Make examples in quickstart all work.

When creating the table in the 'Storing data' section the table created
was called 'person', rather than 'user' making the other examples not
work because 'user' was referred to rather than 'person'.

I added countries to the entered data so that later queries in the
quickstart return some data.

I changed examples of returned data in 'Inspecting databases and tables'
match what is actually returned.

In 'Reading data from tables' I make the examples return real data.

docs/quickstart.rst

@@ -41,18 +41,18 @@ To store some data you need to get a reference to a table. 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'
+   # get a reference to the table 'user'
-   table = db['person']
+   table = db['user']
 
 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))
+   table.insert(dict(name='John Doe', age=46, country='China'))
 
    # 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'))
+   table.insert(dict(name='Jane Doe', age=37, country='France', gender='female'))
 
 .. _dict: http://docs.python.org/2/library/stdtypes.html#dict
 
@@ -72,17 +72,17 @@ first. To start exploring, let's find out what tables are stored in the
 database:
 
    >>> print(db.tables)
-   set([u'user', u'action'])
+   [u'user']
 
 Now, let's list all columns available in the table ``user``:
 
    >>> print(db['user'].columns)
-   [u'id', u'name', u'email', u'pwd', u'country']
+   [u'id', u'country', u'age', u'name', u'gender'] 
 
 Using ``len()`` we can get the total number of rows in a table:
 
    >>> print(len(db['user']))
-   187
+   2
 
 Reading data from tables
 ------------------------
@@ -94,13 +94,13 @@ Now let's get some real data out of the table::
 If we simply want to iterate over all rows in a table, we can omit :py:meth:`all() <dataset.Table.all>`::
 
    for user in db['user']:
-      print(user['email'])
+      print(user['age'])
 
 We can search for specific entries using :py:meth:`find() <dataset.Table.find>` and
 :py:meth:`find_one() <dataset.Table.find_one>`::
 
    # All users from China
-   users = table.find(country='China')
+   chinese_users = table.find(country='China')
 
    # Get a specific user
    john = table.find_one(name='John Doe')
@@ -126,8 +126,8 @@ The :py:meth:`query() <dataset.Table.query>` method can also be used to
 access the underlying `SQLAlchemy core API <http://docs.sqlalchemy.org/ru/latest/orm/query.html#the-query-object>`_, which allows for the
 programmatic construction of more complex queries::
 
-   table = db['users'].table
+   table = db['user'].table
-   statement = table.select(table.c.name.like('%Snoopy%'))
+   statement = table.select(table.c.name.like('%John%'))
    result = db.query(statement)