# HG changeset patch
# User amit
# Date 1285133060 -19800
# Node ID eb5dd4c7c5beeed511def9de88fe176f8d18afff
# Parent  c1242f073db39d8b22cb83b6e3dcd0a9f1ac4acc
Initial commit statistics

diff -r c1242f073db3 -r eb5dd4c7c5be statistics.rst
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+Hello friends and welcome to the tutorial on statistics using Python
+
+{{{ Show the slide containing title }}}
+
+{{{ Show the slide containing the outline slide }}}
+
+In this tutorial, we shall learn
+ * Doing simple statistical operations in Python  
+ * Applying these to real world problems 
+
+You will need Ipython with pylab running on your computer
+to use this tutorial.
+
+Also you will need to know about loading data using loadtxt to be 
+able to follow the real world application.
+
+We will first start with the most necessary statistical 
+operation i.e finding mean.
+
+We have a list of ages of a random group of people ::
+   
+   age_list=[4,45,23,34,34,38,65,42,32,7]
+
+One way of getting the mean could be getting sum of 
+all the elements and dividing by length of the list.::
+
+    sum_age_list =sum(age_list)
+
+sum function gives us the sum of the elements.::
+
+    mean_using_sum=sum_age_list/len(age_list)
+
+This obviously gives the mean age but python has another 
+method for getting the mean. This is the mean function::
+
+       mean(age_list)
+
+Mean can be used in more ways in case of 2 dimensional lists.
+Take a two dimensional list ::
+     
+     two_dimension=[[1,5,6,8],[1,3,4,5]]
+
+the mean function used in default manner will give the mean of the 
+flattened sequence. Flattened sequence means the two lists taken 
+as if it was a single list of elements ::
+
+    mean(two_dimension)
+    flattened_seq=[1,5,6,8,1,3,4,5]
+    mean(flattened_seq)
+
+As you can see both the results are same. The other is mean 
+of each column.::
+   
+   mean(two_dimension,0)
+   array([ 1. ,  4. ,  5. ,  6.5])
+
+or along the two rows seperately.::
+   
+   mean(two_dimension,1)
+   array([ 5.  ,  3.25])
+
+We can see more option of mean using ::
+   
+   mean?
+
+Similarly we can calculate median and stanard deviation of a list
+using the functions median and std::
+      
+      median(age_list)
+      std(age_list)
+
+
+    
+Now lets apply this to a real world example ::
+    
+We will a 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.
+
+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 AA (Absent) ** Social 72
+* Total marks 244
+*
+
+Now lets try and find the mean of English marks of all students.
+
+For this we do. ::
+
+     L=loadtxt('/home/fossee/sslc2.txt',usecols=(3,),delimiter=';')
+     L
+     mean(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,). The 'comma' is added
+because usecols is a sequence.
+
+To get the median marks. ::
+   
+   median(L)
+   
+Standard deviation. ::
+	
+	std(L)
+
+
+Now lets try and and get the mean for all the subjects ::
+
+     L=loadtxt('sslc2.txt',usecols=(3,4,5,6,7),delimiter=';')
+     mean(L,0)
+     array([ 73.55452504,  53.79828941,  62.83342759,  50.69806158,  63.17056881])
+
+As we can see from the result mean(L,0). The resultant sequence  
+is the mean marks of all students that gave the exam for the five subjects.
+
+and ::
+    
+    mean(L,1)
+
+    
+is the average accumalative marks of individual students. Clearly, mean(L,0)
+was a row wise calcultaion while mean(L,1) was a column wise calculation.
+
+
+{{{ Show summary slide }}}
+
+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.
+
+{{{ Show the "sponsored by FOSSEE" slide }}}
+
+
+This tutorial was created as a part of FOSSEE project, NME ICT, MHRD India
+
+Hope you have enjoyed and found it useful.
+Thankyou
+ 
+.. Author              : Amit Sethi
+   Internal Reviewer 1 : 
+   Internal Reviewer 2 : 
+   External Reviewer   :
+