuration to `settings.py`:

```python

INSTALLED_APPS = (
    ...,
    "fflag",
)

MIDDLEWARE = (
    ...,
    "django.contrib.auth", # FFlagMiddleware uses request.user.id
    "django.contrib.sessions", # FFlagMiddleware uses request.session to store unauthenticated client id
    ...,
    "fflags.middleware.FFlagMiddleware",
)

# Persistent cache storage strongly recommended for feature-flags.
# You can configure custom persistent cache using following config:

FFLAG_CACHE_NAME = "fflag"

CACHES = {
    ...,
    "fflag": {
        'BACKEND': 'django.core.cache.backends.db.DatabaseCache',
        'NAME': 'fflag_flags',
    }
}
```

## Usage

When you write new code, you may wrap it with following if:
```python
if request.fflag_enabled("my_new_feature"):
    ...
```

or without request:
```python
if fflag_enabled(uniq_client_id, "my_new_feature"):
    ...
```

That means that your code will be not executed until feature flag is enabled.

To manage feature flags you may use following management commands:

 - **manage.py fflag_list** - print list of used feature flags. 
 - **manage.py fflag_list_enabled_for** - print list enabled feature flags for specified id (usually user id).
 - **manage.py fflag_get** - print information for specified feature flag.
 - **manage.py fflag_set_part** - set feature flag enable part. For example 0.4 will enable functionality for ~40% of users.
 - **manage.py fflag_add_part_ids** - extend feature flag part enabled ids (usually user ids)
 - **manage.py fflag_set_part_ids** - override feature flag part enabled ids (usually user ids)


**IMPORTANT NOTE**: Feature flag will be invisible by management commands after `fflag_enabled` function will be called.
dataget 0.4.15
A framework-agnostic datasets library for Machine Learning research and education.
License: MIT
Requires Python: >=3.6.1,<4.0.0

# Dataget

Dataget is an easy to use, framework-agnostic, dataset library that gives you quick access to a collection of Machine Learning datasets through a simple API.

Main features:

* **Minimal**: Downloads entire datasets with just 1 line of code.
* **Framework Agnostic**: Loads data as `numpy` arrays or `pandas` dataframes which can be easily used with the majority of Machine Learning frameworks.
* **Transparent**: By default stores the data in your current project so you can easily inspect it.
* **Memory Efficient**: When a dataset doesn't fit in memory it will return metadata instead so you can iteratively load it.
* **Integrates with Kaggle**: Supports loading datasets directly from Kaggle in a variety of formats.

Checkout the [documentation](https://cgarciae.github.io/dataget/) for the list of available datasets.

## Getting Started

In dataget you just have to do two things:

* Instantiate a `Dataset` from our collection.
* Call the `get` method to download the data to disk and load it into memory.

Both are usually done in one line:

```python
import dataget


X_train, y_train, X_test, y_test = dataget.image.mnist().get()
```

This example downloads the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset to `./data/image_mnist` and loads it as `numpy` arrays.

### Kaggle Support

Kaggle [promotes](https://www.kaggle.com/docs/datasets#supported-file-types) the use of `csv` files and `dataget` loves it! With dataget you can quickly download any dataset from the platform and have immediate access to the data:

```python
import dataget

df_train, df_test = dataget.kaggle(dataset="cristiangarcia/pointcloudmnist2d").get(
    files=["train.csv", "test.csv"]
)
```
To start using Kaggle datasets just make sure you have properly installed and configured the [Kaggle API](https://github.com/Kaggle/kaggle-api). In the future we want to expand Kaggle support in the following ways:

* Be able to load any file that `numpy` or `pandas` can read.
* Have generic support for other types of datasets like images, audio, video, etc. 
    * e.g `dataget.data.kaggle(..., type="image").get(...)`


## Installation
```bash
pip install dataget
```

## Contributing
Adding a new dataset is easy! Read our guide on [Creating a Dataset](https://cgarciae.github.io/dataget/dataset/) if you are interested in contributing a dataset.

## License
MIT License