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

What missing values are and how to deal with them

Data describing real-world phenomena often has a lot of missing data. Lack of data is a fact that cannot be overlooked, especially if the analyst wants to do an advanced study of the dataset to understand how much the variables in it are correlated.

The consequences of mishandling missing values can be many:

  • The statistical power of variables with missing values is diminished, especially when a substantial number of values is missing for a single variable.
  • The representativeness of the dataset subject to missing values may also be diminished, and thus the dataset in question may not correctly represent the substantive characteristics of the set of all observations of a phenomenon.
  • Any statistical estimates may not converge to whole population values, thus generating bias.
  • The results of the analysis conducted may not be correct.

But let's see what the causes could be that generate missing values in...