Python数据分析 : 影印版

Preface

1. Preliminaries

What Is This Book About?

Why Python for Data Analysis?

Python as Glue

Solving the "Two-Language" Problem

Why Not Python?

Essential Python Libraries

NumPy

pandas

matplotlib

IPython

SciPy

Installation and Setup

Windows

Apple OS X

GNU/Linux

Python 2 and Python 3

Integrated Development Environments (IDEs)

Community and Conferences

Navigating This Book

Code Examples

Data for Examples

Import Conventions

Jargon

Acknowledgements

2. Introductory Examples

1.usa.gov data from bit.ly

Counting Time Zones in Pure Python

Counting Time Zones with pandas

MovieLens 1M Data Set

Measuring rating disagreement

US Baby Names 1880-2010

Analyzing Naming Trends

Conclusions and The Path Ahead

3. IPython: An Interactive Computing and Development Environment

IPython Basics

Tab Completion

Introspection

The %run Command

Executing Code from the Clipboard

Keyboard Shortcuts

Exceptions and Tracebacks

Magic Commands

Qt-based Rich GUI Console

Matplotlib Integration and Pylab Mode

Using the Command History

Searching and Reusing the Command History

Input and Output Variables

Logging the Input and Output

Interacting with the Operating System

Shell Commands and Aliases

Directory Bookmark System

Software Development Tools

Interactive Debugger

Timing Code: %time and %timeit

Basic Profiling: %prun and %run-p

Profiling a Function Line-by-Line

IPython HTML Notebook

Tips for Productive Code Development Using IPython

Reloading Module Dependencies

Code Design Tips

Advanced IPython Features

Making Your Own Classes IPython-friendly

Profiles and Configuration

Credits

4. NumPy Basics: Arrays and Vectorized Computation

The NumPy ndarray: A Multidimensional Array Object

Creating ndarrays

Data Types for ndarrays

……

5．Gettinq Started with pandas

6．Data Loading，Storage，and File Formats

7．Data Wrangling：Clean t Transform l Merge t Reshape

8．Plotting and Visualization．

9．Data Aggregation and Group Operations

10．Time Series

11．Financial and Economic Data Applications

12．Advanced NumPy

Appendix：Python Language Essentials

Index

你是否在寻找一本完整介绍Python操纵、处理、提取和压缩结构化数据的指南《Python数据分析（影印版）》包含了许多实例分析，通过若干个Pvthon库——包括NumPy，pandas，matplotlib和IPython——为伤：展示了如何高效地解决大量数据分析的问题。
　　《Python数据分析（影印版）》由Wes McKinney撰写，他是pandas库的主要作者。《Python数据分析（影印版）》也是一本具有实践性的指南，指导那些使用Python进行科学计算的数据密集型应用。它适用于刚刚开始使用Python的分析师，或者是进入科学计算领域的Python程序员。

使用IPytflon交互式shell作为你的主要开发环境
　　学习NumPy（Numerical Python）的基础和高级特性
　　接触pandas库中的数据分析工具
　　使用高性能工具来加载、抽取、转换、合并和改造数据
　　使用rrmtplotlib来创建散点图和静态或者交互式可视化数据
　　运用pandas的groupby功能来对数据集进行切片、切块和汇总
　　通过具体实例来学习如何解决web分析、社交科学、金融和经济领域的问题