Python equivalence of R's match() for indexing

Question

So i essentially want to implement the equivalent of R's match() function in Python, using Pandas dataframes - without using a for-loop.

In R match() returns a vector of the positions of (first) matches of its first argument in its second.

Let's say that I have two df A and B, of which both include the column C. Where

A$C = c('a','b')
B$C = c('c','c','b','b','c','b','a','a')

In R we would get

match(A$C,B$C) = c(7,3)

What is an equivalent method in Python for columns in pandas data frames, that doesn't require looping through the values.

Answer 1

Here is a one liner:

B.reset_index().set_index('c').loc[A.c, 'index'].values

This solution returns the results in the same order as the input A, as match does in R, so it is a better equivalent than @jezrael's answer, because

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Full example:

A = pd.DataFrame({'c':['a','b']})
B = pd.DataFrame({'c':['c','c','b','b','c','b','a','a']})

B.reset_index().set_index('c').loc[A.c, 'index'].values

Output array([6, 2])