This is why I love sharing these things on the internet. People see things I would never notice. Thanks!

My guess is that there is no "definition" and that a machine learning model was trained using songs labeled as "acoustic". This is probably the result of either some database Spotify pulls from or the artists themselves.

Spotify defines it as the likelihood that a track is acoustic on a scale from 0-1. You could interpret this as how acoustic sounding a track is.

This is a machine-made metric, though, and you can take it with a grain of salt if you wish. Docs on these are here.

I scraped data from Wikipedia (link) for the Billboard top-ten singles each year from 1958 to 2022. I then used the Spotify API (link) to get features about each song.

Valence is a measure of positivity (lower is more negative, scale from 0-1). Acousticness is how confident their algorithm is that the track is acoustic (0-1).

Tools used: Python, Spotipy, Pandas, Matplotlib

You can find a blog post about this analysis here. If you want to view the code and data used to generate this plot, you can find it here.

This is mostly a function of price. I showed the cleaning fee as a percentage of the total cost on the blog. This percentage held true for both the US and non-US cities.

It's legal. Scraping from public sites has been upheld by multiple federal courts in the US.

What type of Airbnb analysis would you like? Also feel free to email me (robert@datafantic.com).

There have been a huge number of complaints about cleaning fees on Airbnb lately. To dig in I scraped listings for 12 cities around the world. This chart was one of the most interesting insights from that analysis.

If you would like to see the full blog post check it out here. To see the code and data for this check it out on Deepnote.

Tools used: Python, Pandas, Numpy, Matplotlib

Approximately 300 listings from each city were scraped from Airbnb. The majority of listings in this chart had a stay duration of 5 days.

Because sometimes one chart doesn’t tell the whole story.

In percent terms true. I’ll add that chart to the blog post I think.

Alas I can only post one chart at a time here.

This subreddit is a fickle mistress. I totally agree that the time series is more interesting.

Hence the term slope. The slope of the line tells you the rate of change.

There are ups and downs, but the recent inflationary pressures are the cause of most of the increase.

Check out the meat price time series here: https://www.datafantic.com/content/images/size/w1600/2022/10/meat.png

And some fruits here and here.

Data gathered from the Bureau of Labor and Statistics. Prices are the US city average not adjust for inflation.

Tools used: Python, Matplotlib, Deepnote

I also wrote a blog with other insights, including that bananas haven’t increased significantly in price in over 10 years. Blog with more graphs here.