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momijizukamori) wrote2020-12-14 08:01 pm
momijizukamori) wrote2020-12-14 08:01 pm
AO3 Stats
Inspired by superborb's post! This was honestly mostly an excuse to play around with
First, some whole-history graphs:
Words read per-month in the twenty fandoms I've read the most fic for (twenty being the max I could easily graph without having to do a bunch of handpicking colors)
Overall words read per year
It's interesting to see there's kind of a yearly cycle where I read less in spring and more in the fall - at a guess, it's because spring is when most of my conventions are, so I'm busy with cosplay stuff? Also interesting to see fandoms cycle in and out.
And then 2020:
Number of works read per month in top ten fandoms
Number of words read per month in top ten fandoms
Yeah uhhhh you can kind of see MDZS dominating both categories after August, heh.
And then top ten ships for year by work count, not graphed because ehhhh lazy:
And by words:
It's interesting to see how JYL/JZX and Giorno/Mista jump up when sorted by words - neither are ships I hunt for, but both have a tendency to be background ships in works featuring my top two ships, and are more likely to get enough screentime to justify a tag in long fics.
And last but not least, a word cloud of all the freeform tags, aka calling myself out for my love of h/c:
I do still have a copy of my data, so if anyone has suggestions for other interesting stats to calculate, I'm all ears!
superborb's post! This was honestly mostly an excuse to play around with pandasand boggle at how much fanfiction I've read. General caveats - because the data is scrapped from the history page, which only records the most recent visit, ongoing/re-read stuff is skewed towards the present; it still lists works even if you didn't finish reading them; and you have to contend with the general messiness of AO3 tagging (I normalized the MDZS tags because works were showing up under.... *counts* ten different tags, but I didn't bother doing any other fandoms that way).
First, some whole-history graphs:
Words read per-month in the twenty fandoms I've read the most fic for (twenty being the max I could easily graph without having to do a bunch of handpicking colors)
Overall words read per year
It's interesting to see there's kind of a yearly cycle where I read less in spring and more in the fall - at a guess, it's because spring is when most of my conventions are, so I'm busy with cosplay stuff? Also interesting to see fandoms cycle in and out.
And then 2020:
Number of works read per month in top ten fandoms
Number of words read per month in top ten fandoms
Yeah uhhhh you can kind of see MDZS dominating both categories after August, heh.
And then top ten ships for year by work count, not graphed because ehhhh lazy:
Lán Zhàn | Lán Wàngjī/Wèi Yīng | Wèi Wúxiàn 909 Leone Abbacchio/Bruno Buccellati 332 Jiāng Chéng | Jiāng Wǎnyín & Wèi Yīng | Wèi Wúxiàn 99 Lán Yuàn | Lán Sīzhuī & Lán Zhàn | Lán Wàngjī 85 Lán Yuàn | Lán Sīzhuī & Wèi Yīng | Wèi Wúxiàn 58 Lán Huàn | Lán Xīchén & Lán Zhàn | Lán Wàngjī 57 Jiāng Yànlí/Jīn Zǐxuān 54 Jiāng Chéng | Jiāng Wǎnyín/Lán Huàn | Lán Xīchén 50 Martin Blackwood/Jonathan Sims 46 Jiāng Chéng | Jiāng Wǎnyín/Niè Huáisāng 43
And by words:
Lán Zhàn | Lán Wàngjī/Wèi Yīng | Wèi Wúxiàn 14569959 Leone Abbacchio/Bruno Buccellati 4362351 Jiāng Yànlí/Jīn Zǐxuān 2192339 Jiāng Chéng | Jiāng Wǎnyín & Wèi Yīng | Wèi Wúxiàn 1859050 Giorno Giovanna/Guido Mista 1334693 Minor or Background Relationship(s) 1155502 Kakyoin Noriaki/Kujo Jotaro 1134426 Jiāng Chéng | Jiāng Wǎnyín/Niè Huáisāng 909096 Lán Yuàn | Lán Sīzhuī & Wèi Yīng | Wèi Wúxiàn 859257 Lán Huàn | Lán Xīchén/Mèng Yáo | Jīn Guāngyáo 858132
It's interesting to see how JYL/JZX and Giorno/Mista jump up when sorted by words - neither are ships I hunt for, but both have a tendency to be background ships in works featuring my top two ships, and are more likely to get enough screentime to justify a tag in long fics.
And last but not least, a word cloud of all the freeform tags, aka calling myself out for my love of h/c:
I do still have a copy of my data, so if anyone has suggestions for other interesting stats to calculate, I'm all ears!
no subject
Very interesting how cyclic your data is! Mine doesn't appear to have such a strong pattern.
no subject
from wordcloud import WordCloud tags = stats.explode('freeforms') by_tags = tags.groupby(by='freeforms').size() tuples = list(zip(by_tags.index, by_tags.values)) wc = WordCloud(background_color="white", min_font_size = 10, width = 800, height = 800).generate_from_frequencies(dict(tuples)) wc.to_file("/path/to/file/tags.png"))There's a whole bunch more tweaking options for wordcloud, including doing shape masks and stuff, but I didn't feel like digging in too deeply.