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
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