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
Resource Information
The item Python data science handbook : essential tools for working with data, Jake VanderPlas represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Python data science handbook : essential tools for working with data, Jake VanderPlas represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
- Edition
- First edition.
- Extent
- 1 online resource (xvi, 529 pages)
- Note
-
- Includes index
- "Powered by Jupyter"-- Page 1 of cover
- 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
- Isbn
- 9781491912133
- 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
- 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
- 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
- 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
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Python-data-science-handbook--essential-tools/LINgOPqDVqo/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Python-data-science-handbook--essential-tools/LINgOPqDVqo/">Python data science handbook : essential tools for working with data, Jake VanderPlas</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
