[School-of-data] Python, pandas and ipython notebooks

[Simon Cropper]

Hi Tony,

Is it your intent to release the python notebooks as open content or 
will it be part of a paid course?

On 17/06/14 19:57, Tony.Hirst wrote:
> FWIW, I've been putting together some notebooks around pandas for an in
> production OU course.
>
> We can't share all the notebooks atm (version & quality control issues!
> Plus things are still in an early stage of development) but I'll try to
> post fragments for comment...
>
> The audience is computing students, so the tone and some of the exercises
> reflect that...
>
> Here's a draft of a section on pandas series and dataframe structures:
> http://nbviewer.ipython.org/github/psychemedia/ou-tm351/blob/master/noteboo
> ks-RFC/Pandas%20Intro%20-%20RFC.ipynb
>
>
> The notebook is intended to be embedded within other materials and used as
> a workbook (if you've ever used Stroud, you may feel the resemblance in
> part!); which means reading and working through the activities, running
> each cell as you come to it, then perhaps editing the cell you just ran
> and running it again.
>
> Any issues or comments, please add them to the tracker at
> https://github.com/psychemedia/ou-tm351/issues
>
> tony
>
> [Michael - I'll try to do a notebook intro post for ScoDa blog this week]
>
> On 17/06/2014 08:38, "Michael Bauer" <michael.bauer at okfn.org> wrote:
>
>> Hi there,
>>
>> On Mon, Jun 16, 2014 at 08:32:42AM +0100, Peter Murray-Rust wrote:
>>>> As I am exploring some new tools in Python, I have thought of doing
>>> this
>>>> analysis using Pandas or something similar. The code would be
>>> integrated
>>>> into iPython Notebooks so others could view the methodology and
>>> augment
>>>> where necessary, and managed in a GitHub repository.
>>>>
>>>>
>>> I'm not (yet?) an expert Pythonista but from the description of the
>>> problem
>>> it sounds like you will need multivariate statistical methods. There are
>>> lots of libraries - I would probably point you at R but Pandas points
>>> you
>>> at http://statsmodels.sourceforge.net. I would probably start with a
>>> Principal Components method to get an idea of the shape of the data -
>>> are
>>> there serious outliers, etc. and then move to classification methods -
>>> supervised and unsupervised, binary and multiple. You're almost
>>> certainly
>>> going to have to deal with missing data .
>>
>> A while back we were thinking about introducing a more advanced framework
>> for everyone who gets bored playing with spreadsheets ;) We were debating
>> on R vs. Python (Although I'm a python programmer I did most of my data
>> work in R (pandas didn't exist when I started out)). Would you want to
>> write a short introduction on python/pandas. What you need to start out
>> and
>> where to find further resources?
>>
>> Michael
>>
>> --
>> Data Diva | skype: mihi_tr | @mihi_tr
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-- 
Cheers Simon

    Simon Cropper - Open Content Creator

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