Replace nan in a numpy array to zero or any number:

a = numpy.array([1,2,3,4,np.nan])

# if copy=False, the replace inplace, default is True, it will be changed to 0 by default
a = numpy.nan_to_num(a, copy=True) 

# if you want it changed to any number, eg. 10.
numpy.nan_to_num(a, copy=False, nan=10) 

Replace inf or -inf with the most positive or negative finite floating-point values or any numbers:

a = numpy.array([1,2,3,4,np.inf])

# change to the most positive or finite floating-point value by default
a = numpy.nan_to_num(a, copy=True)

# if you want it changed to any number, eg. 10.
a = numpy.nan_to_num(a, copy=True, posinf=10)

# if you want it changed to any number, eg. 10., same goes to neginf
a = numpy.nan_to_num(a, copy=True, posinf=10, neginf=-10)

The parameter posinf and neginf only works when your numpy version is equal or higher than 1.17.

Source:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.nan_to_num.html