Python数据分析 第3版(影印版)

本书由Python pandas项 目创始人Wes McKinney亲 笔撰写，详细介绍利用 Python进行操作、处理、 清洗和规整数据等方面的 具体细节和基本要点。你 将在阅读过程中学习到新 版本的pandas、NumPy、 IPython和Jupyter。 本书由Wes McKinney创 作，他是Python pandas项 目的创始人。本书是对 Python数据科学工具的实 操化、现代化的介绍，非 常适合刚学Python的数据 分析师或刚学数据科学以 及科学计算的Python编程 者。数据文件和相关的材 料可以在GitHub上找到： 使用IPython shell和 Jupyter notebook进行探索 性计算；学习NumPy （Numerical Python）的 基础和高级特性；入门 pandas库中的数据分析工 具；使用灵活工具对数据 进行载入、清洗、变换、 合并和重塑；使用 matplotlib创建富含信息的 可视化；将pandas的 groupby功能应用于对数据 集的切片、分块和汇总； 分析并操作规则和不规则 的时间序列数据；利用完 整的、详细的示例学习如 何解决现实中数据分析问 题。

Preface 1.Preliminaries 1.1 What Is This Book About What Kinds of Data 1.2 Whv Python for Data Analysis Python as Glue Solving the Two—Language Problem WhvNot Python 1.3 Essential Python Libraries NumPy pandas matplotlib IPython and Iupyter SciPy scikit-learn statsmodels Other Packages 1.4 Installation and Setup Miniconda on Windows GNU／Linux Miniconda on macOS Installing Necessary Packages Integrated Development Environments and Text Editors 1.5 Community and Conferences 1.6 Navigating This Book Code Examples Data for Examples Import Conventions 2.Python Language Basics，IPython，and Jupyter Notebooks 2.1 The Python Interpreter 2.2 IPython Basics Running the IPython Shell Running the Jupyter Notebook Tab Completion Introspection 2.3 Python Language Basics Language Semantics ScalarTypes Control Flow 2.4 Conclusion 3.Built.In Data Structures，Functions，and Files 3.1 Data Structures and Sequences Tuple List Dictionary Set Built—In Sequence Functions List，Set，and Dictionary Comprehensions 3.2 Functions Namespaces，Scope，and Local Functions Returning Multiple Values Functions Are Objects Anonymous(Lambda)Functions Generators Errors and Exception Handling 3.3 Files and the Operating System Bytes and Unicode with Files 3.4 Conclusion 4.NumPy Basic：Arrays and Vectorized Computation 4.1 The NumPy ndarray：A Multidimensional Array Object Creating ndarrays DataTypesforndarrays Arithmetic with NumPy Arrays Basic Indexing and Slicing Boolean Indexing Fancy Indexing Transposing Arrays and Swapping Axes 4.2 Pseudorandom Number Generation 4.3 Universal Functions：Fast Element—Wise Array Functions 4.4 Array—Oriented Programming with Arrays Expressing Conditional Logic as Array Operations Mathematical and Statistical Methods Methods for Boolean Arrays Sorting Unique and Other Set Logic 4.5 File Input and Output with Arrays 4.6 Linear Algebra 4.7 Example：Random Walks Simulating Many Random Walks at Once 4.8 Conclusion 5.Getting Startedwith pandas 5.1 Introduction to pandas Data Structures Series DataFrame Index Objects 5.2 Essential Functionality Reindexing Dropping Entries from an Axis Indexing，Selection，and Filtering Arithmetic and Data Alignment Function Application and Mapping Sorting and Ranking Axis Indexes with Duplicate Labels 5.3 Summarizing and Computing Descriptive Statistics Correlation and C：ovariance Unique Values，Value Counts，and Membership 5.4 Conclusion 6.Data Loading，Storage，and File Formats 6.1 Reading and Writing Data in Text Format Reading Text Files in Pieces WiRing Data to Text Format Working with Other Delimited Formats TSON Data XML and HTML：Wleb Scraping 6.2 Binary Data Formats Reading Microsoft Excel Files Using HDF5 Format 6.3 Interacting with Web APIs 6.4 Interacting with Databases 6.5 Conclusion …… 7.DataCleaningand Preparation 8.Data Wrangling：Join，Combine，and Reshape 9.Plotting andVisualization 10.Data Aggregation and Group Operations 11.TimeSeries 12.Introduction to Modeling Libraries in Python 13.DataAnalysis Examples A.AdvancedNumPy B.MoreontheIPython System lndex