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Python數(shù)據(jù)分析 第3版（影印版）
Python數(shù)據(jù)分析 第3版（影印版）
Wes McKinney 著
出版時間：2022年11月
頁數(shù)：561
“Wes全面更新了新版的內(nèi)容，確保了本書仍然是使用 Python和pandas進行數(shù)據(jù)分析時的首選資源。我強烈向你推薦此書。”
-Paul Barry
講師，Head First Python(O'Reilly出版)的作者

這是一本使用Python操作、處理、清洗和處理數(shù)據(jù)集的權威手冊。這本實用指南的第3版針對Python 3.10和pandas 1.4進行了更新，包含多個實際案例研究，向你展示了如何有效地解決各種數(shù)據(jù)分析問題。你在本書中將學習pandas、NumPy和Jupyter的最新版本。
作者Wes McKinney是pandas項目的創(chuàng)建人，書中對Python中的多種數(shù)據(jù)科學工具作了實用且與時俱進的介紹。本書非常適合剛接觸Python的分析師以及剛接觸數(shù)據(jù)科學和科學計算的Python程序員。數(shù)據(jù)文件和相關材料都可以在GitHub上找到。
● 使用Jupyter notebook和IPython shell進行探索性計算
● 學習NumPy的基礎功能和高級功能
● 學習pandas庫中的數(shù)據(jù)分析工具
● 使用各種靈活的工具來加載、清理、轉換、合并和重塑數(shù)據(jù)
● 使用matplotlib創(chuàng)建內(nèi)容豐富的可視化圖表
● 運用pandas的groupBy工具對數(shù)據(jù)集進行切片、切塊和匯總
● 分析和處理規(guī)則以及不規(guī)則的時間序列數(shù)據(jù)
● 通過全面詳盡的例子學習如何解決真實世界的數(shù)據(jù)分析問題
  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
書名：Python數(shù)據(jù)分析 第3版（影印版）
作者：Wes McKinney 著
國內(nèi)出版社：東南大學出版社
出版時間：2022年11月
頁數(shù)：561
書號：978-7-5766-0250-0
原版書書名：Python for Data Analysis, 3e
原版書出版商：O'Reilly Media
Wes McKinney
 
Wes McKinney是紐約的一名數(shù)據(jù)分析高手和企業(yè)主。在2007年獲得MIT的數(shù)學學士學位之后，他到位于康涅狄格州格林威治市（Greenwich，CT）的AQR Capital Management公司從事定量金融方面的工作。由于不滿那些數(shù)據(jù)分析工具的各種不好用，他開始學習Python，并于2008年開始構建pandas項目。他目前是Python科學計算社區(qū)的活躍分子，而且積極倡導在數(shù)據(jù)分析、金融以及統(tǒng)計應用中使用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.
購買選項
定價：148.00元
書號：978-7-5766-0250-0
出版社：東南大學出版社