I have 3 arrays, x, y, and q. Arrays x and y have the same length, q is a query array. Assume all values in x and q are unique. For each value of q, I would like to find the index of the corresponding value in x. I would then like to query that index in y. If a value from q does not appear in x, I would like to return np.nan. As a concrete example, consider the following arrays: x = np.array([1, 2, 3]) y = np.array([4, 5, 6]) q = np.array([2, 0]) Since only the value 2 occurs in x, the correct return value would be: out = np.array([5, np.nan]) With for loops, this can be done like so: out = [] for i in range(len(q)): for j in range(len(x)): if np.allclose(q, x[j]): out.append(y[j]) break else: out.append(np.nan) output = np.array(out) Obviously this is quite slow. Is there a simpler way to do this with numpy builtins like np.argwhere? Or would it be easier to use pandas? Continue reading...
