updated documentation

Browse Source
This commit is contained in:
Gregor Aisch 2013-04-03 12:28:42 +02:00
parent 258e3f3059
commit 5e5b902116
3 changed files with 9 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/api.rst
View File

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

2
docs/index.rst
View File

@ -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? 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.* *In short, dataset makes reading and writing data in databases as simple as reading and writing JSON files.*

9
docs/quickstart.rst
View File

@ -74,13 +74,18 @@ Now let's get some real data out of the table::
users = db['user'].all() 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 # All users from China
users = table.find(country='China') users = table.find(country='China')
# Get a specific user # 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:: Using :py:meth:`distinct() <dataset.Table.distinct>` we can grab a set of rows with unique values in one or more columns::