dataset/quickstart.rst at 4bfd6e5d0c155f3854494e2963cc98617d01d640

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 Quickstart
 ==========
 .. toctree::
    :maxdepth: 2
 Hi, welcome to the five-minute quick-start tutorial.
 At first you need to import the dataset package :) ::
    import dataset
 To connect to a database you need to identify it using what is called an engine url. Here are a few examples::
    # connecting to a SQLite database
    db = dataset.connect('sqlite:///factbook.db')
    # connecting to a MySQL database
    db = dataset.connect('mysql://user:password@localhost/mydatabase')
    # connecting to a PostgreSQL database
    db = dataset.connect('postgresql://scott:tiger@localhost:5432/mydatabase')
 If you want to learn more about how to connect to various databases, have a look at the `SQLAlchemy documentation`_.
 .. _SQLAlchemy documentation: http://docs.sqlalchemy.org/en/latest/core/engines.html#engine-creation-api
 Storing data
 ------------
 At first you need to get a reference to the table in which you want to store your data. You don't
 need to worry about whether the table already exists or not, since dataset will create it automatically::
    # get a reference to the table 'person'
    table = db['person']
 Now storing data in a table is a matter of a single function call. Just pass a `dict`_ to *insert*. Note
 that you don't need to create the columns *name* and *age* – dataset will do this automatically::
    # Insert a new record.
    table.insert(dict(name='John Doe', age=46))
    # dataset will create "missing" columns any time you insert a dict with an unknown key
    table.insert(dict(name='Jane Doe', age=37, gender='female'))
    # If you need to insert many items at once, you can speed up things by using insert_many:
    table.insert_many(list_of_persons)
 .. _dict: http://docs.python.org/2/library/stdtypes.html#dict
 Updating existing entries is easy, too::
    table.update(dict(name='John Doe', age=47), ['name'])
 Reading data from tables
 ------------------------
 Checking::
    table = db['population']
    # Let's grab a list of all items/rows/entries in the table:
    table.all()
    table.distinct()
 Searching for specific entries::
    # Returns the first item where the column country equals 'China'
    table.find_one(country='China')
    # Returns all items
    table.find(country='China')
 Querying data
 -------------
 Querying data is easy. Dataset returns an iteratable result object::
    result = db.query('SELECT ...')
    for row in result:
       print row
 Freezing your data
 ------------------