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

What is the goal of EDA?

The objective of EDA is to make sure that the dataset to be used later for more complex processes is first of all clean, that is, it has no missing values and no outliers that could divert possible subsequent analyses. In addition, it is important to select during this phase the variables that actually bring information, trying to drop those that determine mostly noise. This eliminates possible sources of inaccuracy in the conclusions to which subsequent processes lead. At this stage, it is also important to study the associations between variables and gain insights from the data analyzed in order to justify any more complex processing to be applied later.

Ultimately, the phases of EDA are as follows:

  1. Understanding your data
  2. Cleaning your data
  3. Discovering associations between variables

Let's look in detail at what types of analysis they involve.

Understanding your data

In this first phase, it is essential to understand the...