Database access library for SQL databases. Focusses on the database side of things (rather than domain model). Just a nice simple SQL database access layer. Supports lots of (SQL) databases and several Python runtimes.

While Django focusses on the database access needs for web applications, SQLAlchemy is aimed on the broad range of database applications. This makes SQLA harder to learn and far more flexible, exposing far more of the relation model.

Layered architecture:

• Core provides and abstraction of Python’s DBAPI. Generates SQL in Python code (EDSL?). Bulk operations, complex queries, etc.

  • Schema handles DDL-type metadata.

  • Expressions handled DML-type operations. Dialects, pooling, etc.

• ORM

Installing is easy:

 $ pip install sqlalchemy


 query = vehicals_table.select()
 print query
 > SELECT vehicals.name
 > FROM vehicls

Query objects are mutable.

query.where(vehicals_table.c.odometer < 10000)

Robot Framework

Yet another testing framework.

Framework for “aceptance testing and acceptance testing driven development”. Developed by Nokia Siemens Networks, contributions by Google? Pretty old.

Yet another attempt to make things easy for non programmers by deleting all the syntax (except the number of spaces between things, of course that’s meaningful; how could anyone not see that?)

Keywords (just sequences of keywords which are defined elsewhere); libraries of keywords.

Gherkin syntax for BDD (for people who think BDD and cucumber are related).

Generates reports in HTML format (plus XML). Includes screenshots when using the Selenium library.

Library to add annotations to screenshots for use in documentation, etc.

Salt

YAML, jinja and Python; all with significant white space.

Configuration management solutions should (in Daniel’s option) have: visibility, repeatability, iterability.

David Lyon gave an introduction to Python on the Raspberry Pi. He mentioned wiringpi2 for general purpose I/O in the same vein as Wiring. I thought he spent gave too much credence to the suitability of general purpose uses of the Raspberry Pi when it’s the ability to hook it up to hardware that’s truly interesting.

Announcements: some startup wants a new co-founder; a new site based on Zope has launched: swimmingpoolregister.nsw.gov.au.

Eugene Van den Bulke gave a talk about using the pandas library for statistical analysis and, in the process, demonstrated the awesomeness of iPython Notebook (see the notebook at goo.gl/c84WQ). Particularly interesting were the I/O libraries for dealing with data in Google Analytics. Alas, he also used “data scientist” without scare quotes.

Someone else, whose name I missed, gave a brief introduction to R as a lightening talk so that we’d all have a basis for comparison. He used RStudio which looked pretty interesting. He also described RPy2, a Python library which provides an interface to R. After the break someone else (was it Jiří Baum?) mentioned gnumpy, which is like Numpy but ships stuff to the GPU for processing.

Tim Ansell spoke about playing with gstreamer pipelines in Python. He gave a quick rundown on gstreamer pipelines, the gst-launch command and the gst Python module. I’ve been meaning to play with gstreamer since an LCA talk a few years ago; perhaps it’s time?