Why do the numpy and random modules give different random numbers for the same seed?

Question

For the same seed, why does random.random() produce different random values when compared to numpy.random(). My understanding is that they both use the Mersenne Twister to generate random values.

import random as rnd
import numpy as np

rnd.seed(1)
np.random.seed(1)

rnd.random()
np.random.rnd()

0.13436...

0.41702...

Answer 1

The random module and numpy.random both use a mt19937 to generate random numbers. Because of this, we can copy the state of one from one generator to the other to see if they have the same underlying implementation.

import random as rnd
import numpy as np

# seed numpy
np.random.seed(1)

# get state from numpy
state = [int(s) for s in list(np.random.get_state()[1])]
state.append(624)
state = tuple(state)
state = (3, tuple(state), None)

# set state for python 
rnd.setstate(state)

print(rnd.random())
print(np.random.rand())

0.417022004702574

0.417022004702574

It looks like the mt19937 engine used gives equivalent results if the state is manually set to be the same. This seems to imply the seed function are implemented differently.