Python(or Numpy) Equivalent Of Match In R

Is there any easy way in python to accomplish what the match function does in R? what match in R does is that it returns a vector of the positions of (first) matches of its first

Solution 1:

>>> a = [5,4,3,2,1]
>>> b = [2,3]
>>> [ b.index(x) if x in b else None for x in a ]
[None, None, 1, 0, None]

Add 1 if you really need position "one based" instead of "zero based".

>>> [ b.index(x)+1 if x in b else None for x in a ]
[None, None, 2, 1, None]

You can make this one-liner reusable if you are going to repeat it a lot:

>>> match = lambda a, b: [ b.index(x)+1 if x in b else None for x in a ]
>>> match
<function <lambda> at 0x04E77B70>
>>> match(a, b)
[None, None, 2, 1, None]

Solution 2:

A faster approach building on Paulo Scardine's answer (difference becomes more meaningful as the size of the arrays increases). If you don't mind losing the one-liner:

from typing import Hashable, List


def match_list(a: List[Hashable], b: List[Hashable]) -> List[int]:
    return [b.index(x) if x in b else None for x in a]


def match(a: List[Hashable], b: List[Hashable]) -> List[int]:
    b_dict = {x: i for i, x in enumerate(b)}
    return [b_dict.get(x, None) for x in a]


import random

a = [random.randint(0, 100) for _ in range(10000)]
b = [i for i in range(100) if i % 2 == 0]


%timeit match(a, b)
>>> 580 µs ± 15.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit match_list(a, b)
>>> 6.13 ms ± 146 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

match(a, b) == match_list(a, b)
>>> True