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Pandas for Everyone: Python Data Analysis

Pandas for Everyone: Python Data Analysis

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  • Estimated Release: Dec 15, 2017
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  • Copyright 2018
  • Dimensions: 7" x 9-1/8"
  • Pages: 350
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-454702-0
  • ISBN-13: 978-0-13-454702-2

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:

  • Setting up your Python and Pandas environment
  • Getting started with Pandas dataframes
  • Using dataframes to calculate and perform basic statistical tasks
  • Plotting in Matplotlib
  • Cleaning data, reshaping dataframes, handling missing values, working with dates, and more
  • Building basic data analytics models
  • Applying machine learning techniques: both supervised and unsupervised
  • Creating reproducible documents using literate programming techniques

Sample Content

Table of Contents

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



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