Yes, if you use ipython and look at the output it is just a normal distribution np.random.normal.
$ ipython
Python 3.9.2 (default, Feb 28 2021, 17:03:44)
Type 'copyright', 'credits' or 'license' for more information
IPython 8.3.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import numpy as np
In [2]: mu, sigma = 0, 0.5
In [3]: np.random.normal(mu, sigma, 1000)
Out[3]:
array([-2.65413158e-01, -6.27418248e-02, 6.94722716e-01, 6.69866563e-01,
-6.66212401e-01, -7.08121404e-01, 6.18151911e-02, -5.52265635e-01,
-9.64723514e-01, 2.24599970e-02, -7.16623197e-01, -1.50668735e-01,
-3.04206225e-01, 2.41371779e-01, -6.36075849e-01, 1.20183754e-01,
-8.21533018e-01, 2.21056970e-01, 2.06465002e-01, 8.02502840e-01,
...
Or you could run the above in a notebook to check.
np.histogram puts it into a format that is compatible for p.quad (or traditionally matplotlib.pyplot.hist).