check for identical rows in different numpy arrays

You can do it as a list comp via:

c = np.array([row in b for row in a])

though this approach will be slower than a pure numpy approach (if it exists).

Here's a vectorised solution:

res = (a[:, None] == b).all(-1).any(-1)

print(res)

array([ True,  True, False,  True])

Note that a[:, None] == b compares each row of a with b element-wise. We then use all + any to deduce if there are any rows which are all True for each sub-array:

print(a[:, None] == b)

[[[ True  True]
  [False  True]
  [False False]]

 [[False  True]
  [ True  True]
  [False False]]

 [[False False]
  [False False]
  [False False]]

 [[False False]
  [False False]
  [ True  True]]]

you can use numpy with apply_along_axis (kind of iteration on specific axis while axis=0 iterate on every cell, axis=1 iterate on every row, axis=2 on matrix and so on

import numpy as np
a = np.array([[1,0],[2,0],[3,1],[4,2]])
b = np.array([[1,0],[2,0],[4,2]])
c = np.apply_along_axis(lambda x,y: x in y, 1, a, b)

Approach #1

We could use a view based vectorized solution -

# https://stackoverflow.com/a/45313353/ @Divakar
def view1D(a, b): # a, b are arrays
    a = np.ascontiguousarray(a)
    b = np.ascontiguousarray(b)
    void_dt = np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
    return a.view(void_dt).ravel(),  b.view(void_dt).ravel()

A,B = view1D(a,b)
out = np.isin(A,B)

Sample run -

In [8]: a
Out[8]: 
array([[1, 0],
       [2, 0],
       [3, 1],
       [4, 2]])

In [9]: b
Out[9]: 
array([[1, 0],
       [2, 0],
       [4, 2]])

In [10]: A,B = view1D(a,b)

In [11]: np.isin(A,B)
Out[11]: array([ True,  True, False,  True])

Approach #2

Alternatively for the case when all rows in b are in a and rows are lexicographically sorted, using the same views, but with searchsorted -

out = np.zeros(len(A), dtype=bool)
out[np.searchsorted(A,B)] = 1

If the rows are not necessarily lexicographically sorted -

sidx = A.argsort()
out[sidx[np.searchsorted(A,B,sorter=sidx)]] = 1