semvec/pypi-packages huggingface dataset:
unityapiclient 0.0.3
Client library for the Unity IDM APIs
License: Apache License 2.0
Requires Python: >=2.6,<2.8
Keywords: unity identity management rest api client library

unityapiclient
==============

.. image:: https://img.shields.io/pypi/v/unityapiclient.svg
    :target: https://pypi.python.org/pypi/unityapiclient

A Python client library for the Unity IDM APIs.

Currently, it only supprorts a subset of the operations exposed by Unity's Administration API. 

Python version
--------------

Python 2.6 or 2.7 are fully supported.

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

To install the client library, simply run:

.. code-block:: bash

    $ pip install unityapiclient
    ��

Third party libraries and dependencies
--------------------------------------

The following libraries will be installed when you install the client library:

* `pytz <https://github.com/newvem/pytz>`_
* `requests <https://github.com/kennethreitz/requests>`_

Usage
-----

The following example assumes a Unity IDM deployment at ``https://unity.example.org``: 

.. code-block:: python

    >>> import unityapiclient
    >>> from unityapiclient.client import UnityApiClient
    >>> client = UnityApiClient('https://unity.example.org',
    ... auth=('unityapiuser', 'secretpassword'))
    >>> entity = client.get_entity(257)
    >>> entity['entityInformation']['state']
    u'valid'

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

Documentation is available at http://eudat-b2access.github.io/unity-api-python-client 

License
-------

Licensed under the Apache 2.0 license, for details see `LICENSE`.
nbtransom 1.0.1
Machines and people collaborating together through Jupyter notebooks.
License: Apache License 2.0
Keywords: human-in-the-loop,active learning,collaborative documents,jupyter notebook,pipelines,machine learning

Active Learning with Jupyter Notebooks
======================================

This is a Python 3 library to read/write cells programmatically in
`Jupyter notebooks <https://jupyter.org/>`_ which anticipates upcoming
`collaborative <https://groups.google.com/forum/#!topic/jupyter/r7QSObF5YSg>`_
features in Jupyter.

We use this at `O'Reilly Media <https://www.oreilly.com/>`_ for
notebooks used to manage machine learning pipelines.
That is to say, *machines and people collaborate on documents*, 
implementing a "human-in-the-loop" design pattern:

-  people adjust hyperparameters for the ML pipelines
-  machines write structured "logs" during ML modeling/evaluation
-  people run ``jupyter notebook`` via SSH tunnel for remote access

For more info about use cases for this library and *active learning* 
in general, see the `JupyterCon 2017 <https://jupytercon.com/>`_ talk
`Humans in the loop <https://conferences.oreilly.com/jupyter/jup-ny/public/schedule/detail/60058>`_


Example Usage
-------------

The following script generates a Jupyter notebook in the ``test.ipynb``
file, then runs it:

::

    python test.py
    jupyter notebook

Then launch the ``test.ipynb`` notebook and from the ``Cells`` menu
select ``Run All`` to view results.

NB: whenever you use the ``put_df()`` function to store data as a 
`Pandas dataframe <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>`_
be sure to include ``import pandas as pd`` at some earlier point in
the notebook.


Dependencies and Installation
-----------------------------

This code has dependencies on:

-  `nbformat <https://github.com/jupyter/nbformat>`_
-  `pandas <https://pandas.pydata.org/>`_

To install from `PyPi <https://pypi.python.org/pypi/nbtransom>`_:

::

    pip install nbtransom


To install from this Git repo:

::

    git clone https://github.com/ceteri/nbtransom.git
    cd nbtransom
    pip install -r requirements.txt


Kudos
-----

`@htmartin <https://github.com/htmartin>`_
`@esztiorm <https://github.com/esztiorm>`_
`@fperez <https://github.com/fperez>`_
`@odewahn <https://github.com/odewahn>`_