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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