Added new image files needed for compilation.
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%Tutorial slides on Python.
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% Author: FOSSEE
% Copyright (c) 2009, FOSSEE, IIT Bombay
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% Title page
\title[Statistics]{Python for Science and Engg: Statistics}
\author[FOSSEE] {FOSSEE}
\institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay}
\date[] {02 April, 2010\\Day 1, Session 3}
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%% Delete this, if you do not want the table of contents to pop up at
%% the beginning of each subsection:
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\begin{frame}<beamer>
\frametitle{Outline}
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\frametitle{Outline}
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% DOCUMENT STARTS
\begin{document}
\begin{frame}
\maketitle
\end{frame}
%% \begin{frame}
%% \frametitle{Outline}
%% \tableofcontents
%% % You might wish to add the option [pausesections]
%% \end{frame}
\section{Computing mean}
\begin{frame}
\frametitle{Value of acceleration due to gravity?}
\begin{itemize}
\item We already have pendulum.txt
\item We know that $ T = 2\pi \sqrt{\frac{L}{g}} $
\item So $ g = \frac{4 \pi^2 L}{T^2} $
\item Calculate ``g'' - acceleration due to gravity for each pair of L and T
\item Hence calculate mean ``g''
\end{itemize}
\end{frame}
\begin{frame}[fragile]
\frametitle{Acceleration due to gravity - ``g''\ldots}
\begin{lstlisting}
In []: g_list = []
In []: for line in open('pendulum.txt'):
.... point = line.split()
.... L = float(point[0])
.... t = float(point[1])
.... g = 4 * pi * pi * L / (t * t)
.... g_list.append(g)
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Mean ``g'' - Classical method}
\begin{lstlisting}
In []: total = 0
In []: for g in g_list:
....: total += g
....:
In []: g_mean = total / len(g_list)
In []: print 'Mean: ', g_mean
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Mean ``g'' - Slightly improved method}
\begin{lstlisting}
In []: g_mean = sum(g_list) / len(g_list)
In []: print 'Mean: ', g_mean
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Mean ``g'' - One liner}
\begin{lstlisting}
In []: g_mean = mean(g_list)
In []: print 'Mean: ', g_mean
\end{lstlisting}
\inctime{10}
\end{frame}
\section{Processing voluminous data}
\begin{frame}
\frametitle{More on data processing}
\begin{block}{}
We have a huge data file--180,000 records.\\How do we do \emph{efficient} statistical computations, i.e. find mean, median, standard deviation etc; draw pie charts?
\end{block}
\end{frame}
\begin{frame}
\frametitle{Structure of the file}
Understanding the structure of sslc1.txt
\begin{itemize}
\item Each line in the file has a student's details(record)
\item Each record consists of fields separated by ';'
\end{itemize}
\emphbar{A;015162;JENIL T P;081;060;77;41;74;333;P;;}
\end{frame}
\begin{frame}
\frametitle{Structure of the file \ldots}
\emphbar{A;015163;JOSEPH RAJ S;083;042;47;AA;72;244;;;}
Each record consists of:
\begin{itemize}
\item Region Code
\item Roll Number
\item Name
\item Marks of 5 subjects: SLang, Flang Maths, Science, Social
\item Total marks
\item Pass/Fail (P/F)
\item Withheld (W)
\end{itemize}
\inctime{5}
\end{frame}
\begin{frame}
\frametitle{Statistical Analysis: Problem statement}
1. Read the data supplied in the file \emph{sslc1.txt} and carry out the following:
\begin{itemize}
\item[a] Draw a pie chart representing proportion of students who scored more than 90\% in each region in Science.
\item[b] Print mean, median and standard deviation of math scores for all regions combined.
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Problem statement: explanation}
\emphbar{a. Draw a pie chart representing proportion of students who scored more than 90\% in each region in Science.}
\begin{columns}
\column{5.25\textwidth}
\hspace*{.5in}
\includegraphics[height=2.6in, interpolate=true]{data/science}
\column{0.8\textwidth}
\end{columns}
\end{frame}
\begin{frame}
\frametitle{Machinery Required}
\begin{itemize}
\item File reading
\item Parsing
\item Dictionaries
\item Arrays
\item Statistical operations
\end{itemize}
\end{frame}
\subsection{Data processing}
\begin{frame}[fragile]
\frametitle{File reading and parsing \ldots}
\emphbar{Reading files line by line is the same as we had done with the pendulum example.}
\begin{lstlisting}
for record in open('sslc1.txt'):
fields = record.split(';')
\end{lstlisting}
\end{frame}
\subsection{Dictionaries}
\begin{frame}[fragile]
\frametitle{Dictionaries: Introduction}
\begin{itemize}
\item Lists index using integers\\
Recall \typ{p = [2, 3, 5, 7]} and\\
\typ{p[1]} is equal to \typ{3}
\item Dictionaries index using strings
\end{itemize}
\end{frame}
\begin{frame}[fragile]
\frametitle{Dictionaries \ldots}
\begin{lstlisting}
In []: d = {'png' : 'image file',
'txt' : 'text file',
'py' : 'python code',
'java': 'bad code',
'cpp': 'complex code'}
In []: d['txt']
Out[]: 'text file'
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Dictionaries \ldots}
\begin{lstlisting}
In []: 'py' in d
Out[]: True
In []: 'jpg' in d
Out[]: False
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Dictionaries \ldots}
\begin{small}
\begin{lstlisting}
In []: d.keys()
Out[]: ['cpp', 'py', 'txt', 'java', 'png']
In []: d.values()
Out[]: ['complex code', 'python code',
'text file', 'bad code',
'image file']
\end{lstlisting}
\end{small}
\inctime{10}
\end{frame}
\begin{frame}[fragile]
\frametitle{Getting back to the problem}
Let our dictionary be:
\begin{lstlisting}
science = {}
\end{lstlisting}
\begin{itemize}
\item Keys will be region codes
\item Values will be the number students who scored more than 90\% in that region in Science
\end{itemize}
\begin{block}{Sample \emph{science} dictionary}
\{'A': 729, 'C': 764, 'B': 1120,'E': 414, 'D': 603, 'F': 500\}
\end{block}
\end{frame}
\begin{frame}[fragile]
\frametitle{Building parsed data \ldots}
\begin{lstlisting}
science = {}
for record in open('sslc1.txt'):
fields = record.split(';')
region_code = fields[0].strip()
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Building parsed data \ldots}
\begin{lstlisting}
if region_code not in science:
science[region_code] = 0
score_str = fields[6].strip()
score = int(score_str) if \
score_str != 'AA' else 0
if score > 90:
science[region_code] += 1
\end{lstlisting}
\end{frame}
\begin{frame}[fragile]
\frametitle{Building parsed data \ldots}
\begin{lstlisting}
print science
print science.keys()
print science.values()
\end{lstlisting}
\end{frame}
\subsection{Visualizing data}
\begin{frame}[fragile]
\frametitle{Pie Chart}
\begin{lstlisting}
pie(science.values())
\end{lstlisting}
\includegraphics[height=2in, interpolate=true]{data/science_nolabel}
\end{frame}
\begin{frame}[fragile]
\frametitle{Pie chart}
\small
\begin{lstlisting}
pie(science.values(),
labels = science.keys())
title('Students scoring 90% and above
in science by region')
savefig('science.png')
\end{lstlisting}
\begin{columns}
\column{5.25\textwidth}
\hspace*{1.1in}
\includegraphics[height=2in, interpolate=true]{data/science}
\column{0.8\textwidth}
\end{columns}
\inctime{10}
\end{frame}
\begin{frame}
\frametitle{Problem statement}
\emphbar{b. Print mean, median and standard deviation of math scores for all regions combined.}
\end{frame}
\begin{frame}[fragile]
\frametitle{Building data for statistics}
\begin{lstlisting}
math_scores = []
for record in open('sslc1.txt'):
fields = record.split(';')
score_str = fields[5].strip()
score = int(score_str) if \
score_str != 'AA' else 0
math_scores.append(score)
\end{lstlisting}
\end{frame}
\subsection{Obtaining statistics}
\begin{frame}[fragile]
\frametitle{Obtaining statistics}
\begin{lstlisting}
print 'Mean: ', mean(math_scores)
print 'Median: ', median(math_scores)
print 'Standard Deviation: ',
std(math_scores)
\end{lstlisting}
\inctime{10}
\end{frame}
\begin{frame}[fragile]
\frametitle{Obtaining statistics: efficiently!}
\begin{lstlisting}
math_array = array(math_scores)
print 'Mean: ', mean(math_array)
print 'Median: ', median(math_array)
print 'Standard Deviation: ',
std(math_array)
\end{lstlisting}
\inctime{5}
\end{frame}
\begin{frame}[fragile]
\frametitle{What tools did we use?}
\begin{itemize}
\item Dictionaries for storing data
\item Facilities for drawing pie charts
\item Efficient array manipulations
\item Functions for statistical computations - mean, median, standard deviation
\end{itemize}
\end{frame}
\end{document}
%% Questions for Quiz %%
%% ------------------ %%
\begin{frame}
\frametitle{\incqno }
A sample line from a Comma Separated Values (CSV) file:\\
\vspace*{0.2in}
\emph{Rossum, Guido, 42, 56, 34, 54}\\
\vspace*{0.2in}
What code would you use to separate the line into fields?
\end{frame}
\begin{frame}[fragile]
\frametitle{\incqno }
\begin{lstlisting}
In []: a = [1, 2, 5, 9]
\end{lstlisting}
How do you find the length of this list?
\end{frame}
\begin{frame}[fragile]
\frametitle{\incqno }
\begin{lstlisting}
In [1]: d = {
'a': 1,
'b': 2
}
In [2]: print d['c']
\end{lstlisting}
What is the output?
\end{frame}
\begin{frame}[fragile]
\frametitle{\incqno }
\begin{lstlisting}
In []: sc = {'A': 10, 'B': 20,
'C': 70}
\end{lstlisting}
Given the above dictionary, what command will you give to plot a
pie-chart?
\end{frame}
\begin{frame}[fragile]
\frametitle{\incqno }
\begin{lstlisting}
In []: marks = [10, 20, 30, 50, 55,
75, 83]
\end{lstlisting}
Given the above marks, how will you calculate the \alert{mean} and
\alert{standard deviation}?
\end{frame}
\begin{frame}[fragile]
\frametitle{\incqno }
\begin{lstlisting}
In []: marks = [10, 20, 30, 50, 55,
75, 83]
\end{lstlisting}
How will you convert the list \texttt{marks} to an \alert{array}?
\end{frame}
%% \begin{frame}[fragile]
%% \frametitle{\incqno }
%% \begin{lstlisting}
%% for x in "abcd":
%% print x
%% a
%% b
%% c
%% d
%% \end{lstlisting}
%% How do you get the following output?
%% \begin{lstlisting}
%% 0 a
%% 1 b
%% 2 c
%% 3 d
%% \end{lstlisting}
%% \end{frame}