I understand very little of what any of this data means, but I am quite proud of how readable the graph is. (with the exception of details like the freq axis ticks...)

In general, it is very hard to interpret a single line in 3D from just a static plot. Even with the 3 projections onto the 3 "walls", it's not easy to intuitively understand the shape of the line and how it behaves.

The regularly spaced vertical lines together with the semi-transparent fill make it surprisingly easy to just understand the shape, simply from looking at it. And basically from any direction.

There aren't too many real-life cases where you have a single line in a 3D space, but I'm definitely going to use this exact visualisation again if I ever come across one!

Data: Measured by a friend, slightly smoothed.

Tools: Matplotlib

sourcecode: https://pastebin.com/raw/WyEkseCA

Honestly, I have no idea what this actually means, it's something about electrical circuits in a solar panel, I just did the cool 3D visualisation...

Suggestions always welcome!

Ideally they’d be evenly distributed so the width of the point cloud represents the density (like in a violin plot), but that was too annoying to implement. Maybe next time!

No hard cutoff, but CA is topics like climate change, covid, the Theranos scandal while the most recent books included in History are a book on Watergate and a book on The Troubles.

Both are the 3rd book in a trilogy, so you gotta earn it! ;)

Death's End: 736

The Relentless Moon 704

Dune 687

Sure: https://pastebin.com/raw/kd1WgRza

The data file is just a CSV with pagecount,genre_id.

I start with creating filtered_pagecounts which is just a list of genres, each genre being a list of y-values.

Add some random x-offsets (line 36) and then plot 1 scatter plot per genre and the box plot on top.

That would be interesting, but the data is inherently biased by my selection and only 3 genres have enough numbers for conclusions anyway.

So actual conclusions would be very hard to make!

But the mean isn’t even shown? 😅

Box plots show 25th, 50th (median) and 75th percentile plus the full range (minus outliers)

I’ve got them grouped into fiction and non-fiction internally, so in my database it does makes sense (:

Nope, not normalized. There’s definitely quite some range in word count / page, I know that The Power Broker is about 15% longer (by word count) than Oathbringer but it has 100 fewer pages.

Especially the very short novellas have sometimes only half (or even fewer) as many words/page.

Sadly word count data is not nicely available…

Yeah, most fantasy books are in the 400-700 range but at the lower end I’ve got a couple novellas and at the upper end I’ve got Sanderson, Tad Williams and Steven Erikson 😅

The “Historical” section is actual “Historical Fiction” (bad label I just realized), so no reference. The “History” genre is probably about 15% references on average.

EDIT: “Historical” (4th genre) is Historical Fiction! “History” (7th) is non-fiction!

Data: My own shelves, exported from my own app (https://shelf.li, you can explore the data by clicking "Try the demo")

Tools: Matplotlib

Genres: Assigned manually, sometimes obviously debatable...

Books over 1100 pages:

• Oathbringer (Brandon Sanderson): 1331
• The Stand (Stephen King): 1325
• The Count of Monte Cristo: 1312
• Die Arena (Stephen King, German): 1279
• The Power Broker (Robert A. Caro): 1246
• World without End (Ken Follett): 1240
• Rhythm of War (Brandon Sanderson): 1230
• Master of the Senate (Robert A. Caro): 1200
• Memories of Ice (Steven Erikson): 1180
• Edge of Eternity (Ken Follett) 1158

Probably Sankeymatic; just google it, it’s a website

Look at mine, in case that helps

https://reddit.com/r/dataisbeautiful/comments/x0mg3s/oc_applications_for_my_first_ever_real_job_as/

18 applications, 9 interviews (4 of which I declined), got 3 offers.

3 weeks from application to signed contract.