day1/cheatsheet3.tex
author Madhusudan.C.S <madhusudancs@gmail.com>
Tue, 10 Nov 2009 16:27:03 +0530
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\documentclass[12pt]{article}
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\title{Interactive Plotting}
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\author{FOSSEE}
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\begin{document}
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\date{}
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\vspace{-1in}
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\begin{center}
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\LARGE{Statistics and Least square fit}\\
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\large{FOSSEE}
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\end{center}
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\section{Statistics}
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Dictionary
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\begin{verbatim}
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In [1]: d = {"Hitchhiker's guide" : 42, 
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  ....:      "Terminator" : "I'll be back"} #Creation
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In [2]: d["Hitchhiker's guide"] # Accessing a value with key
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In [3]: "Hitchhiker's guide" in d #Checking for a key
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In [4]: d.keys() # Obtaining List of Keys
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In [5]: d.values() # Obtaining List of Values
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\end{verbatim}
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Iterating through List indices
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\begin{verbatim}
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In [1]: names = ["Guido","Alex", "Tim"]
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In [2]: for i, name in enumerate(names):
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   ...:     print i, name
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\end{verbatim}
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\begin{verbatim}
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In [1]: score = int(score_str) if score_str != 'AA' else 0
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\end{verbatim}
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Drawing Pie Charts
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\begin{verbatim}
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In [1]: pie(science.values(), labels=science.keys())
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\end{verbatim}
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sum() and len() functions
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\begin{verbatim}
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In [1]: mean = sum(math_scores) / len(math_scores)
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\end{verbatim}
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Numpy Arrays
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\begin{verbatim}
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In [1]: a = array([1, 2, 3]) #Creating
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In [2]: b = array([4, 5, 6])
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In [3]: a + b #Sum; Element-wise
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\end{verbatim}
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Numpy statistical operations 
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\begin{verbatim}
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In [1]: mean(math_scores)
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In [2]: median(math_scores)
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In [3]: stats.mode(math_scores)
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In [4]: std(math_scores)
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\end{verbatim}
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Generating Van der Monde matrix
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\begin{verbatim}
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In [1]: A = vander(L, 2)
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\end{verbatim}
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Getting a Least Squares Fit curve
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\begin{verbatim}
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In [1]: coef, res, r, s = lstsq(A,TSq)
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In [2]: Tline = coef[0]*L + coef[1]
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In [3]: plot(L, Tline)
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\end{verbatim}
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\end{document}
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