The next code section will also show that you can not take the first number of any random state using np.random.get_state() and expect to recreate that exact scenario. But then you'd already know what your random seed was before, so you wouldn't need to do it that way. It may not seem so at first though, because you could use np.ed(123), retrieve the same number with seed = np.random.get_state(), reset the seed with np.ed(444), and then (seemingly) set it back to the 123 scenario with np.ed(seed). Statement 2 - It will, however, be of little use to you: And using seed = np.random.get_state() will give you 123. You can easily see 123 as the first number in the array contained in the second element. I'm using a screenshot since using print(state) will flood your console because of the size of the array in the second element of the tuple. Below is a closer look at state (I'm using the Variable explorer in Spyder). If you set the random seed using np.ed(123), you can retrieve the random state as a tuple using state = np.random.get_state(). Statement 1 - you can find the random seed using np.random.get_state(). The output from the following code sections will show you why both statements are correct.

It will, however, be of little use to you.

After setting the random seed using np.ed(X) you can find it again using np.random.get_state().

This contribution is intended to serve as a clarification to the right answer from ali_m, and as an important correction to the suggestion from Dong Justin. # set the state back to what it was originally # randomly initialize the RNG from some platform-dependent source of entropy The tuple returned by get_state can be used much like a seed in order to create reproducible sequences of random numbers.

This array has more than enough bits to represent every possible internal state of the RNG (2 624 * 32 > 2 19937-1). The output of get_state is a tuple whose second element is a (624,) array of 32 bit integers. You can get and set the internal state of the RNG directly using np.random.get_state and np.t_state. It's therefore impossible to map every RNG state to a unique integer seed. The Mersenne Twister RNG used by numpy has 2 19937-1 possible internal states, whereas a single 64 bit integer has only 2 64 possible values.

The short answer is that you simply can't (at least not in general).

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