PyCon Australia 2013, Day Two

These are my notes from day two of PyCon Australia 2013 in Hobart, Tasmania. Posted by Thomas Sutton on July 7, 2013

I’m attending PyCon Australia 2013 in Hobart, Tasmania. DjangoCon Australia 2013 and day one of PyCon Australia 2013 were both fantastic, and I expect today to be just as good. I’ll try to update this post over the course of the day. Not real live blogging, more delayed telecast blogging.

I’m typing these notes during the sessions, so there may be errors and omissions. Any such problems are my fault and not that of the speakers.

Today’s programme is pretty much jam packed with interesting-looking talks. You’ll be able to find videos of all the talks on eventually.

Tennessee Leeuwenburg on Sharing stuff with Python

My notes on this talk are not as coherent as I’d like. You’ll be better off with the video when it is available.

A recent study at Cambridge found the global cost of debugging software is $312 billion per annum.


The differences between zebras and donkeys aren’t just chrome. There’s no way to get from zebra to donkey.

Distinction between genotype and phenotype; “wild type” of phenotype persisting over generations? I should do some reading and clear this part up.

The lines on a phylogenetic tree are “work” in the sense that some effort needs to happen to make transitions.

The common perception that decisions made at the start of a project are cheap or free is a myth; we don’t have visibility on requirements, etc. that will allow us to make such decisions.

Don’t believe everything you think

Teams of people working together.

Role as manager is to bring the whole “thing” together, but plagued by the sense that doing wrong thing.

There’s always a reason for things: new to role, Dunning-Kruger effect.

In order to assess competence, you have to be competent. Not in a general sense of being smart, or a good person: specific expertise in the domain at hand.


Film about motivation at work by RSA Animate. MIT study of challenges with 3 levels of rewards. Rewards are effective for purely mechanical tasks, but larger rewards led to poorer performance in tasks with more than rudimentary cognitive complexity.

Replicated in rural India with comparitively huge rewards. Has been replicated over and over again.

Money is not an effective motivation beyond the level required to make it not a constant focus. Real motivators are:

  • Autonomy
  • Mastery
  • Purpose

How can we give our managers, our managees, our customers the control they all need?

A process

  1. Information & input is interesting.
  2. Synthesis & integration developers.
  3. Change & moving on refocuses.

1-2) Reading and consulting 2-3) Acting bubbling producing 3-1) Accepting adapting inspecting

How does this map to some of the organisational psychology models I saw in a talk at the Perth Agile meetup last year?

Disconnect between team members can arise from the position of each people within the cycle; their existing background, knowledge, etc.


  • Motivation Disconnect
  • Delusion of competence

The manager disconnect

Two types of managers:

  • the technical manager: support technical issues, perhaps with less ability to set directions, etc.

  • the domain expert manager: perhaps disconnected from technical issues, “it’s very simple”.

Productivity of teams can be promoted:

  • Diversity of team members can help to resolve local minima.
  • Development of trust between team members, that each will contribute, etc.
  • Performing as a group
  • Equality of access to information
  • Divide thinking and productivity across the team
  • Individual productivity and flow

Transparency of information

Transparency at all costs; giving people knowledge and visibility will develop trust and let them let you do things.

PyPy’s Speed Center lets users and community members see the impact of work, trust that what’s happening is important.

Using tools like iPython Notebook to allow people to examine/test/demonstrate code; it helps to minimise the distance between us and them. Demonstrating disagreement between data and models, giving evidence which lets them do their scientist thing: it’s not a competition. Making things tangible and concrete allows diverse team members – scientists, technologies, managers – to communicate effectively.


You need to know what their charts look like & what their numbers look like. How to make them in matplotlib.

Ed Schofield on Modern scientific computing and big data analytics in Python

Covering “big data” in more details than just as a buzz word; Python tools for working with big data, etc; some examples on a running iPython Notebook server; and some crystal-ball gazing.

Ed did a PhD in machine learn, then postdoc in bioinformatics; training and consulting in using Python for related topics.

Most scientists and engineers are programming for 50%+ of their work time; as self-taught programmers, many of them are using the wrong tools or in the wrong way.

Big Data

The nature of “big data” is somewhat relative but one, very practical, definition is: data that is too large to fit in main memory.

Exponential decay in cost per GB of storage is driving data. 300m photos uploaded Facebook per day; 86m CT scans in the US every year; average of 1 minute of driving by UPS drives is an $86m saving.

Predictive analysis is easy with big data, harder with medium data, small data is hard. Having more data makes learning easier, more effective, etc.

With big data, it’s impractical to move the data to the code; we have to send code to the data.

Trend in clock speeds levelling off.

EC2 instances cost 1/320th the cost per time unit of a human’s time. This gap will continue to grow.

Parallelisation with MapReduce, etc.

MapReduce isn’t suitable for iterative algorithms. Other models include Spark (Erlang, from somewhere in the UCA system).

Noise reduction, signal processing (image super-resolution), prediction, clustering (unsupervised learning), classification, compression (JPEG).

Python 3

Pretty much the entire stack of scientific Python libraries work on Python 3.3 but a few are missing:

  • scikit-learn
  • PyTables is only alpha
  • Numba is difficult
  • RPy2
  • statsmodels
  • boto - needs help
  • mrjob - mapreduce (uses boto)
  • disco
  • matplotlib basemap


Big data is relatively new, but “big CPU” problems are not. Route finding (as in Google Maps), circuit layout minimising power consumption, protein layout prediciton. The field of high-performance computing intersects with that big data.

Traditional high-performance computing was based on proprietary platforms with much nicer reliability properties than commodity hardware. HPC has a lot of issues which traditional platforms don’t: synchronisation, communication, disparity of access to memory, etc.

Using proper tools can ammeliorate some problems: using BLAS libraries avoids GIL problems (for the BLAS computations, at least).

Parallel iPython.

Apache Hadoop is a big, complex Java system for distributed computing. Using the mrjob library makes interacting easy.

Most of the world’s supercomputers spend most of their time running Monte Carlo simulations; not necessary to use MapReduce framework, but can help.

  • UK real-time traffic data.
  • DB of 20 million songs.

Running an mrjob computation is easy to scale:

# Single process on local machine
python input.txt

# Parallelised on local machine
python input.txt -r local

# Parallelised on EC2
python input.txt -r emr --conf-path=aws-details.yml


  • NumPy forms the cornerstone of scientific computing in Python.

  • PANDAS provides a high-level interface.

  • SciPy fills the “numerical recipes” role.

Other important projects include

  • NLTK for natural language processing.


  • London olympics metal tally
  • Landsat satellite imagery

Password: 118490219357

Next verison of iPython will include which can generate a PDF from a notebook file. Support for displaying as a slideshow.

iPython Notebook with PANDAS to load olympics medal tally data from CSV, then filter and plot it.

Parallelisation within iPython starting a cluster and using the %px magic.

NumPy using native BLAS libraries will parallelise automatically. Example of matrix inversion with htop so we see all the cores doing work.

Signal processing with gaussian and other kernels. Image reconstrution, using FFT to detect and remove periodic noise in an image.

See the videos for the sklearn notebooks.

SciKit Learn examples

samples = get_some_data_from_somewhere()

# Separate the labels from the data
y = samples[:, 0]
samples = samples[:, 1:]

# Fit a model
clf = LinearSVC(), y)

Replaced my thesis with 6 lines of Python.

Where to now?

If you’re at the sprints, why not help port scikit-learn to Python 3?

Kaggle run machine learning competitions and provide real data sets for research. One example is smart metre data and identifying electrical appliances.

1.3 GB of bird audio recordings (also Kaggle).


Is mrjob the best of the similar libraries?

Some of the other libraries offer more powerful

It seems to be the easiest to set up and get working with.

How easy is it to get the parallelisation working out side of iPython Notebook? How easy is it to use iPython and iPython Notebook to load and explore an existing code base?

I don’t think there’s any need to use the iPython interface, but it should all work so long as you have it installed and setup.

You can load and run existing code with the iPython magic commands. Tend to use iPython Notebook in the same way as a REPL.

What’s the Sydney course like?

Working with these tools and actually doing exercises, etc.

What packages should we be using to plot and visualise data, etc?

Almost certainly matplotlib.

Would you use PANDAS for CSV data and NumPy for gridded data (with no sensible labels)?

Yes probably easier to use PANDAS to get data in, even if you just want the NumPy data.

Tools for processing logs and such?

scipy.stats and statsmodels

Numeric libraries?

Intel math kernel library or, on Mac, Apple’s veclib.

Richard Jones on Don’t Do This

Poking around in some of Python’s strange corners and find some things that you probably shouldn’t do.

Corner cases of the grammar

Mixin classes: base classes can be an expression:

class Foo(JSON or Marhsal):

So can exceptions:

catch eval('NameError'):

If you use a generator here, you can catch some exceptions (like the webscale logging in MongoDB).


The __code__ attibute on a function object contains the code of a function. It’s a mutable attribute so you can change it.

Modules have a __code__ attribute too (the .pyc files are just mashalled the __code__ attribute marshalled).

Parse XML document, generate AST, compile the AST. Add a finder, loader, etc. and you can now load modules written in .pyxml files. Can be used to implement E/DSLs, macros, etc.

locals() and globals() give you a handle to a dictionary of the appopriate scopes. Using inspect.currentframe().f_back.f_locals.update() to dump crap from JSON into a scope.

The __bases__ is also mutable. Just change your mixin at runtime.

ContextManagers to do namespacing. And do useful things like make every variable in your scope global.

The [q]( package will dump an expression and it’s value into $TMPDIR/q. Inspects code at locations in the callstack to tell whether it’s being called as a function or decorator.

Type-based overloading a la Java with the overload package. Allows you to overload functions and methods and classes and such. Uses introspection on things like __defaults__, __code__.co_argcount, __code__.co_varnames, __annotations__ (for types), __code__.co_flags (for *args and **kwargs).

Back to q:

import q; q(foo)

But modules aren’t callable. Replaces the module in sys.modules['q'] with a class; but the module gets garbage collected so Q does a lot of stuff to make things work (imports in class defn, etc.) Instead, make modules callable!

Callability is handled in the C types code. There’s a member in a C structure which points to the “call” function which can run the callable object. Use the ctypes module to get C function points, reach into the type structure.

Do other things to built-in types with ctypes. See forbiddenfruit which can add stuff like days() to int so you can be more rubyish:



With great power comes great responsibility.

A lot of these things have been added to the language over time. They are very powerful and, sure, you can do stupid things with them but it’s genuinely useful too.

Brianna Laugher on Dynamic visualisation in the IPython Notebook

Slides and material available at Using iPython Notebook with matplotlib plotting, e.g, gridded data and rendering it on WMS maps.

iPython Notebook has a rich display system, this is essentially different versions of __repr__: just add a png() @property method to your classes which returns an iPython Image.

Using Leaflet and OpenLayers to display map. PyDAP to access data from a DAP server when it’s needed. Can input and output a bunch of formats (including WMS so it can be used as a map layer).

PyDAP only produces WMS output in the EPSG:4326 projection (not the normal online projection). You need to find a matching baselayer.

Code we need to hook all this up and display it in iPython Notebook. About 30 lines of Python, need to put it in an iframe w/ b64 encoded source (iframe for JS isolation; b64 or, if external, just URL).

See also:

Russell Keith-Magee on Tinkering with Tkinter

Trades Cloud, Django core developer, Django Software Foundation president.

Introduction (or, for the old people, re-introduction) to a neglected corner of the Python standard library: tkinter, Python’s interface to TK (from TCL/TK). Extremely simple interface based on passing strings around.

TK had truly awful UI (nothing like system defaults), limited UI widgets, poor documentation, poor layout engine. All this changed in tk 8.5: new theme layer and layout system which match native widgets (some are native widgets) make sense, the docs don’t suck any more (and give examples in multiple languages, not just TCL).

Rich text widget (syntax highlighting, etc.), canvas, raw access to TK bindings.

1988 Borland TurboPascal w/ excellent compilers and user interface, 1994 UNIX w/ gcc and gdb, 2000 working with Python but pdb is awful, 2013 still no useful interface for debuggers, etc. Open source software development tools have not improved (in UI terms) in 25 years, if anything they’re worse.

The UNIX philosophy says “do one thing well”; not “do one thing with a horrible user interface”. We can fix this!

Cricket is a UI to discover and run test suites:

pip install cricket
python -m cricket.django

Andrew Walker on Managing scientific simulations with Python with RQ (Redis Queue)

The complexity of simulations can vary significantly. This presentation focusses on cases when the task is too large for a single process, to big for a single machine, too small for a super computer, inappropriate for the cloud. Ideally about 20-50 cores.

Python tools

  • iPython Parallel - this is the first place to go for parallelisation if you’re an experiences developer. Supports a bunch of communication methods.

  • celery - work queue for workers to consume. See Ian Oswelds? HPC notes.

Challenges of these tools include lots of one-off configuration with transport mechanisms, storage, etc. They can be difficult to get right (more moving parts) and more effort to monitor.

Was looking for a solution that can be picked up in a single afternoon, with no configuration files to edit.

Redis Queue

Redis is a “data-structure server” which lets you associate keys with a range of value types. Written in C with bindings in most popular languages. Supported types include lists, maps, sets, etc.

Redis Queue is a pure Python package for doing job queuing using Redis to store the queue data.

  • The rqworker command allows you to start a worker.

  • The rqinfo command allows you to see the status of a queue.

  • rq-dashboard is a web interface to the queue.

  • Calling q.enqueue('operator.add', 2, 3) adds a job to the queue to run the operator.add function with the specified arguments.

The travelling salesman problem

A naive solution (enumerate all permutations, calculate lengths, sort) is fine for 8 nodes, 20 is too slow. This is pretty characteristic behaviour in these simulation problems.

A better approach might be to randomly generate permutations and calculate their lengths. This will let you approximate a good enough answer in an amount of time that you control.

Use Redis Queue to distibute the algorithm.


  • Need to intervene to add robustness

  • Need to distribute source code to workers

  • Everything must be serializable

  • Potentially memory hungry (pickled 500 copies of a graph and stuff them into Redis)

  • Overhead of spinning up many Python processes (though if you leak resources this could be a pro).

  • There’s some cleanup required (.pid files, etc.)

  • Make sure it will help enough.

  • Make sure you can’t solve the problem better (algorithms)

  • Understand the costs (maintenance, resources)


Picking functions, etc.

Use function names and let Python resolve the names. Don’t try pickling functions, callables, etc.


Try to design relatively independent parallel units of work.

Perhaps there’s some parallel with test cases in a testing framework?


Debugging, break points, etc.

Almost certainly won’t work at all. Usually develop and debug on a smaller case, then distribute working code using thr queue.

PyCloud library has an extended pickle which can handle some cases of sending code around.

iPython Parallel does some of that too. If you need it, the other tools are probably more suitable.

Roger Barnes on Building data flows with Celery and SQLAlchemy

11 years at a business intelligence vendor; this is based on a reporting system built as a contractor.

Data warehouring (AKA data integration); providing a framework for data processing in Python rather than a heavy-duty big data analytics stuff.

Data warehousing involves processing and integrating data for reporting purposes in ways that are timely, unambiguous, accurate, complete from the disparate sources across an organisation.

Focusing here on extracting, transforming and storing data.

Python for rapid prototyping, code re-user, leveraging existing libraries. Also good for decoupling data flow management, data processing, business logic from each other. Alas, there aren’t many systems in the Python space which address these issues (most people roll their own): Bubbles

Moving data around

  • Flat files
  • NoSQL databases
  • Relational databases.


Python library providing SQL database access and, separately, ORM functionality. Very widely used with good support for various databases and Python environments.

ORM layer is unnecessary when doing the sort of work involved in the systems we’re building.

Units of work

Single units of work should be encapsulated as processors. Such tasks might include reading data from a CSV file, selecting out of a database, etc.

An example might be

class SalesHistoryProcessor(CSVExtractMixin, DatabaseProcessor):
	"Read data from a CSV into a database table."

class AbstractDeriveTransform(DBProcessor):
	table_name = None
	key_columns = None
	select_columns = None
	target_columns = None

class DeriveFooTransform(AbstractDeriveTransform):
	table_name = 'Sales'
	key_columns = ['txn_id']
	select_columns = ['name', 'location']
	target_columns = ['foo']

    def process(self):
		return derive_foo(...)


Celery is a distributed task queue. Canvas is a workflow system built on top of Celery. Combines tasks in groups, chains, etc.


Organised into layers:

  1. Extraction layer finds and imports data.
  2. Transform layer cleans and normalises data.
  3. Output aggregared data into reporting structures.

Can be useful to keep copies of data in each layer, especially during development.


Are there any tools to help with schema migration with SQLAlchemy?

There are two libraries for use with SQLAlchemy.

One thing I’ve done in this system is having new columns in the source automatically carry through to the end.

There seems to be a lot of structure here?

The 11 years in BI was a Java shop.

The project was originally using functions, but it changed into classes as common code was refactored, etc. The layering, shared code and mixins seems to work properly.

How do you pass information through the system?

You can pass parameters to tasks, etc. but we aren’t using any thing like that. The system uses a single database and we let the data speak for itself.

Some tables have timestamps (per column, even) which are used to filter for incremental processing, etc.

There are a few broker options in celery.

Redis and Rabbit both worked just fine for this (not SQLAlchemy, not getting events and monitoring, etc.)

Lightning Talks

One: Russell Stuart

Write; another parsing library, supports LR(1) parsers. Wrote it to parse SQL statements. 821 parsing modules on PyPI. Everyone uses ply (it’s number 506). is fast (twice as fast as nearest competitor).

Pyparsing has sample code and a book, has Sphinx documentation including a tutorial.

Comes with an SQLite3 SQL parser (tested against their test suite). And a lua to Python compiler.

Order of magnitude simpler code.

Two: Engineering

Intersection of science, technology and society. How can we help society.

Book Citizen Engineer. We need to engage, communicate, lead as engineers. We can make differences as individuals and as a profession.

  • Children: help teach and make things accessible to kids.
  • Community:
  • Our rights: lobby and help with technological protections
  • Computing: user groups, open source, conferences, advocacy, etc.
  • World: science, research, etc. all need support from technologists.

Three: Russell Keith-Magee

Django user groups around Australia and the world.

User groups:

  • Perth
  • Melbourne
  • Sydney
  • Brisbane only has a Python users group
  • Adelaide doesn’t have any groups.
  • Hobart doesn’t have any groups.
  • Canberra

DjangoCon US in Chicago this year, PyCon US 2014 in Montreal, DjangoCon EU 2014 on the French Riviera, DjangoCon Australia 2014 will happen (please volunteer).

DjangoCon Australia 2013 t-shirt

Four: Frank

Version 3 of the talk. Global climate change. Thunderbirds were really good puppets, saved people.

Crowdsourcing information about climate change.

Taiga serves NetCDF files (similar to the mapping system described in Brianna Laugher’s talk).

Five: Flask Analytics

Rhys Elsmore - @rhyselsmore

Buffet is like a worker queue, except celary has never run out of chocolate cake.

Flask Analytics captures every request, no JS involved.

Lots of funny jokes.

Six: David Beitey


Useful Python tools:

  • meme package.

  • natural convert raw data values into “human”. Dates, time deltas, sizes, etc.

  • dogpile.caching is a successor to the caching functionality in Beaker.

  • fanstatic does stuff for JS/CSS resources.

  • TileStache is a tile cache.

  • uWSGI container

Seven: Jacon Kaplan-Moss

pip install python-nation

Doesn’t send Facebook updates; didn’t search disk for naked photos; didn’t steal private keys; didn’t take a picture.

Eight: art

I don’t like art theory, conceptual art, etc. so I turned off, sorry. All I got was the obligatory mention of Fountain with the inevitable joke about taking the piss.

Nine: Appium

Appium is Selenium for apps (iOS and Android). fjords

Ten: COMPCON2013

Computing student conference.


Eleven: Moore’s Cloud

The people who hacked on the light thing did some cool things.

Twelve: Even Brumley

Python and the Playstation Move. There’s a good public API to play with.

The world’s first RESTful API for interpretive dance.

Bluetooth device with a bunch of sensors and a few outputs (RGB LED, rumble, etc.)

API in C with SWIG bindings in Python, etc. Installation involves Cmake, so don’t bother unless you have time and animals to sacrifice.

Made a RESTful API for it last night after Mark’s talk.

Thirteen: Brett

Having a favourite language is cool, limiting yourself to that one language isn’t cool. Practical Object-Oriented Design in Ruby


  • Ruby
  • Golang
  • Rust
  • Clojure
  • Prolog

Try something different.

Fourteen: Tim Ansell

PyCon Australia was pretty great. We should all go to the sprints too.

Linux Australia does lots of things (it’s more “Open Source Australia”).

  • Drupal Downunder
  • Wordcamp
  • Joomla day

Also go to Barcamps.

Also OSDC.

Also user groups.

Videos from this and other Python conferences and events are at


Thanks to all the speakers, volunteers, organisers, sponsors and attendees.

Yay for Google, ACS, Tasmania Department of Economic Development, TASICT, PSF, Anchor, CSIRO, DSF, Aptira, Secret Lab, New Relic, Heroku, Biarri, Redhat, Rackspace, Github, Freelancer, Infoxchange, and others. 2014 Jan 6-10 in Perth, CFP closes on July 20th.

NCSS challenge for high school students

Kiwi PyCon Sept 7-9 in Auckland

PyCon Australia 2014 & 2015 will be in Brisbane. From the city that flooded LCA 2011. First week of August.

Special Thanks

  • Wrest Point was a great venue.

  • Ritual Coffee Tasmania did great coffee. (BeautifulPlumage)

  • Next Day Video did the recording.

  • Tim Ansell is the godfather of PyCon Australia.

  • Kate and Neil Davenport did the t-shirts.

  • The volunteers and session chairs.

  • The speakers

  • The miniconf organisers (Tristan Goode and Tim Fifield; Russell Keith-Magee)

  • Chris Neugebauer (Co-organiser), Richard Jones (programme committee chair), Josh Deprez (Code Wars), Casey Farrell (Miniconfs), Matthew D’Orazio (Treasurer), Joshua Hesketh (Co-organiser).

Not finished yet!

  • The after party is a Jack Greene from 18:30 tonight.

  • Sprints start tomorrow at 09:00.

This post was published on July 7, 2013 and last modified on October 4, 2021. It is tagged with: event, python, conference.