Extending Excel with Python and R

Extending Excel with Python and R

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  • Author: Steven Sanderson
  • Publisher: Packt Publishing Ltd
  • ISBN: 1804615544
  • Category : Computers
  • Languages : en
  • Pages : 345

Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionFor businesses, data analysis and visualization are crucial for informed decision-making; however, Excel’s limitations can make these tasks time-consuming and challenging. Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics, the company behind the ownR platform engineering solution for R, Python, and other data science languages. This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. Whether you're a beginner or an expert, this book has everything you need to unlock Excel's full potential and take your data analysis skills to the next level. By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed.What you will learn Read and write Excel files with R and Python libraries Automate Excel tasks with R and Python scripts Use R and Python to execute Excel VBA macros Format Excel sheets using R and Python packages Create graphs with ggplot2 and Matplotlib in Excel Analyze Excel data with statistical methods and time series analysis Explore various methods to call R and Python functions from Excel Who this book is for If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. It provides a comprehensive introduction to the topics covered, making it suitable for both beginners and intermediate learners. A basic understanding of Excel, Python, and R is all you need to get started.


Extending Power BI with Python and R

Extending Power BI with Python and R

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  • Author: Luca Zavarella
  • Publisher: Packt Publishing Ltd
  • ISBN: 1801076677
  • Category : Computers
  • Languages : en
  • Pages : 559

Perform more advanced analysis and manipulation of your data beyond what Power BI can do to unlock valuable insights using Python and R Key FeaturesGet the most out of Python and R with Power BI by implementing non-trivial codeLeverage the toolset of Python and R chunks to inject scripts into your Power BI dashboardsImplement new techniques for ingesting, enriching, and visualizing data with Python and R in Power BIBook Description Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you'll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R. What you will learnDiscover best practices for using Python and R in Power BI productsUse Python and R to perform complex data manipulations in Power BIApply data anonymization and data pseudonymization in Power BILog data and load large datasets in Power BI using Python and REnrich your Power BI dashboards using external APIs and machine learning modelsExtract insights from your data using linear optimization and other algorithmsHandle outliers and missing values for multivariate and time-series dataCreate any visualization, as complex as you want, using R scriptsWho this book is for This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.


Extending Power BI with Python and R

Extending Power BI with Python and R

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  • Author: Luca Zavarella
  • Publisher: Packt Publishing Ltd
  • ISBN: 1837635862
  • Category : Computers
  • Languages : en
  • Pages : 815

Ingest, transform, manipulate, and visualize your data beyond Power BI's capabilities. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Discover best practices for using Python and R in Power BI by implementing non-trivial code Enrich your Power BI dashboards using external APIs and machine learning models Create any visualization, as complex as you want, using Python and R scripts Book DescriptionThe latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter.What you will learn Configure optimal integration of Python and R with Power BI Perform complex data manipulations not possible by default in Power BI Boost Power BI logging and loading large datasets Extract insights from your data using algorithms like linear optimization Calculate string distances and learn how to use them for probabilistic fuzzy matching Handle outliers and missing values for multivariate and time-series data Apply Exploratory Data Analysis in Power BI with R Learn to use Grammar of Graphics in Python Who this book is for This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.


Essentials of Excel VBA, Python, and R

Essentials of Excel VBA, Python, and R

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  • Author: John Lee
  • Publisher: Springer Nature
  • ISBN: 3031142365
  • Category : Business & Economics
  • Languages : en
  • Pages : 698

This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This first volume is designed for advanced courses in financial statistics, investment analysis and portfolio management. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the second volume for dedicated content on financial derivatives, risk management, and machine learning.


Advancing Into Analytics

Advancing Into Analytics

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  • Author: George Mount
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1492094315
  • Category : Computers
  • Languages : en
  • Pages : 251

Data analytics may seem daunting, but if you're familiar with Excel, you have a head start that can help you make the leap into analytics. Advancing into Analytics will lower your learning curve. Author George Mount, founder and CEO of Stringfest Analytics, clearly and gently guides intermediate Excel users to a solid understanding of analytics and the data stack. This book demonstrates key statistical concepts from spreadsheets and pivots your existing knowledge about data manipulation into R and Python programming. With this practical book at your side, you'll learn how to: Explore a dataset for potential research questions to check assumptions and to build hypotheses Make compelling business recommendations using inferential statistics Load, view, and write datasets using R and Python Perform common data wrangling tasks such as sorting, filtering, and aggregating using R and Python Navigate and execute code in Jupyter notebooks Identify, install, and implement the most useful open source packages for your needs And more.


Essentials of Excel VBA, Python, and R

Essentials of Excel VBA, Python, and R

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  • Author: John Lee
  • Publisher: Springer Nature
  • ISBN: 3031142837
  • Category : Business & Economics
  • Languages : en
  • Pages : 521

This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.


Extending R

Extending R

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  • Author: John M. Chambers
  • Publisher: CRC Press
  • ISBN: 1315349876
  • Category : Mathematics
  • Languages : en
  • Pages : 428

Up-to-Date Guidance from One of the Foremost Members of the R Core Team Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming guidance for projects of all scales, and introduces new, valuable techniques that extend R. The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R’s data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages. A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R. A 2017 Choice Outstanding Academic Title


Python Lab1 Excel Openpyxl

Python Lab1 Excel Openpyxl

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  • Author: R. Zimmerman
  • Publisher:
  • ISBN: 9781711078229
  • Category :
  • Languages : en
  • Pages : 84

#1 New Release Black and White Edition. Are you curious about the Python language and wondering how to read and write Excel files? This book is a hands-on lab with simple code examples that perform one basic task: compare two Excel files and output an Excel file of differences. At the end of the lab, you will know enough about Python to work with your own Excel files, even if you're new to Python or programming. My examples use the free Anaconda data science platform Python 3.7, running on a Windows computer, utilizing the Spyder application. The step-by-step examples walk through each line of code, with screenshots of the corresponding Excel files so you can follow along as the program moves through the code. In the course of the lab, you'll learn these Python concepts. 1. What is a Library? 2. Comments 3. Strings, Types, and Variables 4. If...else statements for comparing data 5. While loops for working with rows of Excel data 6. Working with the file system (files/directories) 7. Creating functions and importing them into your main code file 8. Working with Excel files using openpyxlThe lab has two parts. Part 1 accomplishes the basic tasks to compare the two Excel files. I think of this as the core code that gets the job done. Part 2 adds some nice-to-have features. * Format headings and column widths in the output Excel file* Search for strings and substrings * Find New Items or Retired Items * Compare Dates * Delete Rows * Delete Worksheets * Check if the output Excel file already exists in your filesystem, and delete it if it does * Create functions and call them from your main code file. Please note, I don't attempt to cover all aspects of Python, only those concepts needed to complete this lab. If you said, "Show me what I need to start using Python with Excel files" this lab answers that simple question. After you complete the lab, you'll definitely be able to say you can program in Python. Python is really powerful, and I hope you enjoy the lab and want to continue to expand your Python skills in the future.In my opinion, a working code example takes all the guesswork out of programming, leaving just the fun of learning something new. You don't have to wonder if you have the correct indentation, your counter is in the right place, or if you forgot the colon at the end of the line when you defined your function. Are you ready? Let's get started!


Advanced Analytics in Power BI with R and Python

Advanced Analytics in Power BI with R and Python

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  • Author: Ryan Wade
  • Publisher: Apress
  • ISBN: 9781484258286
  • Category : Computers
  • Languages : en
  • Pages : 330

This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that. What You Will Learn Create advanced data visualizations through R using the ggplot2 package Ingest data using R and Python to overcome the limitations of Power Query Apply machine learning models to your data using R and Python Incorporate advanced AI in Power BI via Microsoft Cognitive Services, IBM Watson, and pre-trained models in SQL Server Machine Learning Services Perform string manipulations not otherwise possible in Power BI using R and Python Who This Book Is For Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more succinct way


Python for Excel

Python for Excel

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  • Author: Felix Zumstein
  • Publisher: "O'Reilly Media, Inc."
  • ISBN: 1492080950
  • Category : Computers
  • Languages : en
  • Pages : 366

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