How to I factorize a list of tuples?

Question

definition
factorize: Map each unique object into a unique integer. Typically, the range of integers mapped to is from zero to the n - 1 where n is

Answer 1

I was going to give this answer

pd.factorize([str(x) for x in tups])

However, after running some test, it did not pan out to be the fastest of them all. Since I already did the work, I will show it here for comparison:

@AChampion

%timeit [d[tup] if tup in d else d.setdefault(tup, next(c)) for tup in tups]
1000000 loops, best of 3: 1.66 µs per loop

@Divakar

%timeit np.unique(get_row_view(np.array(tups)), return_inverse=1)[1]
# 10000 loops, best of 3: 58.1 µs per loop

@self

%timeit pd.factorize([str(x) for x in tups])
# 10000 loops, best of 3: 65.6 µs per loop

@root

%timeit pd.Series(tups).factorize()[0] 
# 1000 loops, best of 3: 199 µs per loop

EDIT

For large data with 100K entries, we have:

tups = [(np.random.randint(0, 10), np.random.randint(0, 10)) for i in range(100000)]

@root

%timeit pd.Series(tups).factorize()[0] 
100 loops, best of 3: 10.9 ms per loop

@AChampion

%timeit [d[tup] if tup in d else d.setdefault(tup, next(c)) for tup in tups]

# 10 loops, best of 3: 16.9 ms per loop

@Divakar

%timeit np.unique(get_row_view(np.array(tups)), return_inverse=1)[1]
# 10 loops, best of 3: 81 ms per loop

@self

%timeit pd.factorize([str(x) for x in tups])
10 loops, best of 3: 87.5 ms per loop