Preface

Part 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

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

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

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

Part 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

……

Part III. Data Manipulation with Pandas

Part IV. Visualization with Matplotlib

Part V. Machine Learning

Index