%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Tutorial slides on Python. % % Author: FOSSEE % Copyright (c) 2009, FOSSEE, IIT Bombay %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass[14pt,compress]{beamer} %\documentclass[draft]{beamer} %\documentclass[compress,handout]{beamer} %\usepackage{pgfpages} %\pgfpagesuselayout{2 on 1}[a4paper,border shrink=5mm] % Modified from: generic-ornate-15min-45min.de.tex \mode { \usetheme{Warsaw} \useoutertheme{infolines} \setbeamercovered{transparent} } \usepackage[english]{babel} \usepackage[latin1]{inputenc} %\usepackage{times} \usepackage[T1]{fontenc} % Taken from Fernando's slides. \usepackage{ae,aecompl} \usepackage{mathpazo,courier,euler} \usepackage[scaled=.95]{helvet} \usepackage{amsmath} \definecolor{darkgreen}{rgb}{0,0.5,0} \usepackage{listings} \lstset{language=Python, basicstyle=\ttfamily\bfseries, commentstyle=\color{red}\itshape, stringstyle=\color{darkgreen}, showstringspaces=false, keywordstyle=\color{blue}\bfseries} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Macros \setbeamercolor{emphbar}{bg=blue!20, fg=black} \newcommand{\emphbar}[1] {\begin{beamercolorbox}[rounded=true]{emphbar} {#1} \end{beamercolorbox} } \newcounter{time} \setcounter{time}{0} \newcommand{\inctime}[1]{\addtocounter{time}{#1}{\tiny \thetime\ m}} \newcommand{\typ}[1]{\lstinline{#1}} \newcommand{\kwrd}[1]{ \texttt{\textbf{\color{blue}{#1}}} } %%% This is from Fernando's setup. % \usepackage{color} % \definecolor{orange}{cmyk}{0,0.4,0.8,0.2} % % Use and configure listings package for nicely formatted code % \usepackage{listings} % \lstset{ % language=Python, % basicstyle=\small\ttfamily, % commentstyle=\ttfamily\color{blue}, % stringstyle=\ttfamily\color{orange}, % showstringspaces=false, % breaklines=true, % postbreak = \space\dots % } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Title page \title[Statistics]{Python for Science and Engg: Statistics} \author[FOSSEE] {FOSSEE} \institute[IIT Bombay] {Department of Aerospace Engineering\\IIT Bombay} \date[] {11 January, 2010\\Day 1, Session 3} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %\pgfdeclareimage[height=0.75cm]{iitmlogo}{iitmlogo} %\logo{\pgfuseimage{iitmlogo}} %% Delete this, if you do not want the table of contents to pop up at %% the beginning of each subsection: \AtBeginSubsection[] { \begin{frame} \frametitle{Outline} \tableofcontents[currentsection,currentsubsection] \end{frame} } \AtBeginSection[] { \begin{frame} \frametitle{Outline} \tableofcontents[currentsection,currentsubsection] \end{frame} } \newcommand{\num}{\texttt{numpy}} % If you wish to uncover everything in a step-wise fashion, uncomment % the following command: %\beamerdefaultoverlayspecification{<+->} %\includeonlyframes{current,current1,current2,current3,current4,current5,current6} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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 = [] 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.append(g) \end{lstlisting} \end{frame} \begin{frame} \frametitle{Computing mean ``g''} \begin{block}{Exercise} Obtain the mean of ``g'' \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Mean ``g''} \begin{lstlisting} total = 0 for g in G: total += g g_mean = total / len(G) print 'Mean: ', g_mean \end{lstlisting} \end{frame} \begin{frame}[fragile] \frametitle{Mean ``g''} \begin{lstlisting} g_mean = sum(G) / len(G) print 'Mean: ', g_mean \end{lstlisting} \end{frame} \begin{frame}[fragile] \frametitle{Mean ``g''} \begin{lstlisting} g_mean = mean(G) 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: English, Hindi, 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} \begin{lstlisting} for record in open('sslc1.txt'): fields = record.split(';') \end{lstlisting} \begin{block}{} \centerline{Recall pendulum example!} \end{block} \end{frame} \subsection{Dictionaries} \begin{frame}[fragile] \frametitle{Dictionaries: Introduction} \begin{itemize} \item lists index: 0 \ldots n \item dictionaries index using strings \end{itemize} \end{frame} \begin{frame}[fragile] \frametitle{Dictionaries \ldots} \begin{lstlisting} In []: d = {'jpg' : 'image file', 'txt' : 'text file', 'py' : 'python 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 []: 'cpp' in d Out[]: False \end{lstlisting} \end{frame} \begin{frame}[fragile] \frametitle{Dictionaries \ldots} \begin{lstlisting} In []: d.keys() Out[]: ['py', 'txt', 'jpg'] In []: d.values() Out[]: ['python code', 'text file', 'image file'] \end{lstlisting} \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 \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'): record = record.strip() 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} \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'): record = record.strip() 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{block}{Exercise} Obtain the mean of Math scores \end{block} \end{frame} \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}