Numpy has a convenience function, np.fft.fftfreq to compute the frequencies associated with FFT components:
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
data = np.random.rand(301) - 0.5
ps = np.abs(np.fft.fft(data))**2
time_step = 1 / 30
freqs = np.fft.fftfreq(data.size, time_step)
idx = np.argsort(freqs)
plt.plot(freqs[idx], ps[idx])
Note that the largest frequency you see in your case is not 30 Hz, but
In [7]: max(freqs)
Out[7]: 14.950166112956811
You never see the sampling frequency in a power spectrum. If you had had an even number of samples, then you would have reached the Nyquist frequency, 15 Hz in your case (although numpy would have calculated it as -15).