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This tutorial teaches students everything they need to get started with Python programming for the fast-growing field of data analysis. Daniel Chen tightly links each new concept with easy-to-apply, relevant examples from modern data analysis. Unlike other beginner's books, this guide helps today's newcomers learn both Python and its popular Pandas data science toolset in the context of tasks they'll really want to perform. Following the proven Software Carpentry approach to teaching programming, Chen introduces each concept with a simple motivating example, slowly offering deeper insights and expanding your ability to handle concrete tasks. Features Establishes a solid foundation for all the Pandas basics needed to be effectiveCovers data frames, statistical calculations, data munging, modeling, machine learning, reproducible documents and much moreTeaches step-by-step through easy, incremental examples, with plenty of opportunities to "code along"Table of ContentsPart I: IntroductionPart II: Data Manipulation Part III: Data MungingPart IV: Data ModelingPart V: ConclusionPart VI: Appendixes Appendix A: Installation Appendix B: Command LineAppendix C: Project TemplatesAppendix D: Using PythonAppendix E: Working DirectoriesAppendix F: EnvironmentsAppendix G: Install PackagesAppendix H: Importing LibrariesAppendix I: ListsAppendix J: TuplesAppendix K: DictionariesAppendix L: Slicing ValuesAppendix M: LoopsAppendix N: ComprehensionsAppendix O: FunctionsAppendix P: Ranges and GeneratorsAppendix Q: Multiple Assignment Appendix R: Numpy ndarrayAppendix S: ClassesAppendix T: Odo: The Shapeshifter.