BUG: Wrong kurtosis outcome due to inadequate fix to previous issues about pandas

[dontgoto]

Good point. Reproducing your example, this does happen in your example. Trying to scale it up to larger input distributions alleviates the issue though.

Your example is a sweet spot for this error, rescaling your distribution to be larger, the zeroing out stops happening very quickly due to the O(count^2) and O(count^3) terms in the numerator and denominator equations counteracting lifting the very small m4 and m2^2 above the e-14 threshold.

Doing a check of the form (pseudocode)
count < 100 and abs(frexp(denominator) - frexp(numerator)) < 24
before doing the zeroing out should alleviate this issue, but I would like to hear someone else's opinion before putting in a PR.

[dontgoto]

Another note: the kurtosis fomulation then still deviates from the scipy implementation by 3, up until a distribution size of about 10x your example, using the same shape of your example.

I was not able to iron out that instability, though.

[j7168908jx]

Another note: the kurtosis fomulation then still deviates from the scipy implementation by 3, up until a distribution size of about 10x your example, using the same shape of your example.

I was not able to iron out that instability, though.

Do you mean that the difference of their output is roughly 3? If you have not set bias=False in scipy or polars, the difference here will be roughly 3.

[dontgoto]

Do you mean that the difference of their output is roughly 3?

Exactly

If you have not set bias=False in scipy or polars, the difference here will be roughly 3.

I did not, so then that's also explained. Then I see no issues with my solution anymore.