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Table of Contents
1. Extending Power BI with Python and R, Second Edition: Perform advanced analysis using the power of analytical languages
2. 1 Where and How to Use R and Python Scripts in Power BI
I. Join our book community on Discord
II. Technical requirements
III. Injecting R or Python scripts into Power BI
i. Data loading
ii. Data transformation
iii. Data visualization
IV. Using R and Python to interact with your data
V. Python and R compatibility across Power BI products
VI. Summary
VII. Test your knowledge
i. Answers
3. 2 Configuring R with Power BI
I. Join our book community on Discord
II. Technical requirements
III. The available R engines
i. The CRAN R distribution
ii. The Microsoft R Open distribution and MRAN
iii. Multi-threading in MRO
IV. Choosing an R engine to install
i. The R engines used by Power BI
ii. Installing the suggested R engines
iii. The R engine for data transformation
iv. The R engine for R script visuals on the Power BI service
V. Installing an IDE for R development
i. Installing RStudio
ii. Installing RTools
iii. Linking Intel’s MKL to R
VI. Configuring Power BI Desktop to work with R
i. Debugging an R script visual
VII. Configuring the Power BI service to work with R
i. Installing the on-premises data gateway in personal mode
ii. Sharing reports that use R scripts in the Power BI service
VIII. R script visuals limitations
IX. Summary
X. Test your knowledge
i. Answers
4. 3 Configuring Python with Power BI
I. Join our book community on Discord
II. Technical requirements
III. The available Python engines
i. Choosing a Python engine to install
ii. The Python engines used by Power BI
iii. Installing the suggested Python engines
iv. The Python engine for data transformation
IV. Creating an environment for data transformations using pip
V. Creating an optimized environment for data transformations using conda
VI. Creating an environment for Python script visuals on the Power BI service
VII. What to do when the Power BI service upgrades the Python engine
VIII. Installing an IDE for Python development
i. Configuring Python with RStudio
ii. Configuring Python with Visual Studio Code
IX. Configuring Power BI Desktop to work with Python
i. Configuring the Power BI service to work with Python
X. Limitations of Python visuals
XI. Summary
XII. Test your knowledge
i. Answers
5. 4 Solving Common Issues When Using Python and R in Power BI
I. Join our book community on Discord
II. Technical Requirements
III. Avoiding the ADO.NET error when running a Python script in Power BI
i. The real cause of the problem
ii. A practical solution to the problem
IV. Avoiding the Formula.Firewall error
i. Incompatible privacy levels
ii. Indirect access to a data source
V. Using multiple datasets in Python and R script steps
i. Applying a full join with Merge
ii. Using arguments of the Python.Execute function
VI. Dealing with dates/times in Python and R script steps
VII. Summary
VIII. Test your knowledge
i. Answers
6. 5 Importing Unhandled Data Objects
I. Join our book community on Discord
II. Technical requirements
III. Importing RDS files in R
i. A brief introduction to Tidyverse
ii. Creating a serialized R object
iii. Using an RDS file in Power BI
IV. Importing PKL files in Python
i. A very short introduction to the PyData world
ii. Creating a serialized Python object
iii. Using a PKL file in Power BI
V. Summary
VI. References
VII. Test your knowledge
i. Answers
7. 6 Using Regular Expressions in Power BI
I. Join our book community on Discord
II. Technical requirements
III. A brief introduction to regexes
i. The basics of regexes
ii. Checking the validity of email addresses
iii. Checking the validity of dates
IV. Validating data using regex in Power BI
i. Using regex in Power BI to validate emails with Python
ii. Using regex in Power BI to validate emails with R
iii. Using regex in Power BI to validate dates with Python
iv. Using regex in Power BI to validate dates with R
V. Loading complex log files using regex in Power BI
i. Apache access logs
ii. Importing Apache access logs in Power BI with Python
iii. Importing Apache access logs in Power BI with R
VI. Extracting values from text using regex in Power BI
i. One regex to rule them all
ii. Using regex in Power BI to extract values with Python
iii. Using regex in Power BI to extract values with R
VII. Summary
VIII. References
IX. Test your knowledge
i. Answers
8. 7 Anonymizing and Pseudonymizing Your Data in Power BI
I. Join our book community on Discord
II. Technical requirements
III. De-identifying data
i. De-identification techniques
ii. Understanding pseudonymization
iii. What is anonymization?
IV. Anonymizing data in Power BI
i. Anonymizing data using Python
ii. Anonymizing data using R
V. Pseudonymizing data in Power BI
i. Pseudonymizing data using Python
ii. Pseudonymizing data using R
VI. Summary
VII. References
VIII. Test your knowledge
i. Answers
9. 8 Logging Data from Power BI to External Sources
I. Join our book community on Discord
II. Technical requirements
III. Logging to CSV files
i. Logging to CSV files with Python
ii. Logging to CSV files with R
IV. Logging to Excel files
i. Logging to Excel files with Python
ii. Logging to Excel files with R
V. Logging to (Azure) SQL Server
i. Installing SQL Server Express
ii. Creating an Azure SQL database
iii. Logging to an (Azure) SQL Server with Python
iv. Logging to an (Azure) SQL server with R
v. Managing credentials in the code
VI. Summary
VII. References
VIII. Test your knowledge
i. Answers
10. 9 Loading Large Datasets beyond the Available RAM in Power BI
I. Join our book community on Discord
II. Technical requirements
III. A typical analytic scenario using large datasets
IV. Importing large datasets with Python
i. Installing Dask on your laptop
ii. Creating a Dask DataFrame
iii. Extracting information from a Dask DataFrame
iv. Importing a large dataset in Power BI with Python
V. Importing large datasets with R
i. Introducing Apache Arrow
ii. Installing arrow on your laptop
iii. Creating and extracting information from an Arrow Dataset object
iv. Importing a large dataset in Power BI with R
VI. Summary
VII. References
11. 10 Boosting Data Loading Speed in Power BI with Parquet Format
I. Join our book community on Discord
II. Technical Requirements
III. From CSV to the Parquet file format
i. Limitations of using Parquet files natively in Power BI
ii. Using Parquet files with Python
iii. Using Parquet files with R
IV. Using the Parquet format to speed up a Power BI report
i. Transforming historical data in Parquet
ii. Appending new data to and analyze the Parquet dataset
iii. Analyzing Parquet data in Power BI with Python
iv. Analyzing Parquet data in Power BI with R
V. Summary
VI. References
VII. Test your knowledge
i. Answers
Extending Power BI with Python and R, Second Edition: Perform advanced analysis using the
power of analytical languages
Welcome to Packt Early Access. We’re giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time.You can dip in and out of this book or follow along from start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.
1. Chapter 1: Where and How to Use R and Python Scripts in Power BI
2. Chapter 2: Configuring R with Power BI
3. Chapter 3: Configuring Python with Power BI
4. Chapter 4: Solving Common Issues When Using Python and R in Power BI
5. Chapter 5: Importing Unhandled Data Objects
6. Chapter 6: Using Regular Expressions in Power BI
7. Chapter 7: Anonymizing and Pseudonymizing Your Data in Power BI
8. Chapter 8: Logging Data from Power BI to External Sources
9. Chapter 9: Loading Large Datasets beyond the Available RAM in Power BI
10. Chapter 10: Boosting Data Loading Speed in Power BI with Parquet Format
11. Chapter 11: Calling External APIs To Enrich Your Data
12. Chapter 12: Calculating Columns Using Complex Algorithms: Distances
13. Chapter 13: Calculating Columns Using Complex Algorithms: Fuzzy Matching
14. Chapter 14: Calculating Columns Using Complex Algorithms: Optimization Problems
16. Chapter 16: Adding Statistics Insights: Outliers and Missing Values
17. Chapter 17: Using Machine Learning Without Premium or Embedded Capacity
18. Chapter 18: Using the SQL Server Extensibility Framework’s R and Python engines with Power BI
19. Chapter19: Exploratory Data Analysis
20. Chapter20: Using the Grammar of Graphics in Python
21. Chapter 21: Advanced Visualizations
22. Chapter 22: Interactive Custom Visuals in R
23. Chapter 23: Infrastructural Review
1 Where and How to Use R and Python Scripts in Power BI
Join our book community on Discord
https://packt.link/EarlyAccessCommunity
Power BI is Microsoft's flagship self-service business intelligence product. It consists of a set of on-premises applications and cloudbased services that help organizations integrate, transform, and analyze data from a wide variety of source systems through a userfriendly interface.The platform is not limited to data visualization. Power BI is much more than this when you consider that its analytics engine (Vertipaq) is the same as SQL Server Analysis Services (SSAS) and Azure Analysis Services and Power Pivot in Excel and it is also the engine used for reports and datasets published to the Power BI Service. In addition, it uses Power Query as its data extraction and transformation engine, which we find in both Analysis Services and Excel. The engine comes with a very powerful and versatile formula language (M) and GUI, thanks to which you can "grind" and shape any type of data into any form.Moreover, Power BI supports DAX as a data analytic formula language, which can be used for advanced calculations and queries on data that has already been loaded into tabular data models.Such a versatile and powerful tool is a godsend for anyone who needs to do data ingestion and transformation in order to build dashboards and reports to summarize a company's business.Recently, the availability of huge
amounts of data, along with the ability to scale the computational power of machines, has made the area of advanced analytics more appealing. So, new mathematical and statistical tools have become necessary in order to provide rich insights. Hence the integration of analytical languages such as Python and R within Power BI.R or Python scripts can only be used within Power BI with specific features. Knowing which Power BI tools can be used to inject R or Python scripts into Power BI is key to understanding whether the problem you want to address is achievable with these analytical languages.This chapter will cover the following topics:
Injecting R or Python scripts into Power BI
Using R and Python to interact with your data R and Python limitations on Power BI products
Technical requirements
This chapter requires you to have Power BI Desktop already installed on your machine (you can download it here: https://aka.ms/pbiSingleInstaller). The version used in this chapter is 2.110.1161.0 64-bit (October 2022).
Injecting R or Python scripts into Power BI
In this first section, Power BI Desktop tools that allow you to use Python or R scripts will be presented and described in detail. Specifically, you will see how to add your own code during the data loading, data transforming, and data viewing phases.
Data loading
One of the first steps required to work with data in Power BI Desktop is to import it from external sources:
1. There are many connectors that allow you to do this, depending on the respective data sources, but you can also do it via scripts in Python and R. In fact, if you click on the Get data icon in the ribbon, not only the most commonly used connectors are shown but you can also select other ones from a more complete list by clicking on More...:
Figure1.1–Browsemoreconnectorstoloadyourdata
2. In the new Get Data window that pops up, simply start typing the string script into the search text box, and immediately the two options for importing data via Python or R appear:
3. Reading the contents of the tooltip, obtained by hovering the mouse over the Python script option, two things should immediately jump out at you:
a) A local installation of Python is required.b) What can be imported through Python is a data frame.The same two observations also apply when selecting R script. The only difference is that it is possible to import a pandas DataFrame when using Python (a DataFrame is a data structure provided by the pandas package), whereas R employs the two-dimensional array-like data structure called an R data frame, which is provided by default by the language.
1. After clicking on the Python script option, a new window will be shown containing a text box for writing the Python code:
Figure1.3–WindowshowingthePythonscripteditor
As you can see, it's definitely a very skimpy editor, but in Chapter3, ConfiguringPythonwithPowerBI, you'll discover how you can utilize your preferred IDE to create your scripts within a more comprehensive and feature-rich editor.
1. Taking a look at the warning message, Power BI reminds you that no Python engine has been detected, so it must be installed. If you already have Python installed and configured, you will not see this message. Clicking on the How to install Python link will cause a Microsoft Docs web page to open, explaining the steps to install Python.
Microsoft suggests installing the base Python distribution, but in order to follow some best practices on environments (selfcontained spaces that allow developers to manage dependencies, libraries, and configurations specific to individual projects), we will
install the Miniconda distribution. The details of how to do this and why will be covered in Chapter3.
1. If you had clicked on R script instead, a window for entering code in R, similar to the one shown in Figure1.4, would have appeared:
Figure1.4–WindowshowingtheRscripteditor
As with Python, in order to run code in R, you need to install the R engine on your machine. Clicking on the How to install R link will open a Docs page where Microsoft suggests installing either Microsoft R Open or the classic CRAN R. Chapter2, ConfiguringR WithPowerBI, will show you which engine to choose and how to configure your favorite IDE to write code in R.In order to import data using Python or R, you need to write code in the editors shown in Figure1.3and Figure1.4, which assign a pandas DataFrame or an R data frame to a variable, respectively. You will see concrete examples throughout this book.Next, let's look at transforming data.
Data transformation
It is possible to apply a transformation to data already imported or being imported, using scripts in R or Python. Should you want to test this on the fly, you can import the following CSV file directly from the web: http://bit.ly/iriscsv. Follow these steps:
1. Simply click on Get data and then Web to import data directly from a web page:
2. You can now enter the previously mentioned URL in the window that pops up:
Figure1.6–ImporttheIrisdatafromtheweb
Right after clicking OK, a window will pop up with a preview of the data to be imported.
1. In this case, instead of importing the data as-is, click on Transform Data in order to access the Power Query data transformation window:
2. It is at this point that you can add a transformation step using a Python or R script by selecting the Transform tab in Power Query Editor:
3. By clicking on Run Python script, you'll cause a window, similar to the one you've already seen in the data import phase, to pop up:
Figure1.9–TheRunPythonscripteditor
If you carefully read the comment in the text box, you will see that the dataset variable is already initialized and contains the data present at that moment in Power Query Editor, including any transformations already applied. At this point, you can insert your Python code in the text box to transform the data into the desired form.
1. A similar window will open if you click on Run R script:
Figure1.10–TheRunRscripteditor
Also, in this case, the dataset variable is already initialized and contains the data present at that moment in Power Query Editor. You can then add your own R code and reference the dataset variable to transform your data in the most appropriate way.Next, let's look at visualizing data.
Data visualization
Finally, your own Python or R scripts can be added to Power BI to create new visualizations, in addition to those already present in the tool out of the box:
1. Assuming we resume the data import activity started in the previous section, once the Iris dataset is loaded, simply click Cancel in the Run R script window, and then click Close & Apply in the Home tab of Power Query Editor:
Figure1.11–ClickClose&ApplytoimporttheIrisdata
2. After the data import is complete, you can select either the R script visual or Python script visual option in the Visualizations pane of Power BI:
3. If you click on Python script visual, a window pops up asking for permission to enable script code execution, as there may be security or privacy risks:
Figure1.12–TheRandPythonscriptvisuals
4. After enabling code execution, in Power BI Desktop, you can see a placeholder for the Python visual image on the report canvas and a Python script editor at the bottom:
Figure1.13–Enablethescriptcodeexecution
Figure1.14–ThePythonvisuallayout
Once you drag the fields you want to use in your Python script into the Values area, you can write your own custom code into the Python script editor and run it to generate a Python visualization.A pretty much identical layout occurs when you select R script visual.
Using R and Python to interact with your data
In the previous section, you saw all the ways you can interact with your data in Power BI via R or Python scripts. Beyond knowing how and where to inject your code into Power BI, it is very important to know how your code will interact with that data. It's here that we see a big difference between the effect of scripts injected via Power Query Editor and scripts used in visuals:
Scripts via Power Query Editor: This type of script will transform the data and persist transformations in the model. This means that it will always be possible to retrieve the transformed data from any object within Power BI. Also, once the scripts have been executed and have taken effect, theywill notbere-executedunlessthedataisrefreshed. Therefore, it is recommended to inject code in R or Python via Power Query Editor when you intend to use the resulting insights in other visuals, or in the data model.
Scripts in visuals: The scripts used within the R and Python script visuals extract particular insights from the data and only make them evident to the user through visualization. Like all the other visuals on a report page, the R and Python script visuals are also interconnected with the other visuals. This means that the script visuals are subject to cross-filtering and therefore, theyarerefreshedeverytimeyouinteractwithother visualsinthereport. That said, it is not possible to persist the results obtained from the visuals scripts in the data model.
TIP
Thanks to the interactive nature of R and Python script visuals due to cross-filtering, it is possible to inject code useful for extracting real-time insights from data. The important thing to keep in mind is that, as previously stated, it is then only possible to visualize such information, or at the most, to write it to external repositories (as you will see in Chapter 8, Logging Data from Power BI to External Sources). Also, although it is possible to access resources on the internet from a visual script when developing in Power BI Desktop, it is no longer possible to do so when the report is published to the Power BI Service (you will see what this is about in the next section) due to security issues. This restriction doesn’t exist for scripts used in Power Query.
In the final section of this chapter, let's look at the limitations of using R and Python when it comes to various Power BI products.
Python and R compatibility across Power BI products
The first question once you are clear on where to inject R and Python scripts in Power BI could be: "IstheuseofRandPython codeallowedinallPowerBIproducts?" In order to cover that, let’s briefly recap the various Power BI products and their usage in general. Here is a concise list:
Power BI Service: This is sometimes called Power BI Online, and it's the Software as a Service (SaaS) version of Power BI. It was created to facilitate the sharing of visual analysis between users through Dashboards and Reports.
Power BI Report Server: This is the on-premises version of Power BI and it extends the capabilities of SQL Server Reporting Services, enabling the sharing of reports created in Power BI Desktop (for Report Server) and in Power BI Report Builder for Power BI Paginated reports.
Power BI Embedded: A Microsoft Azure service that allows dashboards and reports to be embedded in an application for users who do not have a Power BI account.
Power BI Desktop: A free desktop application for Windows that allows you to use almost all of the features that Power BI offers. It is not the right tool for sharing results between users, but it allows you to share them on Power BI Service and Power BI Report Server. The desktop versions that allow publishing on the two mentioned services are distinct and support slightly different sets of features. They are named Power BI Desktop and Power BI Desktop for Power BI Report Server, respectively.
Power BI Mobile: A mobile application, available on Windows, Android, and iOS, that allows secure access to Power BI Service and Power BI Report Server, and that allows you to browse and share dashboards and reports, but not edit them.
Power BI Report Builder: A free desktop application for Windows that allows you to create Paginated reports. These can then be published and shared in Power BI Service and in Power BI Report Server.
Apart from the licenses, which we will not go into here, a summary figure of the relationships between the previously mentioned products follows:
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