Merge pull request #80 from ode79/make_quickstart_examples_work

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Make examples in quickstart all work.
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Friedrich Lindenberg 2014-02-27 19:26:00 +01:00
commit d5ae74afbf
1 changed files with 11 additions and 11 deletions
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docs/quickstart.rst
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@ -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 to worry about whether the table already exists or not, since dataset
will create it automatically:: 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 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 pass a `dict`_ to *insert*. Note that you don't need to create the columns
*name* and *age* – dataset will do this automatically:: *name* and *age* – dataset will do this automatically::
# Insert a new record. # 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 # 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 .. _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: database:
>>> print(db.tables) >>> print(db.tables)
set([u'user', u'action']) [u'user']
Now, let's list all columns available in the table ``user``: Now, let's list all columns available in the table ``user``:
>>> print(db['user'].columns) >>> 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: Using ``len()`` we can get the total number of rows in a table:
>>> print(len(db['user'])) >>> print(len(db['user']))
187 2
Reading data from tables 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>`:: 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']: 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 We can search for specific entries using :py:meth:`find() <dataset.Table.find>` and
:py:meth:`find_one() <dataset.Table.find_one>`:: :py:meth:`find_one() <dataset.Table.find_one>`::
# All users from China # All users from China
users = table.find(country='China') chinese_users = table.find(country='China')
# Get a specific user # Get a specific user
john = table.find_one(name='John Doe') 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 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:: 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) result = db.query(statement)