Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Extending Power BI with Python and R
  • Toc
  • feedback
Extending Power BI with Python and R

Extending Power BI with Python and R

By : Zavarella
4.7 (10)
close
Extending Power BI with Python and R

Extending Power BI with Python and R

4.7 (10)
By: 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)
close
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

Implementing distances using Python

The scenario on which we will implement the distance algorithms just described involves a dataset of US hotels, containing the latitude and longitude of each. The goal is to enrich the dataset by adding the distances to the nearest airports.

The hotel data is publicly available on Back4App (https://bit.ly/data-hotels-usa). For convenience, we extracted only 100 hotels from New York City and we will calculate for each of them the distances from the LaGuardia and John F. Kennedy airports (you can find the airport data here: https://datahub.io/core/airport-codes) using the Haversine (spherical model) and Karney (ellipsoidal model) methods. You can find the already extracted datasets for your convenience in the Chapter10 folder of the GitHub repository. In detail, you will find the hotel data in the hotels-ny.xlsx file and the airport data in the airport-codes.csv file.

Calculating distances with Python

As we mentioned earlier, we are not those...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete