semvec/pypi-packages huggingface dataset:
magic_lightning_remote 0.1.0
Web controller for the magic lightning irda lamplight (acheap rgb irda controlled lightbulb)
License: BSD
Keywords: magic_lightning_remote

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Magic lightning remote
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.. image:: https://img.shields.io/travis/XayOn/magic_lightning_remote.svg
        :target: https://travis-ci.org/XayOn/magic_lightning_remote

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


Web controller for the magic lightning irda lamplight 
(a cheap rgb irda controlled lightbulb)

I currently have this one: 

_ http://es.aliexpress.com/store/product/3W-E27-16-Color-80LM-LED-RGB-Magic-Light-Bulb-Wireless-Remote-Control/1147259_32224553056.html

As you can see, is really cheap!

To be able to use this, you'll probably want a micro-pc and a IR receiver/emmiter.
I've used a prototype board, a receiver and an infrared led, nothing more.

* Free software: BSD license
* Documentation: https://magic_lightning_remote.readthedocs.org.

Features
--------

* Web interface emulating the full remote control
* Simple API enabling RestTasker control




History
-------

0.1.0 (2015-05-18)
---------------------

* First release on PyPI.
dummipy 0.0.1
Categorical variable friendly pandas data frames
License: BSD

dummipy
=======

Quickstart
----------

.. code:: bash

    $ pip install dummipy

Let it out of the box...

::

    from sklearn.linear_model import LinearRegression
    from dummipy import cereal

    type(cereal)
    # CategoricalDataFrame

    cereal.head()
    reg = LinearRegression()
    reg.fit(cereal[['mfr', 'vitamins', 'fat']], cereal.calories)

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

You'll need ```pandas`` <http://pandas.pydata.org/>`__, but any old
version will do the trick. There is no ``pandas`` version pegged in the
``setup.py`` file so installing ``dummipy`` won't mess up your existing
sci-py setup.

.. code:: bash

    $ pip install dummipy

Use
---

Just use it like any old data frame. That's really all there is to it.

::

    import dummipy as dp

    df = dp.CategoricalDataFrame({
        "x": range(5),
        "y": ["a", "b", "c", "a", "b"]
    })


    df = pd.read_csv("foo.csv")
    df = dp.CategoricalDataFrame(df)