My SQL for Data Science Capstone Project: Country Data and Olympic Success (Part 3)
So, when we left off we were looking at population, GDP per capita, and urbanization metrics. Nothing jumped out except the urbanization…
(tl;dr the first five posts in this series… I’m looking at connections between population and economic metrics and country success at the olympics. To start at the beginning, check out part 1.)
(Standard warning in all my posts. I’m a beginner. Nothing below should be taken as the best way of doing things, a good way of doing things, or even a right way of doing things. If you think you know better than me, you probably do. Please comment with better practices)
Ok. I’ll get back to the cool connection between being “pretty urban” and olympically successful, because that’s cool. I wanna jump in to a couple other metrics first to see what we see.
So, generally, the countries with bigger populations were more successful in the olympics. What if we try to factor that out. What about medals per capita as a metric of success? Interesting stuff…
Golds_per_cap*1000000 Country
0 69.631289 Norway
1 52.428762 Liechtenstein
2 47.377741 Sweden
3 44.739101 Hungary
4 35.725647 Finland
... ... ...
84 0.037755 Cote d'Ivoire
85 0.034217 Nepal
86 0.031848 Mozambique
87 0.030257 Peru
88 0.010257 Vietnam
Medals_per_cap*1000000 Country
0 235.929431 Liechtenstein
1 190.288682 Norway
2 162.389302 Finland
3 151.925282 Sweden
4 117.543703 Hungary
... ... ...
84 0.113067 Ecuador
85 0.097842 Jordan
86 0.063697 Mozambique
87 0.041030 Vietnam
88 0.034217 NepalLiechtenstein and Scandinavia looking strong.
I need to know a few more fun facts… who sends the most athletes per capita of population?
Some of our old friends…
Country Athletes_per_cap*1000000
0 Liechtenstein 9673.106666
1 Luxembourg 1586.735149
2 Finland 986.424796
3 Norway 913.680412
4 Sweden 824.807895
... ... ...
84 Pakistan 2.535955
85 Mozambique 2.484170
86 Vietnam 1.579649
87 Indonesia 1.485284
88 India 1.018559Finally, who is most successful per athlete sent to the olympics? By percentage shot at a medal…
Country gold_per_athlete medals_per_athlete
0 United States 13 29
1 Russia 13 33
2 India 9 13
3 Germany 8 23
4 Pakistan 7 21
... ... ... ...
84 Slovenia 0 4
85 Latvia 0 3
86 Bahrain 0 2
87 Luxembourg 0 0
88 Liechtenstein 0 2Ah, our friend Liechenstein’s athletes are rather unsuccessful, that’s what you get for sending 1% of your population to participate. So who all is at the top?
Country gold_per_athlete medals_per_athlete
0 United States 13 29
1 Russia 13 33
2 India 9 13
3 Germany 8 23
4 Pakistan 7 21
5 Norway 7 20
6 China 6 19
7 Cameroon 6 7
8 Cuba 6 16
9 Hungary 6 17
10 Croatia 6 17
11 Ethiopia 5 14
12 United Kingdom 5 16
13 Italy 5 15
14 Zimbabwe 5 7
15 Sweden 5 18
16 Denmark 5 16
17 Uruguay 5 10
18 Fiji 5 5
19 Kenya 4 13Ok. Let’s get back to “The Joys of Semi-Urbanism”…
By these bar graphs, the second tier of urbanization is drastically more successful than other countries. What if we change our definition of success though? Let’s look at the quartile breakdown of medals per capita…
The story has become a lot less drastic. It’s hard to tell a story about the olympic success of semi-urbanized and non-urbanized countries.
Let’s try our new metric for success with a different country metric… GDP per capita it is…
Oh, this looks promising…
(Many months later…)
So after letting this project sit for a while, I decided to go ahead and turn something in and move on in my life. This was a fun experiment. I didn’t really answer any questions, but I learned a lot about pandas, matplotlib, Jupyter Notebooks, and getting distracted.
To close out this project, I leave you with this lovely scatter plot, that should tell you everything you need to know about everything…
Catch ya next time…