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)
Section 1: Best Practices for Using R and Python in Power BI
Section 2: Data Ingestion and Transformation with R and Python in Power BI
Section 3: Data Enrichment with R and Python in Power BI
Section 3: Data Visualization with R in Power BI

Chapter 14: Exploratory Data Analysis

In Chapter 13, Using Machine Learning without Premium or Embedded Capacity, we mentioned that using Auto Machine Learning (AutoML) solutions on a dataset blindly often does not lead to very accurate models. This is because it is necessary to understand the most inherent characteristics of the dataset by using statistical tools at an earlier stage to extract useful information in order to get a better model.

The approach to be used for this type of dataset analysis is called Exploratory Data Analysis (EDA) and was first introduced by John Turkey to encourage statisticians to explore data and formulate hypotheses that would lead to new data collection and experiments to eventually enrich patterns among the variables in a dataset.

In this chapter, you will learn about the following topics:

  • What is the goal of EDA?
  • EDA with Python and R
  • EDA in Power BI