Here is a guide to installing Python for data analysis on a Mac, along with a few extra tips to get going.

The Anaconda distribution is a very convenient version of scientific Python that installs a lot of modules as well as a Launcher that offers three GUI apps:

• IPython Console, 
  
• Spyder (a MATLAB-GUIDE-like application) and 
  
• Jupyter Notebook. 
  

One of the great things about Anaconda is the ease of updating everything. On one hand, most of what we do doesn't need updating, but on the other the whole SciPy ecosystem is evolving so quickly that it seems silly to update less than twice a year. The GUI apps can be updated by pressing buttons in the Launcher, while Anaconda as a whole can be updated with two lines.

As shown here, you just need to open the command line (e.g. Terminal on Mac) and type
    $ conda update conda 
    $ conda update anaconda
The first line will update the conda package manager, the second line will use conda to update all the modules in Anaconda.

Update: Some packages that are not in Anaconda will require installation with pip. To install pip, follow these instructions here. If you have pip already installed you can update pip:
    $pip install --upgrade pip
and then use it to install a bootstrap package:
    $pip install scikits.bootstrap
This bootstrap package can then used as per this tutorial.

OK so I needed to import a .csv table into Matlab, but knew it could be tricky. A friend wrote some Matlab code for me once to do this and it was a page-and-half of fiddly code, filled with string trimming and required quite prescriptive column and row names. 

I knew pandas could handle csv files nicely, but was keen to keep things in Matlab since the rest of the code was there etc. So I google for the best way to do it and got this. Its a whole blog called Abandon Matlab on how awful Matlab is! Has chapter and verse about how Matlab does not handle csv files with mixed data... I did not go looking for this.

In late 2014, to install Octave and Oct2py for use in IPython on a Mac, take the following steps.

1. Download the Octave binary package from Octave Forge and install
   
   
2. Then install oct2py, by opening a terminal window and entering
   $ pip install oct2py
   as per this page.
   
   
3. Python and oct2py are still not aware of Octave's location. To make them aware, edit your bash profile. As per this page, open a terminal window and enter
   $ touch ~/.bash_profile; open ~/.bash_profile
   This will open your bash profile in TextEdit.
   
   
4. To the bash profile use the method from this page; add the following line of text
   export PATH=/usr/local/octave/3.8.0/bin:$PATH
   You may need to adjust this text as per the version of Octave you are using.
   
   
5. Now as per this page you should be ready to either call Octave as a library of IPython, e.g.
   >>> from oct2py import octave as oc
   >>> x = oc.zeros(3,3)
   >>> print x, x.dtype
   [[ 0.  0.  0.]
    [ 0.  0.  0.]
   [ 0.  0.  0.]] float64
   
   
6. Or you can use oct2py in IPython magic mode like
   In [1]: %load_ext oct2py.ipython
   In [2]: x = %octave [1 2; 3 4];
   x
   Out[2]: array([[ 1.,  2.],
          [ 3.,  4.]])
   as per this page.

Yes this would have been better as a Notebook ;)

An example of an IPython Notebook that converts Python plots into fun, annotated cartoons, suitable for presenting pseudo-quantitative hypotheses: XKCD plots in Matplotlib. It demonstrates some of the beauty of the IPython Notebook format (.ipynb), by being a blog post as python code (or vice versa?). The dual identity of .ipynb files and their mixed-media format solves many problems with other data analysis software: the lack of narrative, the difficulty of sharing and the challenge of 'provenance' - knowing how a result was arrived at, from what data and which analytic methods.

A post by Fernando Perez, the founder of IPython, describes the philosophical aims of the Notebook model (e.g. transparency, easy shareability), and gives some more substantial examples: "Literate computing" and computational reproducibility: IPython in the age of data-driven journalism. In particular, he describes how massive economic pain was inflicted by governments with support from some economists' buggy Excel spreadsheet (Reinhart-Rogoff), and how that this is something that would not have happened with a shared .ipynb file. Such a file was produced in 3 hours by a member of the Python community!

In my view the clear superiority of Python over Matlab is based on things that did not exist when I first reviewed switching in mid-2011. Both the Notebook interface of IPython and the Pandas data analysis library were released with around the end of 2011, and since then both tools have gathered an incredible ecosystem around them. Furthermore, the IPython Notebook now also has 'magic' functions that allow one to call Octave (a free Matlab-like language) and R (a major language for stats) commands in a notebook, features that have evolved dramatically in recent years. Indeed, the first book about IPython was only released in April 2013.

Update: Other great (new-ish?) features of Python are pip install and StackOverflow - they make things much easier than my experience in 2011.

I am pretty sick of seeing the above, the Matlab license checkout error message. It comes up when my work copy of Matlab is open. Since my Matlab license only gives permission to use a single copy, the license server prevents me from using it on my work laptop at home. 

Within 20 minutes of seeing this error message, I had downloaded and installed the Anaconda distribution of Python, and used it to do the calculation I wanted to do (a simple linear regression), even though I am not familiar with the Python language. Google served as my help search tool.

It was fewer lines of code, more straightforward than the Matlab script I was going to use and has a better interface (the IPython Notebook). From now on, the lab will be moving away from Matlab and working to use Python wherever possible. We are installing the Anaconda distribution on the server next to our data - so we also avoid having to move files across the network just to analyze.

-Adam

Update, 6th December 2014. 
I just updated my browser to Yosemite, and now Matlab 2012b is completely broken on my laptop.

PhD Studentships in Neuroscience
Behavioral Neuroscience
Laboratory of Neurogenetics
Duke-NUS Medical School
Institute of Molecular and Cell Biology

The Singapore-based Duke-NUS Graduate Medical School is a collaboration between Duke University and the National University of Singapore. It has a mission for research and training biomedical scientists and physicians. The Laboratory of Neurogenetics under the direction of Assistant Professor Adam Claridge-Chang focuses on analysing essential brain functions with behavioural, genetic, anatomical and physiological methods, with an aim to discovering some of the basic components and mechanisms that underlie psychiatric and cognitive dysfunction. The laboratory uses Drosophila neurogenetic methods combined with ongoing molecular, instrumentation and analytical tool development and application to understand the molecules and circuits that support learning. For additional information please visit www.claridgechang.net.

The lab has an open PhD studentship in behavioral neuroscience.

A candidate must have:

• A high level of motivation and a keen interest in neuroscience
• A BSc, MSc or equivalent in a relevant field
• A background and knowledge of neuroscience
• Strong computer skills for recording, analyzing and organizing data
• An ability to work closely with other scientists and an ability to quickly learn new techniques
• Excellent communication, teamwork and project management skills
• Experience in scientific sourcing & procurement

A candidate’s application will be strengthened by:

• Relevant skill set to perform the following techniques to their project: behavioural experimentation including videographic analysis, neurogenetic and neuropharmacological experiments, the use and programming of software for data analysis and statistics (including MATLAB, Excel, R and/or other statistical packages), apparatus design & testing, real-time apparatus control methods including stimulus delivery and control, fluorescence microscopy, laser confocal microscopy, image analysis (ImageJ, Zeiss LSM Browser, PhotoShop, Metamorph and/or MATLAB) and neuroanatomical analysis (e.g. Neurolucida)
• Knowledge of other techniques including: Drosophila genetics, molecular biology (subcloning methods including restriction digests, PCR and gel electrophoresis), methods for analysis of DNA and protein sequence (BLAST, Vector NTI, etc.), protein analytical methods, use of genetically-encoded indicators (GFP and derivatives, luciferase), analysis of gene expression (Q-PCR, Northern blotting, in situ hybridization), immunohistochemistry, fine-scale brain dissection, graphic design (e.g. Adobe Illustrator), database use (e.g. Filemaker Pro), electronics, electrophysiology, optogenetic physiology and two-photon microscopy.
• Training will be available in some or all of these techniques where needed and appropriate.

Your doctoral studies would include but are not limited to the following:

• To pursue one or more projects using the above described methods and others as necessary for the project
• To learn new methodologies when necessary or useful
• To participate in the education and training of other staff as appropriate
• To acquire and maintain knowledge of the literature relevant to the project
• To attend appropriate scientific seminars and meetings and to remain current with the broader field of neurogenetics
• To effectively analyse your results
• To regularly communicate your results to the rest of the laboratory members
• To work with colleagues on collaborative projects
• To share responsibility for the housekeeping of the laboratory area, general maintenance tasks and fly stock maintenance
• To conduct scientific sourcing and procurement
• To share responsibility with others for the safety in the laboratory
• To perform any other duties as may be required from time to time to ensure the efficient running of the laboratory

Interested applicants are welcome to email

1. a letter describing your interest in neuroscience
2. a detailed resume
3. supporting documents
4. contact details for at least two referees

by 13th May 2014 to Adam. Please note that your subject title and/or cover letter should indicate the reference number  (Ref no: PN/ACC/PHD-BN/13052014/LW). If you found this page via an advertisement, please use the advert's unique code instead. Only short-listed candidates will be contacted.

Funding for the position can be through the SINGA program (Singapore International Graduate Award) for which the deadline is 1st June 2014 to start study in January 2015.

Please contact us at least six weeks prior to the SINGA deadline. Other scholarships are available, please review options here. 

Research Assistant in Behavioral Neuroscience
Neuroscience and Behavioral Disorders Program
Duke-NUS Medical School

The Singapore-based Duke-NUS Graduate Medical School is a collaboration between Duke University and the National University of Singapore. It has a mission for research and training biomedical scientists and physicians. The Laboratory of Neurogenetics under the direction of Assistant Professor Adam Claridge-Chang focuses on analysing essential brain functions with behavioural, genetic, anatomical and physiological methods, with an aim to discovering some of the basic components and mechanisms that underlie psychiatric and cognitive dysfunction. The laboratory uses Drosophila neurogenetic methods combined with ongoing molecular, instrumentation and analytical tool development and application to understand the molecules and circuits that support learning. For additional information please visit www.claridgechang.net.

The lab is seeking to appoint a research assistant to investigate how the brain alters preference for an olfactory stimulus.

A candidate must have:

• A high level of motivation and a keen interest in neuroscience
• A BSc, MSc or equivalent in a relevant field
• A background and knowledge of neuroscience
• Strong computer skills for recording, analyzing and organizing data
• An ability to work closely with other scientists and an ability to quickly learn new techniques
• Excellent communication, teamwork and project management skills
• Experience in scientific sourcing & procurement

A candidates application will be strengthened by:

• Relevant skill set to perform the following techniques to their project: behavioural experimentation including videographic analysis, neurogenetic and neuropharmacological experiments, the use and programming of software for data analysis and statistics (including MATLAB, Excel, R and/or other statistical packages), apparatus design & testing, real-time apparatus control methods including stimulus delivery and control, fluorescence microscopy, laser confocal microscopy, image analysis (ImageJ, Zeiss LSM Browser, PhotoShop, Metamorph and/or MATLAB) and neuroanatomical analysis (e.g. Neurolucida)
• Knowledge of other techniques including: Drosophila genetics, molecular biology (subcloning methods including restriction digests, PCR and gel electrophoresis), methods for analysis of DNA and protein sequence (BLAST, Vector NTI, etc.), protein analytical methods, use of genetically-encoded indicators (GFP and derivatives, luciferase), analysis of gene expression (Q-PCR, Northern blotting, in situ hybridization), immunohistochemistry, fine-scale brain dissection, graphic design (e.g. Adobe Illustrator), database use (e.g. Filemaker Pro), electronics, electrophysiology, optogenetic physiology and two-photon microscopy. Training will be available in some or all of these techniques where needed and appropriate.

Your main duties would include but are not limited to the following:

• To pursue one or more projects using the above described methods and others as necessary for the project
• To learn new methodologies when necessary or useful
• To participate in the education and training of other staff as appropriate
• To acquire and maintain knowledge of the literature relevant to the project
• To attend appropriate scientific seminars and meetings and to remain current with the broader field of neurogenetics
• To effectively analyse your results
• To regularly communicate your results to the rest of the laboratory members
• To work with colleagues on collaborative projects
• To share responsibility for the housekeeping of the laboratory area, general maintenance tasks and fly stock maintenance
• To conduct scientific sourcing and procurement
• To share responsibility with others for the safety in the laboratory
• To perform any other duties as may be required from time to time to ensure the efficient running of the laboratory

Interested applicants are welcome to email

1. a letter describing your interest in neuroscience
2. a detailed resume,
3. supporting documents
4. contact details for at least two referees

by 13th june 2014 to Adam. Please note that your subject title and/or cover letter should indicate the reference number  (Ref no: PN/ACC/RA-BN/13052014/LW). If you found this page via an advertisement, please use the advertisement's unique code instead. Only short-listed candidates will be contacted.