Python数据分析 第3版（影印版）
Python数据分析 第3版（影印版）
Wes McKinney 著
出版时间：2022年11月
页数：561
“Wes全面更新了新版的内容，确保了本书仍然是使用 Python和pandas进行数据分析时的首选资源。我强烈向你推荐此书。”
-Paul Barry
讲师，Head First Python(O'Reilly出版)的作者

这是一本使用Python操作、处理、清洗和处理数据集的权威手册。这本实用指南的第3版针对Python 3.10和pandas 1.4进行了更新，包含多个实际案例研究，向你展示了如何有效地解决各种数据分析问题。你在本书中将学习pandas、NumPy和Jupyter的最新版本。
作者Wes McKinney是pandas项目的创建人，书中对Python中的多种数据科学工具作了实用且与时俱进的介绍。本书非常适合刚接触Python的分析师以及刚接触数据科学和科学计算的Python程序员。数据文件和相关材料都可以在GitHub上找到。
● 使用Jupyter notebook和IPython shell进行探索性计算
● 学习NumPy的基础功能和高级功能
● 学习pandas库中的数据分析工具
● 使用各种灵活的工具来加载、清理、转换、合并和重塑数据
● 使用matplotlib创建内容丰富的可视化图表
● 运用pandas的groupBy工具对数据集进行切片、切块和汇总
● 分析和处理规则以及不规则的时间序列数据
● 通过全面详尽的例子学习如何解决真实世界的数据分析问题
  1. Preface
  2. 1. Preliminaries
  3. 1.1 What Is This Book About?
  4. 1.2 Why Python for Data Analysis?
  5. 1.3 Essential Python Libraries
  6. 1.4 Installation and Setup
  7. 1.5 Community and Conferences
  8. 1.6 Navigating This Book
  9. 2. Python Language Basics, IPython, and Jupyter Notebooks
  10. 2.1 The Python Interpreter
  11. 2.2 IPython Basics
  12. 2.3 Python Language Basics
  13. 2.4 Conclusion
  14. 3. Built-In Data Structures, Functions, and Files
  15. 3.1 Data Structures and Sequences
  16. 3.2 Functions
  17. 3.3 Files and the Operating System
  18. 3.4 Conclusion
  19. 4. NumPy Basics: Arrays and Vectorized Computation
  20. 4.1 The NumPy ndarray: A Multidimensional Array Object
  21. 4.2 Pseudorandom Number Generation
  22. 4.3 Universal Functions: Fast Element-Wise Array Functions
  23. 4.4 Array-Oriented Programming with Arrays
  24. 4.5 File Input and Output with Arrays
  25. 4.6 Linear Algebra
  26. 4.7 Example: Random Walks
  27. 4.8 Conclusion
  28. 5. Getting Started with pandas
  29. 5.1 Introduction to pandas Data Structures
  30. 5.2 Essential Functionality
  31. 5.3 Summarizing and Computing Descriptive Statistics
  32. 5.4 Conclusion
  33. 6. Data Loading, Storage, and File Formats
  34. 6.1 Reading and Writing Data in Text Format
  35. 6.2 Binary Data Formats
  36. 6.3 Interacting with Web APIs
  37. 6.4 Interacting with Databases
  38. 6.5 Conclusion
  39. 7. Data Cleaning and Preparation
  40. 7.1 Handling Missing Data
  41. 7.2 Data Transformation
  42. 7.3 Extension Data Types
  43. 7.4 String Manipulation
  44. 7.5 Categorical Data
  45. 7.6 Conclusion
  46. 8. Data Wrangling: Join, Combine, and Reshape
  47. 8.1 Hierarchical Indexing
  48. 8.2 Combining and Merging Datasets
  49. 8.3 Reshaping and Pivoting
  50. 8.4 Conclusion
  51. 9. Plotting and Visualization
  52. 9.1 A Brief matplotlib API Primer
  53. 9.2 Plotting with pandas and seaborn
  54. 9.3 Other Python Visualization Tools
  55. 9.4 Conclusion
  56. 10. Data Aggregation and Group Operations
  57. 10.1 How to Think About Group Operations
  58. 10.2 Data Aggregation
  59. 10.3 Apply: General split-apply-combine
  60. 10.4 Group Transforms and “Unwrapped” GroupBys
  61. 10.5 Pivot Tables and Cross-Tabulation
  62. 10.6 Conclusion
  63. 11. Time Series
  64. 11.1 Date and Time Data Types and Tools
  65. 11.2 Time Series Basics
  66. 11.3 Date Ranges, Frequencies, and Shifting
  67. 11.4 Time Zone Handling
  68. 11.5 Periods and Period Arithmetic
  69. 11.6 Resampling and Frequency Conversion
  70. 11.7 Moving Window Functions
  71. 11.8 Conclusion
  72. 12. Introduction to Modeling Libraries in Python
  73. 12.1 Interfacing Between pandas and Model Code
  74. 12.2 Creating Model Descriptions with Patsy
  75. 12.3 Introduction to statsmodels
  76. 12.4 Introduction to scikit-learn
  77. 12.5 Conclusion
  78. 13. Data Analysis Examples
  79. 13.1 Bitly Data from 1.USA.gov
  80. 13.2 MovieLens 1M Dataset
  81. 13.3 US Baby Names 1880–2010
  82. 13.4 USDA Food Database
  83. 13.5 2012 Federal Election Commission Database
  84. 13.6 Conclusion
  85. A. Advanced NumPy
  86. A.1 ndarray Object Internals
  87. A.2 Advanced Array Manipulation
  88. A.3 Broadcasting
  89. A.4 Advanced ufunc Usage
  90. A.5 Structured and Record Arrays
  91. A.6 More About Sorting
  92. A.7 Writing Fast NumPy Functions with Numba
  93. A.8 Advanced Array Input and Output
  94. A.9 Performance Tips
  95. B. More on the IPython System
  96. B.1 Terminal Keyboard Shortcuts
  97. B.2 About Magic Commands
  98. B.3 Using the Command History
  99. B.4 Interacting with the Operating System
  100. B.5 Software Development Tools
  101. B.6 Tips for Productive Code Development Using IPython
  102. B.7 Advanced IPython Features
  103. B.8 Conclusion
  104. Index
书名：Python数据分析 第3版（影印版）
作者：Wes McKinney 著
国内出版社：东南大学出版社
出版时间：2022年11月
页数：561
书号：978-7-5766-0250-0
原版书书名：Python for Data Analysis, 3e
原版书出版商：O'Reilly Media
Wes McKinney
 
Wes McKinney是纽约的一名数据分析高手和企业主。在2007年获得MIT的数学学士学位之后，他到位于康涅狄格州格林威治市（Greenwich，CT）的AQR Capital Management公司从事定量金融方面的工作。由于不满那些数据分析工具的各种不好用，他开始学习Python，并于2008年开始构建pandas项目。他目前是Python科学计算社区的活跃分子，而且积极倡导在数据分析、金融以及统计应用中使用Python。
 
 
The animal on the cover of Python for Data Analysis is a golden-tailed, or pen-tailed, tree shrew (Ptilocercus lowii). The golden-tailed tree shrew is the only one of its species in the genus Ptilocercus and family Ptilocercidae; all the other tree shrews are of the family Tupaiidae. Tree shrews are identified by their long tails and soft red-brown fur. As nicknamed, the golden-tailed tree shrew has a tail that resembles the feather on a quill pen. Tree shrews are omnivores, feeding primarily on insects, fruit, seeds, and small vertebrates.
Found predominantly in Indonesia, Malaysia, and Thailand, these wild mammals are known for their chronic consumption of alcohol. Malaysian tree shrews were found to spend several hours consuming the naturally fermented nectar of the bertam palm, equalling about 10 to 12 glasses of wine with 3.8% alcohol content. Despite this, no golden-tailed tree shrew has ever been intoxicated, thanks largely to their impressive ability to break down ethanol, which includes metabolizing the alcohol in a way not used by humans. Also more impressive than any of their mammal counterparts, including humans, is their brain-to-body mass ratio.
Despite its name, the golden-tailed shrew is not a true shrew; instead it is more closely related to primates. Because of their close relation, tree shrews have become an alternative to primates in medical experimentation for myopia, psychosocial stress, and hepatitis.
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定价：148.00元
书号：978-7-5766-0250-0
出版社：东南大学出版社
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