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Python data science handbook : essential tools for working with data

Python data science handbook : essential tools for working with data (Loan 12 times)

Material type
단행본
Personal Author
Vanderplas, Jacob T.
Title Statement
Python data science handbook : essential tools for working with data / Jake VanderPlas.
Publication, Distribution, etc
Sebastopol, CA :   O'Reilly Media, Inc.,   2017.  
Physical Medium
xvi, 529 p. : ill. ; 24 cm.
ISBN
9781491912058 (paperback) 1491912057 (paperback)
General Note
Includes index.  
Content Notes
IPython: beyond normal Python -- Introduction to NumPy -- Data manipulation with Pandas -- Visualization with Matplotlib -- Machine learning.
Subject Added Entry-Topical Term
Python (Computer program language). Data mining.
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020 ▼a 1491912057 (paperback)
035 ▼a (KERIS)REF000018433214
040 ▼a BTCTA ▼b eng ▼e rda ▼c BTCTA ▼d YDXCP ▼d OCLCQ ▼d CLE ▼d BDX ▼d JRZ ▼d OCLCF ▼d CHVBK ▼d OCLCO ▼d TMA ▼d CUY ▼d NZHMA ▼d DLC ▼d 211009
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100 1 ▼a Vanderplas, Jacob T.
245 1 0 ▼a Python data science handbook : ▼b essential tools for working with data / ▼c Jake VanderPlas.
260 ▼a Sebastopol, CA : ▼b O'Reilly Media, Inc., ▼c 2017.
300 ▼a xvi, 529 p. : ▼b ill. ; ▼c 24 cm.
500 ▼a Includes index.
505 0 ▼a IPython: beyond normal Python -- Introduction to NumPy -- Data manipulation with Pandas -- Visualization with Matplotlib -- Machine learning.
650 0 ▼a Python (Computer program language).
650 0 ▼a Data mining.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.312 V239p Accession No. 121242308 Availability In loan Due Date 2021-06-23 Make a Reservation Available for Reserve R Service M

Contents information

Author Introduction

제이크 밴더플래스(지은이)

파이썬 과학 스택의 장기 사용자이자 개발자다. 현재 워싱턴 대학교의 학제간 연구 부장으로 근무하면서 독자적인 천문학 연구를 수행하고 있으며 다양한 분야의 과학자와 함께 상담 및 컨설팅을 진행하고 있다.

Information Provided By: : Aladin

Table of 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.

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