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Extending Power BI with Python and R : Perform Advanced Analysis Using the Power of Analytical Languages / Luca Zavarella and Rajat Talwar.

Format:
Author/Creator:
Series:
Language:
English
Subjects (All):
Physical Description:
1 online resource (815 pages)
Edition:
Second edition.
Place of Publication:
Birmingham, England : Packt Publishing Ltd., [2024]
Summary:
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.
Contents:
  • Cover
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Where and How to Use R and Python Scripts in Power BI
  • Technical requirements
  • Injecting R or Python scripts into Power BI
  • Data loading
  • Data transformation
  • Data visualization
  • Using R and Python to interact with your data
  • Python and R compatibility across Power BI products
  • Summary
  • Test your knowledge
  • Chapter 2: Configuring R with Power BI
  • The available R engines
  • The CRAN R distribution
  • The Microsoft R Open distribution and MRAN
  • Multi-threading in MRO
  • Choosing an R engine to install
  • The R engines used by Power BI
  • Installing the suggested R engines
  • The R engine for data transformation
  • The R engine for R script visuals on the Power BI service
  • What to do when the Power BI service upgrades the R engine
  • Installing an IDE for R development
  • Installing RStudio
  • Installing RTools
  • Linking Intel's MKL to R
  • Configuring Power BI Desktop to work with R
  • Debugging an R script visual
  • Configuring the Power BI service to work with R
  • Installing the on-premises data gateway in personal mode
  • Sharing reports that use R scripts in the Power BI service
  • R script visuals limitations
  • Chapter 3: Configuring Python with Power BI
  • The available Python engines
  • Choosing a Python engine to install
  • The Python engines used by Power BI
  • Installing the suggested Python engines
  • The Python engine for data transformation
  • Creating an environment for data transformations using pip
  • Creating an optimized environment for data transformations using conda
  • Creating an environment for Python script visuals on the Power BI service
  • What to do when the Power BI service upgrades the Python engine.
  • Installing an IDE for Python development
  • Configuring Python with RStudio
  • Configuring Python with Visual Studio Code
  • Working with the Python Interactive window in Visual Studio Code
  • Configuring Power BI Desktop to work with Python
  • Configuring the Power BI service to work with Python
  • Sharing reports that use Python scripts in the Power BI service
  • Limitations of Python visuals
  • Chapter 4: Solving Common Issues When Using Python and R in Power BI
  • Avoiding the ADO.NET error when running a Python script in Power BI
  • The real cause of the problem
  • A practical solution to the problem
  • Avoiding the Formula.Firewall error
  • Incompatible privacy levels
  • Indirect access to a data source
  • The easy way
  • Combining queries and/or transformations
  • Encapsulating queries into functions
  • Using multiple datasets in Python and R script steps
  • Applying a full join with Merge
  • Using arguments of the Python.Execute function
  • Dealing with dates/times in Python and R script steps
  • Chapter 5: Importing Unhandled Data Objects
  • Importing RDS files in R
  • A brief introduction to Tidyverse
  • Creating a serialized R object
  • Configuring the environment and installing Tidyverse
  • Creating the RDS files
  • Using an RDS file in Power BI
  • Importing an RDS file into the Power Query Editor
  • Importing an RDS file in an R script visual
  • Importing PKL files in Python
  • A very short introduction to the PyData world
  • Creating a serialized Python object
  • Configuring the environment and installing the PyData packages
  • Creating the PKL files
  • Using a PKL file in Power BI
  • Importing a PKL file into the Power Query Editor
  • Importing a PKL file in a Python script visual
  • References.
  • Test your knowledge
  • Chapter 6: Using Regular Expressions in Power BI
  • A brief introduction to regexes
  • The basics of regexes
  • Literal characters
  • Special characters in regex
  • The ^ and anchors
  • OR operators
  • Negated character classes
  • Shorthand character classes
  • Quantifiers
  • The dot
  • Greedy and lazy matches
  • Checking the validity of email addresses
  • Checking the validity of dates
  • Validating data using regex in Power BI
  • Using regex in Power BI to validate emails with Python
  • Using regex in Power BI to validate emails with R
  • Using regex in Power BI to validate dates with Python
  • Using regex in Power BI to validate dates with R
  • Loading complex log files using regex in Power BI
  • Apache access logs
  • Importing Apache access logs in Power BI with Python
  • Importing Apache access logs in Power BI with R
  • Extracting values from text using regex in Power BI
  • One regex to rule them all
  • Using regex in Power BI to extract values with Python
  • Using regex in Power BI to extract values with R
  • References
  • Chapter 7: Anonymizing and Pseudonymizing Your Data in Power BI
  • De-identifying data
  • De-identification techniques
  • Information removal
  • Data masking
  • Data swapping
  • Generalization
  • Data perturbation
  • Tokenization
  • Hashing
  • Encryption
  • Understanding pseudonymization
  • What is anonymization?
  • Anonymizing data in Power BI
  • Anonymizing data using Python
  • Anonymizing data using R
  • Pseudonymizing data in Power BI
  • Pseudonymizing data using Python
  • Pseudonymizing data using R
  • Chapter 8: Logging Data from Power BI to External Sources
  • Logging to CSV files
  • Logging to CSV files with Python.
  • Using the pandas module
  • Logging emails to CSV files in Power BI with Python
  • Logging to CSV files with R
  • Using Tidyverse functions
  • Logging dates to CSV files in Power BI with R
  • Logging to Excel files
  • Logging to Excel files with Python
  • Using the pandas module
  • Logging emails and dates to Excel files in Power BI with Python
  • Logging to Excel files with R
  • Using the readxl and openxlsx packages
  • Logging emails and dates to Excel in Power BI with R
  • Logging to (Azure) SQL Server
  • Installing SQL Server Express
  • Creating an Azure SQL Database
  • Logging to an (Azure) SQL server with Python
  • Using the pyodbc module
  • Logging emails and dates to an Azure SQL Database in Power BI with Python
  • Logging to an (Azure) SQL Server with R
  • Using the DBI and odbc packages
  • Logging emails and dates to an Azure SQL Database in Power BI with R
  • Managing credentials in the code
  • Creating environment variables
  • Using environment variables in Python
  • Using environment variables in R
  • Chapter 9: Loading Large Datasets Beyond the Available RAM in Power BI
  • A typical analytic scenario using large datasets
  • Importing large datasets with Python
  • Installing Dask on your laptop
  • Creating a Dask DataFrame
  • Extracting information from a Dask DataFrame
  • Importing a large dataset in Power BI with Python
  • Importing large datasets with R
  • Introducing Apache Arrow
  • Installing arrow on your laptop
  • Creating and extracting information from an Arrow Dataset object
  • Importing a large dataset in Power BI with R
  • Chapter 10: Boosting Data Loading Speed in Power BI with Parquet Format
  • From CSV to the Parquet file format.
  • Limitations of using Parquet files natively in Power BI
  • Using Parquet files with Python
  • Analyzing Parquet data with Dask
  • Analyzing Parquet data with PyArrow
  • Performance differences between Dask and PyArrow
  • Using Parquet files with R
  • Analyzing Parquet data with Arrow for R
  • Using the Parquet format to speed up a Power BI report
  • Transforming historical data in Parquet
  • Appending new data to and analyzing the Parquet dataset
  • Analyzing Parquet data in Power BI with Python
  • Analyzing Parquet data in Power BI with R
  • Chapter 11: Calling External APIs to EnrichYour Data
  • What is a web service?
  • Registering for Bing Maps web services
  • Geocoding addresses using Python
  • Using an explicit GET request
  • Using an explicit GET request in parallel
  • Using the Geocoder library in parallel
  • Geocoding addresses using R
  • Using the tidygeocoder package in parallel
  • Accessing web services using Power BI
  • Geocoding addresses in Power BI with Python
  • Geocoding addresses in Power BI with R
  • Chapter 12: Calculating Columns Using Complex Algorithms: Distances
  • What is a distance?
  • The distance between two geographic locations
  • Some theory first
  • Spherical trigonometry
  • The law of Cosines distance
  • The law of Haversines distance
  • Vincenty's distance
  • What kind of distance to use and when
  • Implementing distances using Python
  • Calculating distances with Python
  • Calculating distances in Power BI with Python
  • Implementing distances using R
  • Calculating distances with R
  • Calculating distances in Power BI with R
  • The distance between two strings
  • Some theory first.
  • The Hamming distance.
Notes:
  • Description based on publisher supplied metadata and other sources.
  • Description based on print version record.
ISBN:
  • 9781837635863
  • 1837635862
OCLC:
1428439710

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