semvec/pypi-packages huggingface dataset:
django-mathcalc 0.1
A simple app Django app to to do some basic calculations
License: MIT

========
Mathcalc
========

Mathcalc is a simple app created for the class requirement.
Detailed documentation (if any!) is with in the source files as comments.

(I'd suggested to go through the source, since that would be an easier task than to
figure out manually)

Quick start
-----------

1. Add "shopcart" to your INSTALLED_APPS setting like this::

   INSTALLED_APPS = [
        ...
        'mathcalc',
   ]

2. Include the shopcart urlconf in your project urls.py like this::

   urlpatterns = [
        ...
        url(r'^mathcalc/', include('mathcalc.urls')),
   ]

3. Start the development server and visit http://127.0.0.1:8000/mathcalc/ to view the
   mathcalc app
DStore-MySQL 0.1.0a2
MySQL Storage layer for DStore
License: MIT

Welcome To DStore-MySQL
#######################

.. image:: https://img.shields.io/coveralls/MarkLark/dstore-mysql.svg
    :target: https://coveralls.io/github/MarkLark/dstore-mysql?branch=master

.. image:: https://img.shields.io/travis/MarkLark/dstore-mysql/master.svg
    :target: https://travis-ci.org/MarkLark/dstore-mysql

.. image:: https://img.shields.io/pypi/v/dstore-mysql.svg
    :target: https://pypi.python.org/pypi/dstore-mysql

.. image:: https://img.shields.io/pypi/pyversions/dstore-mysql.svg
    :target: https://pypi.python.org/pypi/dstore-mysql

DStore-MySQL is a MySQL storage layer for DStore.
This allows you to use the same Model descriptions to Create, Read, Update and Delete from a MySQL DataBase.


Installing
==========

PyMan is available from the PyPi repository.

This means that all you have to do to install DStore-MySQL is run the following in a console:

.. code-block:: console

    $ pip install dstore-mysql

Minimal Example
===============

.. code-block:: python

    from dstore import Model, var, mod
    from dstore_mysql import MySQLStore

    class Car( Model ):
        _namespace = "cars.make"
        _vars = [
            var.RowID,
            var.String( "manufacturer", 32, mods = [ mod.NotNull() ] ),
            var.String( "make", 32, mods = [ mod.NotNull() ] ),
            var.Number( "year", mods = [ mod.NotNull(), mod.Min( 1950 ), mod.Max( 2017 ) ] ),
        ]

    # Create the MemoryStore instance, and add Models to it
    store = MySQLStore( self.models )
    store.init_app()
    store.set_config({
        "DSTORE_DB_HOST"  : "localhost",
        "DSTORE_DB_USER"  : "username",
        "DSTORE_DB_PASSWD": "password",
        "DSTORE_DB_DB"    : "dstoredb"
    })
    store.connect()
    store.create_all()

    # Destroy all instances and shut down the application
    store.destroy_all()
    store.disconnect()
    store.destroy_app()


Documentation: `ReadTheDocs <http://dstore-mysql.readthedocs.io/>`_

Source Code: `GitHub <https://github.com/MarkLark/dstore-mysql>`_
django-shopcart 0.1
A simple app Django app to understand how shopping cart models work
License: MIT

========
Shopcart
========

Shopcart is a simple app created for the class requirement.
Detailed documentation (if any!) is with in the source files as comments.

(I'd suggested to go through the source, since that would be an easier task than to
figure out manually)

Quick start
-----------

1. Add "shopcart" to your INSTALLED_APPS setting like this::

   INSTALLED_APPS = [
        ...
        'shopcart',
   ]

2. Include the shopcart urlconf in your project urls.py like this::

   urlpatterns = [
        ...
        url(r'^shopcart/', include('shopcart.urls')),
   ]

3. Start the development server and visit http://127.0.0.1:8000/shopcart/ to view the
   shopcart app
heamy 0.0.7
A set of useful tools for competitive data science.
License: MIT
Keywords: heamy

=====
heamy
=====

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

.. image:: https://img.shields.io/travis/rushter/heamy.svg
        :target: https://travis-ci.org/rushter/heamy

.. image:: https://coveralls.io/repos/github/rushter/heamy/badge.svg?branch=master
        :target: https://coveralls.io/github/rushter/heamy?branch=master

A set of useful tools for competitive data science.


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

To install Heamy, simply:

.. code:: bash

    $ pip install -U heamy


Features
--------
* Automatic caching (data preprocessing, predictions from models)
* Ensemble learning (stacking, blending, weighted average, etc.).


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

* Documentation: http://heamy.readthedocs.io/en/latest/        
* Examples: https://github.com/rushter/heamy/tree/master/examples