Coverart for item
The Resource Python data science handbook : essential tools for working with data, Jake VanderPlas

Python data science handbook : essential tools for working with data, Jake VanderPlas

Label
Python data science handbook : essential tools for working with data
Title
Python data science handbook
Title remainder
essential tools for working with data
Statement of responsibility
Jake VanderPlas
Creator
Author
Subject
Genre
Language
eng
Cataloging source
IDEBK
http://library.link/vocab/creatorName
Vanderplas, Jacob T
Dewey number
006.3
Illustrations
  • illustrations
  • maps
Index
index present
LC call number
QA76.9.D343
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Python (Computer program language)
  • Data mining
  • COMPUTERS
  • Data mining
  • Python (Computer program language)
  • Data Mining
  • Datenanalyse
  • Datenmanagement
  • Python
Label
Python data science handbook : essential tools for working with data, Jake VanderPlas
Instantiates
Publication
Copyright
Note
  • Includes index
  • "Powered by Jupyter"-- Page 1 of cover
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
Control code
964358680
Dimensions
unknown
Edition
First edition.
Extent
1 online resource (xvi, 529 pages)
Form of item
online
Isbn
9781491912133
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
color illustrations, color maps
http://library.link/vocab/ext/overdrive/overdriveId
  • 971881
  • 0d776164-392c-4f9b-92ff-ec5099b11d3e
Specific material designation
remote
System control number
(OCoLC)964358680
Label
Python data science handbook : essential tools for working with data, Jake VanderPlas
Publication
Copyright
Note
  • Includes index
  • "Powered by Jupyter"-- Page 1 of cover
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 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
Control code
964358680
Dimensions
unknown
Edition
First edition.
Extent
1 online resource (xvi, 529 pages)
Form of item
online
Isbn
9781491912133
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
color illustrations, color maps
http://library.link/vocab/ext/overdrive/overdriveId
  • 971881
  • 0d776164-392c-4f9b-92ff-ec5099b11d3e
Specific material designation
remote
System control number
(OCoLC)964358680

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012
    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
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