Efficiently copying values from one ndarray to another on unequal sized arrays

If column three is getting updated and you want to use pandas:

import numpy as np
import pandas as pd

a1 = np.array([[ 0.0,  2.0,  10.0 ],
               [ 0.0,  2.0,  10.0 ],
               [ 0.0,  3.0,  10.0 ],
               [ 1.0,  3.0,  10.0 ],
               [ 1.0, 13.0,  10.0 ],
               [ 2.0,  2.0,  10.0 ],
               [ 2.0,  5.0,  10.0 ]])

a2 = np.array([[ 0.0,  2.0,  0.0   ],
               [ 0.0,  3.0,  0.713 ],
               [ 1.0,  3.0,  0.713 ],
               [ 1.0, 13.0,  1.0   ],
               [ 2.0,  2.0,  0.0   ]])


d1 = pd.DataFrame(a1)

d2 = pd.DataFrame(a2)

d3 = d2.set_index([0,1])[[2]].combine_first(d1.set_index([0,1])[[2]]).reset_index().to_numpy()
d3

Output:

array([[ 0.   ,  2.   ,  0.   ],
       [ 0.   ,  2.   ,  0.   ],
       [ 0.   ,  3.   ,  0.713],
       [ 1.   ,  3.   ,  0.713],
       [ 1.   , 13.   ,  1.   ],
       [ 2.   ,  2.   ,  0.   ],
       [ 2.   ,  5.   , 10.   ]])

Concatenate a2 and a1 and leave only unique rows for first 2 columns.

a_all = np.r_[a2, a1]
a_all = a_all[np.unique(a_all[:, :2], axis=0, return_index=True)[1]]

The solution has 2 parts. First, you need to identify which keys in a1 aren't in a2, and then you need to figure out which row of a2 each row of a1 is associated with.

Here's my solution:

equiv = np.all(np.equal(a1[:,None,:2],a2[None,:,:2]),-1)
mask = np.any(equiv,-1)
ind = np.argmax(equiv,0)

a1[mask,2] = a2[ind,2]

I start by broadcasting both arrays to conforming dimensions and computing the equivalence matrix that tells me for each row of a1 and a2 which are equal for both elements.

Then, it's easy to figure out which rows of a1 are not included in a2 and make a boolean mask from the previous result. We can also find the element number associated with each pair.

Finally, you associate every value of the last column of a1 that has a correspondence in a2 with the associated element in a2.

Would you consider other packages like Pandas?

import pandas as pd

d2 = pd.DataFrame(a2).set_index([0,1])
d1 = pd.DataFrame(a1).set_index([0,1])

d1.update(d2)
d1.reset_index().values

Output:

array([[ 0.   ,  2.   ,  0.   ],
       [ 0.   ,  2.   ,  0.   ],
       [ 0.   ,  3.   ,  0.713],
       [ 1.   ,  3.   ,  0.713],
       [ 1.   , 13.   ,  1.   ],
       [ 2.   ,  2.   ,  0.   ],
       [ 2.   ,  5.   , 10.   ]])