Python Data Science Handbook (Jupyter Notebook Version)

Copyright
Table of Contents
Preface
	What Is Data Science?
	Who Is This Book For?
	Why Python?
		Python 2 Versus Python 3
	Outline of This Book
	Using Code Examples
	Installation Considerations
	Conventions Used in This Book
	O’Reilly Safari
	How to Contact Us
Chapter 1. IPython: Beyond Normal Python
	Shell or Notebook?
		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
	Keyboard Shortcuts in the IPython Shell
		Navigation Shortcuts
		Text Entry Shortcuts
		Command History Shortcuts
		Miscellaneous Shortcuts
	IPython Magic Commands
		Pasting Code Blocks: %paste and %cpaste
		Running External Code: %run
		Timing Code Execution: %timeit
		Help on Magic Functions: ?, %magic, and %lsmagic
	Input and Output History
		IPython’s 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
	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
Chapter 2. Introduction to NumPy
	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
	The Basics of NumPy Arrays
		NumPy Array Attributes
		Array Indexing: Accessing Single Elements
		Array Slicing: Accessing Subarrays
		Reshaping of Arrays
		Array Concatenation and Splitting
	Computation on NumPy Arrays: Universal Functions
		The Slowness of Loops
		Introducing UFuncs
		Exploring NumPy’s UFuncs
		Advanced Ufunc Features
		Ufuncs: Learning More
	Aggregations: Min, Max, and Everything in Between
		Summing the Values in an Array
		Minimum and Maximum
		Example: What Is the Average Height of US Presidents?
	Computation on Arrays: Broadcasting
		Introducing Broadcasting
		Rules of Broadcasting
		Broadcasting in Practice
	Comparisons, Masks, and Boolean Logic
		Example: Counting Rainy Days
		Comparison Operators as ufuncs
		Working with Boolean Arrays
		Boolean Arrays as Masks
	Fancy Indexing
		Exploring Fancy Indexing
		Combined Indexing
		Example: Selecting Random Points
		Modifying Values with Fancy Indexing
		Example: Binning Data
	Sorting Arrays
		Fast Sorting in NumPy: np.sort and np.argsort
		Partial Sorts: Partitioning
		Example: k-Nearest Neighbors
	Structured Data: NumPy’s Structured Arrays
		Creating Structured Arrays
		More Advanced Compound Types
		RecordArrays: Structured Arrays with a Twist
		On to Pandas
Chapter 3. Data Manipulation with Pandas
	Installing and Using Pandas
	Introducing Pandas Objects
		The Pandas Series Object
		The Pandas DataFrame Object
		The Pandas Index Object
	Data Indexing and Selection
		Data Selection in Series
		Data Selection in DataFrame
	Operating on Data in Pandas
		Ufuncs: Index Preservation
		UFuncs: Index Alignment
		Ufuncs: Operations Between DataFrame and Series
	Handling Missing Data
		Trade-Offs in Missing Data Conventions
		Missing Data in Pandas
		Operating on Null Values
	Hierarchical Indexing
		A Multiply Indexed Series
		Methods of MultiIndex Creation
		Indexing and Slicing a MultiIndex
		Rearranging Multi-Indices
		Data Aggregations on Multi-Indices
	Combining Datasets: Concat and Append
		Recall: Concatenation of NumPy Arrays
		Simple Concatenation with pd.concat
	Combining Datasets: Merge and Join
		Relational Algebra
		Categories of Joins
		Specification of the Merge Key
		Specifying Set Arithmetic for Joins
		Overlapping Column Names: The suffixes Keyword
		Example: US States Data
	Aggregation and Grouping
		Planets Data
		Simple Aggregation in Pandas
		GroupBy: Split, Apply, Combine
	Pivot Tables
		Motivating Pivot Tables
		Pivot Tables by Hand
		Pivot Table Syntax
		Example: Birthrate Data
	Vectorized String Operations
		Introducing Pandas String Operations
		Tables of Pandas String Methods
		Example: Recipe Database
	Working with Time Series
		Dates and Times in Python
		Pandas Time Series: Indexing by Time
		Pandas Time Series Data Structures
		Frequencies and Offsets
		Resampling, Shifting, and Windowing
		Where to Learn More
		Example: Visualizing Seattle Bicycle Counts
	High-Performance Pandas: eval() and query()
		Motivating query() and eval(): Compound Expressions
		pandas.eval() for Efficient Operations
		DataFrame.eval() for Column-Wise Operations
		DataFrame.query() Method
		Performance: When to Use These Functions
	Further Resources
Chapter 4. Visualization with Matplotlib
	General Matplotlib Tips
		Importing matplotlib
		Setting Styles
		show() or No show()? How to Display Your Plots
		Saving Figures to File
	Two Interfaces for the Price of One
	Simple Line Plots
		Adjusting the Plot: Line Colors and Styles
		Adjusting the Plot: Axes Limits
		Labeling Plots
	Simple Scatter Plots
		Scatter Plots with plt.plot
		Scatter Plots with plt.scatter
		plot Versus scatter: A Note on Efficiency
	Visualizing Errors
		Basic Errorbars
		Continuous Errors
	Density and Contour Plots
		Visualizing a Three-Dimensional Function
	Histograms, Binnings, and Density
		Two-Dimensional Histograms and Binnings
	Customizing Plot Legends
		Choosing Elements for the Legend
		Legend for Size of Points
		Multiple Legends
	Customizing Colorbars
		Customizing Colorbars
		Example: Handwritten Digits
	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
	Text and Annotation
		Example: Effect of Holidays on US Births
		Transforms and Text Position
		Arrows and Annotation
	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
	Customizing Matplotlib: Configurations and Stylesheets
		Plot Customization by Hand
		Changing the Defaults: rcParams
		Stylesheets
	Three-Dimensional Plotting in Matplotlib
		Three-Dimensional Points and Lines
		Three-Dimensional Contour Plots
		Wireframes and Surface Plots
		Surface Triangulations
	Geographic Data with Basemap
		Map Projections
		Drawing a Map Background
		Plotting Data on Maps
		Example: California Cities
		Example: Surface Temperature Data
	Visualization with Seaborn
		Seaborn Versus Matplotlib
		Exploring Seaborn Plots
		Example: Exploring Marathon Finishing Times
	Further Resources
		Matplotlib Resources
		Other Python Graphics Libraries
Chapter 5. Machine Learning
	What Is Machine Learning?
		Categories of Machine Learning
		Qualitative Examples of Machine Learning Applications
		Summary
	Introducing Scikit-Learn
		Data Representation in Scikit-Learn
		Scikit-Learn’s Estimator API
		Application: Exploring Handwritten Digits
		Summary
	Hyperparameters and Model Validation
		Thinking About Model Validation
		Selecting the Best Model
		Learning Curves
		Validation in Practice: Grid Search
		Summary
	Feature Engineering
		Categorical Features
		Text Features
		Image Features
		Derived Features
		Imputation of Missing Data
		Feature Pipelines
	In Depth: Naive Bayes Classification
		Bayesian Classification
		Gaussian Naive Bayes
		Multinomial Naive Bayes
		When to Use Naive Bayes
	In Depth: Linear Regression
		Simple Linear Regression
		Basis Function Regression
		Regularization
		Example: Predicting Bicycle Traffic
	In-Depth: Support Vector Machines
		Motivating Support Vector Machines
		Support Vector Machines: Maximizing the Margin
		Example: Face Recognition
		Support Vector Machine Summary
	In-Depth: Decision Trees and Random Forests
		Motivating Random Forests: Decision Trees
		Ensembles of Estimators: Random Forests
		Random Forest Regression
		Example: Random Forest for Classifying Digits
		Summary of Random Forests
	In Depth: Principal Component Analysis
		Introducing Principal Component Analysis
		PCA as Noise Filtering
		Example: Eigenfaces
		Principal Component Analysis Summary
	In-Depth: Manifold Learning
		Manifold Learning: “HELLO”
		Multidimensional Scaling (MDS)
		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
	In Depth: k-Means Clustering
		Introducing k-Means
		k-Means Algorithm: Expectation–Maximization
		Examples
	In Depth: Gaussian Mixture Models
		Motivating GMM: Weaknesses of k-Means
		Generalizing E–M: Gaussian Mixture Models
		GMM as Density Estimation
		Example: GMM for Generating New Data
	In-Depth: Kernel Density Estimation
		Motivating KDE: Histograms
		Kernel Density Estimation in Practice
		Example: KDE on a Sphere
		Example: Not-So-Naive Bayes
	Application: A Face Detection Pipeline
		HOG Features
		HOG in Action: A Simple Face Detector
		Caveats and Improvements
	Further Machine Learning Resources
		Machine Learning in Python
		General Machine Learning
Index
About the Author
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