Python for Data Analysis
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Sagemath and Sagemathcloud
Viviane Pons Ma^ıtrede conf´erence,Universit´eParis-Sud Orsay [email protected] { @PyViv SageMath and SageMathCloud Introduction SageMath SageMath is a free open source mathematics software I Created in 2005 by William Stein. I http://www.sagemath.org/ I Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 2 / 7 SageMath Source and language I the main language of Sage is python (but there are many other source languages: cython, C, C++, fortran) I the source is distributed under the GPL licence. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 3 / 7 SageMath Sage and libraries One of the original purpose of Sage was to put together the many existent open source mathematics software programs: Atlas, GAP, GMP, Linbox, Maxima, MPFR, PARI/GP, NetworkX, NTL, Numpy/Scipy, Singular, Symmetrica,... Sage is all-inclusive: it installs all those libraries and gives you a common python-based interface to work on them. On top of it is the python / cython Sage library it-self. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 4 / 7 SageMath Sage and libraries I You can use a library explicitly: sage: n = gap(20062006) sage: type(n) <c l a s s 'sage. interfaces .gap.GapElement'> sage: n.Factors() [ 2, 17, 59, 73, 137 ] I But also, many of Sage computation are done through those libraries without necessarily telling you: sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]]) sage : G . g a p () Group( [ (3,4), (1,2,3)(4,5) ] ) Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 5 / 7 SageMath Development model Development model I Sage is developed by researchers for researchers: the original philosophy is to develop what you need for your research and share it with the community. -
Wireshark User's Guide
Wireshark User’s Guide For Wireshark 2.1 Ulf Lamping <ulf.lamping[AT]web.de> Richard Sharpe, NS Computer Software and Services P/L <rsharpe[AT]ns.aus.com> Ed Warnicke <hagbard[AT]physics.rutgers.edu> Wireshark User’s Guide: For Wireshark 2.1 by Ulf Lamping, Richard Sharpe, and Ed Warnicke Copyright © 2004-2014 Ulf Lamping, Richard Sharpe, Ed Warnicke Permission is granted to copy, distribute and/or modify this document under the terms of the GNU General Public License, Version 2 or any later version published by the Free Software Foundation. All logos and trademarks in this document are property of their respective owner. Preface ...................................................................................................................... viii 1. Foreword ....................................................................................................... viii 2. Who should read this document? ....................................................................... viii 3. Acknowledgements .......................................................................................... viii 4. About this document ......................................................................................... ix 5. Where to get the latest copy of this document? ....................................................... ix 6. Providing feedback about this document ............................................................... ix 1. Introduction ............................................................................................................. -
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. -
Virtualization of the RIOT Operating System
Computer Systems and Telematics — Distributed, Embedded Systems Diploma Thesis Virtualization of the RIOT Operating System Ludwig Ortmann Matr. 3914103 Supervisor: Dr. Emmanuel Baccelli Assisting Supervisor: Prof. Dr.-Ing. Jochen Schiller Institute of Computer Science, Freie Universität Berlin, Germany March 2, 2015 iii I hereby declare to have written this thesis on my own. I have used no other literature and resources than the ones referenced. All text passages that are literal or logical copies from other publications have been marked accordingly. All figures and pictures have been created by me or their sources are referenced accordingly. This thesis has not been submitted in the same or a similar version to any other examination board. Berlin, March 2, 2015 (Ludwig Ortmann) Abstract Abstract Software developers in the growing field of the Internet of Things face many hurdles which arise from the limitations of embedded systems and wireless networking. The employment of hardware and network virtualization promises to allow developers to test and debug hard- ware independent code without being affected by these limitations. This thesis presents RIOT native, a hardware and network emulation implementation for the RIOT operating system, which enables developers to compile and run RIOT as a process in their host operat- ing system. Running the operating system as a process allows for the use of debugging tools and techniques only available on desktop computers otherwise, the integration of common network analysis tools, and the emulation of arbitrary network topologies. By enabling the use of these tools and techniques for the development of software for distributed embedded systems, the hurdles they impose on the development process are significantly reduced. -
Alternatives to Python: Julia
Crossing Language Barriers with , SciPy, and thon Steven G. Johnson MIT Applied Mathemacs Where I’m coming from… [ google “Steven Johnson MIT” ] Computaonal soPware you may know… … mainly C/C++ libraries & soPware … Nanophotonics … oPen with Python interfaces … (& Matlab & Scheme & …) jdj.mit.edu/nlopt www.w.org jdj.mit.edu/meep erf(z) (and erfc, erfi, …) in SciPy 0.12+ & other EM simulators… jdj.mit.edu/book Confession: I’ve used Python’s internal C API more than I’ve coded in Python… A new programming language? Viral Shah Jeff Bezanson Alan Edelman julialang.org Stefan Karpinski [begun 2009, “0.1” in 2013, ~20k commits] [ 17+ developers with 100+ commits ] [ usual fate of all First reacBon: You’re doomed. new languages ] … subsequently: … probably doomed … sll might be doomed but, in the meanBme, I’m having fun with it… … and it solves a real problem with technical compuBng in high-level languages. The “Two-Language” Problem Want a high-level language that you can work with interacBvely = easy development, prototyping, exploraon ⇒ dynamically typed language Plenty to choose from: Python, Matlab / Octave, R, Scilab, … (& some of us even like Scheme / Guile) Historically, can’t write performance-criBcal code (“inner loops”) in these languages… have to switch to C/Fortran/… (stac). [ e.g. SciPy git master is ~70% C/C++/Fortran] Workable, but Python → Python+C = a huge jump in complexity. Just vectorize your code? = rely on mature external libraries, operang on large blocks of data, for performance-criBcal code Good advice! But… • Someone has to write those libraries. • Eventually that person may be you. -
Data Visualization in Python
Data visualization in python Day 2 A variety of packages and philosophies • (today) matplotlib: http://matplotlib.org/ – Gallery: http://matplotlib.org/gallery.html – Frequently used commands: http://matplotlib.org/api/pyplot_summary.html • Seaborn: http://stanford.edu/~mwaskom/software/seaborn/ • ggplot: – R version: http://docs.ggplot2.org/current/ – Python port: http://ggplot.yhathq.com/ • Bokeh (live plots in your browser) – http://bokeh.pydata.org/en/latest/ Biocomputing Bootcamp 2017 Matplotlib • Gallery: http://matplotlib.org/gallery.html • Top commands: http://matplotlib.org/api/pyplot_summary.html • Provides "pylab" API, a mimic of matlab • Many different graph types and options, some obscure Biocomputing Bootcamp 2017 Matplotlib • Resulting plots represented by python objects, from entire figure down to individual points/lines. • Large API allows any aspect to be tweaked • Lengthy coding sometimes required to make a plot "just so" Biocomputing Bootcamp 2017 Seaborn • https://stanford.edu/~mwaskom/software/seaborn/ • Implements more complex plot types – Joint points, clustergrams, fitted linear models • Uses matplotlib "under the hood" Biocomputing Bootcamp 2017 Others • ggplot: – (Original) R version: http://docs.ggplot2.org/current/ – A recent python port: http://ggplot.yhathq.com/ – Elegant syntax for compactly specifying plots – but, they can be hard to tweak – We'll discuss this on the R side tomorrow, both the basics of both work similarly. • Bokeh – Live, clickable plots in your browser! – http://bokeh.pydata.org/en/latest/ -
Mastering Machine Learning with Scikit-Learn
www.it-ebooks.info Mastering Machine Learning with scikit-learn Apply effective learning algorithms to real-world problems using scikit-learn Gavin Hackeling BIRMINGHAM - MUMBAI www.it-ebooks.info Mastering Machine Learning with scikit-learn Copyright © 2014 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: October 2014 Production reference: 1221014 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78398-836-5 www.packtpub.com Cover image by Amy-Lee Winfield [email protected]( ) www.it-ebooks.info Credits Author Project Coordinator Gavin Hackeling Danuta Jones Reviewers Proofreaders Fahad Arshad Simran Bhogal Sarah -
Download User Guide
SpyderX User’s Guide 1 Table of Contents INTRODUCTION 4 WHAT’S IN THE BOX 5 SYSTEM REQUIREMENTS 5 SPYDERX COMPARISON CHART 6 SERIALIZATION AND ACTIVATION 7 SOFTWARE LAYOUT 11 SPYDERX PRO 12 WELCOME SCREEN 12 SELECT DISPLAY 13 DISPLAY TYPE 14 MAKE AND MODEL 15 IDENTIFY CONTROLS 16 DISPLAY TECHNOLOGY 17 CALIBRATION SETTINGS 18 MEASURING ROOM LIGHT 19 CALIBRATION 20 SAVE PROFILE 23 RECAL 24 1-CLICK CALIBRATION 24 CHECKCAL 25 SPYDERPROOF 26 PROFILE OVERVIEW 27 SHORTCUTS 28 DISPLAY ANALYSIS 29 PROFILE MANAGEMENT TOOL 30 SPYDERX ELITE 31 WORKFLOW 31 WELCOME SCREEN 32 SELECT DISPLAY 33 DISPLAY TYPE 34 MAKE AND MODEL 35 IDENTIFY CONTROLS 36 DISPLAY TECHNOLOGY 37 SELECT WORKFLOW 38 STEP-BY-STEP ASSISTANT 39 STUDIOMATCH 41 EXPERT CONSOLE 45 MEASURING ROOM LIGHT 46 CALIBRATION 47 SAVE PROFILE 50 2 RECAL 51 1-CLICK CALIBRATION 51 CHECKCAL 52 SPYDERPROOF 53 SPYDERTUNE 54 PROFILE OVERVIEW 56 SHORTCUTS 57 DISPLAY ANALYSIS 58 SOFTPROOFING/DEVICE SIMULATION 59 PROFILE MANAGEMENT TOOL 60 GLOSSARY OF TERMS 61 FAQ’S 63 INSTRUMENT SPECIFICATIONS 66 Main Company Office: Manufacturing Facility: Datacolor, Inc. Datacolor Suzhou 5 Princess Road 288 Shengpu Road Lawrenceville, NJ 08648 Suzhou, Jiangsu P.R. China 215021 3 Introduction Thank you for purchasing your new SpyderX monitor calibrator. This document will offer a step-by-step guide for using your SpyderX calibrator to get the most accurate color from your laptop and/or desktop display(s). 4 What’s in the Box • SpyderX Sensor • Serial Number • Welcome Card with Welcome page details • Link to download the -
Installing Python Ø Basics of Programming • Data Types • Functions • List Ø Numpy Library Ø Pandas Library What Is Python?
Python in Data Science Programming Ha Khanh Nguyen Agenda Ø What is Python? Ø The Python Ecosystem • Installing Python Ø Basics of Programming • Data Types • Functions • List Ø NumPy Library Ø Pandas Library What is Python? • “Python is an interpreted high-level general-purpose programming language.” – Wikipedia • It supports multiple programming paradigms, including: • Structured/procedural • Object-oriented • Functional The Python Ecosystem Source: Fabien Maussion’s “Getting started with Python” Workshop Installing Python • Install Python through Anaconda/Miniconda. • This allows you to create and work in Python environments. • You can create multiple environments as needed. • Highly recommended: install Python through Miniconda. • Are we ready to “play” with Python yet? • Almost! • Most data scientists use Python through Jupyter Notebook, a web application that allows you to create a virtual notebook containing both text and code! • Python Installation tutorial: [Mac OS X] [Windows] For today’s seminar • Due to the time limitation, we will be using Google Colab instead. • Google Colab is a free Jupyter notebook environment that runs entirely in the cloud, so you can run Python code, write report in Jupyter Notebook even without installing the Python ecosystem. • It is NOT a complete alternative to installing Python on your local computer. • But it is a quick and easy way to get started/try out Python. • You will need to log in using your university account (possible for some schools) or your personal Google account. Basics of Programming Indentations – Not Braces • Other languages (R, C++, Java, etc.) use braces to structure code. # R code (not Python) a = 10 if (a < 5) { result = TRUE b = 0 } else { result = FALSE b = 100 } a = 10 if (a < 5) { result = TRUE; b = 0} else {result = FALSE; b = 100} Basics of Programming Indentations – Not Braces • Python uses whitespaces (tabs or spaces) to structure code instead. -
Fira Code: Monospaced Font with Programming Ligatures
Personal Open source Business Explore Pricing Blog Support This repository Sign in Sign up tonsky / FiraCode Watch 282 Star 9,014 Fork 255 Code Issues 74 Pull requests 1 Projects 0 Wiki Pulse Graphs Monospaced font with programming ligatures 145 commits 1 branch 15 releases 32 contributors OFL-1.1 master New pull request Find file Clone or download lf- committed with tonsky Add mintty to the ligatures-unsupported list (#284) Latest commit d7dbc2d 16 days ago distr Version 1.203 (added `__`, closes #120) a month ago showcases Version 1.203 (added `__`, closes #120) a month ago .gitignore - Removed `!!!` `???` `;;;` `&&&` `|||` `=~` (closes #167) `~~~` `%%%` 3 months ago FiraCode.glyphs Version 1.203 (added `__`, closes #120) a month ago LICENSE version 0.6 a year ago README.md Add mintty to the ligatures-unsupported list (#284) 16 days ago gen_calt.clj Removed `/**` `**/` and disabled ligatures for `/*/` `*/*` sequences … 2 months ago release.sh removed Retina weight from webfonts 3 months ago README.md Fira Code: monospaced font with programming ligatures Problem Programmers use a lot of symbols, often encoded with several characters. For the human brain, sequences like -> , <= or := are single logical tokens, even if they take two or three characters on the screen. Your eye spends a non-zero amount of energy to scan, parse and join multiple characters into a single logical one. Ideally, all programming languages should be designed with full-fledged Unicode symbols for operators, but that’s not the case yet. Solution Download v1.203 · How to install · News & updates Fira Code is an extension of the Fira Mono font containing a set of ligatures for common programming multi-character combinations. -
Introduction to Ggplot2
Introduction to ggplot2 Dawn Koffman Office of Population Research Princeton University January 2014 1 Part 1: Concepts and Terminology 2 R Package: ggplot2 Used to produce statistical graphics, author = Hadley Wickham "attempt to take the good things about base and lattice graphics and improve on them with a strong, underlying model " based on The Grammar of Graphics by Leland Wilkinson, 2005 "... describes the meaning of what we do when we construct statistical graphics ... More than a taxonomy ... Computational system based on the underlying mathematics of representing statistical functions of data." - does not limit developer to a set of pre-specified graphics adds some concepts to grammar which allow it to work well with R 3 qplot() ggplot2 provides two ways to produce plot objects: qplot() # quick plot – not covered in this workshop uses some concepts of The Grammar of Graphics, but doesn’t provide full capability and designed to be very similar to plot() and simple to use may make it easy to produce basic graphs but may delay understanding philosophy of ggplot2 ggplot() # grammar of graphics plot – focus of this workshop provides fuller implementation of The Grammar of Graphics may have steeper learning curve but allows much more flexibility when building graphs 4 Grammar Defines Components of Graphics data: in ggplot2, data must be stored as an R data frame coordinate system: describes 2-D space that data is projected onto - for example, Cartesian coordinates, polar coordinates, map projections, ... geoms: describe type of geometric objects that represent data - for example, points, lines, polygons, ... aesthetics: describe visual characteristics that represent data - for example, position, size, color, shape, transparency, fill scales: for each aesthetic, describe how visual characteristic is converted to display values - for example, log scales, color scales, size scales, shape scales, .. -
Introduction to MS-DOS
1.Introduction to MS-DOS : MS-DOS (Microsoft Disk Operating System) was the Microsoft-marketed version of the first widely-installed operating system in personal computers. It was essentially the same operating system that (Bill Gates's) young company developed for IBM as Personal Computer - Disk Operating System in 1981. Most users of either DOS system simply referred to their system as Disk Operating System. Like PC-DOS, MS-DOS was (and still is) a non-graphical line-oriented command- driven operating system, with a relatively simple interface but not overly "friendly" user interface. Its prompt to enter a command looks like this: C:\> MS-DOS does not care about anything called an icon, wallpaper or screen saver. Rather than being considered as a Graphical User Interface (GUI) MS-DOS is what is known as a command-line interface. You type commands on what is called the command line. MS-DOS is a single-user, single-tasking computer operating system. In spite of its very small size and relative simplicity, it is one of the most successful operating systems that has been developed to date. In DOS, a file name consists of eight character followed by a 3 character file extension. The size of a file is restricted to a 4 byte file descriptor, which limits a file’s maximum size to approximately 4 billion characters. The first release of DOS could not read or write to disk drives so users could only read and write to a floppy disc. DOS was not a state of the art operating system, even for its time.