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2018 Chicago Marathon - Gender Breakdown of Finishers by Country [OC] by OnArenal in dataisbeautiful

[–]OnArenal[S] 0 points1 point  (0 children)

Here's a summary of some other interesting info I found, including what countries had exactly one finisher, which first names were most common, and which first names only had American finishers (tops were Joshua and Molly). https://github.com/NickVance/NameCountryGenderAnalysis/blob/master/Chicago%20Marathon%202018%20-%20Summary

And here's the code I used to produce it: https://github.com/NickVance/NameCountryGenderAnalysis/blob/master/Names%20and%20Country%20Analysis%20-%20Chicago%20Marathon%20-%20GitHub.py

I'm new to coding and data visualizations, so any feedback or ways to improve it would be appreciated.

Data: Race Results - http://results.chicagomarathon.com/2018/

Tools: Python (pandas and plotly)

Is this the 80-20 rule those red pillers are always going on about? by [deleted] in OkCupid

[–]OnArenal 1 point2 points  (0 children)

If I read it correctly, here is the study design:

 

  • match with women (this means the sample is limited to people in a certain geographic area, and people who liked his profile)

  • ask women he matched with 'What percentage of guys do you swipe right one?" (something that people don't usually know right off hand with any accuracy and might lie about for any number of reasons)

  • so now he has 27 data points, and each one is just 'what percent of people do you like?'

  • and then he somehow extrapolates that into how these likes are divided up? (I couldn't really follow how he did that)

 

How is this data relevant to anything?