This eBook includes the following formats, accessible from your Account page after purchase:
EPUB The open industry format known for its reflowable content and usability on supported mobile devices.
MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.
PDF The popular standard, used most often with the free Adobe® Reader® software.
This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.
Also available in other formats.
Register your product to gain access to bonus material or receive a coupon.
This tutorial teaches everything you 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.
Each chapter is illuminated with a concept map: an intuitive visual index of what you'll learn -- and an easy way to refer back to what you've already learned. An extensive set of easy-to-read appendices help you fill knowledge gaps wherever they may exist. Coverage includes:
About the Author
Part I: Introduction
Chapter 1: Pandas DataFrame Basics
Chapter 2: Pandas Data Structures
Chapter 3: Introduction to Plotting
Part II: Data Manipulation
Chapter 4: Data Assembly
Chapter 5: Missing Data
Chapter 6: Tidy Data
Part III: Data Munging
Chapter 7: Data Types
Chapter 8: Strings and Text Data
Chapter 9: Apply
Chapter 10: Groupby operations: split-apply-combine
Chapter 11: The datetime Data Type
Part IV: Data Modeling
Chapter 12: Linear Models
Chapter 13: Generalized Linear Models
Chapter 14: Model Diagnostics
Chapter 15: Regularization
Chapter 16: Clustering
Part V: Conclusion
Chapter 17: Life Outside of Pandas
Chapter 18: Towards a Self-Directed Learner
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