Python for Data Analysis. Data Wrangling with Pandas, NumPy, and IPython. 2nd Edition

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• Preface
  • 1. New for the Second Edition
  • 2. Conventions Used in This Book
  • 3. Using Code Examples
  • 4. OReilly Safari
  • 5. How to Contact Us
  • 6. Acknowledgments
    • In Memoriam: John D. Hunter (19682012)
    • Acknowledgments for the Second Edition (2017)
    • Acknowledgments for the First Edition (2012)
• 1. Preliminaries
  • 1.1. What Is This Book About?
    • What Kinds of Data?
  • 1.2. Why Python for Data Analysis?
    • Python as Glue
    • Solving the Two-Language Problem
    • Why Not Python?
  • 1.3. Essential Python Libraries
    • NumPy
    • pandas
    • matplotlib
    • IPython and Jupyter
    • SciPy
    • scikit-learn
    • statsmodels
  • 1.4. Installation and Setup
    • Windows
    • Apple (OS X, macOS)
    • GNU/Linux
    • Installing or Updating Python Packages
    • Python 2 and Python 3
    • Integrated Development Environments (IDEs) and Text Editors
  • 1.5. Community and Conferences
  • 1.6. Navigating This Book
    • Code Examples
    • Data for Examples
    • Import Conventions
    • Jargon
• 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
    • The %run Command
      • Interrupting running code
    • Executing Code from the Clipboard
    • Terminal Keyboard Shortcuts
    • About Magic Commands
    • Matplotlib Integration
  • 2.3. Python Language Basics
    • Language Semantics
      • Indentation, not braces
      • Everything is an object
      • Comments
      • Function and object method calls
      • Variables and argument passing
      • Dynamic references, strong types
      • Attributes and methods
      • Duck typing
      • Imports
      • Binary operators and comparisons
      • Mutable and immutable objects
    • Scalar Types
      • Numeric types
      • Strings
      • Bytes and Unicode
      • Booleans
      • Type casting
      • None
      • Dates and times
    • Control Flow
      • if, elif, and else
      • for loops
      • while loops
      • pass
      • range
      • Ternary expressions
• 3. Built-in Data Structures, Functions, and Files
  • 3.1. Data Structures and Sequences
    • Tuple
      • Unpacking tuples
      • Tuple methods
    • List
      • Adding and removing elements
      • Concatenating and combining lists
      • Sorting
      • Binary search and maintaining a sorted list
      • Slicing
    • Built-in Sequence Functions
      • enumerate
      • sorted
      • zip
      • reversed
    • dict
      • Creating dicts from sequences
      • Default values
      • Valid dict key types
    • set
    • List, Set, and Dict Comprehensions
      • Nested list comprehensions
  • 3.2. Functions
    • Namespaces, Scope, and Local Functions
    • Returning Multiple Values
    • Functions Are Objects
    • Anonymous (Lambda) Functions
    • Currying: Partial Argument Application
    • Generators
      • Generator expresssions
      • itertools module
    • Errors and Exception Handling
      • Exceptions in IPython
  • 3.3. Files and the Operating System
    • Bytes and Unicode with Files
  • 3.4. Conclusion
• 4. NumPy Basics: Arrays and Vectorized Computation
  • 4.1. The NumPy ndarray: A Multidimensional Array Object
    • Creating ndarrays
    • Data Types for ndarrays
    • Arithmetic with NumPy Arrays
    • Basic Indexing and Slicing
      • Indexing with slices
    • Boolean Indexing
    • Fancy Indexing
    • Transposing Arrays and Swapping Axes
  • 4.2. Universal Functions: Fast Element-Wise Array Functions
  • 4.3. 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
  • 4.4. File Input and Output with Arrays
  • 4.5. Linear Algebra
  • 4.6. Pseudorandom Number Generation
  • 4.7. Example: Random Walks
    • Simulating Many Random Walks at Once
  • 4.8. Conclusion
• 5. Getting Started with 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
      • Selection with loc and iloc
    • Integer Indexes
    • Arithmetic and Data Alignment
      • Arithmetic methods with fill values
      • Operations between DataFrame and Series
    • Function Application and Mapping
    • Sorting and Ranking
    • Axis Indexes with Duplicate Labels
  • 5.3. Summarizing and Computing Descriptive Statistics
    • Correlation and Covariance
    • 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
    • Writing Data to Text Format
    • Working with Delimited Formats
    • JSON Data
    • XML and HTML: Web Scraping
      • Parsing XML with lxml.objectify
  • 6.2. Binary Data Formats
    • Using HDF5 Format
    • Reading Microsoft Excel Files
  • 6.3. Interacting with Web APIs
  • 6.4. Interacting with Databases
  • 6.5. Conclusion
• 7. Data Cleaning and Preparation
  • 7.1. Handling Missing Data
    • Filtering Out Missing Data
    • Filling In Missing Data
  • 7.2. Data Transformation
    • Removing Duplicates
    • Transforming Data Using a Function or Mapping
    • Replacing Values
    • Renaming Axis Indexes
    • Discretization and Binning
    • Detecting and Filtering Outliers
    • Permutation and Random Sampling
    • Computing Indicator/Dummy Variables
  • 7.3. String Manipulation
    • String Object Methods
    • Regular Expressions
    • Vectorized String Functions in pandas
  • 7.4. Conclusion
• 8. Data Wrangling: Join, Combine, and Reshape
  • 8.1. Hierarchical Indexing
    • Reordering and Sorting Levels
    • Summary Statistics by Level
    • Indexing with a DataFrames columns
  • 8.2. Combining and Merging Datasets
    • Database-Style DataFrame Joins
    • Merging on Index
    • Concatenating Along an Axis
    • Combining Data with Overlap
  • 8.3. Reshaping and Pivoting
    • Reshaping with Hierarchical Indexing
    • Pivoting Long to Wide Format
    • Pivoting Wide to Long Format
  • 8.4. Conclusion
• 9. Plotting and Visualization
  • 9.1. A Brief matplotlib API Primer
    • Figures and Subplots
      • Adjusting the spacing around subplots
    • Colors, Markers, and Line Styles
    • Ticks, Labels, and Legends
      • Setting the title, axis labels, ticks, and ticklabels
      • Adding legends
    • Annotations and Drawing on a Subplot
    • Saving Plots to File
    • matplotlib Configuration
  • 9.2. Plotting with pandas and seaborn
    • Line Plots
    • Bar Plots
    • Histograms and Density Plots
    • Scatter or Point Plots
    • Facet Grids and Categorical Data
  • 9.3. Other Python Visualization Tools
  • 9.4. Conclusion
• 10. Data Aggregation and Group Operations
  • 10.1. GroupBy Mechanics
    • Iterating Over Groups
    • Selecting a Column or Subset of Columns
    • Grouping with Dicts and Series
    • Grouping with Functions
    • Grouping by Index Levels
  • 10.2. Data Aggregation
    • Column-Wise and Multiple Function Application
    • Returning Aggregated Data Without Row Indexes
  • 10.3. Apply: General split-apply-combine
    • Suppressing the Group Keys
    • Quantile and Bucket Analysis
    • Example: Filling Missing Values with Group-Specific Values
    • Example: Random Sampling and Permutation
    • Example: Group Weighted Average and Correlation
    • Example: Group-Wise Linear Regression
  • 10.4. Pivot Tables and Cross-Tabulation
    • Cross-Tabulations: Crosstab
  • 10.5. Conclusion
• 11. Time Series
  • 11.1. Date and Time Data Types and Tools
    • Converting Between String and Datetime
  • 11.2. Time Series Basics
    • Indexing, Selection, Subsetting
    • Time Series with Duplicate Indices
  • 11.3. Date Ranges, Frequencies, and Shifting
    • Generating Date Ranges
    • Frequencies and Date Offsets
      • Week of month dates
    • Shifting (Leading and Lagging) Data
      • Shifting dates with offsets
  • 11.4. Time Zone Handling
    • Time Zone Localization and Conversion
    • Operations with Time ZoneAware Timestamp Objects
    • Operations Between Different Time Zones
  • 11.5. Periods and Period Arithmetic
    • Period Frequency Conversion
    • Quarterly Period Frequencies
    • Converting Timestamps to Periods (and Back)
    • Creating a PeriodIndex from Arrays
  • 11.6. Resampling and Frequency Conversion
    • Downsampling
      • Open-High-Low-Close (OHLC) resampling
    • Upsampling and Interpolation
    • Resampling with Periods
  • 11.7. Moving Window Functions
    • Exponentially Weighted Functions
    • Binary Moving Window Functions
    • User-Defined Moving Window Functions
  • 11.8. Conclusion
• 12. Advanced pandas
  • 12.1. Categorical Data
    • Background and Motivation
    • Categorical Type in pandas
    • Computations with Categoricals
      • Better performance with categoricals
    • Categorical Methods
      • Creating dummy variables for modeling
  • 12.2. Advanced GroupBy Use
    • Group Transforms and Unwrapped GroupBys
    • Grouped Time Resampling
  • 12.3. Techniques for Method Chaining
    • The pipe Method
  • 12.4. Conclusion
• 13. Introduction to Modeling Libraries in Python
  • 13.1. Interfacing Between pandas and Model Code
  • 13.2. Creating Model Descriptions with Patsy
    • Data Transformations in Patsy Formulas
    • Categorical Data and Patsy
  • 13.3. Introduction to statsmodels
    • Estimating Linear Models
    • Estimating Time Series Processes
  • 13.4. Introduction to scikit-learn
  • 13.5. Continuing Your Education
• 14. Data Analysis Examples
  • 14.1. 1.USA.gov Data from Bitly
    • Counting Time Zones in Pure Python
    • Counting Time Zones with pandas
  • 14.2. MovieLens 1M Dataset
    • Measuring Rating Disagreement
  • 14.3. US Baby Names 18802010
    • Analyzing Naming Trends
      • Measuring the increase in naming diversity
      • The last letter revolution
      • Boy names that became girl names (and vice versa)
  • 14.4. USDA Food Database
  • 14.5. 2012 Federal Election Commission Database
    • Donation Statistics by Occupation and Employer
    • Bucketing Donation Amounts
    • Donation Statistics by State
  • 14.6. Conclusion
• A. Advanced NumPy
  • A.1. ndarray Object Internals
    • NumPy dtype Hierarchy
  • A.2. Advanced Array Manipulation
    • Reshaping Arrays
    • C Versus Fortran Order
    • Concatenating and Splitting Arrays
      • Stacking helpers: r_ and c_
    • Repeating Elements: tile and repeat
    • Fancy Indexing Equivalents: take and put
  • A.3. Broadcasting
    • Broadcasting Over Other Axes
    • Setting Array Values by Broadcasting
  • A.4. Advanced ufunc Usage
    • ufunc Instance Methods
    • Writing New ufuncs in Python
  • A.5. Structured and Record Arrays
    • Nested dtypes and Multidimensional Fields
    • Why Use Structured Arrays?
  • A.6. More About Sorting
    • Indirect Sorts: argsort and lexsort
    • Alternative Sort Algorithms
    • Partially Sorting Arrays
    • numpy.searchsorted: Finding Elements in a Sorted Array
  • A.7. Writing Fast NumPy Functions with Numba
    • Creating Custom numpy.ufunc Objects with Numba
  • A.8. Advanced Array Input and Output
    • Memory-Mapped Files
    • HDF5 and Other Array Storage Options
  • A.9. Performance Tips
    • The Importance of Contiguous Memory
• B. More on the IPython System
  • B.1. Using the Command History
    • Searching and Reusing the Command History
    • Input and Output Variables
  • B.2. Interacting with the Operating System
    • Shell Commands and Aliases
    • Directory Bookmark System
  • B.3. Software Development Tools
    • Interactive Debugger
      • Other ways to make use of the debugger
    • Timing Code: %time and %timeit
    • Basic Profiling: %prun and %run -p
    • Profiling a Function Line by Line
  • B.4. Tips for Productive Code Development Using IPython
    • Reloading Module Dependencies
    • Code Design Tips
      • Keep relevant objects and data alive
      • Flat is better than nested
      • Overcome a fear of longer files
  • B.5. Advanced IPython Features
    • Making Your Own Classes IPython-Friendly
    • Profiles and Configuration
  • B.6. Conclusion
• Index

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