How to Analyze YouTube (Italia)

How to Analyze YouTube (Italia)

17 ago
|
Data Goblins
|
Italia

17 ago

Data Goblins

Italia

Feb 15

Feb 15 How to Analyze YouTube & YouTube Music History with Power BI (or Excel)

Kurt Buhler

Other Topics, Getting/Transforming Data

MAKE A FUN POWER BI PORTFOLIO PROJECT

*…by analyzing your*
*YouTube watch history*
*or*
*YouTube music listen history*
Summary
- Request your data via takeout.google.com
- When the data is ready, download & extract it to an appropriate directory. Copy the filepath.
- Open the .pbix or .xlsx linked at the bottom of the article, which contain some sample data
- In Power Query (under ‘Transform data’), change the parameter to the filepath of the .json
- Select ‘Close & Apply’ and the data will be transformed & loaded into the tables
- Analyze the data how you like;
- You can combine the data with other information from takeout.google.com (i.e. device used) or music metadata from musicbrainz (i.e. to get the track length or album names)

The starting point - analyzing music listening history

One of my favorite articles this year was by Matthew Roche,
demonstrating how you can analyze your Spotify listen history using Power BI
.



Intrigued by my own listening habits, I wondered if this was something I could do with my platform-of-choice:
YouTube Music
(formerly Google Play Music).

Using personal data for learning

We know ourselves best, of all people. That’s why personal data is so interesting; we can easily relate to and interpret what we are looking at. It’s also a good learning exercise, because when it comes to analyzing and visualizing such data, we also become our own users. It’s a good exercise in creating something meaningful for answering questions and informing action; you quickly realize what works and doesn’t. Further, if you want to practice with a new tool or method, using personal data is a good way to do it.

In 2020 I did this in an effort to teach myself Python from scratch. I completed my first
Digital Shadow Project
, which was an exercise in collecting & analyzing data across two years - comparing 2019 with

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