Bessel functions of the first kind, $$J_l$$ where $$l$$ is an integer, are found in scipy.special.jn. This is a function that takes two arguments: the first one is the value of $$l$$ and the second one is the value of the point at which to evaluate the functions. If one is interested in $$J_0$$ or $$J_1$$ only, then there are faster implementations of these functions as j0 and j1.

If the goal is to find the zeros of the Bessel function, then rather than using a root finding routine (such as scipy.optimize.newton) it is more efficient to call scipy.special.jn_zeros. This function also takes two arguments, the first one is the value of $$l$$ and the second one is the number of zeros to display (it starts at the first positive zero).

For Bessel functions of half-integer order, so called spherical Bessel function, the situation is a little bit more complicated. The appropriate function is scipy.special.sph_jn. To compute $$J_{l + 1/2}$$, the first argument to sph_jn is the value of $$l$$. The second argument is the value of the point at which to evaluate the function. Where it gets complicated is in the return value of the function. sph_jn returns a pair of arrays. The first array contains the values of the function, and the second array contains the values of the derivative. Each array has length $$l + 1$$ and contains a value for all spherical Bessel functions with $$l$$ smaller than or equal to the supplied value.

It is useful to wrap this function into something friendlier:

def j3o2(x):
"""Compute the spherical Bessel function J_{3/2}."""
return sph_jn(1, x)

def j5o2(x):
"""Compute the spherical Bessel function J_{5/2}."""
return sph_jn(2, x)


To obtain the zeros of the spherical Bessel function, one has to call a root finding procedure. For instance, scipy.optimize.newton does the job quite well.

John D. Cook has a great page about Bessel functions and another one about how to work with the implementation in Scipy.