.. Objectives

.. ----------

.. By the end of this tutorial you will --

.. 1. Get to know simple statistics functions like mean,std etc .. (Remembering)

.. #. Apply them on a real world example. (Applying)

.. Prerequisites

.. -------------

.. Getting started with IPython

.. Loading Data from files

.. Getting started with Lists

.. Accessing Pieces of Arrays

.. Author              : Amit Sethi

   Internal Reviewer   : Puneeth

   External Reviewer   :

   Checklist OK?       : <put date stamp here, if OK> [2010-10-05]

.. #[punch; add slides, exercises!]

Hello friends and welcome to the tutorial on Statistics using Python

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In this tutorial, we shall learn

 * Doing statistical operations in Python  

   * Summing set of numbers

   * Finding there mean

   * Finding there Median

   * Finding there Standard Deviation 

.. #[punch: since loadtxt is anyway a pre-req, I would recommend you

.. to use a data file and load data from that. that is good, since you

.. would get to deal with arrays, instead of lists. 

.. Talking of rows and columns of 2-D lists etc is confusing. Also,

.. converting to float can be avoided. The tutorial will feel more

.. natural, is what I think. 

.. The idea of separating the main problem and giving toy examples

.. doesn't sound good. Use the same problem to explain stuff. Or use a

.. smaller data-set or something. Using lists doesn't seem natural.]

For this tutorial We will use data file that is at the a path

``/home/fossee/sslc2.txt``.  It contains record of students and their

performance in one of the State Secondary Board Examination. It has

180,000 lines of record. We are going to read it and process this

data.  We can see the content of file by double clicking on it. It

might take some time to open since it is quite a large file.  Please

don't edit the data.  This file has a particular structure.

We can do ::

   cat /home/fossee/sslc2.txt

to check the contents of the file.

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Each line in the file is a set of 11 fields separated 

by semi-colons Consider a sample line from this file.  

A;015163;JOSEPH RAJ S;083;042;47;00;72;244;;; 

The following are the fields in any given line.

* Region Code which is 'A'

* Roll Number 015163

* Name JOSEPH RAJ S

* Marks of 5 subjects: ** English 083 ** Hindi 042 ** Maths 47 **

Science 35 ** Social 72

* Total marks 244

Lets try and load this data as an array and then run various function on

it.

To get the data as an array we do. ::

     L=loadtxt('/home/amit/sslc2.txt',usecols=(3,4,5,6,7,),delimiter=';')

     L

loadtxt function loads data from an external file.Delimiter specifies

the kind of character are the fields of data seperated by.  usecols

specifies the columns to be used so (3,4,5,6,7) loads those

colums. The 'comma' is added because usecols is a sequence.

As we can see L is an array. We can get the shape of this array using::

   L.shape

   (185667, 5)

Lets start applying statistics operations on these. We will start with

the most basic, summing. How do you find the sum of marks of all

subjects for the first student.

As we know from our knowledge of accessing pieces of arrays. To acess

the first row we will do ::

   L[0,:]

Now to sum this we can say ::

    totalmarks=sum(L[0,:]) 

    totalmarks

To get the mean we can do ::

   totalmarks/len(L[0,:])

or simply ::

   mean(L[0,:])

But we have such a large data set calculating one by one the mean of

each student is impossible. Is there a way to reduce the work.

For this we will look into the documentation of mean by doing::

    mean?

As we know L is a two dimensional array. We can calculate the mean

across each of the axis of the array. The axis of rows is referred by

number 0 and columns by 1. So to calculate mean accross all colums we

will pass extra parameter 1 for the axis.::

    mean(L,1)

L here is the two dimensional array.

Similarly to calculate average marks scored by all the students for each

subject can be calculated using ::

   mean(L,0)

Next lets now calculate the median of English marks for the all the students

We can access English marks of all students using ::

   L[:,0]

To get the median we will do ::

   median(L[:,0])

For all the subjects we can use the same syntax as mean and calculate

median across all rows using ::

       median(L,0)

Similarly to calculate standard deviation for English we can do::

	  std(L[:,0])

and for all rows::

    std(L,0)

Following is an exercise that you must do. 

%% %% In the given file football.txt at path /home/fossee/football.txt , one column is player name,second is goals at home and third goals away.

   1.Find the total goals for each player

   2.Mean home and away goals

   3.Standard deviation of home and away goals 

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This brings us to the end of the tutorial.

we have learnt

 * How to do the standard statistical operations sum , mean

   median and standard deviation in Python.

 * Combine text loading and the statistical operation to solve

   real world problems.

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This tutorial was created as a part of FOSSEE project, NME ICT, MHRD India

Hope you have enjoyed and found it useful.

Thank you!