Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

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.
Table of Contents (22 chapters)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

Diagnosing missing values in R and Python

Before thinking about imputing missing values in a dataset, we must first know the extent to which the missing values affect each individual variable.

You can find the code used in this section in the Chapter12\R\03-diagnose-missing-values-in-r.R and Chapter12\Python\03-diagnose-missing-values-in-python.py files. In order to properly run the code and the code of the following sections, you need to install the requisite R and Python packages as follows:

  1. Open the Anaconda prompt.
  2. Enter the conda activate pbi_powerquery_env command.
  3. Enter the pip install missingno command.
  4. Enter the pip install upsetplot command.
  5. Then, open RStudio and make sure it is referencing your latest CRAN R (version 4.0.2 in our case).
  6. Click on the Console window and enter install.packages('naniar'). Then press Enter.
  7. Enter install.packages('imputeTS'). Then press Enter.
  8. Enter install.packages('forecast&apos...