Kiwi PyCon 2013 – Auckland, New Zealand

Last week I went to Auckland for Kiwi Pycon 2013. I arrived a couple of days early and did some sightseeing with Angus, including a visit to Tangleball hackerspace in Auckland

Had an enjoyable start to the conference at the tutorial sessions on Friday. In the morning I went to Let’s learn twisted run by Aurynn Shaw and then in the afternoon I helped out at Introduction to Data Processing with Python run by Angus Gratton.

As I found at pyconau, the quality of the talks over the weekend was great. They were all recorded and are being published in lots of places:

Based on feedback about my pyconau talk, I redeveloped a similar talk for Kiwi PyCon. At the end of the talk, I didn’t repeat peoples questions so they haven’t been included in the video.

The slides and the IPython notebooks I used are available on GitHub – I’m interested in any feedback people have about the talk or about my workflow.

Pyconau 2013 – Hobart

Enjoying Hobart after pyconau
Enjoying Hobart after pyconau

Last week I had a fantastic time at Pyconau 2013 in Hobart. The videos of the talks are now available on the pyconau youtube channel, including the two Friday mini conferences, Djancon and OpenStack. They are being added to the pyvideo site too:

With the multiple streams of talks, there are lots that I missed and want to watch. Thanks to the organisers for arranging for the video and publishing them so quickly.

Exploring Science on Twitter with IPython Notebook and Python Pandas

I gave a talk about the way I’m using python for my research. Looking up at the large auditorium before my talk was unsettling, but once I started I enjoyed it. I’ve put my slides and the ipython notebooks I used to generate them onto GitHub

I had some interesting discussions with people afterwards. As I say at the beginning of the talk, I’m really happy to hear suggestions of how I can improve how I’m doing things, so please use the comments if you have any suggestions about my workflow or feedback about the talk.

Learning how to install python packages

In an earlier post I described my python setup for Debian, this time I’ve kept notes while working yesterday to get a stable python environment on Mac OS X Mountain Lion (10.8.1). I’ve used the notes to write up the steps that worked, there were a lot of dead ends along the way that I haven’t included.

Since the upgrade to Mountain Lion, I’ve had a lot of problems with my python environment not working, and in trying to sort it out I’d somehow managed to end up with a broken easy_install. I think I achieved that by downloading and installing the latest command line tools from the Apple Developer website instead of through XCode. So I’d recommend sticking with the one installed through XCode. But at that stage I wasn’t taking notes about what I was trying, so I’m not sure.

I fixed easy_install by installing distribute:

I noticed that my path had /usr/bin ahead of /usr/local/bin, but this should probably be the other way around.

This is being set by /etc/paths  and the ~/.bashrc

Changed /etc/paths to have:

Then started setting up my ipython, scipy and pandas environment. Using “distribute” in the virtual environment seems to work better at building some packages.

The scipy install failed, and I fixed it by installing the dev version as suggested here –

The matplotlib install failed, so I then followed these instructions – (but not step 5) to install X11 and pkg-config as follows:

download and install X11 from

logged out and in to activate X11.

(not sure this actually helped)

The x11 libraries are not on the path (which I thought was what pkg-config was meant to fix?) Fixed by using:


So I now have a virtual environment with the right Python packages installed, and I understand a bit more about working out how to install packages when they fail. I still need to work out what the difference is between pip, distribute and easy_install.