Why Is Pandas Map Slower Than List Comprehension

Does someone know why pandas/numpy map is slower then list comprehension? I thought I could optimize my code replacing the list comprehensions by map. Since map doesn't need the li

Solution 1:

Here are my results:

list comprehension:

In [33]: %timeit df["A"] = [x for x indf[0]]
10 loops, best of 3: 72.6 ms per loop

simple column assignment:

In [34]: %timeit df["A"] = df[0]
The slowest run took 5.75 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 661 µs per loop

using .map() method:

In [35]: map_df = pd.Series(np.random.randint(0, 10**6, 100000))

In [36]: %timeit df["A"] = df[0].map(map_df)
10 loops, best of 3: 19.8 ms per loop