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Python for data analysis : data wrangling with Pandas, NumPy, and IPython /
This second edition offers instructions for manipulating, processing, cleaning, and crunching datasets in Python.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2017.
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
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| 008 | 171003t20172018cau o 001 0 eng d | ||
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| 020 | |z 9781491957660 | ||
| 020 | |z 9781491957653 | ||
| 029 | 1 | |a AU@ |b 000061341364 | |
| 029 | 1 | |a GBVCP |b 1014936462 | |
| 035 | |a (OCoLC)1005138881 | ||
| 037 | |a CL0500000897 |b Safari Books Online | ||
| 050 | 4 | |a QA76.73.P98 | |
| 082 | 0 | 4 | |a 005.13/3 |
| 049 | |a UAMI | ||
| 100 | 1 | |a McKinney, Wes, |e author. | |
| 245 | 1 | 0 | |a Python for data analysis : |b data wrangling with Pandas, NumPy, and IPython / |c Wes McKinney. |
| 246 | 3 | 0 | |a Data wrangling with Pandas, NumPy, and IPython |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c 2017. | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource (1 volume) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed Seprember 29, 2017). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Preliminaries -- Python language basics, IPython, and Jupyter notebook -- Built-in data structures, functions, and files -- NumPy basics : arrays and vectorized computation -- Getting started with pandas -- Data loading, storage, and file formats -- Data cleaning and preparation -- Data wrangling : join, combine, and reshape -- Plotting and visualization -- Data aggregation and group operations -- Time series -- Advanced pandas -- Introduction to modeling libraries in Python -- Data analysis examples -- Advanced NumPy -- More on the IPython system. | |
| 505 | 0 | |a Copyright; Table of Contents; Preface; Section 1. New for the Second Edition; Section 2. Conventions Used in This Book; Section 3. Using Code Examples; Section 4. O'Reilly Safari; Section 5. How to Contact Us; Section 6. Acknowledgments; In Memoriam: John D. Hunter (1968-2012); Acknowledgments for the Second Edition (2017); Acknowledgments for the First Edition (2012); Chapter 1. Preliminaries; 1.1 What Is This Book About?; What Kinds of Data?; 1.2 Why Python for Data Analysis?; Python as Glue; Solving the "Two-Language" Problem; Why Not Python?; 1.3 Essential Python Libraries; NumPy; pandas. | |
| 505 | 8 | |a MatplotlibIPython and Jupyter; SciPy; scikit-learn; statsmodels; 1.4 Installation and Setup; Windows; Apple (OS X, macOS); GNU/Linux; Installing or Updating Python Packages; Python 2 and Python 3; Integrated Development Environments (IDEs) and Text Editors; 1.5 Community and Conferences; 1.6 Navigating This Book; Code Examples; Data for Examples; Import Conventions; Jargon; Chapter 2. Python Language Basics, IPython, and Jupyter Notebooks; 2.1 The Python Interpreter; 2.2 IPython Basics; Running the IPython Shell; Running the Jupyter Notebook; Tab Completion; Introspection. | |
| 505 | 8 | |a The %run CommandExecuting Code from the Clipboard; Terminal Keyboard Shortcuts; About Magic Commands; Matplotlib Integration; 2.3 Python Language Basics; Language Semantics; Scalar Types; Control Flow; Chapter 3. Built-in Data Structures, Functions, and Files; 3.1 Data Structures and Sequences; Tuple; List; Built-in Sequence Functions; dict; set; List, Set, and Dict Comprehensions; 3.2 Functions; Namespaces, Scope, and Local Functions; Returning Multiple Values; Functions Are Objects; Anonymous (Lambda) Functions; Currying: Partial Argument Application; Generators. | |
| 505 | 8 | |a Errors and Exception Handling3.3 Files and the Operating System; Bytes and Unicode with Files; 3.4 Conclusion; Chapter 4. NumPy Basics: Arrays and Vectorized Computation; 4.1 The NumPy ndarray: A Multidimensional Array Object; Creating ndarrays; Data Types for ndarrays; Arithmetic with NumPy Arrays; Basic Indexing and Slicing; Boolean Indexing; Fancy Indexing; Transposing Arrays and Swapping Axes; 4.2 Universal Functions: Fast Element-Wise Array Functions; 4.3 Array-Oriented Programming with Arrays; Expressing Conditional Logic as Array Operations; Mathematical and Statistical Methods. | |
| 505 | 8 | |a Methods for Boolean ArraysSorting; Unique and Other Set Logic; 4.4 File Input and Output with Arrays; 4.5 Linear Algebra; 4.6 Pseudorandom Number Generation; 4.7 Example: Random Walks; Simulating Many Random Walks at Once; 4.8 Conclusion; Chapter 5. Getting Started with pandas; 5.1 Introduction to pandas Data Structures; Series; DataFrame; Index Objects; 5.2 Essential Functionality; Reindexing; Dropping Entries from an Axis; Indexing, Selection, and Filtering; Integer Indexes; Arithmetic and Data Alignment; Function Application and Mapping; Sorting and Ranking. | |
| 520 | |a This second edition offers instructions for manipulating, processing, cleaning, and crunching datasets in Python. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Data mining. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 650 | 7 | |a Datamining. |2 humord | |
| 650 | 7 | |a Programvare. |2 humord | |
| 650 | 7 | |a Python. |2 humord | |
| 776 | 0 | 8 | |i Print version: |a McKinney, Wes. |t Python for data analysis : data wrangling with pandas, NumPy, and IPython. |b Second edition. |d Sebastopol, California : O'Reilly Media, 2017, ©2018 |z 9781491957660 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781491957653/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||