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| combine multiple excel files into one workbook separate sheets python: Foundations for Analytics with Python Clinton W. Brownley, 2016-08-16 If you’re like many of Excel’s 750 million users, you want to do more with your data—like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python. After author Clinton Brownley takes you through Python basics, you’ll be able to write simple scripts for processing data in spreadsheets as well as databases. You’ll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary. Create and run your own Python scripts by learning basic syntax Use Python’s csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments |
| combine multiple excel files into one workbook separate sheets python: M Is for (Data) Monkey Ken Puls, Miguel Escobar, 2015-06-01 Power Query is one component of the Power BI (Business Intelligence) product from Microsoft, and M is the name of the programming language created by it. As more business intelligence pros begin using Power Pivot, they find that they do not have the Excel skills to clean the data in Excel; Power Query solves this problem. This book shows how to use the Power Query tool to get difficult data sets into both Excel and Power Pivot, and is solely devoted to Power Query dashboarding and reporting. |
| combine multiple excel files into one workbook separate sheets python: Python for Excel Felix Zumstein, 2021-03-04 While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently. Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started. Use Python without extensive programming knowledge Get started with modern tools, including Jupyter notebooks and Visual Studio code Use pandas to acquire, clean, and analyze data and replace typical Excel calculations Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports Use xlwings to build interactive Excel tools that use Python as a calculation engine Connect Excel to databases and CSV files and fetch data from the internet using Python code Use Python as a single tool to replace VBA, Power Query, and Power Pivot |
| combine multiple excel files into one workbook separate sheets python: Excel VBA Programming For Dummies Michael Alexander, John Walkenbach, 2018-11-06 Take your Excel programming skills to the next level To take Excel to the next level, you need to understand and implement the power of Visual Basic for Applications (VBA). Excel VBA Programming For Dummies introduces you to a wide array of new Excel options, beginning with the most important tools and operations for the Visual Basic Editor. Inside, you’ll find an overview of the essential elements and concepts for programming with Excel. In no time, you’ll discover techniques for handling errors and exterminating bugs, working with range objects and controlling program flow, and much more. With friendly advice on the easiest ways to develop custom dialog boxes, toolbars, and menus, readers will be creating Excel applications custom fit to their unique needs! Fully updated for the new Excel 2019 Step-by-step instructions for creating VBA macros to maximize productivity Guidance on customizing your applications so they work the way you want All sample programs, VBA code, and worksheets are available at dummies.com Beginning VBA programmers rejoice! This easy-to-follow book makes it easier than ever to excel at Excel VBA! |
| combine multiple excel files into one workbook separate sheets python: Excel 2007 Matthew MacDonald, 2007 Publisher description |
| combine multiple excel files into one workbook separate sheets python: Excel Basics to Blackbelt Elliot Bendoly, 2008-07-07 Excel Basics to Blackbelt is intended to serve as an accelerated guide to decision support designs. Its structure is designed to enhance the skills in Excel of those who have never used it for anything but possibly storing phone numbers, enabling them to reach a level of mastery that will allow them to develop user interfaces and automated applications. To accomplish this, the major theme of the text is 'the integration of the basic'; as a result readers will be able to develop decision support tools that are at once highly intuitive from a working-components perspective but also highly significant from the perspective of practical use and distribution. Applications integration discussed includes the use of MS MapPoint, XLStat and RISKOptimizer, as well as how to leverage Excel's iteration mode, web queries, visual basic code, and interface development. There are ample examples throughout the text. |
| combine multiple excel files into one workbook separate sheets python: Practical Python Data Wrangling and Data Quality Susan E. McGregor, 2021-12-03 There are awesome discoveries to be made and valuable stories to be told in datasets--and this book will help you uncover them. Whether you already work with data or just want to understand its possibilities, the techniques and advice in this practical book will help you learn how to better clean, evaluate, and analyze data to generate meaningful insights and compelling visualizations. Through foundational concepts and worked examples, author Susan McGregor provides the concepts and tools you need to evaluate and analyze all kinds of data and communicate your findings effectively. This book provides a methodical, jargon-free way for practitioners of all levels to harness the power of data. Use Python 3.8+ to read, write, and transform data from a variety of sources Understand and use programming basics in Python to wrangle data at scale Organize, document, and structure your code using best practices Complete exercises either on your own machine or on the web Collect data from structured data files, web pages, and APIs Perform basic statistical analysis to make meaning from data sets Visualize and present data in clear and compelling ways. |
| combine multiple excel files into one workbook separate sheets python: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| combine multiple excel files into one workbook separate sheets python: Mastering Shiny Hadley Wickham, 2021-04-29 Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production |
| combine multiple excel files into one workbook separate sheets python: Getting started with Power Query in Power BI and Excel Reza Rad, Leila Etaati, 2021-08-27 Any data analytics solution requires data population and preparation. With the rise of data analytics solutions these years, the need for this data preparation becomes even more essential. Power BI is a helpful data analytics tool that is used worldwide by many users. As a Power BI (or Microsoft BI) developer, it is essential to learn how to prepare the data in the right shape and format needed. You need to learn how to clean the data and build it in the structure that can be modeled easily and used high performant for visualization. Data preparation and transformation is the backend work. If you consider building a BI system as going to a restaurant and ordering food. The visualization is the food you see on the table nicely presented. The quality, the taste, and everything else comes from the hard work in the kitchen. The part that you don’t see or the backend in the world of Power BI is Power Query. You may be already familiar with some other data preparation and data transformation technologies, such as T-SQL, SSIS, Azure Data Factory, Informatica, etc. Power Query is a data transformation engine capable of preparing the data in the format you need. The good news is that to learn Power Query; you don’t need to know programming. Power Query is for citizen data engineers. However, this doesn’t mean that Power Query is not capable of performing advanced transformation. Unfortunately, because Power Query and data preparation is the kitchen work of the BI system, many Power BI users skip the learning of it and become aware of it somewhere along their BI project. Once they get familiar with it, they realize there are tons of things they could have implemented easier, faster, and in a much more maintainable way using Power Query. In other words, they learn mastering Power Query is the key skill toward mastering Power BI. We have been working with Power Query since the very early release of that in 2013, named Data Explorer, and wrote blog articles and published videos about it. The number of articles we published under this subject easily exceeds hundreds. Through those articles, some of the fundamentals and key learnings of Power Query are explained. We thought it is good to compile some of them in a book. A good analytics solution combines a good data model, good data preparation, and good analytics and calculations. Reza has written another book about the Basics of modeling in Power BI and a book on Power BI DAX Simplified. This book is covering the data preparation and transformations aspects of it. This book is for you if you are building a Power BI solution. Even if you are just visualizing the data, preparation and transformations are an essential part of analytics. You do need to have the cleaned and prepared data ready before visualizing it. This book is complied into a series of two books, which will be followed by a third book later; Getting started with Power Query in Power BI and Excel (this book) Mastering Power Query in Power BI and Excel (already available to be purchased separately) Power Query dataflows (will be published later) Although this book is written for Power BI and all the examples are presented using the Power BI. However, the examples can be easily applied to Excel, Dataflows, and other tools and services using Power Query. |
| combine multiple excel files into one workbook separate sheets python: VBA and Macros Bill Jelen, Tracy Syrstad, 2010 Provides a step-by-step guide to using Visual Basic for Applications (VBA) and macros to import data and produce reports in Microsoft Excel 2010. |
| combine multiple excel files into one workbook separate sheets python: Communicating Data with Tableau Ben Jones, 2014-06-16 Go beyond spreadsheets and tables and design a data presentation that really makes an impact. This practical guide shows you how to use Tableau Software to convert raw data into compelling data visualizations that provide insight or allow viewers to explore the data for themselves. Ideal for analysts, engineers, marketers, journalists, and researchers, this book describes the principles of communicating data and takes you on an in-depth tour of common visualization methods. You’ll learn how to craft articulate and creative data visualizations with Tableau Desktop 8.1 and Tableau Public 8.1. Present comparisons of how much and how many Use blended data sources to create ratios and rates Create charts to depict proportions and percentages Visualize measures of mean, median, and mode Lean how to deal with variation and uncertainty Communicate multiple quantities in the same view Show how quantities and events change over time Use maps to communicate positional data Build dashboards to combine several visualizations |
| combine multiple excel files into one workbook separate sheets python: R For Dummies Andrie de Vries, Joris Meys, 2012-06-06 Master the programming language of choice among statisticians and data analysts worldwide Coming to grips with R can be tough, even for seasoned statisticians and data analysts. Enter R For Dummies, the quick, easy way to master all the R you'll ever need. Requiring no prior programming experience and packed with practical examples, easy, step-by-step exercises, and sample code, this extremely accessible guide is the ideal introduction to R for complete beginners. It also covers many concepts that intermediate-level programmers will find extremely useful. Master your R ABCs ? get up to speed in no time with the basics, from installing and configuring R to writing simple scripts and performing simultaneous calculations on many variables Put data in its place ? get to know your way around lists, data frames, and other R data structures while learning to interact with other programs, such as Microsoft Excel Make data dance to your tune ? learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and much more Visualize it ? learn to use R's powerful data visualization features to create beautiful and informative graphical presentations of your data Get statistical ? find out how to do simple statistical analysis, summarize your variables, and conduct classic statistical tests, such as t-tests Expand and customize R ? get the lowdown on how to find, install, and make the most of add-on packages created by the global R community for a wide variety of purposes Open the book and find: Help downloading, installing, and configuring R Tips for getting data in and out of R Ways to use data frames and lists to organize data How to manipulate and process data Advice on fitting regression models and ANOVA Helpful hints for working with graphics How to code in R What R mailing lists and forums can do for you |
| combine multiple excel files into one workbook separate sheets python: SPSS For Dummies Arthur Griffith, 2007-03-07 SPSS (Statistical Package for the Social Sciences) is a data management and analysis software that allows users to generate solid, decision-making results by performing statistical analysis This book provides just the information needed: installing the software, entering data, setting up calculations, and analyzing data Covers computing cross tabulation, frequencies, descriptive ratios, means, bivariate and partial correlations, linear regression, and much more Explains how to output information into striking charts and graphs For ambitious users, also covers how to program SPSS to take their statistical analysis to the next level |
| combine multiple excel files into one workbook separate sheets python: Getting Started with Arduino Massimo Banzi, 2011-09-13 Presents an introduction to the open-source electronics prototyping platform. |
| combine multiple excel files into one workbook separate sheets python: Power BI DAX Simplified Reza Rad, 2021-07-18 DAX is the language of data analysis in Microsoft Power BI, Azure Analysis Services, and Excel Power Pivot. DAX is a powerful language that can quickly empower you to analyze year-over-year or rolling 12 months calculations. It is rare to find an analytics solution using Microsoft technologies (especially Power BI) that doesn’t require some calculations to be written by DAX. As a Power BI (or Microsoft BI) developer, it is essential to learn this language and master it. Learning a language is not just learning the structure and functions. It is learning how, where, and when to use it so that you can solve real-world problems with it. In my training and consulting experience on Power BI, I realized that DAX is the weak point for many Power BI users. DAX itself is not a complex language. It is merely a language of expression. The complexity of learning DAX is not the formula or the functions. It is how to use it in real-world scenarios and how it performs on a dataset or visual. I have been writing many blogs about DAX for many years. My blog articles are all coming from my experience working with Power BI. I found it helpful to compile them all in a book. Because my blog articles practically explain things, I thought it better to title it as a practical way of learning DAX by examples. Indeed, there are books, articles, and Microsoft documentation on how each function works where and how. However, learning these through an example would bring a new way of understanding it. A good analytics solution is a combined outcome of a good data model, good data preparation, and good analytics and calculations. I have written another book about the Basics of modeling in Power BI. This book is covering the calculation and DAX aspects of it. This book is for you if you are building a Power BI solution. Even if you are just visualizing the data, calculations are an essential part of analytics. You do need to have the calculation ready before visualizing it. This is not a book to explain every single function in DAX. The approach in this book is to have practical examples. Every chapter is based on real-world examples of using a combination of functions to solve a challenge. You can start from any chapter and finish at any chapter. The order of chapters suggested in this book is just a guideline to help you have a smooth flow of topics. Each chapter can be read without needing other chapters. Examples of this book are designed in a way that you can use the learning straight away in your Power BI file. |
| combine multiple excel files into one workbook separate sheets python: Advanced Guide to Python 3 Programming John Hunt, 2023-11-02 Advanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level. This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and all remaining chapters have been updated for the latest version of Python as well as for any of the libraries they use. There are eleven sections within the book covering Python Language Concepts, Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive Programming, Networking and Data Analytics. Each section is self-contained and can either be read on its own or as part of the book as a whole. It is aimed at those who have learnt the basics of the Python 3 language but wish to delve deeper into Python’s eco system of additional libraries and modules. |
| combine multiple excel files into one workbook separate sheets python: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| combine multiple excel files into one workbook separate sheets python: Collect, Combine, and Transform Data Using Power Query in Excel and Power BI Gil Raviv, 2018-10-08 Using Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you’re finished, you’ll be ready to wrangle any data–and transform it into actionable knowledge. Prepare and analyze your data the easy way, with Power Query · Quickly prepare data for analysis with Power Query in Excel (also known as Get & Transform) and in Power BI · Solve common data preparation problems with a few mouse clicks and simple formula edits · Combine data from multiple sources, multiple queries, and mismatched tables · Master basic and advanced techniques for unpivoting tables · Customize transformations and build flexible data mashups with the M formula language · Address collaboration challenges with Power Query · Gain crucial insights into text feeds · Streamline complex social network analytics so you can do it yourself For all information workers, analysts, and any Excel user who wants to solve their own business intelligence problems. |
| combine multiple excel files into one workbook separate sheets python: The Linux Command Line, 2nd Edition William Shotts, 2019-03-05 You've experienced the shiny, point-and-click surface of your Linux computer--now dive below and explore its depths with the power of the command line. The Linux Command Line takes you from your very first terminal keystrokes to writing full programs in Bash, the most popular Linux shell (or command line). Along the way you'll learn the timeless skills handed down by generations of experienced, mouse-shunning gurus: file navigation, environment configuration, command chaining, pattern matching with regular expressions, and more. In addition to that practical knowledge, author William Shotts reveals the philosophy behind these tools and the rich heritage that your desktop Linux machine has inherited from Unix supercomputers of yore. As you make your way through the book's short, easily-digestible chapters, you'll learn how to: • Create and delete files, directories, and symlinks • Administer your system, including networking, package installation, and process management • Use standard input and output, redirection, and pipelines • Edit files with Vi, the world's most popular text editor • Write shell scripts to automate common or boring tasks • Slice and dice text files with cut, paste, grep, patch, and sed Once you overcome your initial shell shock, you'll find that the command line is a natural and expressive way to communicate with your computer. Just don't be surprised if your mouse starts to gather dust. |
| combine multiple excel files into one workbook separate sheets python: Professional Excel Development Rob Bovey, 2009 The definitive guide to developing applications with Microsoft Excel, this book is written by four authors who are Excel MVPs and run their own companies developing Excel-based applications. |
| combine multiple excel files into one workbook separate sheets python: Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008-07-07 Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. |
| combine multiple excel files into one workbook separate sheets python: Python All-in-One For Dummies John C. Shovic, Alan Simpson, 2019-05-07 Your one-stop resource on all things Python Thanks to its flexibility, Python has grown to become one of the most popular programming languages in the world. Developers use Python in app development, web development, data science, machine learning, and even in coding education classes. There's almost no type of project that Python can't make better. From creating apps to building complex websites to sorting big data, Python provides a way to get the work done. Python All-in-One For Dummies offers a starting point for those new to coding by explaining the basics of Python and demonstrating how it’s used in a variety of applications. Covers the basics of the language Explains its syntax through application in high-profile industries Shows how Python can be applied to projects in enterprise Delves into major undertakings including artificial intelligence, physical computing, machine learning, robotics and data analysis This book is perfect for anyone new to coding as well as experienced coders interested in adding Python to their toolbox. |
| combine multiple excel files into one workbook separate sheets python: Introduction to Data Technologies Paul Murrell, 2009-02-23 Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve thei |
| combine multiple excel files into one workbook separate sheets python: SAS Programming for R Users Jordan Bakerman, 2019-12-09 SAS Programming for R Users, based on the free SAS Education course of the same name, is designed for experienced R users who want to transfer their programming skills to SAS. Emphasis is on programming and not statistical theory or interpretation. You will learn how to write programs in SAS that replicate familiar functions and capabilities in R. This book covers a wide range of topics including the basics of the SAS programming language, how to import data, how to create new variables, random number generation, linear modeling, Interactive Matrix Language (IML), and many other SAS procedures. This book also explains how to write R code directly in the SAS code editor for seamless integration between the two tools. Exercises are provided at the end of each chapter so that you can test your knowledge and practice your programming skills. |
| combine multiple excel files into one workbook separate sheets python: Python Programming on Win32 Mark J. Hammond, Andy Robinson, 2000 Demonstrates how to use the Python programming language (an object- oriented scripting language) as a development and administrations tool for Win32. Focused on tasks rather than programming (although a brief tutorial is provided) the authors cover how Python works on Windows; the key integration technologies supported by Python on Windows; and examples of what Python can do with databases, email, Internet protocols, NT services, communications, and other areas. Annotation copyrighted by Book News, Inc., Portland, OR |
| combine multiple excel files into one workbook separate sheets python: Programming Excel with VBA Flavio Morgado, 2016-11-09 Learn to harness the power of Visual Basic for Applications (VBA) in Microsoft Excel to develop interesting, useful, and interactive Excel applications. This book will show you how to manipulate Excel with code, allowing you to unlock extra features, accuracy, and efficiency in working with your data. Programming Excel 2016 with VBA is a complete guide to Excel application development, using step-by-step guidance, example applications, and screenshots in Excel 2016. In this book, you will learn: How to interact with key Excel objects, such as the application object, workbook object, and range object Methods for working with ranges in detail using code Usage of Excel as a database repository How to exchange data between Excel applications How to use the Windows API to expand the capabilities of Excel A step-by-step method for producing your own custom Excel ribbon Who This Book Is For:Developers and intermediate-to-advanced Excel users who want to dive deeper into the capabilities of Excel 2016 using code. |
| combine multiple excel files into one workbook separate sheets python: Web Scraping with Python Ryan Mitchell, 2015-06-15 Learn web scraping and crawling techniques to access data from any web source in any format. Teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. |
| combine multiple excel files into one workbook separate sheets python: Mastering Power Query in Power BI and Excel Reza Rad, Leila Etaati, 2021-08-27 Any data analytics solution requires data population and preparation. With the rise of data analytics solutions these years, the need for this data preparation becomes even more essential. Power BI is a helpful data analytics tool that is used worldwide by many users. As a Power BI (or Microsoft BI) developer, it is essential to learn how to prepare the data in the right shape and format needed. You need to learn how to clean the data and build it in a structure that can be modeled easily and used high performant for visualization. Data preparation and transformation is the backend work. If you consider building a BI system as going to a restaurant and ordering food. The visualization is the food you see on the table nicely presented. The quality, the taste, and everything else come from the hard work in the kitchen. The part that you don’t see or the backend in the world of Power BI is Power Query. You may already be familiar with other data preparation and transformation technologies, such as T-SQL, SSIS, Azure Data Factory, Informatica, etc. Power Query is a data transformation engine capable of preparing the data in the format you need. The good news is that to learn Power Query; you don’t need to know programming. Power Query is for citizen data engineers. However, this doesn’t mean that Power Query is not capable of performing advanced transformation. Power Query exists in many Microsoft tools and services such as Power BI, Excel, Dataflows, Power Automate, Azure Data Factory, etc. Through the years, this engine became more powerful. These days, we can say this is essential learning for anyone who wants to do data analysis with Microsoft technology to learn Power Query and master it. We have been working with Power Query since the very early release of that in 2013, named Data Explorer, and wrote blog articles and published videos about it. The number of articles we published under this subject easily exceeds hundreds. Through those articles, some of the fundamentals and key learnings of Power Query are explained. We thought it is good to compile some of them in a book series. A good analytics solution combines a good data model, good data preparation, and good analytics and calculations. Reza has written another book about the Basics of modeling in Power BI and a book on Power BI DAX Simplified. This book is covering the data preparation and transformations aspects of it. This book series is for you if you are building a Power BI solution. Even if you are just visualizing the data, preparation and transformations are an essential part of analytics. You do need to have the cleaned and prepared data ready before visualizing it. This book is compiled into a series of two books, which will be followed by a third book later; Getting started with Power Query in Power BI and Excel (already available to be purchased separately) Mastering Power Query in Power BI and Excel (This book) Power Query dataflows (will be published later) This book deeps dive into real-world challenges of data transformation. It starts with combining data sources and continues with aggregations and fuzzy operations. The book covers advanced usage of Power Query in scenarios such as error handling and exception reports, custom functions and parameters, advanced analytics, and some helpful table and list functions. The book continues with some performance tuning tips and it also explains the Power Query formula language (M) and the structure of it and how to use it in practical solutions. Although this book is written for Power BI and all the examples are presented using the Power BI. However, the examples can be easily applied to Excel, Dataflows, and other tools and services using Power Query. |
| combine multiple excel files into one workbook separate sheets python: Excel 2016 VBA and Macros Bill Jelen, Tracy Syrstad, 2015-11-03 MASTER CORE EXCEL 2016 TOOLS FOR BUILDING POWERFUL, RELIABLE SPREADSHEETS! Use this guide to automate virtually any routine task: save yourself hours, days, maybe even weeks! Make Excel do things you thought were impossible, discover macro techniques you won’t find anywhere else, and create automated reports that are amazingly powerful. Bill Jelen and Tracy Syrstad help you instantly visualize information, so you can act on it... capture data from anywhere, and use it anywhere... automate Excel 2016’s best new features. You’ll find simple, step-by-step instructions, real-world case studies, and 50 workbooks packed with bonus examples, macros, and solutions–straight from MrExcel! Get started fast with Excel macro development Work efficiently with ranges, cells, and formulas Build super-fast applications with arrays Automate Excel’s new pivot table enhancements Collect user data with custom dialogs Make your macros more reliable and resilient Pull data from the Internet with web queries Use advanced classes, collections, and custom functions Build sophisticated business analysis solutions Read and write to Access or SQL Server databases Control other Office programs, and Windows itself Write code that also works on older Excel versions Start writing Office Store-style Excel Apps About MrExcel Library Every book in the MrExcel Library pinpoints a specific set of crucial Excel tasks and presents focused skills and examples for performing them rapidly and effectively. Selected by Bill Jelen, Microsoft Excel MVP and mastermind behind the leading Excel solutions website MrExcel.com, these books will Dramatically increase your productivity–saving you 50 hours a year or more Present proven, creative strategies for solving real-world problems Show you how to get great results, no matter how much data you have Help you avoid critical mistakes that even experienced users make This book is part of Que’s Content Update Program. As Microsoft updates features of Excel, sections of this book will be updated or new sections will be added to match the updates to the software. See inside for details. |
| combine multiple excel files into one workbook separate sheets python: Using R for Introductory Statistics John Verzani, 2018-10-03 The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package=UsingR)), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing. |
| combine multiple excel files into one workbook separate sheets python: Programming in Python 3 Mark Summerfield, 2008-12-16 Python 3 is the best version of the language yet: It is more powerful, convenient, consistent, and expressive than ever before. Now, leading Python programmer Mark Summerfield demonstrates how to write code that takes full advantage of Python 3’s features and idioms. The first book written from a completely “Python 3” viewpoint, Programming in Python 3 brings together all the knowledge you need to write any program, use any standard or third-party Python 3 library, and create new library modules of your own. Summerfield draws on his many years of Python experience to share deep insights into Python 3 development you won’t find anywhere else. He begins by illuminating Python’s “beautiful heart”: the eight key elements of Python you need to write robust, high-performance programs. Building on these core elements, he introduces new topics designed to strengthen your practical expertise—one concept and hands-on example at a time. This book’s coverage includes Developing in Python using procedural, object-oriented, and functional programming paradigms Creating custom packages and modules Writing and reading binary, text, and XML files, including optional compression, random access, and text and XML parsing Leveraging advanced data types, collections, control structures, and functions Spreading program workloads across multiple processes and threads Programming SQL databases and key-value DBM files Utilizing Python’s regular expression mini-language and module Building usable, efficient, GUI-based applications Advanced programming techniques, including generators, function and class decorators, context managers, descriptors, abstract base classes, metaclasses, and more Programming in Python 3 serves as both tutorial and language reference, and it is accompanied by extensive downloadable example code—all of it tested with the final version of Python 3 on Windows, Linux, and Mac OS X. |
| combine multiple excel files into one workbook separate sheets python: The Improv Handbook Tom Salinsky, Deborah Frances-White, 2017-10-19 The Improv Handbook is the most comprehensive, smart, helpful and inspiring guide to improv available today. Applicable to comedians, actors, public speakers and anyone who needs to think on their toes, it features a range of games, interviews, descriptions and exercises that illuminate and illustrate the exciting world of improvised performance. First published in 2008, this second edition features a new foreword by comedian Mike McShane, as well as new exercises on endings, managing blind offers and master-servant games, plus new and expanded interviews with Keith Johnstone, Neil Mullarkey, Jeffrey Sweet and Paul Rogan. The Improv Handbook is a one-stop guide to the exciting world of improvisation. Whether you're a beginner, an expert, or would just love to try it if you weren't too scared, The Improv Handbook will guide you every step of the way. |
| combine multiple excel files into one workbook separate sheets python: Learning Tableau 2019 Joshua N. Milligan, 2019-03-27 Create powerful data visualizations and unlock intelligent business insights with Tableau Key FeaturesExplore all the latest Tableau 2019 features and redefine business analytics for your organizationCreate impressive data visualizations and interactive dashboards to get insights from your dataLearn effective data storytelling to transform how your business leverages data and makes decisionsBook Description Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics. This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis. By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making. What you will learnDevelop stunning visualizations that explain complexity with clarityExplore the exciting new features of Tableau Desktop and Tableau PrepConnect to various data sources to bring all your data togetherUncover techniques to prep and structure your data for easy analysisCreate and use calculations to solve problems and enrich analyticsMaster advanced topics such as sets, LOD calcs, and much moreEnable smart decisions with clustering, distribution, and forecastingShare your data stories to build a culture of trust and actionWho this book is for This Tableau book is for anyone who wants to understand data. If you’re new to Tableau, don’t worry. This book builds on the foundations to help you understand how Tableau really works and then builds on that knowledge with practical examples before moving on to advanced techniques. Working experience with databases will be useful but is not necessary to get the most out of this book. |
| combine multiple excel files into one workbook separate sheets python: SPSS Programming and Data Management Raynald Levesque, 2007 |
| combine multiple excel files into one workbook separate sheets python: Python Projects Laura Cassell, Alan Gauld, 2014-12-04 A guide to completing Python projects for those ready to take their skills to the next level Python Projects is the ultimate resource for the Python programmer with basic skills who is ready to move beyond tutorials and start building projects. The preeminent guide to bridge the gap between learning and doing, this book walks readers through the where and how of real-world Python programming with practical, actionable instruction. With a focus on real-world functionality, Python Projects details the ways that Python can be used to complete daily tasks and bring efficiency to businesses and individuals alike. Python Projects is written specifically for those who know the Python syntax and lay of the land, but may still be intimidated by larger, more complex projects. The book provides a walk-through of the basic set-up for an application and the building and packaging for a library, and explains in detail the functionalities related to the projects. Topics include: *How to maximize the power of the standard library modules *Where to get third party libraries, and the best practices for utilization *Creating, packaging, and reusing libraries within and across projects *Building multi-layered functionality including networks, data, and user interfaces *Setting up development environments and using virtualenv, pip, and more Written by veteran Python trainers, the book is structured for easy navigation and logical progression that makes it ideal for individual, classroom, or corporate training. For Python developers looking to apply their skills to real-world challenges, Python Projects is a goldmine of information and expert insight. |
| combine multiple excel files into one workbook separate sheets python: Handbook on Geospatial Infrastructure in Support of Census Activities , 2009 The handbook demonstrates how the use and application of contemporary geospatial technologies and geographical databases are beneficial at all stages of the population and housing census process. |
| combine multiple excel files into one workbook separate sheets python: Business Intelligence in Microsoft SharePoint 2013 Norm Warren, Mariano Neto, Stacia Misner, Ivan Sanders, Scott A. Helmers, 2013-05-15 Dive into the business intelligence features in SharePoint 2013—and use the right combination of tools to deliver compelling solutions. Take control of business intelligence (BI) with the tools offered by SharePoint 2013 and Microsoft SQL Server 2012. Led by a group of BI and SharePoint experts, you’ll get step-by-step instructions for understanding how to use these technologies best in specific BI scenarios—whether you’re a SharePoint administrator, SQL Server developer, or business analyst. Discover how to: Manage the entire BI lifecycle, from determining key performance indicators to building dashboards Use web-based Microsoft Excel services and publish workbooks on a SharePoint Server Mash up data from multiple sources and create Data Analysis Expressions (DAX) using PowerPivot Create data-driven diagrams that provide interactive processes and context with Microsoft Visio Services Use dashboards, scorecards, reports, and key performance indicators to monitor and analyze your business Use SharePoint to view BI reports side by side, no matter which tools were used to produced them |
| combine multiple excel files into one workbook separate sheets python: Financial Analysis with Microsoft Excel Timothy R. Mayes, Todd M. Shank, 1996 Start mastering the tool that finance professionals depend upon every day. FINANCIAL ANALYSIS WITH MICROSOFT EXCEL covers all the topics you'll see in a corporate finance course: financial statements, budgets, the Market Security Line, pro forma statements, cost of capital, equities, and debt. Plus, it's easy-to-read and full of study tools that will help you succeed in class. |
| combine multiple excel files into one workbook separate sheets python: R Markdown Yihui Xie, J.J. Allaire, Garrett Grolemund, 2018-07-27 R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages. In this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards, Tufte handouts, xaringan/reveal.js presentations, websites, books, journal articles, and interactive tutorials Advanced topics: Parameterized reports, HTML widgets, document templates, custom output formats, and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages, including knitr, rmarkdown, bookdown, blogdown, shiny, xaringan, and animation. He has published three other books, Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, and blogdown: Creating Websites with R Markdown. J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown, flexdashboard, learnr, and radix. Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse. |
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