How to Efficiently Find Row Indexes of Multiple Values in NumPy Arrays?

Finding Row Indexes of Multiple Values in NumPy Arrays

Given an array X and a set of target rows searched_values, the task is to retrieve the corresponding row indexes. This problem can be solved efficiently using various NumPy functions.

Approach 1: Broadcasting

A simple approach using broadcasting:

np.where((X==searched_values[:,None]).all(-1))[1]

Approach 2: Memory-Efficient Conversion

For memory efficiency, convert each row to a unique linear index and use np.in1d:

dims = X.max(0)+1
out = np.where(np.in1d(np.ravel_multi_index(X.T,dims),\
                    np.ravel_multi_index(searched_values.T,dims)))[0]

Approach 3: Memory-Efficient Search

Another memory-efficient solution using np.searchsorted:

dims = X.max(0)+1
X1D = np.ravel_multi_index(X.T,dims)
searched_valuesID = np.ravel_multi_index(searched_values.T,dims)
sidx = X1D.argsort()
out = sidx[np.searchsorted(X1D,searched_valuesID,sorter=sidx)]

Note: This approach assumes that each row in searched_values has a match in X.

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