That's right. Quite ironic indeed!

I wanted to look for those names that have escaped the dichotomial tradition and have not fallen into one normative gender in practice.

It shows the contigency of it since it proves that gender-neutral names exist and also, perhaps only as a curiosity, which names would be the hardest to assume their gender.

Dick,M,29348,

Dick,F,48,

:|

Jessie,M, 113014

Jessie,F, 172923

With a (male_count - female_count) / (male_count + female_count) of -0.2095 it was set apart.

It would have been one of the most popular with a different criteria. Sorry mate :(

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Names like Alex are a priori unisex, however, I wanted to consider only the most "gender neutral" in practice (in the sense of that formula):

Alex,M,286229,

Alex,F,9486,

Most probably but I don't know. Source data is not that clear.

I would focus rather on the unisex-ness of these names (in English) rather than their popularity. Also, some of those might be really old.

However, I have found names like Mckinley which I think is quite Scotish.

Source of the data: https://archive.ics.uci.edu/ml/datasets/Gender+by+Name

Edit: Tool: Matplotlib and python

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-US: Baby Names from Social Security Card Applications - National Data, 1880 to 2019

-UK: Baby names in England and Wales Statistical bulletins, 2011 to 2018

-Canada: British Columbia 100 Years of Popular Baby names, 1918 to 2018

-Australia: Popular Baby Names, Attorney-General's Department, 1944 to 2019

I only considered those that satisfy that: |male_count - female_count| / (male_count + female_count) <= 0.2

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A longer list (sorted by total count):

Riley, Casey, Jackie, Jaime, Kerry, Frankie, Quinn, Pat, Emerson, Robbie, Emery, Justice, Blair, Amari, Carey, Elisha, Kris, Finley, Stevie, Shea, Alva, Mckinley, Ivory, Armani, Jaylin, Lavern, Devyn, Leighton, Arden, Santana

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English is not my first language and I am really new to data science, sry if there are mistakes.