How we turned over 18000 Pitchfork music reviews data into interesting insights
So, I took on an interesting project with a Pitchfork dataset I found on Kaggle. Pitchfork is one of the most popular music reviews sites in the world, they were founded in 1999 and I must confess, they are known for being a bit unconventional.

Anyways, the dataset came in form of six tables and in SQLite format having 18, 393 reviews in total. I’ll skip the rest of the technical stuff till the bottom of this post, so I don’t bore core music readers.
First thing of note is: I limited my analysis to Rap, Rock and Jazz, because I felt it would be clearer to compare 3 genres than, say, 7.
I attempted to answer a few questions, let’s dive right in.1. How were reviews distributed across the 3 genres?So, I plotted a pie chart to show how the reviews were distributed across the 3 genres.2. How have the 3 genres performed when you compare ratings side-by-side?Of course, we all want to know. So, I plotted a nice line graph to show the average rating for each genre per year, from the 1960s to 2016.
Now, you’re probably wondering how we have reviews from the 60s when Pitchfork started in 1999. Ah, right. They actually went back to review albums that dropped before they started and here's a graph:3. What are the best performing albums, and artists for each genre?Rap
Best performing artists:
Highest rated albums:
Jazz
Best performing artists:
Highest rated albums:
Rock
Best performing artists:
Highest rated albums:4. Is there a relationship between the length of a review and the final rating it gives?So, I plotted the number of characters (letters, numbers, spaces, symbols) in each review against the average rating for that character length. Here's the graph and how about this for a laugh?5. Who are Pitchfork’s top reviewers, what are their roles, and what arethe average ratings they give?Here's the graph

Technical stuff
The data came in 6 tables in SQLite so I used Alteryx to clean, understand the data and do some preliminary operations like counting the number of characters for each review, then I exported each table as sheets in one Excel File.
I did all filtering and visualizations in Tableau.

Please, feel free to leave your comments below
and clap for us.