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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all? IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you? ll learn how to use:

• IPython and Jupyter: provide computational environments for data scientists using Python
• NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
• Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
• Matplotlib: includes capabilities for a flexible range of data visualizations in Python
• Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
  
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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.