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    "info": {
        "author": "Paul Wolf",
        "author_email": "paul.wolf@yewleaf.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Framework :: Django",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "Djaq: Django Queries\n====================\n\ncopyright (c) Paul Wolf\n\nTo Install:\n\n::\n\n    git clone git@github.com:paul-wolf/djaq.git\n    cd djaq\n\nCreate the virtualenv:\n\n::\n\n    virtualenv -p python3 .venv    \n\nActivate the virtual environment:\n\n::\n\n    source ../.venv/bin/activate\n\nThe module itself does not install Django and there are no further\nrequirements. The example Django application is in ./bookshop. To\ninstall dependencies for the sample application:\n\n::\n\n    cd bookshop\n    pip install -r requirements.txt\n\nMake sure the virtualenv is activated! The sample database is already\npart of source code now (sqlite). The example application comes with a\nmanagement command to run queries:\n\n::\n\n    ./manage.py djaq \"(Publisher.name, max(Book.price) - round(avg(Book.price)) as diff) Book b\"  --format json\n\nIf using in code, you would do this:\n\n::\n\n    from xquery.exp import XQuery as XQ\n    xq = XQ(\"(avg(b.price) as average_book_price) Book b\"\n    print(xq.json())\n\nThere are several generators to choose from to iterate records:\n\n::\n\n    XQuery.json()  # return json objects\n    XQuery.dicts() # dicts\n    XQuery.tuples() # tuples of values\n    XQuery.objs()  # We return for each record, an instance of XQueryInstance\n\nThe XQueryInstance is basically a namespace so you can do this:\n\n::\n\n    for inst in XQ('(Book.name, Book.price)').objs():\n        print(inst.name)\n        print(inst.price)\n\netc.\n\nOutput of the command should look like this:\n\n::\n\n    \u25b6 ./manage.py djaq \"(Publisher.name, max(Book.price) - round(avg(Book.price)) as diff) Book b\"  --format json\n    SELECT books_publisher.name, (max(books_book.price) - round(avg(books_book.price))) FROM books_book LEFT JOIN books_publisher ON (books_book.publisher_id = books_publisher.id)  GROUP BY books_publisher.name LIMIT 10\n    {\"publisher_name\": \"Avila, Garza and Ward\", \"diff\": 14.0}\n    {\"publisher_name\": \"Boyer-Clements\", \"diff\": 16.0}\n    {\"publisher_name\": \"Clark, Garza and York\", \"diff\": 15.0}\n    {\"publisher_name\": \"Clarke PLC\", \"diff\": 14.0}\n    {\"publisher_name\": \"Griffin-Blake\", \"diff\": 16.0}\n    {\"publisher_name\": \"Hampton-Davis\", \"diff\": 13.0}\n    {\"publisher_name\": \"Jones LLC\", \"diff\": 15.0}\n    {\"publisher_name\": \"Lane-Kim\", \"diff\": 15.0}\n    {\"publisher_name\": \"Norris-Bennett\", \"diff\": 14.0}\n    {\"publisher_name\": \"Singleton-King\", \"diff\": 17.0}\n\nNotice the SQL used to retrieve data is printed first.\n\nThe syntax is like this:\n\n::\n\n    <join_operator> (<column_exp>, ... ) ModelName{<filter_expression>} Alias\n\nMost of these are optional. You don\u2019t have to reference models in an\nexpression with Alias; you don\u2019t need a filter in curly braces, etc.\nThere is no point in providing the join_operator on the first relation.\nYou can have as many of these as you wish to add more relations. You can\neven skip the join relation on subsequent relations: it will default to\nLEFT JOIN. Don\u2019t bother doing this:\n\n::\n\n    (Books.name, Publisher.name) Book -> Publisher\n\nbecause you can just do this:\n\n::\n\n    (Books.name, Publisher.name) Book\n\nIt will know to create the join to Publisher. But you might want to\ninclude it for the purpose of having an alias for Publisher:\n\n::\n\n    (b.name, p.name) Book b -> Publisher p\n\nUse \u201cas\u201d operator to name columns:\n\n::\n\n    (b.name as book_name, p.name as publisher) Book b -> Publisher p\n\nGet the average price of books for each publisher:\n\n::\n\n    (avg(b.price)) Book b\n\nGet the difference off the maximum price:\n\n::\n\n    (Publisher.name, max(Book.price) - avg(Book.price) as diff) Book b\n\nCount books per publisher:\n\n::\n\n    (Publisher.name, count(Book.id) as num_books) Book b\n\nCount books with ratings up to and over 3:\n\n::\n\n    (sum(b.rating > 3), sum(b.rating <= 3)) Book b\n\nGet average, maximum, minimum price of books:\n\n::\n\n    (avg(b.price), max(b.price), min(b.price)) Book b\n\n##TODO\n\n-  Improve group by detection\n-  Reference named xqueries as relations (for subqueries)\n-  IN operator either subquery, list, m2m, QuerySet\n-  startswith, endswith custom functions etc.\n-  m2m field relations\n-  backwards relations\n-  subquery, outter reference\n-  map/reduce\n-  Native sql backticks\n-  Parameter checking and type casting\n-  Emit names in double quotes\n-  CSV import\n   https://github.com/edcrewe/django-csvimport/blob/master/csvimport/parser.py\n-  format sql https://pypi.org/project/format-sql/",
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        "summary": "An alternative Django query language",
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