Python數(shù)據(jù)分析（影印版）

定　價(jià)：￥74.00

作　者：	（美）麥金尼（Wes McKinney）編
出版社：	東南大學(xué)出版社
叢編項(xiàng)：
標(biāo)　簽：	暫缺

購(gòu)買(mǎi)這本書(shū)可以去

ISBN：	9787564142049	出版時(shí)間：	2013-05-01	包裝：	平裝
開(kāi)本：	16開(kāi)	頁(yè)數(shù)：	452	字?jǐn)?shù)：

內(nèi)容簡(jiǎn)介

　　你是否在尋找一本完整介紹Python操縱、處理、提取和壓縮結(jié)構(gòu)化數(shù)據(jù)的指南？本書(shū)包含了許多實(shí)例分析，通過(guò)若干個(gè)Python庫(kù)——包括NumPy，pandas，matplotlib和IPython——為你展示了如何高效地解決大量數(shù)據(jù)分析的問(wèn)題?！禤ython數(shù)據(jù)分析（影印版）》由麥金尼撰寫(xiě)，他是pandas庫(kù)的主要作者。本書(shū)也是一本具有實(shí)踐性的指南，指導(dǎo)那些使用Python進(jìn)行科學(xué)計(jì)算的數(shù)據(jù)密集型應(yīng)用。它適用于剛剛開(kāi)始使用Python的分析師，或者是進(jìn)入科學(xué)計(jì)算領(lǐng)域的Python程序員。使用IPyth1on交互式shell作為你的主要開(kāi)發(fā)環(huán)境學(xué)習(xí)NumPy（NumericalPython）的基礎(chǔ)和高級(jí)特性接觸patldas庫(kù)中的數(shù)據(jù)分析工具。《Python數(shù)據(jù)分析（影印版）》內(nèi)容：使用高性能工具來(lái)加載、抽取、轉(zhuǎn)換、合并和改造數(shù)據(jù) 使用matplotlib來(lái)創(chuàng)建散點(diǎn)圖和靜態(tài)或者交互式可視化數(shù)據(jù)運(yùn)用pandas的groupby功能來(lái)對(duì)數(shù)據(jù)集進(jìn)行切片、切塊和匯總通過(guò)具體實(shí)例來(lái)學(xué)習(xí)如何解決web分析、社交科學(xué)、金融和經(jīng)濟(jì)領(lǐng)域的問(wèn)題

作者簡(jiǎn)介

　　Wes McKinney，是pandas的主要作者，pandas是Python中流行的數(shù)據(jù)分析開(kāi)源庫(kù)。他一開(kāi)始是AQR資產(chǎn)管理公司的量化分析師，后來(lái)創(chuàng)辦了Lambda Foundry——一家企業(yè)數(shù)據(jù)分析公司。Wes是Python和開(kāi)源社區(qū)的活躍講師和參與者。

圖書(shū)目錄

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