Skip NaN values to get distance

Part of my dataset (in reality my dataset size (106,1800)):

df =

    1           1.1     2           2.1     3           3.1     4           4.1     5           5.1
0   43.1024     6.7498  NaN         NaN     NaN         NaN     NaN         NaN     NaN         NaN
1   46.0595     1.6829  25.0695     3.7463  NaN         NaN     NaN         NaN     NaN         NaN
2   25.0695     5.5454  44.9727     8.6660  41.9726     2.6666  84.9566     3.8484  44.9566     1.8484
3   35.0281     7.7525  45.0322     3.7465  14.0369     3.7463  NaN         NaN     NaN         NaN
4   35.0292     7.5616  45.0292     4.5616  23.0292     3.5616  45.0292     6.7463  NaN         NaN

What I am able to do now based on Tom's answer:

• I manually wrote 1-st 2 rows like p and q value:

p =

[[45.1024,7.7498],[45.1027,7.7513],[45.1072,7.7568],[45.1076,7.7563]]

q=

[[45.0595,7.6829],[45.0595,7.6829],[45.0564,7.6820],[45.0533,7.6796],[45.0501,7.6775]]

THEN:

__all__ = ['frdist']


def _c(ca, i, j, p, q):

    if ca[i, j] > -1:
        return ca[i, j]
    elif i == 0 and j == 0:
        ca[i, j] = np.linalg.norm(p[i]-q[j])
    elif i > 0 and j == 0:
        ca[i, j] = max(_c(ca, i-1, 0, p, q), np.linalg.norm(p[i]-q[j]))
    elif i == 0 and j > 0:
        ca[i, j] = max(_c(ca, 0, j-1, p, q), np.linalg.norm(p[i]-q[j]))
    elif i > 0 and j > 0:
        ca[i, j] = max(
            min(
                _c(ca, i-1, j, p, q),
                _c(ca, i-1, j-1, p, q),
                _c(ca, i, j-1, p, q)
            ),
            np.linalg.norm(p[i]-q[j])
            )
    else:
        ca[i, j] = float('inf')

    return ca[i, j]

THEN:

def frdist(p, q):

    # Remove nan values from p
    p = np.array([i for i in p if np.any(np.isfinite(i))], np.float64)
    q = np.array([i for i in q if np.any(np.isfinite(i))], np.float64)

    len_p = len(p)
    len_q = len(q)

    if len_p == 0 or len_q == 0:
        raise ValueError('Input curves are empty.')

    # p and q will no longer be the same length
    if len(p[0]) != len(q[0]):
        raise ValueError('Input curves do not have the same dimensions.')

    ca = (np.ones((len_p, len_q), dtype=np.float64) * -1)

    dist = _c(ca, len_p-1, len_q-1, p, q)
    return(dist)

frdist(p, q)

It works. But how I could apply p and q to the whole dataset? Not by choosing row by row?

Finally I need to get 106 to 106 symmetric matrix with 0 diagonal

from Skip NaN values to get distance