Python数据科学手册

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