semvec/pypi-packages huggingface dataset:
facetswrapper 0.1b1
A wrapper for the Facets project (https://pair-code.github.io/facets/).
Requires Python: >=3.6
Keywords: dataviz data_visualization wrapper

# facets-wrapper

A wrapper to two visualizations in [Facets](https://pair-code.github.io/facets/), namely Facets Overview and Facets Dive.

## Facets Overview wrapper
* The general idea follows one of the provided [original examples](https://raw.githubusercontent.com/PAIR-code/facets/master/facets_overview/Overview_demo.ipynb).
* The wrapper takes only one dataframe as input, but allows to use optionally a list of columns to perform a groupby. In such a case, the groups of the groupby correspond to the multiple datasets of the original example.
* The CSS of the wrapping HTML page is used to tweak the HTML code returned by facets, in order to accommodate gracefully more than 2 groups of data. A sprinkle of JavaScript is also used to enhance some functionalities.

## Facets Dive wrapper
* Not yet implemented, planned for end of may 2019.
dottorrent 1.10.1
High-level Python 3 library for creating .torrent files
Keywords: bittorrent torrent bencode

dottorrent
==========

High-level Python 3 library for creating .torrent files

Documentation
-------------

Full documentation on how to use dottorrent
is available at `dottorrent.readthedocs.io <http://dottorrent.readthedocs.io>`_.

.. image:: https://readthedocs.org/projects/dottorrent/badge/


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

Stable releases are available on PyPI and can be installed using ``pip``.
::

	pip install dottorrent


To install from the latest development sources, clone the git repo and run
``pip install .``

CLI and GUI
-----------

Looking for a command-line interface? Check out `dottorrent-cli <https://github.com/kz26/dottorrent-cli>`_.

Looking for a GUI? Check out `dottorrent-gui <https://github.com/kz26/dottorrent-gui>`_.

License
-------

`MIT <https://opensource.org/licenses/MIT>`_
dottorrent-gui 1.3.11
An advanced GUI torrent file creator with batch functionality, powered by PyQt and dottorrent
Keywords: bittorrent torrent

==============
dottorrent-gui
==============

An advanced GUI torrent file creator with batch functionality, powered by PyQt and
`dottorrent <https://github.com/kz26/dottorrent>`_

.. image:: img/screenshot1.png

--------
Features
--------

* Fast (capable of several hundred MB/s)
* Cross-platform
* Full Unicode support
* Automatic and manual piece size selection, up to 64MB
* Batch torrent creation mode
* Filename exclusion patterns
* HTTP/web seeds support `(BEP 19) <http://www.bittorrent.org/beps/bep_0019.html>`_
* Private flag support `(BEP 27) <http://www.bittorrent.org/beps/bep_0027.html>`_
* User-definable source string
* Optional MD5 file hash inclusion
* `Import/export of profiles <https://github.com/kz26/dottorrent-gui/wiki/Profiles>`_ (trackers, web seeds, source string, filename exclusion patterns)

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

Windows
-------

Binary releases of stable versions for 64-bit Windows can be found at
`https://github.com/kz26/dottorrent-gui/releases <https://github.com/kz26/dottorrent-gui/releases>`_.
Extract to a folder and run ``dottorrent_gui.exe``. You may need to download and install the `Microsoft Visual C++ Redistributable for Visual Studio 2015 <https://www.microsoft.com/en-us/download/details.aspx?id=48145>`_.

Linux and Mac OS X
------------------

**Requirements**

* Python 3.3+
* PyQt5 5.7+

Latest stable release: ``pip install dottorrent-gui``

Development: ``git clone`` this repository, then ``pip install .``

To run: ``dottorrent-gui``

-------------
Portable Mode
-------------

dottorrent-gui can be configured to run in portable mode, good for running from USB drives and network shares.
To enable this, simply create an empty file named ``dottorrent-gui.ini`` in the same directory as the
main excecutable.

-------
License
-------

© 2017 Kevin Zhang. Made available under the terms of the
`GNU General Public License v3 <http://choosealicense.com/licenses/gpl-3.0/>`_.
vulcan 0.1.14
Terminal-based flashcard application, for developers, that uses machine learning to schedule reviews


# Vulcan

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/2c05478170d74f6ba28cf5efc379cf69)](https://www.codacy.com/app/shyal/vulcan?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=shyal/vulcan&amp;utm_campaign=Badge_Grade)

Vulcan is a terminal based flashcard application, for developers, that uses machine learning to schedule reviews.

[https://vulcan.sh](https://vulcan.sh)

## running

```
$ pip3 install vulcan
$ vulcan
```
pynosql 0.0.4
A NOSQL DB Library for Python

# PyNOSQL Library for Python
[![Build Status](https://travis-ci.org/stajkowski/pynosql.svg?branch=master)](https://travis-ci.org/stajkowski/pynosql)

# Summary
PyNOSQL is a simple library that wraps two popular NOSQL databases DynamoDB|MongoDB and provides the concept of a model
for maintaining, expanding, and reducing data structures stored in NOSQL tables.  Extend model.Model and pass to various
Dynamo operations to benefit from default values, adding/removing elements, and simplifying use of boto3 with DynamoDB.

# Versions
0.0.1 - Support for basic CRUD operations in DynamoDB
0.0.2 - Support for basic update of model with GET|PUT
0.0.3 - Support for NotFound on single record and empty list for multiple records

# Getting Started

1. Instantiate a client and pass to the desired provider:

    ```

    from pynosql.credentials.aws import AWSCredentials
    from pynosql.clients.aws import AWSClient
    from pynosql.providers.dynamo import DynamoDBProvider

    credentials = AWSCredentials(
        'AKIAIOSFODNN7EXAMPLE',
        'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'
    )

    dynamo = DynamoDBProvider(
        AWSClient(self.credentials, 'us-west-2')
    )

    ```

2. Extend base.model.Model to create the desired data structure to be stored in
your NOSQL DB:

    ```

    from pynosql.base.model import Model

    class TestModel(Model):

        BASE = {
            'test1': None,
            'test2': None,
            'test3': None,
            'test4': None
        }

        def __init__(self):
            super(TestModel, self).__init__(self.BASE)

    ```

3. Pass the model with the appropriate call to DynamoDB:

    ```

    key = {
        'test1': 'value1',
        'test2': 'value2'
    }

    response = dynamo.get_record(TestModel(), 'TestTable', Key=key)

    ```

4. For paginating all results into the model, just keep passing the model
instance into your DynamoDB calls.  The new records will be appended to the
list within the model.