exploratory data analysis using (29 Ergebnisse)

Soft cover. Zustand: Very Good. 1st Edition. Very good copy in softcover. Grey spine with white title.

Zustand: New. 2018. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. pp. 380 Illus.

Paperback. Zustand: Used; Like New. Dispatched, from the UK, within 48 hours of ordering. This book is in mint condition. Both the pages and the cover are completely intact, without zero sign of previous usage.

Zustand: New. pp. 562.

UNK. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,500grams, ISBN:9783319015163.

Zustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,500grams, ISBN:9783319015163.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,500grams, ISBN:9783319015163.

Book/CD-ROM. Zustand: Brand New. 547 pages. 9.00x6.00x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware -'Exploratory Data Analysis using Python: A Quick Reference Guide' serves as an invaluable companion for data enthusiasts seeking a concise and practical overview of exploratory data analysis (EDA) techniques using the Python programming language. Exploratory Data Analysis serves as an important step of any data analysis task, offering a systematic approach to understanding, cleaning, and interpreting data before carrying out more complex modeling. It not only enhances the reliability of subsequent analyses but also empowers data practitioners to make informed decisions, leading to more robust and actionable insights. This book is designed to be a quick reference, offering insights into key Python libraries and methods for effective data exploration. Whether you're a beginner or an experienced data analyst, this guide provides essential tools and tips to navigate through the intricate process of EDA, enabling you to uncover patterns, trends, and valuable insights within your datasets efficiently.Books on Demand GmbH, Überseering 33, 22297 Hamburg 88 pp. Englisch.

Taschenbuch. Zustand: Neu. Become a Python Data Analyst | Perform exploratory data analysis and gain insight into scientific computing using Python | Alvaro Fuentes | Taschenbuch | Kartoniert / Broschiert | Englisch | 2018 | Packt Publishing | EAN 9781789531701 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Zustand: New. Ronald K. Pearson currently works for GeoVera, a property insurance company in Fairfield, California, primarily in the analysis of text data. He holds a PhD in Electrical Engineering and Computer Science from the Massachussetts Institute.

Zustand: New. 2020. 2nd Edition. paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Neuware - From fundamental techniques to advanced strategies for handling big data, visualization, and more, this book equips you with skills to excel in real-world data analysis projects.Key Features: This book targets features in pandas 2.x and beyond Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any datasetBook Description:The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.With this latest edition unlock the full potential of pandas 2.x onwards. Whether you're a beginner or an experienced data analyst, this book offers a wealth of practical recipes to help you excel in your data analysis projects. This cookbook covers everything from fundamental data manipulation tasks to advanced techniques for handling big data, visualization, and more. Each recipe is designed to address common real-world challenges, providing clear explanations and step-by-step instructions to guide you through the process.Explore cutting-edge topics such as idiomatic pandas coding, efficient handling of large datasets, and advanced data visualization techniques.¿ Whether you're looking to sharpen or expand your skills, the 'Pandas Cookbook' is your essential companion for mastering data analysis and manipulation with pandas 2.x, and beyond.What You Will Learn: The pandas type system and how to best navigate it Import/export DataFrames to/from common data formats Data exploration in pandas through dozens of practice problems Grouping, aggregation, transformation, reshaping, and filtering data Merge data from different sources through pandas SQL-like operations Leverage the robust pandas time series functionality in advanced analyses Scale pandas operations to get the most out of your system The large ecosystem that pandas can coordinate with and supplementWho this book is for:This book is for Python developers, data scientists, engineers, and analysts. pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandasTable of Contents Pandas Foundations Selection / Indexing Pandas data types Pandas Input/Output Algorithms and how to apply them Visualization Reshaping Dataframes Groupby Temporal Data Types and Algorithms Exploratory Data Analysis The pandas ecosystem.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of 'interesting' - good, bad, and ugly - features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data.

Paperback. Zustand: Brand New. 2014 edition. 280 pages. 9.00x6.00x0.50 inches. In Stock.

Paperback. Zustand: Brand New. 363 pages. 9.25x6.10x0.86 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in economic analysis, decision-making and business planning. This textbook, Exploratory Data Analysis in Business and Economics , aims to familiarise students of economics and business as well as practitioners in firms with the basic principles, techniques, and applications of descriptive statistics and data analysis. Drawing on practical examples from business settings, it demonstrates the basic descriptive methods of univariate and bivariate analysis. The textbook covers a range of subject matter, from data collection and scaling to the presentation and univariate analysis of quantitative data, and also includes analytic procedures for assessing bivariate relationships. It does not confine itself to presenting descriptive statistics, but also addresses the use of computer programmes such as Excel, SPSS, and STATA, thus treating all of the topics typically covered in a university course on descriptive statistics. The German edition of this textbook is one of the bestsellers on the German market for literature in statistics.

Zustand: New. Only book in which EPI, a form of information theory, is discussed in terms of its applications rather its theoretical underpinningsValuable new approach for those working in data mining, pattern recognition, and algorithms and statisticsIn.

Hardcover. Zustand: Brand New. 1st edition. 363 pages. 9.50x6.75x0.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Sustainability' is often used in a qualitative sense. However, there is at present a great need to quantitatively measure (and monitor) its many qualitative aspects in real systems. Real systems are regarded as sustainable if they can maintain their current, desirable productivity and character without creating unfavorable condi tions elsewhere or in the future [1-4]. Sustainability therefore incorporates both concern for the future of the current system (temporal sustainability) and concern about the degree to which some areas and cultures of the planet are improved at the expense of other areas and cultures (spatial sustainability). That is, sustainability is to hold over both space and time. Sustainability encompasses many disciplines. For example, economic systems are not sustainable if they degrade their natural resource base and impoverish some sectors of the human population [5, 6]. Indices are needed that will measure sustainability through time, and over space, at several scales. These indices must also have the ability to aggregate the many disciplinary facets of sustainability, often incorporatedthrough a large number of environmental, social, and economic variables. Such a multidisciplinary dynamic system can be regarded as sustainable if it maintains a desirable steady state or regime', including fluctuations that are desirable (such as those that respond to natural disturbances [8]).

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 'Sustainability' is often used in a qualitative sense. However, there is at present a great need to quantitatively measure (and monitor) its many qualitative aspects in real systems. Real systems are regarded as sustainable if they can maintain their current, desirable productivity and character without creating unfavorable condi tions elsewhere or in the future [1-4]. Sustainability therefore incorporates both concern for the future of the current system (temporal sustainability) and concern about the degree to which some areas and cultures of the planet are improved at the expense of other areas and cultures (spatial sustainability). That is, sustainability is to hold over both space and time. Sustainability encompasses many disciplines. For example, economic systems are not sustainable if they degrade their natural resource base and impoverish some sectors of the human population [5, 6]. Indices are needed that will measure sustainability through time, and over space, at several scales. These indices must also have the ability to aggregate the many disciplinary facets of sustainability, often incorporatedthrough a large number of environmental, social, and economic variables. Such a multidisciplinary dynamic system can be regarded as sustainable if it maintains a desirable steady state or regime', including fluctuations that are desirable (such as those that respond to natural disturbances [8]).

Hardcover. Zustand: Brand New. 464 pages. 9.18x6.12x9.21 inches. In Stock.

Zustand: New.

Hardcover. Zustand: Brand New. 400 pages. 9.25x6.25x1.50 inches. In Stock.

Zustand: New. Ronald K. Pearson currently works for GeoVera, a property insurance company in Fairfield, California, primarily in the analysis of text data. He holds a PhD in Electrical Engineering and Computer Science from the Massachussetts Institute.