TAILIEUCHUNG - Basic data analysis and more - a guided tour using python

n these lecture notes, a selection of frequently required statistical tools will be introduced and illustrated. They allow to post-process data that stem from, ., large-scale numerical simulations. From a point of view of data analysis, the concepts and techniques introduced here are of general interest and are, at best, employed by computational aid. Consequently, an exemplary implementation of the presented techniques using the Python programming language is provided. | arXiv 25 Jul 2012 Published in Reinhard Leidl and Alexander K. Hartmann eds. Modern Computational Science 2012 - Optimization Lecture Notes from the 4th International Summer School BIS-Verlag Oldenburg 2012. ISBN 978-3-8142-2269-1 Basic Data Analysis and More A Guided Tour Using python Oliver Melchert Institute of Physics Faculty of Mathematics and Science Carl von Ossietzky Universitat Oldenburg D-26111 Oldenburg Germany Abstract. In these lecture notes a selection of frequently required statistical tools will be introduced and illustrated. They allow to post-process data that stem from . large-scale numerical simulations aka sequence of random experiments . From a point of view of data analysis the concepts and techniques introduced here are of general interest and are at best employed by computational aid. Consequently an exemplary implementation of the presented techniques using the python programming language is provided. The contents of these lecture notes is rather selective and represents a computational experimentalist s view on the subject of basic data analysis ranging from the simple computation of moments for distributions of random variables to more involved topics such as hierarchical cluster analysis and the parallelization of python code. Note that in order to save space all python snippets presented in the following are undocumented. In general this has to be considered as bad programming style. However the supplementary material . the example programs you can download from the MCS homepage is well documented see Ref. 1 . 1 Basic data analysis Melchert Contents 1 Basic python selected features. 2 2 Basic data analysis. 6 Distribution of random variables. 6 Histograms. 21 Bootstrap resampling. 25 The chi-square test. 27 3 Object oriented programming in python. 31 Implementing an undirected graph. 32 A simple histogram data structure. 38 4 Increase your efficiency using Scientific Python .

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