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Data Wrangling With Python Pdf Download
Data Wrangling With Python Pdf Download


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(707) 827-7019(800) 889-8969 All trademarks and registered trademarks appearing on are the property of their respective owners. Originally from Los Angeles, she learned Python while working at the Washington Post in 2008. 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By learning just enough Python to get stuff done. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process Chapter 1Introduction to PythonWhy PythonGetting Started with PythonSummaryChapter 2Python BasicsBasic Data TypesData ContainersWhat Can the Various Data Types Do?Helpful Tools: type, dir, and helpPutting It All TogetherWhat Does It All Mean?SummaryChapter 3Data Meant to Be Read by MachinesCSV DataJSON DataXML DataSummaryChapter 4Working with Excel FilesInstalling Python PackagesParsing Excel FilesGetting Started with ParsingSummaryChapter 5PDFs and Problem Solving in PythonAvoid Using PDFs!Programmatic Approaches to PDF ParsingParsing PDFs Using pdfminerLearning How to Solve ProblemsUncommon File TypesSummaryChapter 6Acquiring and Storing DataNot All Data Is Created EqualFact CheckingReadability, Cleanliness, and LongevityWhere to Find DataCase Studies: Example Data InvestigationStoring Your Data: When, Why, and How?Databases: A Brief IntroductionWhen to Use a Simple FileAlternative Data StorageSummaryChapter 7Data Cleanup: Investigation, Matching, and FormattingWhy Clean Data?Data Cleanup BasicsSummaryChapter 8Data Cleanup: Standardizing and ScriptingNormalizing and Standardizing Your DataSaving Your DataDetermining What Data Cleanup Is Right for Your ProjectScripting Your CleanupTesting with New DataSummaryChapter 9Data Exploration and AnalysisExploring Your DataAnalyzing Your DataSummaryChapter 10Presenting Your DataAvoiding Storytelling PitfallsVisualizing Your DataPresentation ToolsPublishing Your DataSummaryChapter 11Web Scraping: Acquiring and Storing Data from the WebWhat to Scrape and HowAnalyzing a Web PageGetting Pages: How to Request on the InternetReading a Web Page with Beautiful SoupReading a Web Page with LXMLSummaryChapter 12Advanced Web Scraping: Screen Scrapers and SpidersBrowser-Based ParsingSpidering the WebNetworks: How the Internet Works and Why Its Breaking Your ScriptThe Changing Web (or Why Your Script Broke)A (Few) Word(s) of CautionSummaryChapter 13APIsAPI FeaturesA Simple Data Pull from Twitters REST APIAdvanced Data Collection from Twitters REST APIAdvanced Data Collection from Twitters Streaming APISummaryChapter 14Automation and ScalingWhy Automate?Steps to AutomateWhat Could Go Wrong?Where to AutomateSpecial Tools for AutomationSimple AutomationLarge-Scale AutomationMonitoring Your AutomationNo System Is FoolproofSummaryChapter 15ConclusionDuties of a Data WranglerBeyond Data WranglingWhere Do You Go from Here?Appendix Comparison of Languages MentionedC, C , and Java Versus PythonR or MATLAB Versus PythonHTML Versus PythonJavaScript Versus PythonNode.js Versus PythonRuby and Ruby on Rails Versus PythonAppendix Python Resources for BeginnersOnline ResourcesIn-Person GroupsAppendix Learning the Command LineBashWindows CMD/Power ShellAppendix Advanced Python SetupStep 1: Install GCCStep 2: (Mac Only) Install HomebrewStep 3: (Mac Only) Tell Your System Where to Find HomebrewStep 4: Install Python 2.7Step 5: Install virtualenv (Windows, Mac, Linux)Step 6: Set Up a New DirectoryStep 7: Install virtualenvwrapperLearning About Our New Environment (Windows, Mac, Linux)Advanced Setup ReviewAppendix Python GotchasHail the WhitespaceThe Dreaded GIL= Versus == Versus is, and When to Just CopyDefault Function ArgumentsPython Scope and Built-Ins: The Importance of Variable NamesDefining Objects Versus Modifying ObjectsChanging Immutable ObjectsType CheckingCatching Multiple ExceptionsThe Power of DebuggingAppendix IPython HintsWhy Use IPython?Getting Started with IPythonMagic FunctionsFinal Thoughts: A Simpler TerminalAppendix Using Amazon Web ServicesSpinning Up an AWS ServerLogging into an AWS Server Title: Data Wrangling with Python By: Jacqueline Kazil, Katharine Jarmul Publisher: O'Reilly Media Formats: Print Ebook Safari Books Online Print: February 2016 Ebook: February 2016 Pages: 508 Print ISBN: 978-1-4919-4881-1 ISBN 10: 1-4919-4881-7 Ebook ISBN: 978-1-4919-4876-7 ISBN 10: 1-4919-4876-0 Jacqueline Kazil Jacqueline Kazil is a data lover. She has worked at small and large start ups before starting her consulting career overseas. You don't need to know a thing about the Python programming language to get started. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. Practical Programming Jun 30 2016Welcome to computer science in the 21st century. Through various step-by-step exercises, youll learn how to acquire, clean, analyze, and present data efficiently. As one of the founders of PyLadies ( Katharine hopes to promote diversity in Python and other open source languages through education and training.


Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. Your Account Shopping Cart 0 items $0.00 Your shopping cart is empty. .. This book is not an exposition on analytical methods using Python as the implementation language. You'll get a comprehensive tutorial that guides you from writing simple, basic Python scripts all the way through complex. Anyone who has t. Computers are used in every part of science from ecology to particle physics. This hands-on guide shows non-programmers like you how to process information thats initially too messy or difficult to access. Katharine Jarmul Katharine Jarmul is a Python developer who enjoys data analysis and acquisition, web scraping, teaching Python and all things Unix. She is active in the Python and data related communities -- Python Software Foundation, PyLadies, Women Data Science DC, and more. For more information on upcoming trainings, reach out to her on Twitter ( or her her web site ( View Katharine Jarmul's full profile page. .. Data Smart Jun 30 2016Data Science gets thrown around in the press like it's magic. In her career, she has worked in technology focusing in finance, government, and journalism. Her career has consisted of many data science and wrangling projects including Geoq, an open source mapping workflow tool, remake, and Top Secret America. bd40bc7c7a

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