实例介绍
【实例简介】
Paperback: 364 pages Publisher: Apress; 1 edition (August 21, 2015) Language: English ISBN-10: 1484209591 ISBN-13: 978-1484209592 Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage
Python Data Analytics Copyright o 2015 by Fabio Nelli This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher's location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through Rightslink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law ISBN-13(pbk):978-1-48420959-2 ISBN-13( electronic):978-1-4842-0958-5 Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark The use in this publication of trade names, trademarks service marks, and similar terms, even if they are notidentified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights While the advice and information in this book are believed to be true and accurate at the date of publication neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein Managing director: Welmoed spahr Lead Editor: Steve Anglin Technical Reviewer: Shubham Singh Tomar Editorial Board: Steve Anglin, Louise Corrigan, Morgan Ertel, Jonathan Gennick, Robert Hutchinson, Michelle lowman, James Markham, Susan McDermott, Matthew Moodie, Jeffrey Pepper, Douglas Pundick, Ben Renow-Clarke Gwenan Spearing, Steve Weiss Coordinating Editor: Mark Powers Copy Editor Brendan Frost Compositor: SPi Global Indexer: SPi Global Artist: SPi global Distributed to the book trade worldwide by Springer Science+ Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax(201)348-4505, e-mai orders-ny@springer-sbm.com,orvisitwww.springeronline.comApressMedia,LlcisaCaliforniaLlc and the sole member (owner) is Springer Science Business Media Finance Inc(SSBM Finance Inc) SSBM Finance Inc is a Delaware corporation. Forinformationontranslationspleasee-mailrights@apress.comorvisitwww.apress.com Apress and friends of ed books may be purchased in bulk for academic, corporate, or promotional use e Book versions and licenses are also available for most titles. For more information, reference our Special Bulk Sales-eBook licensing web pa ageatwww.apress.com/bulk-sales Any source code or other supplementary materials referenced by the author in this text is available to readersatwww.apress.com/9781484209592.Fordetailedinformationabouthowtolocateyourbookssource codegotowww.apress.com/source-code/.ReaderscanalsoaccesssourcecodeatSpringerlinkinthe Supplementary Material section for each chapter Contents at a glance About the author muxi About the technical reviewer mmmmxix Acknowledgments ■■■■■■ XX Chapter 1: An Introduction to Data Analysis ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口■■■■■■■■■a■口 Chapter 2: Introduction to the python's World aat ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 13 Chapter 3: The NumPy library ■■■■■■ 35 Chapter 4: The pandas library-An Introduction mmma mama. 63 Chapter 5: pandas: Reading and Writing Data mammon 103 Chapter 6: pandas in Depth: Data Manipulation ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口 131 Chapter 7: Data Visualization with matplotlib n167 Chapter 8: Machine learning with scikit-learn ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 237 Chapter 9: An Example-Meteorological Data nan 265 Chapter 10: Embedding the Javascript D3 Library in IPython Notebook m 289 Chapter 11: Recognizing Handwritten Digitsmmammmmn 311 Appendix A: Writing Mathematical Expressions with Latex a317 Appendix B: Open Data Sources 327 ndex ■■■■ 331 Contents About the author… About the technical reviewer mmmmxix Acknowledgments ■■■■■■ XX Chapter 1: An Introduction to Data Analysis BBBRIERRRBREAIIBBBBRIIIIREEaIna, 1 Data analysis Knowledge Domains of the Data analyst 2 Computer Science Mathematics and statistics 面日日面日面重日日日面日面日面日面面日面日日面日面日面日日面面日面日日面日面日面日面日面面面日日面面日面重日日日面日面日面日面面面日日面日面口 Machine Learning and artificial Intelligence 2333 Professional Fields of application Understanding the Nature of the Data When the data become Information When the Information Becomes Knowledge. Types of Data.o 日面B日面面DB … 4 The data analysis process. Problem definition Data extraction Data preparation ......................................................................................7 Data Exploration/visualization Predictive Modeling Model validation Deployment Quantitative and Qualitative Data analysis 9 CONTENTS Open Data 10 Python and Data Analysis Conclusions……12 Chapter 2: Introduction to the Python's World ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口■■■■■■■■■■■■■■■■■■■ Python--The Programming Language Python--The Interpreter. Cvthon 15 jython 15 15 Python 2 and Python 3. Installing Python 16 Python Distributions 16 Anaconda…16 Enthought Canopy 17 Python(xy)… 18 Using Python 18 Python Shell 18 Run an Entire Program code 18 mplement the Code Using an IDE.........…………………19 Interact with Python 19 Writing Python Code 19 Make calculations 20 mport New Libraries and Functions……, Functional Programming(only for Python 3.4) 22 Indentation 24 PYthon 24 Python Shell IPython Qt-Console 26 CONTENTS PyPI--The Python Package Index .28 The IDEs for Python nnnnnnnnnnnnnnnnnnnnnnnnnnnnnD 28 IDLE (Integrated DeveLopment Environment pyder 29 Eclipse( Sublime Eclipse∴ 31 NinjalDE… 32 Komodo de SciP 32 NumPy Pandas… matplotlib… Conclusions ….34 Chapter 3: The NumPy Library. 35 NumPy: a Little history . nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnDnnnDDnnDDnnDnDDnnDDnnDnDDnnDDnD 35 The NumPy Installation nnnnnnnnnnnnnnnnnnnnnnnnnD 35 Ndarray: The Heart of the Library 36 Create an Array Types of data The dtype option Intrinsic Creation of an array . Basic Operations… 40 Arithmetic Operators 41 The matrix product Increment and decrement operators Universal Functions(ufunc) 面日日日面日日面B日日BB面日量量日日目面日面日面日面日面面日面日日面日面日面日面日面面面日日面面日面重日日日面日面日面日面面面日 2344 Aggregate Functions CONTENTS dexing Slicing and Iterating 5 dexing…,....,,,,……,45 Slicing Iterating an array ..d 68 Conditions and boolean arrays 50 Shape Manipulation 50 Array manipulation 51 Joining arrays 51 Splitting arrays 52 General concepts 54 Copies or views of objects ...............................................................................54 Vectorization Broadcasting..................... Structured Arrays 58 Reading and writing array data on Files 59 Loading and Saving Data in Binary Files Reading File with Tabular Data Conclusions 61 Chapter 4: The pandas Library-An Introduction ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 63 pandas: The Python Data Analysis Library 63 Installation 64 Installation from anaconda …64 Installation from PyPl Installation on linux ,65 Installation from source A Module repository for Windows. Test Your pandas Installation 66 Getting Started with pandas ,67 CONTENTS Introduction to pandas Data Structures .67 The series 68 The dataframe 75 The Index objects ............................................................................81 other functionalities on indexes gogo. 83 Reindexing…83 Dropping….85 Arithmetic and data alignment 86 Operations between Data Structures 87 Flexible arithmetic Methods Operations between Dataframe and series. Function Application and Mapping 89 Functions by Element 89 Functions by row or column Statistics Functions Sorting and Ranking…… 91 Correlation and covariance “ Not a number”Daa……………95 Assigning a nan value .............................................................................96 Filtering Out Nan values .96 Filling in Nan Occurrences Hierarchical Indexing and Leveling 97 Reordering and sorting levels . Summary Statistic by Level. Conclusions 101 CONTENTS Chapter 5: pandas: Reading and Writing Data.a ng103 10 API Tools 103 CSv and textual files 104 Reading data in cSv or Text Files.ot. eeeeeeeaaee. 104 sing RegExp for Parsing TXT Files 106 Reading TXT Files into Parts or Partially 108 Writing data in cSv Reading and Writing HTML Files 111 Writing Data in HTML 日日日日日日日日日日日日日日日日日正日日正日日正日日日正日日日面正 Reading data from an HTML File. Reading data from XML 114 Reading and Writing Data on Microsoft Excel Files 116 JSoN Data……118 The format hdf5…121 Pickle--Python Object Serialization 122 Serialize a Python object with pIckle…… 122 Pickling with pandas 1B面日日面B面面BB面日面BB面面B日B面面B面日面B面面BB面日面BBB面面BB面面B面日面B面面B 123 Interacting with Databases 124 Loading and writing data with sQlite3 124 Loading and Writing Data with postgreSQL 126 Reading and Writing Data with a NosQL Database MongoDB 128 Conclusions… 130 Chapter 6: pandas in Depth: Data Manipulation mammmmmam 131 Data Preparation 131 Merging 132 Concatenating ….136 Combining Pivoting…..140 Removing… .142 【实例截图】
【核心代码】
Paperback: 364 pages Publisher: Apress; 1 edition (August 21, 2015) Language: English ISBN-10: 1484209591 ISBN-13: 978-1484209592 Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage
Python Data Analytics Copyright o 2015 by Fabio Nelli This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher's location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through Rightslink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law ISBN-13(pbk):978-1-48420959-2 ISBN-13( electronic):978-1-4842-0958-5 Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark The use in this publication of trade names, trademarks service marks, and similar terms, even if they are notidentified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights While the advice and information in this book are believed to be true and accurate at the date of publication neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein Managing director: Welmoed spahr Lead Editor: Steve Anglin Technical Reviewer: Shubham Singh Tomar Editorial Board: Steve Anglin, Louise Corrigan, Morgan Ertel, Jonathan Gennick, Robert Hutchinson, Michelle lowman, James Markham, Susan McDermott, Matthew Moodie, Jeffrey Pepper, Douglas Pundick, Ben Renow-Clarke Gwenan Spearing, Steve Weiss Coordinating Editor: Mark Powers Copy Editor Brendan Frost Compositor: SPi Global Indexer: SPi Global Artist: SPi global Distributed to the book trade worldwide by Springer Science+ Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax(201)348-4505, e-mai orders-ny@springer-sbm.com,orvisitwww.springeronline.comApressMedia,LlcisaCaliforniaLlc and the sole member (owner) is Springer Science Business Media Finance Inc(SSBM Finance Inc) SSBM Finance Inc is a Delaware corporation. Forinformationontranslationspleasee-mailrights@apress.comorvisitwww.apress.com Apress and friends of ed books may be purchased in bulk for academic, corporate, or promotional use e Book versions and licenses are also available for most titles. For more information, reference our Special Bulk Sales-eBook licensing web pa ageatwww.apress.com/bulk-sales Any source code or other supplementary materials referenced by the author in this text is available to readersatwww.apress.com/9781484209592.Fordetailedinformationabouthowtolocateyourbookssource codegotowww.apress.com/source-code/.ReaderscanalsoaccesssourcecodeatSpringerlinkinthe Supplementary Material section for each chapter Contents at a glance About the author muxi About the technical reviewer mmmmxix Acknowledgments ■■■■■■ XX Chapter 1: An Introduction to Data Analysis ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口■■■■■■■■■a■口 Chapter 2: Introduction to the python's World aat ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 13 Chapter 3: The NumPy library ■■■■■■ 35 Chapter 4: The pandas library-An Introduction mmma mama. 63 Chapter 5: pandas: Reading and Writing Data mammon 103 Chapter 6: pandas in Depth: Data Manipulation ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口 131 Chapter 7: Data Visualization with matplotlib n167 Chapter 8: Machine learning with scikit-learn ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 237 Chapter 9: An Example-Meteorological Data nan 265 Chapter 10: Embedding the Javascript D3 Library in IPython Notebook m 289 Chapter 11: Recognizing Handwritten Digitsmmammmmn 311 Appendix A: Writing Mathematical Expressions with Latex a317 Appendix B: Open Data Sources 327 ndex ■■■■ 331 Contents About the author… About the technical reviewer mmmmxix Acknowledgments ■■■■■■ XX Chapter 1: An Introduction to Data Analysis BBBRIERRRBREAIIBBBBRIIIIREEaIna, 1 Data analysis Knowledge Domains of the Data analyst 2 Computer Science Mathematics and statistics 面日日面日面重日日日面日面日面日面面日面日日面日面日面日日面面日面日日面日面日面日面日面面面日日面面日面重日日日面日面日面日面面面日日面日面口 Machine Learning and artificial Intelligence 2333 Professional Fields of application Understanding the Nature of the Data When the data become Information When the Information Becomes Knowledge. Types of Data.o 日面B日面面DB … 4 The data analysis process. Problem definition Data extraction Data preparation ......................................................................................7 Data Exploration/visualization Predictive Modeling Model validation Deployment Quantitative and Qualitative Data analysis 9 CONTENTS Open Data 10 Python and Data Analysis Conclusions……12 Chapter 2: Introduction to the Python's World ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■口■■■■■■■■■■■■■■■■■■■ Python--The Programming Language Python--The Interpreter. Cvthon 15 jython 15 15 Python 2 and Python 3. Installing Python 16 Python Distributions 16 Anaconda…16 Enthought Canopy 17 Python(xy)… 18 Using Python 18 Python Shell 18 Run an Entire Program code 18 mplement the Code Using an IDE.........…………………19 Interact with Python 19 Writing Python Code 19 Make calculations 20 mport New Libraries and Functions……, Functional Programming(only for Python 3.4) 22 Indentation 24 PYthon 24 Python Shell IPython Qt-Console 26 CONTENTS PyPI--The Python Package Index .28 The IDEs for Python nnnnnnnnnnnnnnnnnnnnnnnnnnnnnD 28 IDLE (Integrated DeveLopment Environment pyder 29 Eclipse( Sublime Eclipse∴ 31 NinjalDE… 32 Komodo de SciP 32 NumPy Pandas… matplotlib… Conclusions ….34 Chapter 3: The NumPy Library. 35 NumPy: a Little history . nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnDnnnDDnnDDnnDnDDnnDDnnDnDDnnDDnD 35 The NumPy Installation nnnnnnnnnnnnnnnnnnnnnnnnnD 35 Ndarray: The Heart of the Library 36 Create an Array Types of data The dtype option Intrinsic Creation of an array . Basic Operations… 40 Arithmetic Operators 41 The matrix product Increment and decrement operators Universal Functions(ufunc) 面日日日面日日面B日日BB面日量量日日目面日面日面日面日面面日面日日面日面日面日面日面面面日日面面日面重日日日面日面日面日面面面日 2344 Aggregate Functions CONTENTS dexing Slicing and Iterating 5 dexing…,....,,,,……,45 Slicing Iterating an array ..d 68 Conditions and boolean arrays 50 Shape Manipulation 50 Array manipulation 51 Joining arrays 51 Splitting arrays 52 General concepts 54 Copies or views of objects ...............................................................................54 Vectorization Broadcasting..................... Structured Arrays 58 Reading and writing array data on Files 59 Loading and Saving Data in Binary Files Reading File with Tabular Data Conclusions 61 Chapter 4: The pandas Library-An Introduction ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 63 pandas: The Python Data Analysis Library 63 Installation 64 Installation from anaconda …64 Installation from PyPl Installation on linux ,65 Installation from source A Module repository for Windows. Test Your pandas Installation 66 Getting Started with pandas ,67 CONTENTS Introduction to pandas Data Structures .67 The series 68 The dataframe 75 The Index objects ............................................................................81 other functionalities on indexes gogo. 83 Reindexing…83 Dropping….85 Arithmetic and data alignment 86 Operations between Data Structures 87 Flexible arithmetic Methods Operations between Dataframe and series. Function Application and Mapping 89 Functions by Element 89 Functions by row or column Statistics Functions Sorting and Ranking…… 91 Correlation and covariance “ Not a number”Daa……………95 Assigning a nan value .............................................................................96 Filtering Out Nan values .96 Filling in Nan Occurrences Hierarchical Indexing and Leveling 97 Reordering and sorting levels . Summary Statistic by Level. Conclusions 101 CONTENTS Chapter 5: pandas: Reading and Writing Data.a ng103 10 API Tools 103 CSv and textual files 104 Reading data in cSv or Text Files.ot. eeeeeeeaaee. 104 sing RegExp for Parsing TXT Files 106 Reading TXT Files into Parts or Partially 108 Writing data in cSv Reading and Writing HTML Files 111 Writing Data in HTML 日日日日日日日日日日日日日日日日日正日日正日日正日日日正日日日面正 Reading data from an HTML File. Reading data from XML 114 Reading and Writing Data on Microsoft Excel Files 116 JSoN Data……118 The format hdf5…121 Pickle--Python Object Serialization 122 Serialize a Python object with pIckle…… 122 Pickling with pandas 1B面日日面B面面BB面日面BB面面B日B面面B面日面B面面BB面日面BBB面面BB面面B面日面B面面B 123 Interacting with Databases 124 Loading and writing data with sQlite3 124 Loading and Writing Data with postgreSQL 126 Reading and Writing Data with a NosQL Database MongoDB 128 Conclusions… 130 Chapter 6: pandas in Depth: Data Manipulation mammmmmam 131 Data Preparation 131 Merging 132 Concatenating ….136 Combining Pivoting…..140 Removing… .142 【实例截图】
【核心代码】
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