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    "info": {
        "author": "Catherine Devlin",
        "author_email": "catherine.devlin@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
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            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3"
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        "description": "=============\nDDL Generator\n=============\n\nInfers SQL DDL (Data Definition Language) from table data.\n\nUse at command line::\n\n    $ ddlgenerator -i postgresql '[{\"Name\": \"Alfred\", \"species\": \"wart hog\", \"kg\": 22}]'\n\n    DROP TABLE generated_table;\n    CREATE TABLE generated_table (\n\t    name VARCHAR(6) NOT NULL, \n\t    kg INTEGER NOT NULL, \n\t    species VARCHAR(8) NOT NULL \n    )\n    ;\n    INSERT INTO generated_table (kg, Name, species) VALUES (22, 'Alfred', 'wart hog');\n    \nReads data from files::\n\n    $ ddlgenerator postgresql mydata.yaml > mytable.sql\n\nEnables one-line creation of tables with their data\n\n    $ ddlgenerator --inserts postgresql mydata.json | psql \n\nTo use in Python::\n\n    >>> from ddlgenerator.ddlgenerator import Table\n    >>> table = Table([{\"Name\": \"Alfred\", \"species\": \"wart hog\", \"kg\": 22}])\n    >>> sql = table.sql('postgresql', inserts=True)\n\nSupported data formats\n----------------------\n\n- Pure Python\n- YAML\n- JSON\n- CSV\n- Pickle\n- HTML\n\nFeatures\n--------\n\n- Supports all SQL dialects supported by SQLAlchemy\n- Coerces data into most specific data type valid on all column's values\n- Takes table name from file name\n- Guesses format of input data if unspecified by file extension\n- with ``-i``/``--inserts`` flag, adds INSERT statements\n- with ``-u``/``--uniques`` flag, surmises UNIQUE constraints from data\n- Handles nested data, creating child tables as needed\n- Reads HTML tables, including those embedded in noisy websites\n\nOptions\n-------\n\n::\n\n      -h, --help            show this help message and exit\n      -k KEY, --key KEY     Field to use as primary key\n      -r, --reorder         Reorder fields alphabetically, ``key`` first\n      -u, --uniques         Include UNIQUE constraints where data is unique\n      -t, --text            Use variable-length TEXT columns instead of VARCHAR\n      -d, --drops           Include DROP TABLE statements\n      -i, --inserts         Include INSERT statements\n      --no-creates          Do not include CREATE TABLE statements\n      --save-metadata-to FILENAME\n\t\t\t    Save table definition in FILENAME for later --use-\n\t\t\t    saved-metadata run\n      --use-metadata-from FILENAME\n\t\t\t    Use metadata saved in FROM for table definition, do\n\t\t\t    not re-analyze table structure\n      -l LOG, --log LOG     log level (CRITICAL, FATAL, ERROR, DEBUG, INFO, WARN)\n\nGenerate SQLAlchemy models\n--------------------------\n\nUse ``sqlalchemy`` as the model to generate Python for defining SQLAlchemy\nmodels::\n\n    $ ddlgenerator sqlalchemy '[{\"Name\": \"Alfred\", \"species\": \"wart hog\", \"kg\": 22}]'\n\n    Table0 = Table('Table0', metadata, \n      Column('species', Unicode(length=8), nullable=False), \n      Column('kg', Integer(), nullable=False), \n      Column('name', Unicode(length=6), nullable=False), \n      schema=None)\n\nGenerate Django models\n----------------------\n\nIf Django is installed on the path then using ``django`` as the model will run the\ngenerated ddl through Django's ``inspectdb`` management command to produce a model\nfile::\n\n    $ ddlgenerator django '[{\"Name\": \"Alfred\", \"species\": \"wart hog\", \"kg\": 22}]'\n\n    # This is an auto-generated Django model module.\n    # You'll have to do the following manually to clean this up:\n    #   * Rearrange models' order\n    #   * Make sure each model has one field with primary_key=True\n    #   * Remove `managed = False` lines if you wish to allow Django to create and delete the table\n    # Feel free to rename the models, but don't rename db_table values or field names.\n    #\n    # Also note: You'll have to insert the output of 'django-admin.py sqlcustom [appname]'\n    # into your database.\n    from __future__ import unicode_literals\n\n    from django.db import models\n\n    class Table0(models.Model):\n        species = models.CharField(max_length=8)\n        kg = models.IntegerField()\n        name = models.CharField(max_length=6)\n        class Meta:\n            managed = False\n            db_table = 'Table0'\n\nLarge tables\n------------\n\nAs of now, ``ddlgenerator`` is not well-designed for table sizes approaching\nyour system's available memory.\n\nOne approach to save time and memory for large tables is to break your input data into multiple\nfiles, then run ``ddlgenerator`` with ``--save-metadata`` against a small \nbut representative sample.  Then run with ``--no-creates`` and ``-use-saved-metadata``\nto generate INSERTs from the remaining files without needing to re-determine the\ncolumn types each time.\n\nInstalling\n----------\n\nRequires Python3.\n\nFrom PyPI::\n\n    pip3 install ddlgenerator\n\nFrom source::\n\n    git clone https://github.com/catherinedevlin/ddl-generator.git\n    cd ddl-generator\n    pip3 install .\n\nAlternatives\n------------\n\n* `csvkit.csvsql <https://csvkit.readthedocs.org/en/0.7.3/scripts/csvsql.html>`_\n* `pandas.read_*` methods\n* `prequel <https://github.com/timClicks/prequel.git>`_ for SQLite\n\nCredits\n-------\n\n- Mike Bayer for sqlalchemy\n- coldfix and Mark Ransom for their StackOverflow answers\n- Audrey Roy for cookiecutter\n- Brandon Lorenz for Django model generation\n\n\n\n\n\n\n\nHistory\n-------\n\n0.1.0 (2014-03-22)\n++++++++++++++++++\n\n* First release on PyPI.\n\n0.1.2 (2014-07-15)\n++++++++++++++++++\n\n* ``data_dispenser`` moved to separate module\n\n0.1.3 (2014-07-16)\n++++++++++++++++++\n\n* Bugfix for long integers found after short strings\n\n0.1.4 (2014-07-25)\n++++++++++++++++++\n\n* Fixed bug: external ``data_dispenser`` unused by 0.1.3!\n\n0.1.5 (2014-07-25)\n++++++++++++++++++\n\n* ``sqlalchemy`` pseudo-dialect added\n\n0.1.6 (2014-07-25)\n++++++++++++++++++\n\n* Generate sqlalchemy inserts\n\n0.1.7 (2014-09-14)\n++++++++++++++++++\n\n* Read via HTTP\n* Support HTML format\n* Generate Django models\n\n0.1.7.1 (2014-09-14)\n++++++++++++++++++++\n\n* Require data-dispenser 0.2.3\n\n0.1.7.3 (2014-10-19)\n++++++++++++++++++++\n\n* Require all formerly recommended dependencies, for simplicity\n* Several bugfixes for complex number-like fields\n\n0.1.8 (2015-02-01)\n++++++++++++++++++\n\n* UNIQUE contstraints handled properly in sqlalchemy output\n\n0.1.8.2 (2015-02-05)\n++++++++++++++++++++\n\n* Cleaner SQLAlchemy generation for fixtures\n\n0.1.9 (2015-02-10)\n++++++++++++++++++\n\n* README fixes from Anatoly Technonik, Mikhail Podgurskiy\n* Parse args passed to ``generate(args, namespace)`` for non-command-line use",
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