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1. Preface
1. New for the Second Edition
2. Conventions Used in This Book
3. Using Code Examples
4. Safari® Books Online
5. How to Contact Us
6. Acknowledgements
1. In Memorium: John D. Hunter (1968-2012)
2. Acknowledgements for the 2nd Edition (2016)
3. Acknowledgements for the 1st Edition (2012)
2. 1. Preliminaries
1. What Is This Book About?
1. What kinds of data?
2. Why Python for Data Analysis?
1. Python as Glue
2. Solving the “Two-Language” Problem
3. Why Not Python?
3. Essential Python Libraries
1. NumPy
2. pandas
3. matplotlib
4. IPython and Jupyter
5. SciPy
6. scikit-learn
7. statsmodels
4. Installation and Setup
1. Windows
2. Apple (OS X, macOS)
3. GNU/Linux
4. Installing or updating Python packages
5. Python 2 and Python 3
6. Integrated Development Environments (IDEs) and Text Editors
5. Community and Conferences
6. Navigating This Book
1. Code Examples
2. Data for Examples
3. Import Conventions
4. Jargon
3. 2. Python Language Basics, IPython, and Jupyter Notebooks
1. The Python Interpreter
2. IPython Basics
1. Running the IPython Shell
2. Running the Jupyter Notebook
3. Tab Completion
4. Introspection
5. The %run Command
6. Executing Code from the Clipboard
7. Terminal Keyboard Shortcuts
8. Exceptions and Tracebacks
9. About Magic Commands
10. Matplotlib Integration
3. Python Language Basics
1. Language Semantics
2. Scalar Types
3. Control Flow
4. 3. Built-in Data Structures, Functions, and Files
1. Data Structures and Sequences
1. Tuple
2. List
3. Built-in Sequence Functions
4. Dict
5. Set
6. List, Set, and Dict Comprehensions
2. Functions
1. Namespaces, Scope, and Local Functions
2. Returning Multiple Values
3. Functions Are Objects
4. Anonymous (lambda) Functions
5. Closures: Functions that Return Functions
6. Extended Call Syntax with *args, **kwargs
7. Currying: Partial Argument Application
8. Generators
3. Files and the operating system
1. Bytes and Unicode with files
5. 4. NumPy Basics: Arrays and Vectorized Computation
1. The NumPy ndarray: A Multidimensional Array Object
1. Creating ndarrays
2. Data Types for ndarrays
3. Operations between Arrays and Scalars
4. Basic Indexing and Slicing
5. Boolean Indexing
6. Fancy Indexing
7. Transposing Arrays and Swapping Axes
2. Universal Functions: Fast Element-wise Array Functions
3. Loop-free programming with arrays
1. Expressing Conditional Logic as Array Operations
2. Mathematical and Statistical Methods
3. Methods for Boolean Arrays
4. Sorting
5. Unique and Other Set Logic
4. File Input and Output with Arrays
5. Linear Algebra
6. Pseudorandom Number Generation
7. Example: Random Walks
1. Simulating Many Random Walks at Once
6. 5. Getting Started with pandas
1. Introduction to pandas Data Structures
1. Series
2. DataFrame
3. Index Objects
2. Essential Functionality
1. Reindexing
2. Dropping entries from an axis
3. Indexing, selection, and filtering
4. Arithmetic and data alignment
5. Function application and mapping
6. Sorting and ranking
7. Axis indexes with duplicate values
3. Summarizing and Computing Descriptive Statistics
1. Correlation and Covariance
2. Unique Values, Value Counts, and Membership
4. Moving ahead
7. 6. Data Loading, Storage, and File Formats
1. Reading and Writing Data in Text Format
1. Reading Text Files in Pieces
2. Writing Data Out to Text Format
3. Manually Working with Delimited Formats
4. JSON Data
5. XML and HTML: Web Scraping
2. Binary Data Formats
1. Using HDF5 Format
2. Reading Microsoft Excel Files
3. Interacting with Web APIs
4. Interacting with Databases
8. 7. Data Cleaning and Preparation
1. Handling Missing Data
1. Filtering Out Missing Data
2. Filling in Missing Data
2. Data Transformation
1. Removing Duplicates
2. Transforming Data Using a Function or Mapping
3. Replacing Values
4. Renaming Axis Indexes
5. Discretization and Binning
6. Detecting and Filtering Outliers
7. Permutation and Random Sampling
8. Computing Indicator/Dummy Variables
3. String Manipulation
1. String Object Methods
2. Regular expressions
3. Vectorized string functions in pandas
9. 8. Data Wrangling: Join, Combine, and Reshape
1. Hierarchical Indexing
1. Reordering and Sorting Levels
2. Summary Statistics by Level
3. Indexing with a DataFrame’s columns
4. Integer Indexes
2. Combining and Merging Data Sets
1. Database-style DataFrame Joins
2. Merging on Index
3. Concatenating Along an Axis
4. Combining Data with Overlap
3. Reshaping and Pivoting
1. Reshaping with Hierarchical Indexing
2. Pivoting “long” to “wide” Format
10. 9. Plotting and Visualization
1. A Brief matplotlib API Primer
1. Figures and Subplots
2. Colors, Markers, and Line Styles
3. Ticks, Labels, and Legends
4. Annotations and Drawing on a Subplot
5. Saving Plots to File
6. matplotlib Configuration
2. Plotting with pandas and seaborn
1. Line Plots
2. Bar Plots
3. Histograms and Density Plots
4. Scatter or Point Plots
5. Facet grids and categorical data
3. Other Python Visualization Tools
11. 10. Data Aggregation and Group Operations
1. GroupBy Mechanics
1. Iterating Over Groups
2. Selecting a Column or Subset of Columns
3. Grouping with Dicts and Series
4. Grouping with Functions
5. Grouping by Index Levels
2. Data Aggregation
1. Column-wise and Multiple Function Application
2. Returning Aggregated Data without Row Indexes
3. Apply: General split-apply-combine
1. Suppressing the group keys
2. Quantile and Bucket Analysis
3. Example: Filling Missing Values with Group-specific Values
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