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

Logging to CSV files

One of the most widely used formats for logging tabular structured information to files is comma-separated values (CSV). Since a CSV file is still a flat text file, CSV is the most popular format for exchanging information between heterogeneous applications.

A CSV file is a representation of a rectangular dataset (matrix), containing numeric or string columns. Each row of the matrix is represented by a list of values (one for each column), separated by a comma, and should have the same number of values. Sometimes, other value delimiters could be used, such as tab (\t), colon (:), and semi-colon (;) characters. The first row could contain the column header names. Usually, a line break, made by CRLF (Carriage Return Line Feed) characters (usually entered as \r\n), or simply by LF (\n) in Unix systems, is used as a row delimiter. So, an example of CSV file content could be the following:

Figure 7.1 – Example of CSV file content