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Update fb percent dataset

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This commit is contained in:
newt 2024-10-09 18:02:31 +01:00
parent 2839aa9237
commit aeeac49182
2 changed files with 23 additions and 23 deletions
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6
python/data science/fbPercentActive.py
View file

@ -19,14 +19,14 @@ data = []
def population(country):
countryCode = pycountry.countries.search_fuzzy(country)[0].alpha_3
res = requests.get('https://restcountries.eu/rest/v2/alpha/' + countryCode)
return json.loads(res.content)['population']
return int(json.loads(res.content)['population'])
for td1, td2 in zip(tds[::2], tds[1::2]):
frame = {}
frame['country'] = td1.text.strip()
frame['active'] = td2.text.strip()
frame['active'] = int(td2.text.strip()) * 1000000
frame['population'] = population(frame['country'])
frame['percentActive'] = (int(frame['active']) * 1000000 / int(frame['population'])) * 100
frame['percentActive'] = (frame['active'] / frame['population']) * 100
data.append(frame)
# Save the data

40
python/data science/fbPercentActive_res.csv
View file

@ -1,21 +1,21 @@
,country,active,population,percentActive
0,India,340,1295210000,26.250569405733433
1,United States,200,323947000,61.738494259863494
2,Indonesia,140,258705000,54.11569161786591
3,Brazil,130,206135893,63.065193600223715
4,Mexico,98,122273473,80.14821007006155
5,Philippines,88,103279800,85.20543223360231
6,Vietnam,71,92700000,76.59115426105717
7,Thailand,54,65327652,82.6602492922905
8,Egypt,47,91290000,51.484280863183265
9,Bangladesh,46,161006790,28.570223653300587
10,Pakistan,45,194125062,23.18093271233572
11,Colombia,38,48759958,77.93279887566761
12,United Kingdom,38,65110000,58.36277069574566
13,Turkey,37,78741053,46.98946558410896
14,France,33,66710000,49.46784590016489
15,Argentina,31,43590400,71.11657612685362
16,Italy,31,60665551,51.099840830589336
17,Nigeria,31,186988000,16.578603974586603
18,Germany,28,81770900,34.24201029950753
19,Peru,27,31488700,85.74504504790607
0,India,340000000,1295210000,26.250569405733433
1,United States,200000000,323947000,61.738494259863494
2,Indonesia,140000000,258705000,54.11569161786591
3,Brazil,130000000,206135893,63.065193600223715
4,Mexico,98000000,122273473,80.14821007006155
5,Philippines,88000000,103279800,85.20543223360231
6,Vietnam,71000000,92700000,76.59115426105717
7,Thailand,54000000,65327652,82.6602492922905
8,Egypt,47000000,91290000,51.484280863183265
9,Bangladesh,46000000,161006790,28.570223653300587
10,Pakistan,45000000,194125062,23.18093271233572
11,Colombia,38000000,48759958,77.93279887566761
12,United Kingdom,38000000,65110000,58.36277069574566
13,Turkey,37000000,78741053,46.98946558410896
14,France,33000000,66710000,49.46784590016489
15,Argentina,31000000,43590400,71.11657612685362
16,Italy,31000000,60665551,51.099840830589336
17,Nigeria,31000000,186988000,16.578603974586603
18,Germany,28000000,81770900,34.24201029950753
19,Peru,27000000,31488700,85.74504504790607

1 country active population percentActive
2 0 India 340 340000000 1295210000 26.250569405733433
3 1 United States 200 200000000 323947000 61.738494259863494
4 2 Indonesia 140 140000000 258705000 54.11569161786591
5 3 Brazil 130 130000000 206135893 63.065193600223715
6 4 Mexico 98 98000000 122273473 80.14821007006155
7 5 Philippines 88 88000000 103279800 85.20543223360231
8 6 Vietnam 71 71000000 92700000 76.59115426105717
9 7 Thailand 54 54000000 65327652 82.6602492922905
10 8 Egypt 47 47000000 91290000 51.484280863183265
11 9 Bangladesh 46 46000000 161006790 28.570223653300587
12 10 Pakistan 45 45000000 194125062 23.18093271233572
13 11 Colombia 38 38000000 48759958 77.93279887566761
14 12 United Kingdom 38 38000000 65110000 58.36277069574566
15 13 Turkey 37 37000000 78741053 46.98946558410896
16 14 France 33 33000000 66710000 49.46784590016489
17 15 Argentina 31 31000000 43590400 71.11657612685362
18 16 Italy 31 31000000 60665551 51.099840830589336
19 17 Nigeria 31 31000000 186988000 16.578603974586603
20 18 Germany 28 28000000 81770900 34.24201029950753
21 19 Peru 27 27000000 31488700 85.74504504790607