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  • 1
    ISBN: 9781787286634 , 1787286630
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Web usage mining ; Cloud computing
    Note: Description based on online resource; title from title page (Safari, viewed March 2, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 2
    Online Resource
    Online Resource
    Birmingham, UK : Packt Publishing
    ISBN: 9781787120310 , 1787120317
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Get to grips with pandas-a versatile and high-performance Python library for data manipulation, analysis, and discovery About This Book Get comfortable using pandas and Python as an effective data exploration and analysis tool Explore pandas through a framework of data analysis, with an explanation of how pandas is well suited for the various stages in a data analysis process A comprehensive guide to pandas with many of clear and practical examples to help you get up and using pandas Who This Book Is For This book is ideal for data scientists, data analysts, Python programmers who want to plunge into data analysis using pandas, and anyone with a curiosity about analyzing data. Some knowledge of statistics and programming will be helpful to get the most out of this book but not strictly required. Prior exposure to pandas is also not required. What You Will Learn Understand how data analysts and scientists think about of the processes of gathering and understanding data Learn how pandas can be used to support the end-to-end process of data analysis Use pandas Series and DataFrame objects to represent single and multivariate data Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources How to access data from external sources such as files, databases, and web services Represent and manipulate time-series data and the many of the intricacies involved with this type of data How to visualize statistical information How to use pandas to solve several common data representation and analysis problems within finance In Detail You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance. With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science. Style and approach Step-by-step instruction on using pandas within an end-to-end framework of performing data analysis Practical demonstration of using Python and pandas using interactive and incremental examples Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your ac...
    Note: Previous edition published: 2015. - Description based on online resource; title from cover (Safari, viewed July 21, 2017)
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  • 3
    ISBN: 9781787286122 , 1787286126
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Information visualization ; Data processing ; JavaScript (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Turn your raw data into real knowledge by creating and deploying complex data visualizations with D3.js About This Book Understand how to best represent your data by developing the right kind of visualization Explore the concepts of D3.js through examples that enable you to quickly create visualizations including charts, network diagrams, and maps Get practical examples of visualizations using real-world data sets that show you how to use D3.js to visualize and interact with information to glean its underlying meaning Who This Book Is For Whether you are new to data and data visualization, a seasoned data scientist, or a computer graphics specialist, this Learning Path will provide you with the skills you need to create web-based and interactive data visualizations. Some basic JavaScript knowledge is expected, but no prior experience with data visualization or D3 is required What You Will Learn Gain a solid understanding of the common D3 development idioms Find out how to write basic D3 code for servers using Node.js Install and use D3.js to create HTML elements within a document Create and style graphical elements such as circles, ellipses, rectangles, lines, paths, and text using SVG Turn your data into bar and scatter charts, and add margins, axes, labels, and legends Use D3.js generators to perform the magic of creating complex visualizations from data Add interactivity to your visualizations, including tool-tips, sorting, hover-to-highlight, and grouping and dragging of visuals Write, test, and distribute a D3-based charting package Make a real-time application with Node and D3 In Detail D3 has emerged as one of the leading platforms to develop beautiful, interactive visualizations over the web. We begin the course by setting up a strong foundation, then build on this foundation as we take you through the entire world of reimagining data using interactive, animated visualizations created in D3.js. In the first module, we cover the various features of D3.js to build a wide range of visualizations. We also focus on the entire process of representing data through visualizations. By the end of this module, you will be ready to use D3 to transform any data into a more engaging and sophisticated visualization. In the next module, you will learn to master the creation of graphical elements from data. Using practical examples provided, you will quickly get to grips with the features of D3.js and use this learning to create your own spectacular da...
    Note: "Learning path.". - Includes bibliographical references and index. - Description based on online resource; title from cover (viewed April 18, 2017)
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  • 4
    ISBN: 9781783985111 , 1783985119
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Python (Computer program language) ; Finance ; Quantitative research ; Open source software ; Electronic books ; Electronic books ; local
    Abstract: Master pandas, an open source Python Data Analysis Library, for financial data analysis In Detail This book will teach you to use Python and the Python Data Analysis Library (pandas) to solve real-world financial problems. Starting with a focus on pandas data structures, you will learn to load and manipulate time-series financial data and then calculate common financial measures, leading into more advanced derivations using fixed- and moving-windows. This leads into correlating time-series data to both index and social data to build simple trading algorithms. From there, you will learn about more complex trading algorithms and implement them using open source back-testing tools. Then, you will examine the calculation of the value of options and Value at Risk. This then leads into the modeling of portfolios and calculation of optimal portfolios based upon risk. All concepts will be demonstrated continuously through progressive examples using interactive Python and IPython Notebook. By the end of the book, you will be familiar with applying pandas to many financial problems, giving you the knowledge needed to leverage pandas in the real world of finance. What You Will Learn Modeling and manipulating financial data using the pandas DataFrame Indexing, grouping, and calculating statistical results on financial information Time-series modeling, frequency conversion, and deriving results on fixed and moving windows Calculating cumulative returns and performing correlations with index and social data Algorithmic trading and backtesting using momentum and mean reversion strategies Option pricing and calculation of Value at Risk Modeling and optimization of financial portfolios Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed June 10, 2015)
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  • 5
    ISBN: 9781783985135 , 1783985135
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Community experience distilled
    Keywords: Python (Computer program language) ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Get to grips with pandas - a versatile and high-performance Python library for data manipulation, analysis, and discovery In Detail This learner's guide will help you understand how to use the features of pandas for interactive data manipulation and analysis. This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas' Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance. With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis. What You Will Learn Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution Learn how pandas builds on NumPy to implement flexible indexed data Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs Index, slice, and transform data to derive meaning from information Load data from files, databases, and web services Manipulate dates, times, and time series data Group, aggregate, and summarize data Visualize techniques for pandas and statistical data
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed May 8, 2015)
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