书籍详情
Python数据分析（影印版）
作者：（美）麦金尼（Wes McKinney）编
出版社：东南大学出版社
出版时间：2013-05-01
ISBN：9787564142049
定价：¥74.00
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内容简介
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？本书包含了许多实例分析，通过若干个Python库——包括NumPy，pandas，matplotlib和IPython——为你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）》由麦金尼撰写，他是pandas库的主要作者。本书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IPyth1on交互式shell作为你的主要开发环境学习NumPy（NumericalPython）的基础和高级特性接触patldas库中的数据分析工具。 《Python数据分析（影印版）》内容：使用高性能工具来加载、抽取、转换、合并和改造数据 使用matplotlib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
作者简介
Wes McKinney，是pandas的主要作者，pandas是Python中流行的数据分析开源库。他一开始是AQR资产管理公司的量化分析师，后来创办了Lambda Foundry——一家企业数据分析公司。Wes是Python和开源社区的活跃讲师和参与者。
目录
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
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
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