#5001 to 10000

[flightfoot]

I'd also love to know how long it took to amass that many fics in your AO3 history, I'm kinda curious what the typical numbers on that is.

#polls #ao3 #I've got a little over 12000 fics in mine #After having an AO3 account for a little over four years

[gracieryder]

#tumblr #poll

[ussjellyfish]

Fanfic Writers!

(it can be a WIP, doesn't have to be published at all)

#fanfic writing #fanfic writers #fanfic #word counts #yes its arbitrary #do with it as you will

[snappysprinkledog]

If you remember, add the fandom or name in the tags!

#fanfic #poll #im aware the intervals are a bit weird but please just go with it #i tried to go with how i mentally tend to group fics

[mads-is-tired]

i was fiddling with my tumblr settings and discovered you can see how many things you've liked!

i have no idea if i even need the highest options but idk some people have been on tumblr for a long time

i'm not going to ask people to reblog but if you do, add your likes and how long you've been on tumblr in the tags!! ✨data✨

#i've liked just over 4000 things and this tumblr account has exsisted since feb 2020 but theres a large like almost 2 year gap of inactivity #mads makes a post #polls #poll

[team-moonlark]

This includes reblogs and original posts! All posts!!

#Polls #actual polls #Poll #Tumblr #tumblr poll #Kailey rambles #Curious to see how many posts everyone has #Also you can include in the tags when you joined tumblr if you want #I have almost 9k + joined in 2017

[pixeljade]

Okay lets ask something. Reblog for sample size etc

#tumblr #poll #tumblr polls #popularity #popularblog

[dyingtobehim]

#miles says something #polls #personally i like like 15-20000

[cocteautwinslyrics]

#another poll im feeling annoying today sorry mutuals bestests

[guinea-pig-enthusiast]

#ao3 #tumblr polls #fanfiction

[adragonhoardingstories]

#ao3 #polls

[robin-hood-for-freedom]

[rhttumbles]

MT

RHTAULTY

import pandas as pd import numpy

covid = pd.read_csv('c:/Users/rhtau/acovid.csv', low_memory=False)

Our data needs to be made categorical therefore we will use the cut function

covid['mortality'] = pd.cut(covid['Covid.19 Deaths'],bins =[0,1000,5000,10000000],labels = ["Low","Average","High"]) covid['Covid.19 Cases'] = pd.cut(covid['Covid.19 Cases'],bins =[0,10000,200000,500000],labels = ["Low","Average","High"]) covid['incomeperperson'] = pd.cut(covid['incomeperperson'], bins =[0,1000,10000,1000000],labels = ["Low","Average","High"])

print('Number of observations(rows)') print(len(covid))

print('Number of Variables') print(len(covid.columns))

print('Counts for covid deaths based Low(0-1000), Medium(1001-5000) or High > 5001') c1 = covid['mortality'].value_counts(sort=False) print(c1)

print('Percentages for covid deaths') p1 = covid['mortality'].value_counts(sort=False, normalize=True) print(p1)

print('Counts for Covid.19 Cases based on Low (0-10000), Medium(10001-200000 or High > 200001') c2 = covid['Covid.19 Cases'].value_counts(sort=False) print(c2)

print('Percentages for Covid.19 Cases based on Low, Medium or High') p2 = covid['Covid.19 Cases'].value_counts(sort=False, normalize=True) print(p2)

print('Counts for Income per person based on Low, Medium or High') c3=covid['incomeperperson'].value_counts(sort=False) print(c3)

print('Counts for Income per person based on Low(0-1000), Medium(1001-10000 or High>10000') c3=covid['incomeperperson'].value_counts(sort=False, normalize=True) print(c3)

Number of observations(rows) 17 Number of Variables 10 Counts for covid deaths based Low(0-1000), Medium(1001-5000) or High > 5001 mortality Low 4 Average 10 High 3 Percentages for covid deaths mortality Low 0.235294 Average 0.588235 High 0.176471 Counts for Covid.19 Cases based on Low (0-10000), Medium(10001-200000 or High > 200001

Covid.19 Cases Low 1 Average 10 High 5

Percentages for Covid.19 Cases based on Low, Medium or High

Covid.19 Cases Low 0.0625 Average 0.6250 High 0.3125 Counts for Income per person based on Low, Medium or High incomeperperson Low 10 Average 7 High 0 Counts for Income per person based on Low(0-1000), Medium(1001-10000 or High>10000 incomeperperson Low 0.588235 Average 0.411765 High 0.000000