As described in the documentation of pandas.DataFrame.sample, the random_state parameter accepts either an integer (as in your case) or a numpy.random.RandomState, which is a container for a Mersenne Twister pseudo random number generator.

If you pass it an integer, it will use this as a seed for a pseudo random number generator. As the name already says, the generator does not produce true randomness. It rather has an internal state (that you can get by calling np.random.get_state()) which is initialized based on a seed. When initialized by the same seed, it will reproduce the same sequence of "random numbers".

If you pass it a RandomState it will use this (already initialized/seeded) RandomState to generate pseudo random numbers. This also allows you to get reproducible results by setting a fixed seed when initializing the RandomState and then passing this RandomState around. Actually you should prefer this over setting the seed of numpys internal RandomState. The reasoning being explained in this answer by Robert Kern and the comments to it. The idea is to have an independent stream which prevents other parts of the program to mess up your reproducibility by changing the seed of numpys internal RandomState.