updated documentation

docs/api.rst

@@ -17,4 +17,4 @@ Table
 
 .. autoclass:: dataset.Table
    :members: columns, drop, insert, update, upsert, find, find_one, distinct, create_column, create_index, all
-   :special-members:
+   :special-members: __len__, __iter__

docs/index.rst

@@ -12,7 +12,7 @@ dataset: databases for lazy people
 
 Although managing data in relational database has plenty of benefits, we find them rarely being used in the typical day-to-day work with small to medium scale datasets. But why is that? Why do we see an awful lot of data stored in static files in CSV or JSON format?
 
-Because **programmers are lazy** they tend to prefer the easiest solution they find. And in **Python**, managing data in a databases simply wasn't the simplest solution to store a bunch of structured data. This is where **dataset** steps in!
+Because **programmers are lazy** they tend to prefer the easiest solution they find. And in **Python**, databases weren't the simplest solution to store a bunch of structured data. This is what **dataset** is going to change!
 
 *In short, dataset makes reading and writing data in databases as simple as reading and writing JSON files.*
 

docs/quickstart.rst

@@ -74,13 +74,18 @@ Now let's get some real data out of the table::
 
    users = db['user'].all()
 
-Searching for specific entries::
+If we simply want to iterate over all rows in a table, we can ommit :py:meth:`all() <dataset.Table.all>`::
+
+   for user in db['user']:
+      print user['email']
+
+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')
 
    # Get a specific user
-   john = table.find_one(email='john.doe@example.org')
+   john = table.find_one(name='John Doe')
 
 Using  :py:meth:`distinct() <dataset.Table.distinct>` we can grab a set of rows with unique values in one or more columns::