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 effective Covers data frames, statistical calculations, data munging, modeling, machine learning, reproducible documents and much more Teaches step-by-step through easy, incremental examples, with plenty of opportunities to "code along" Table of Contents Part I: Introduction Part II: Data Manipulation Part III: Data Munging Part IV: Data Modeling Part V: Conclusion Part VI: Appendixes Appendix A: Installation Appendix B: Command Line Appendix C: Project Templates Appendix D: Using Python Appendix E: Working Directories Appendix F: Environments Appendix G: Install Packages Appendix H: Importing Libraries Appendix I: Lists Appendix J: Tuples Appendix K: Dictionaries Appendix L: Slicing Values Appendix M: Loops Appendix N: Comprehensions Appendix O: Functions Appendix P: Ranges and Generators Appendix Q: Multiple Assignment Appendix R: Numpy ndarray Appendix S: Classes Appendix T: Odo: The Shapeshifter.

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