Preface 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 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 …… 3．Data Manipulation with Pandas 4．Visualization with Matplotlib 5．Machine Learning Index
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