Python Data Science Handbook. 2nd Edition

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• Preface
  • What Is Data Science?
  • Who Is This Book For?
  • Why Python?
  • Outline of the Book
  • Installation Considerations
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
• I. Jupyter: Beyond Normal Python
• 1. Getting Started in IPython and Jupyter
  • Launching the IPython Shell
  • Launching the Jupyter Notebook
  • Help and Documentation in IPython
    • Accessing Documentation with ?
    • Accessing Source Code with ??
    • Exploring Modules with Tab Completion
      • Tab completion of object contents
      • Tab completion when importing
      • Beyond tab completion: Wildcard matching
  • Keyboard Shortcuts in the IPython Shell
    • Navigation Shortcuts
    • Text Entry Shortcuts
    • Command History Shortcuts
    • Miscellaneous Shortcuts
• 2. Enhanced Interactive Features
  • IPython Magic Commands
    • Running External Code: %run
    • Timing Code Execution: %timeit
    • Help on Magic Functions: ?, %magic, and %lsmagic
  • Input and Output History
    • IPythons In and Out Objects
    • Underscore Shortcuts and Previous Outputs
    • Suppressing Output
    • Related Magic Commands
  • IPython and Shell Commands
    • Quick Introduction to the Shell
    • Shell Commands in IPython
    • Passing Values to and from the Shell
    • Shell-Related Magic Commands
• 3. Debugging and Profiling
  • Errors and Debugging
    • Controlling Exceptions: %xmode
    • Debugging: When Reading Tracebacks Is Not Enough
  • Profiling and Timing Code
    • Timing Code Snippets: %timeit and %time
    • Profiling Full Scripts: %prun
    • Line-by-Line Profiling with %lprun
    • Profiling Memory Use: %memit and %mprun
  • More IPython Resources
    • Web Resources
    • Books
• II. Introduction to NumPy
• 4. Understanding Data Types in Python
  • A Python Integer Is More Than Just an Integer
  • A Python List Is More Than Just a List
  • Fixed-Type Arrays in Python
  • Creating Arrays from Python Lists
  • Creating Arrays from Scratch
  • NumPy Standard Data Types
• 5. The Basics of NumPy Arrays
  • NumPy Array Attributes
  • Array Indexing: Accessing Single Elements
  • Array Slicing: Accessing Subarrays
    • One-Dimensional Subarrays
    • Multidimensional Subarrays
    • Subarrays as No-Copy Views
    • Creating Copies of Arrays
  • Reshaping of Arrays
  • Array Concatenation and Splitting
    • Concatenation of Arrays
    • Splitting of Arrays
• 6. Computation on NumPy Arrays: Universal Functions
  • The Slowness of Loops
  • Introducing Ufuncs
  • Exploring NumPys Ufuncs
    • Array Arithmetic
    • Absolute Value
    • Trigonometric Functions
    • Exponents and Logarithms
    • Specialized Ufuncs
  • Advanced Ufunc Features
    • Specifying Output
    • Aggregations
    • Outer Products
  • Ufuncs: Learning More
• 7. Aggregations: min, max, and Everything in Between
  • Summing the Values in an Array
  • Minimum and Maximum
    • Multidimensional Aggregates
    • Other Aggregation Functions
  • Example: What Is the Average Height of US Presidents?
• 8. Computation on Arrays: Broadcasting
  • Introducing Broadcasting
  • Rules of Broadcasting
    • Broadcasting Example 1
    • Broadcasting Example 2
    • Broadcasting Example 3
  • Broadcasting in Practice
    • Centering an Array
    • Plotting a Two-Dimensional Function
• 9. Comparisons, Masks, and Boolean Logic
  • Example: Counting Rainy Days
  • Comparison Operators as Ufuncs
  • Working with Boolean Arrays
    • Counting Entries
    • Boolean Operators
  • Boolean Arrays as Masks
  • Using the Keywords and/or Versus the Operators &/|
• 10. Fancy Indexing
  • Exploring Fancy Indexing
  • Combined Indexing
  • Example: Selecting Random Points
  • Modifying Values with Fancy Indexing
  • Example: Binning Data
• 11. Sorting Arrays
  • Fast Sorting in NumPy: np.sort and np.argsort
  • Sorting Along Rows or Columns
  • Partial Sorts: Partitioning
  • Example: k-Nearest Neighbors
• 12. Structured Data: NumPys Structured Arrays
  • Exploring Structured Array Creation
  • More Advanced Compound Types
  • Record Arrays: Structured Arrays with a Twist
  • On to Pandas
• III. Data Manipulation with Pandas
• 13. Introducing Pandas Objects
  • The Pandas Series Object
    • Series as Generalized NumPy Array
    • Series as Specialized Dictionary
    • Constructing Series Objects
  • The Pandas DataFrame Object
    • DataFrame as Generalized NumPy Array
    • DataFrame as Specialized Dictionary
    • Constructing DataFrame Objects
      • From a single Series object
      • From a list of dicts
      • From a dictionary of Series objects
      • From a two-dimensional NumPy array
      • From a NumPy structured array
  • The Pandas Index Object
    • Index as Immutable Array
    • Index as Ordered Set
• 14. Data Indexing and Selection
  • Data Selection in Series
    • Series as Dictionary
    • Series as One-Dimensional Array
    • Indexers: loc and iloc
  • Data Selection in DataFrames
    • DataFrame as Dictionary
    • DataFrame as Two-Dimensional Array
    • Additional Indexing Conventions
• 15. Operating on Data in Pandas
  • Ufuncs: Index Preservation
  • Ufuncs: Index Alignment
    • Index Alignment in Series
    • Index Alignment in DataFrames
  • Ufuncs: Operations Between DataFrames and Series
• 16. Handling Missing Data
  • Trade-offs in Missing Data Conventions
  • Missing Data in Pandas
    • None as a Sentinel Value
    • NaN: Missing Numerical Data
    • NaN and None in Pandas
  • Pandas Nullable Dtypes
  • Operating on Null Values
    • Detecting Null Values
    • Dropping Null Values
    • Filling Null Values
• 17. Hierarchical Indexing
  • A Multiply Indexed Series
    • The Bad Way
    • The Better Way: The Pandas MultiIndex
    • MultiIndex as Extra Dimension
  • Methods of MultiIndex Creation
    • Explicit MultiIndex Constructors
    • MultiIndex Level Names
    • MultiIndex for Columns
  • Indexing and Slicing a MultiIndex
    • Multiply Indexed Series
    • Multiply Indexed DataFrames
  • Rearranging Multi-Indexes
    • Sorted and Unsorted Indices
    • Stacking and Unstacking Indices
    • Index Setting and Resetting
• 18. Combining Datasets: concat and append
  • Recall: Concatenation of NumPy Arrays
  • Simple Concatenation with pd.concat
    • Duplicate Indices
      • Treating repeated indices as an error
      • Ignoring the index
      • Adding MultiIndex keys
    • Concatenation with Joins
    • The append Method
• 19. Combining Datasets: merge and join
  • Relational Algebra
  • Categories of Joins
    • One-to-One Joins
    • Many-to-One Joins
    • Many-to-Many Joins
  • Specification of the Merge Key
    • The on Keyword
    • The left_on and right_on Keywords
    • The left_index and right_index Keywords
  • Specifying Set Arithmetic for Joins
  • Overlapping Column Names: The suffixes Keyword
  • Example: US States Data
• 20. Aggregation and Grouping
  • Planets Data
  • Simple Aggregation in Pandas
  • groupby: Split, Apply, Combine
    • Split, Apply, Combine
    • The GroupBy Object
      • Column indexing
      • Iteration over groups
      • Dispatch methods
    • Aggregate, Filter, Transform, Apply
      • Aggregation
      • Filtering
      • Transformation
      • The apply method
    • Specifying the Split Key
      • A list, array, series, or index providing the grouping keys
      • A dictionary or series mapping index to group
      • Any Python function
      • A list of valid keys
    • Grouping Example
• 21. Pivot Tables
  • Motivating Pivot Tables
  • Pivot Tables by Hand
  • Pivot Table Syntax
    • Multilevel Pivot Tables
    • Additional Pivot Table Options
  • Example: Birthrate Data
• 22. Vectorized String Operations
  • Introducing Pandas String Operations
  • Tables of Pandas String Methods
    • Methods Similar to Python String Methods
    • Methods Using Regular Expressions
    • Miscellaneous Methods
      • Vectorized item access and slicing
      • Indicator variables
  • Example: Recipe Database
    • A Simple Recipe Recommender
    • Going Further with Recipes
• 23. Working with Time Series
  • Dates and Times in Python
    • Native Python Dates and Times: datetime and dateutil
    • Typed Arrays of Times: NumPys datetime64
    • Dates and Times in Pandas: The Best of Both Worlds
  • Pandas Time Series: Indexing by Time
  • Pandas Time Series Data Structures
  • Regular Sequences: pd.date_range
  • Frequencies and Offsets
  • Resampling, Shifting, and Windowing
    • Resampling and Converting Frequencies
    • Time Shifts
    • Rolling Windows
  • Example: Visualizing Seattle Bicycle Counts
    • Visualizing the Data
    • Digging into the Data
• 24. High-Performance Pandas: eval and query
  • Motivating query and eval: Compound Expressions
  • pandas.eval for Efficient Operations
  • DataFrame.eval for Column-Wise Operations
    • Assignment in DataFrame.eval
    • Local Variables in DataFrame.eval
  • The DataFrame.query Method
  • Performance: When to Use These Functions
  • Further Resources
• IV. Visualization with Matplotlib
• 25. General Matplotlib Tips
  • Importing Matplotlib
  • Setting Styles
  • show or No show? How to Display Your Plots
    • Plotting from a Script
    • Plotting from an IPython Shell
    • Plotting from a Jupyter Notebook
    • Saving Figures to File
    • Two Interfaces for the Price of One
      • MATLAB-style Interface
      • Object-oriented interface
• 26. Simple Line Plots
  • Adjusting the Plot: Line Colors and Styles
  • Adjusting the Plot: Axes Limits
  • Labeling Plots
  • Matplotlib Gotchas
• 27. Simple Scatter Plots
  • Scatter Plots with plt.plot
  • Scatter Plots with plt.scatter
  • plot Versus scatter: A Note on Efficiency
  • Visualizing Uncertainties
    • Basic Errorbars
    • Continuous Errors
• 28. Density and Contour Plots
  • Visualizing a Three-Dimensional Function
  • Histograms, Binnings, and Density
  • Two-Dimensional Histograms and Binnings
    • plt.hist2d: Two-Dimensional Histogram
    • plt.hexbin: Hexagonal Binnings
    • Kernel Density Estimation
• 29. Customizing Plot Legends
  • Choosing Elements for the Legend
  • Legend for Size of Points
  • Multiple Legends
• 30. Customizing Colorbars
  • Customizing Colorbars
    • Choosing the Colormap
    • Color Limits and Extensions
    • Discrete Colorbars
  • Example: Handwritten Digits
• 31. Multiple Subplots
  • plt.axes: Subplots by Hand
  • plt.subplot: Simple Grids of Subplots
  • plt.subplots: The Whole Grid in One Go
  • plt.GridSpec: More Complicated Arrangements
• 32. Text and Annotation
  • Example: Effect of Holidays on US Births
  • Transforms and Text Position
  • Arrows and Annotation
• 33. Customizing Ticks
  • Major and Minor Ticks
  • Hiding Ticks or Labels
  • Reducing or Increasing the Number of Ticks
  • Fancy Tick Formats
  • Summary of Formatters and Locators
• 34. Customizing Matplotlib: Configurations and Stylesheets
  • Plot Customization by Hand
  • Changing the Defaults: rcParams
  • Stylesheets
    • Default Style
    • FiveThiryEight Style
    • ggplot Style
    • Bayesian Methods for Hackers Style
    • Dark Background Style
    • Grayscale Style
    • Seaborn Style
• 35. Three-Dimensional Plotting in Matplotlib
  • Three-Dimensional Points and Lines
  • Three-Dimensional Contour Plots
  • Wireframes and Surface Plots
  • Surface Triangulations
  • Example: Visualizing a Möbius Strip
• 36. Visualization with Seaborn
  • Exploring Seaborn Plots
    • Histograms, KDE, and Densities
    • Pair Plots
    • Faceted Histograms
  • Categorical Plots
    • Joint Distributions
    • Bar Plots
  • Example: Exploring Marathon Finishing Times
  • Further Resources
  • Other Python Visualization Libraries
• V. Machine Learning
• 37. What Is Machine Learning?
  • Categories of Machine Learning
  • Qualitative Examples of Machine Learning Applications
    • Classification: Predicting Discrete Labels
    • Regression: Predicting Continuous Labels
    • Clustering: Inferring Labels on Unlabeled Data
    • Dimensionality Reduction: Inferring Structure of Unlabeled Data
  • Summary
• 38. Introducing Scikit-Learn
  • Data Representation in Scikit-Learn
    • The Features Matrix
    • The Target Array
  • The Estimator API
    • Basics of the API
    • Supervised Learning Example: Simple Linear Regression
      • 1. Choose a class of model
      • 2. Choose model hyperparameters
      • 3. Arrange data into a features matrix and target vector
      • 4. Fit the model to the data
      • 5. Predict labels for unknown data
    • Supervised Learning Example: Iris Classification
    • Unsupervised Learning Example: Iris Dimensionality
    • Unsupervised Learning Example: Iris Clustering
  • Application: Exploring Handwritten Digits
    • Loading and Visualizing the Digits Data
    • Unsupervised Learning Example: Dimensionality Reduction
    • Classification on Digits
  • Summary
• 39. Hyperparameters and Model Validation
  • Thinking About Model Validation
    • Model Validation the Wrong Way
    • Model Validation the Right Way: Holdout Sets
    • Model Validation via Cross-Validation
  • Selecting the Best Model
    • The Bias-Variance Trade-off
    • Validation Curves in Scikit-Learn
  • Learning Curves
  • Validation in Practice: Grid Search
  • Summary
• 40. Feature Engineering
  • Categorical Features
  • Text Features
  • Image Features
  • Derived Features
  • Imputation of Missing Data
  • Feature Pipelines
• 41. In Depth: Naive Bayes Classification
  • Bayesian Classification
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
    • Example: Classifying Text
  • When to Use Naive Bayes
• 42. In Depth: Linear Regression
  • Simple Linear Regression
  • Basis Function Regression
    • Polynomial Basis Functions
    • Gaussian Basis Functions
  • Regularization
    • Ridge Regression (L2 Regularization)
    • Lasso Regression (L1 Regularization)
  • Example: Predicting Bicycle Traffic
• 43. In Depth: Support Vector Machines
  • Motivating Support Vector Machines
  • Support Vector Machines: Maximizing the Margin
    • Fitting a Support Vector Machine
    • Beyond Linear Boundaries: Kernel SVM
    • Tuning the SVM: Softening Margins
  • Example: Face Recognition
  • Summary
• 44. In Depth: Decision Trees and Random Forests
  • Motivating Random Forests: Decision Trees
    • Creating a Decision Tree
    • Decision Trees and Overfitting
  • Ensembles of Estimators: Random Forests
  • Random Forest Regression
  • Example: Random Forest for Classifying Digits
  • Summary
• 45. In Depth: Principal Component Analysis
  • Introducing Principal Component Analysis
    • PCA as Dimensionality Reduction
    • PCA for Visualization: Handwritten Digits
    • What Do the Components Mean?
    • Choosing the Number of Components
  • PCA as Noise Filtering
  • Example: Eigenfaces
  • Summary
• 46. In Depth: Manifold Learning
  • Manifold Learning: HELLO
  • Multidimensional Scaling
    • MDS as Manifold Learning
    • Nonlinear Embeddings: Where MDS Fails
  • Nonlinear Manifolds: Locally Linear Embedding
  • Some Thoughts on Manifold Methods
  • Example: Isomap on Faces
  • Example: Visualizing Structure in Digits
• 47. In Depth: k-Means Clustering
  • Introducing k-Means
  • ExpectationMaximization
  • Examples
    • Example 1: k-Means on Digits
    • Example 2: k-Means for Color Compression
• 48. In Depth: Gaussian Mixture Models
  • Motivating Gaussian Mixtures: Weaknesses of k-Means
  • Generalizing EM: Gaussian Mixture Models
  • Choosing the Covariance Type
  • Gaussian Mixture Models as Density Estimation
  • Example: GMMs for Generating New Data
• 49. In Depth: Kernel Density Estimation
  • Motivating Kernel Density Estimation: Histograms
  • Kernel Density Estimation in Practice
  • Selecting the Bandwidth via Cross-Validation
  • Example: Not-so-Naive Bayes
    • Anatomy of a Custom Estimator
    • Using Our Custom Estimator
• 50. Application: A Face Detection Pipeline
  • HOG Features
  • HOG in Action: A Simple Face Detector
    • 1. Obtain a Set of Positive Training Samples
    • 2. Obtain a Set of Negative Training Samples
    • 3. Combine Sets and Extract HOG Features
    • 4. Train a Support Vector Machine
    • 5. Find Faces in a New Image
  • Caveats and Improvements
  • Further Machine Learning Resources
• Index

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