Extending Power BI with Python and R
eBook - ePub

Extending Power BI with Python and R

Perform advanced analysis using the power of analytical languages

  1. 814 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Extending Power BI with Python and R

Perform advanced analysis using the power of analytical languages

About this book

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 Description

The 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.

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Yes, you can access Extending Power BI with Python and R by Luca Zavarella in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Preface
  2. Where and How to Use R and Python Scripts in Power BI
  3. Configuring R with Power BI
  4. Configuring Python with Power BI
  5. Solving Common Issues When Using Python and R in Power BI
  6. Importing Unhandled Data Objects
  7. Using Regular Expressions in Power BI
  8. Anonymizing and Pseudonymizing Your Data in Power BI
  9. Logging Data from Power BI to External Sources
  10. Loading Large Datasets Beyond the Available RAM in Power BI
  11. Boosting Data Loading Speed in Power BI with Parquet Format
  12. Calling External APIs to Enrich Your Data
  13. Calculating Columns Using Complex Algorithms: Distances
  14. Calculating Columns Using Complex Algorithms: Fuzzy Matching
  15. Calculating Columns Using Complex Algorithms: Optimization Problems
  16. Adding Statistical Insights: Associations
  17. Adding Statistical Insights: Outliers and Missing Values
  18. Using Machine Learning without Premium or Embedded Capacity
  19. Using SQL Server External Languages for Advanced Analytics and ML Integration in Power BI
  20. Exploratory Data Analysis
  21. Using the Grammar of Graphics in Python with plotnine
  22. Advanced Visualizations
  23. Interactive R Custom Visuals
  24. Answers
  25. Glossary
  26. Other Books You May Enjoy
  27. Index