{"product_id":"139149","title":"Data analysis using Python libraries ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPBvgEqmvrGxZp035Ms3uU6Xmujr.png?v=1765073300\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n \u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003eData analysis using Python libraries \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/118523424\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eThe most complete way to learn data analysis\u003cbr\u003e From how to use Python libraries to hands-on practice using real data.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e The trusted and respected Python data analysis book is back in its third edition.\u003cbr\u003e Wes McKinney, the creator of the Python Pandas project, explains how to use Python libraries in a practical and modern way.\u003cbr\u003e The content has been updated based on the latest versions of Python and Pandas, and various examples are examined to learn how to effectively solve data analysis problems.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e We introduce various Python libraries, including Pandas, NumPy, IPython, Matplotlib, and Jupyter, and cover not only new features but also advanced usage methods that reduce memory usage and improve performance.\u003cbr\u003e We also introduce the modeling tool statsmodels and the scikit-learn library. \u003cbr\u003eLet's practice with real data, such as newborn name statistics and a presidential election database, and become experts who select the right tools for the data and analyze it effectively.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eCHAPTER 1 Before You Begin\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 1.1 Contents covered\u003cbr\u003e 1.2 Why Use Python for Data Analysis?\u003cbr\u003e 1.3 Required Python Libraries\u003cbr\u003e 1.4 Installation and Setup\u003cbr\u003e 1.5 Community and Conference\u003cbr\u003e 1.6 How to Explore This Book\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2 Python Basics, IPython, and Jupyter Notebooks\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 2.1 Python Interpreter\u003cbr\u003e 2.2. IPython Basics\u003cbr\u003e 2.3 Python Basics\u003cbr\u003e 2.4 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 3 Built-in data structures, functions, and files\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 3.1 Data Structures and Sequential Data Types\u003cbr\u003e 3.2 Function\u003cbr\u003e 3.3 Files and the Operating System\u003cbr\u003e 3.4 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 4 NumPy Basics: Array and Vector Operations\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 4.1 Multidimensional array object ndarray\u003cbr\u003e 4.2 Random number generation\u003cbr\u003e 4.3 Universal functions: functions that quickly process each element of an array. \u003cbr\u003e4.4 Array-based programming using arrays\u003cbr\u003e 4.5 File input\/output of array data\u003cbr\u003e 4.6 Linear Algebra\u003cbr\u003e 4.7 Example of climbing stairs\u003cbr\u003e 4.8 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5: GETTING STARTED WITH PANDAS\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 5.1 Introduction to Pandas Data Structures\u003cbr\u003e 5.2 Core Features\u003cbr\u003e 5.3 Calculating and summarizing descriptive statistics\u003cbr\u003e 5.4 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6 Data Loading and Saving, File Formats\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 6.1 How to read and write data from text files\u003cbr\u003e 6.2 Binary Data Format\u003cbr\u003e 6.3 Using with Web APIs\u003cbr\u003e 6.4 Using with a Database\u003cbr\u003e 6.5 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7 DATA CLEANING AND PREPARATION\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 7.1 Handling Missing Data\u003cbr\u003e 7.2 Data Transformation\u003cbr\u003e 7.3 Extended Data Types\u003cbr\u003e 7.4 Handling Strings\u003cbr\u003e 7.5 Categorical data\u003cbr\u003e 7.6 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 Preparing Data: Joins, Merging, and Transformations\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 8.1 Hierarchical Index\u003cbr\u003e 8.2 Merging Data\u003cbr\u003e 8.3 Reconfiguration and Pivot\u003cbr\u003e 8.4 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 9 GRAPHS AND VISUALIZATIONS\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 9.1 A Brief Overview of the Matplotlib API\u003cbr\u003e 9.2 Drawing Graphs with Seaborn in Pandas \u003cbr\u003e9.3 Other Python Visualization Tools\u003cbr\u003e 9.4 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 10 DATA AGGREGATION AND GROUP OPERATIONS\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 10.1 Considerations on Group Operations\u003cbr\u003e 10.2 Data Aggregation\u003cbr\u003e 10.3 The apply method: Generalized split-apply-merge\u003cbr\u003e 10.4 Group transformations and unwrapped groupby\u003cbr\u003e 10.5 Pivot Tables and Crosstabulations\u003cbr\u003e 10.6 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 11 TIME SERIES\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 11.1 Date and Time Data Types and Tools\u003cbr\u003e 11.2 Time Series Basics\u003cbr\u003e 11.3 Date range, frequency, and movement\u003cbr\u003e 11.4 Handling Time Zones\u003cbr\u003e 11.5 Periods and Period Operations\u003cbr\u003e 11.6 Resampling and Frequency Conversion\u003cbr\u003e 11.7 Moving Window Function\u003cbr\u003e 11.8 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 12 Python Modeling Libraries\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 12.1 Interfacing Pandas with Model Code\u003cbr\u003e 12.2 Creating a model with patsy\u003cbr\u003e 12.3 Introduction to statsmodels\u003cbr\u003e 12.4 Introduction to scikit-learn\u003cbr\u003e 12.5 In conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 13 DATA ANALYSIS EXAMPLES\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 13.1 Bitly's 1.USA.gov data\u003cbr\u003e 13.2 Movie Lens movie rating data\u003cbr\u003e 13.3 Newborn Names\u003cbr\u003e 13.4 USDA Nutrient Information\u003cbr\u003e 13.5 2012 Federal Election Commission Database\u003cbr\u003e 13.6 In conclusion\u003cbr\u003e \u003cbr\u003eAPPENDIX A Advanced NumPy\u003cbr\u003e A.1 ndarray object structure\u003cbr\u003e A.2 Advanced Array Manipulation Techniques\u003cbr\u003e A.3 Broadcasting\u003cbr\u003e A.4 Advanced ufunc usage\u003cbr\u003e A.5 Structured Arrays and Record Arrays\u003cbr\u003e A.6 Learn more about alignment\u003cbr\u003e A.7 Writing fast NumPy functions using Numba\u003cbr\u003e A.8 Advanced Array Input\/Output\u003cbr\u003e A.9 Useful Performance Tips\u003cbr\u003e\u003cbr\u003e APPENDIX B: Learn More About the IPython System\u003cbr\u003e B.1 Terminal Keyboard Shortcuts\u003cbr\u003e B.2 Magic Commands\u003cbr\u003e B.3 Using Command History\u003cbr\u003e B.4 Using with the Operating System\u003cbr\u003e B.5 Software Development Tools\u003cbr\u003e B.6 Tips for Productive Code Development Using IPython\u003cbr\u003e B.7 Advanced IPython Features\u003cbr\u003e B.8 In conclusion\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4160\/MidCate008\/415976575.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eA Guide to Using Data Analysis Libraries from Pandas Core Developers\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e [Data Analysis Using Python Libraries], which was published as a revised and supplemented edition in 2013 and has been consistently loved by domestic readers up until the second edition in 2019, is now back in its third edition. \u003cbr\u003eOver the past decade, Python has firmly established itself as a popular language widely used in numerous fields, from data science to machine learning and deep learning, and is constantly updated to serve its users.\u003cbr\u003e The third edition has been refined to reflect changes in the latest versions of Python, NumPy, Pandas, and other projects.\u003cbr\u003e As this book is widely used as a textbook in universities and as a reference book in the workplace, we have put a lot of effort into ensuring that its contents remain relevant for years to come.\u003cbr\u003e I hope this book will be a valuable resource for anyone who needs to work with data using Python.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWhat's changed in the 3rd edition\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e - Code updated based on Python 3.11 and Pandas 2.0.\u003cbr\u003e - Reflects NumPy 1.23 and the latest version of Jupyter\u003cbr\u003e - Added new content\u003cbr\u003e Categorical data type\u003cbr\u003e Data group transformation and unwrapped groupby\u003cbr\u003e How to Use IPython's Magic Commands and Command History\u003cbr\u003e \u003cbr\u003eThis book introduces various basic methods of handling data with Python.\u003cbr\u003e Covers the fundamentals of the Python programming language and libraries that help you efficiently solve data analysis problems.\u003cbr\u003e Although the book's title includes \"data analysis,\" it focuses on Python programming, libraries, and tools rather than data analysis methodology.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e - Data engineers, data scientists, machine learning engineers, and statisticians who are responsible for data analysis practices.\u003cbr\u003e - IT-related undergraduate students who want to analyze data using Python's representative libraries.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eKey Contents\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e - How to use NumPy basics and advanced features\u003cbr\u003e - Loading, cleaning, joining, and transforming data with Pandas\u003cbr\u003e - Creating useful visualizations with Matplotlib\u003cbr\u003e - Dividing and summarizing data using the Pandas groupby function\u003cbr\u003e - Analyzing and manipulating regular and irregular time series data \u003cbr\u003e- Learn how to solve analytical problems by examining real data.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTranslator's Note\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Just as the printer was about to press out the third Korean edition, the release of Pandas 2.0 brought back memories of when I translated the first edition 10 years ago.\u003cbr\u003e While translating a book written in Pandas 0.14, the process of fixing many parts as new versions kept coming out was incredibly difficult. Fortunately, this time, installing Pandas 2.0, re-examining the example code, sending a PR to the author, and even requesting confirmation was not as difficult as when working on the first edition.\u003cbr\u003e :)\u003cbr\u003e\u003cbr\u003e Pandas has become a stable library that can be used together for a long time, and this book has also been able to be read for a long time, as the author hoped.\u003cbr\u003e If you found the second edition helpful, I would recommend it without hesitation to anyone who wants to explore data using Python. \u003cbr\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS\u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n \u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e- \u003cstrong\u003eDate of issue:\u003c\/strong\u003e May 1, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 696 pages | 1,240g | 183*235*28mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169210973\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 116921097X \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893342535722,"sku":"139149","price":54.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/38f7952a779ea3b60299b497eba08ccb.jpg?v=1765397392","url":"https:\/\/librairie.coreenne.fr\/en\/products\/139149","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}