Thanks to this task, I have learned the average time complexity of python’s dict family. Unlike my initial expectation, it shows just O(1) for insertion operation. I thought the inner structure of it would be balanced tree such as the usual implementation of std::map in C++. [Reference]

A non-empty array A consisting of N integers is given.

The leader of this array is the value that occurs in more than half of the elements of A.

An equi leader is an index S such that 0 ≤ S < N − 1 and two sequences A, A, …, A[S] and A[S + 1], A[S + 2], …, A[N − 1] have leaders of the same value.

For example, given array A such that:

​ A = 4 A = 3 A = 4 A = 4 A = 4 A = 2

we can find two equi leaders:

• 0, because sequences: (4) and (3, 4, 4, 4, 2) have the same leader, whose value is 4.
• 2, because sequences: (4, 3, 4) and (4, 4, 2) have the same leader, whose value is 4.

The goal is to count the number of equi leaders.

Write a function:

def solution(A)

that, given a non-empty array A consisting of N integers, returns the number of equi leaders.

For example, given:

​ A = 4 A = 3 A = 4 A = 4 A = 4 A = 2

the function should return 2, as explained above.

Write an efficient algorithm for the following assumptions:

• N is an integer within the range [1..100,000];
• each element of array A is an integer within the range [−1,000,000,000..1,000,000,000].

• Detected Time Complexity: O(N)

As seen in CPython implementation, defaultdict is a type consisting of Python dictionary object (PyDictObject) and default_factory. Aside from the running cost of default_factory, it has the same time complexity as dict (Stackoverflow, UCI ICS-46). defaultdict explicits Hash Table and shows just O(1) for insertion operation in average (Python Wiki).

from collections import defaultdict

def solution(A):
# write your code in Python 3.6
marker_l = defaultdict(lambda : 0)
marker_r = defaultdict(lambda : 0)

for i in range(len(A)):
marker_r[A[i]] += 1