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I'm trying to locate the most recent rows within my Dataframe that contain the same values in two separate columns.

Presently, I am doing this slowly with looping, but I suspect there's a way to cleverly use the apply method or some other vectorized function to do this faster. My present code:

def enumerate_matching(df):
    a = list(df['A'])
    b = list(df['B'])
    matching = []

    for i in range(0, len(a)-1):
        for j in range(i+1, len(b)):
            if a[i] == b[j]:
                matching.append(i)
                matching.append(i+j)
                break
    return matching

Is there a faster method to do this?

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2 Answers 2

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you could use set to get the intersection (it has a complexity logarithmic in the size of the sets a and b)

 a = set(df['A'])
 b = set(df['B'])
 a.intersection(b)
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If you want to do the matching line by line, you should do: 

np.sum(df['A'] == df['B'])
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