Why does timeit results in almost constant time for all memoization runs?

We compute the Fibonacci number:

def fibo_memo(i, memo={}):
    if i <= 0:
        return 0
    elif i == 1:
        return 1
    elif i in memo:
        return memo[i]
    else:
        memo[i] = fibo_memo(i-2, memo) + fibo_memo(i-1, memo)
        return memo[i]
def fibo_dp(i):
    if i <= 0:
        return 0
    elif i == 1:
        return 1
    dp = [0] * (i + 1)
    dp[1] = 1
    for j in range(2, i + 1):
        dp[j] = dp[j-1] + dp[j-2]
    return dp[i]
assert(fibo_memo(100) == fibo_dp(100))

Now time it:

i = 10
%timeit fibo_memo(i)  # 73 ns
%timeit fibo_dp(i)  # 309 ns
i = 100
%timeit fibo_memo(i)  # 73 ns
%timeit fibo_dp(i)  # 2.54 micro seconds
i = 1000
%timeit fibo_memo(i)  # 73 ns
%timeit fibo_dp(i)  # 33 micro seconds

Why does memoization result in almost constant time, unlike dynamic programming?

The mutable default argument of fibo_memo, which holds the memoized values, persists between iterations of %timeit. So all but the first iteration complete near-instantly. (And in fact, for i = 10 and i = 100, the first iteration is probably near-instant too, since the assert check populated the memoization dict up to 100.)

For a like-for-like comparison, fibo_memo should be called with an empty memoization dict each time:

i = 10
%timeit fibo_memo(i, {})
%timeit fibo_dp(i)
i = 100
%timeit fibo_memo(i, {})
%timeit fibo_dp(i)
i = 1000
%timeit fibo_memo(i, {})
%timeit fibo_dp(i)

In my quick run on Google Colab, this shows very similar scaling behaviour for both solutions (roughly the expected O(N), with the memoization solution taking about 3 times as long as the DP for each value of i).