Nyquist Frequency on semi-unevenly sampled data I have a data set that has 'kind of' constant sampling rate - it switches between 1 min and 2 min. About 70% of the times, samples are taken every 1 minute, and about 30%, samples are taken every 2 minutes. 

I tried using FFT, but it didn't work because samples aren't evenly spaced. Instead I applied Lomb-Scargle transformation, and obtained a good looking plot. Upper plot is a Lomb-Scargle periodogram, and lower plot is the original sample data with varying sampling rate (1 min or 2 min).

What should I use for Nyquist Frequency in this case?
the lomb scargle in matlab, (plomb) returns a frequency vector as the second output. i would be surprised if the routine you are using doesn’t do the same.  there is a common tendency for python signal processing libraries to be functionally equivalent to matlab conventions.

your plots show symmetry around a “center” frequency which is the same as what a FFT does for real inputs, except a FFT would not extend to start a third interval.  The high half frequencies(above $\approx$ .017 to .033)  are the negative frequencies in FFT speak. you can ignore them when you have a real signal. 

There really isn’t a simple “Nyquist” frequency with nonuniform sampling. 

The documentation of the library you are using should detail how frequency is treated. If it doesn’t, you can find a routine that does or do your own calibration by using test sines of known frequency.