Is there a faster alternative to np.diff?

Question

I'm concerned with the speed of the following function:

def cch(tau):
    return np.sum(abs(-1*np.diff(cartprod)-tau)<0.001)

Where "cartprod" is a variable for a list that looks like this:

cartprod = np.ndarray([[0.0123,0.0123],[0.0123,0.0459],...])

The length of this list is about 25 million. Basically, I'm trying to find a significantly faster way to return a list of differences for every pair list in that np.ndarray. Is there an algorithmic way or function that's faster than np.diff? Or, is np.diff the end all be all? I'm also open to anything else.

EDIT: Thank you all for your solutions!

Answer 1

I think you're hitting a wall by repeatedly returning multiple np.arrays of length ~25 million rather than np.diff being slow. I wrote an equivalent function that iterates over the array and tallies the results as it goes along. The function needs to be jitted with numba to be fast. I hope that is acceptable.

arr = np.random.rand(25000000, 2)

def cch(tau, cartprod):
    return np.sum(abs(-1*np.diff(cartprod)-tau)<0.001)
%timeit cch(0.01, arr)

@jit(nopython=True)
def cch_jit(tau, cartprod):
    count = 0
    tau = -tau
    for i in range(cartprod.shape[0]):
        count += np.less(np.abs(tau - (cartprod[i, 1]- cartprod[i, 0])), 0.001)
    return count
%timeit cch_jit(0.01, arr)

produces

294 ms ± 2.82 ms 
42.7 ms ± 483 µs 

which is about ~6 times faster.