进行n-1次插入和删除操作，所以总的时间复杂度为O((n-1)logn)。空间复杂度：优先队列的大小为n，所以空间复杂度为O(n)。

以下是哈夫曼编码的实验代码：

import heapq
from collections import defaultdict

def huffman_encoding(s):
    # 统计字符频率
    freq = defaultdict(int)
    for c in s:
        freq[c] += 1

    # 初始化优先队列（最小堆）
    min_heap = [[weight, [char, ""]] for char, weight in freq.items()]
    heapq.heapify(min_heap)

    # 构建哈夫曼树
    while len(min_heap) > 1:
        lo = heapq.heappop(min_heap)
        hi = heapq.heappop(min_heap)
        for pair in lo[1:]:
            pair[1] = '0' + pair[1]
        for pair in hi[1:]:
            pair[1] = '1' + pair[1]
        heapq.heappush(min_heap, [lo[0] + hi[0]] + lo[1:] + hi[1:])

    # 提取哈夫曼编码
    huff_dict = dict(heapq.heappop(min_heap)[1:])
    encoded_str = "".join(huff_dict[c] for c in s)

    return huff_dict, encoded_str

# 测试
s = "this is an example for huffman encoding"
huff_dict, encoded_str = huffman_encoding(s)
print("哈夫曼编码字典：", huff_dict)
print("编码后的字符串：", encoded_str)

注意：这个代码示例仅适用于给定的输入字符串，如果需要处理其他字符串，请将s替换为相应的输入。