交互式Python实验室

Hash Map Counting

Playground在加载时自动运行。点击运行我的代码来追踪你的Python代码 — 每一步实时动画展示。

解释·逐行

回放·逐步

挑战·引导式提示

XP·...

数据流水线

intermediate 路径

Binary Search

Selection Sort

Stack Operations

Insertion Sort

Hash Map Counting

Two Pointers

Queue Operations

Anagram Check

Sliding Window Max Sum

Merge Sorted Arrays

解释

Line 1

'the' × 3, 'quick' × 2, 'fox' × 1, 'jumps' × 1. 7 words total.

为什么用这种方法？

Real-world text often has a Zipfian distribution — a few words appear very frequently, most appear rarely. Hash map counting exploits this by only storing unique keys.

Structured Explanation

Overview

哈希表计数：用平均O(1)的快速查找来统计和跟踪元素。

Analogy

就像点名表——记录每个名字(键)被签到的次数(值)。

Step-by-step

创建空字典
遍历每个元素
存在则+1，否则初始化为1
用.get(key,0)避免KeyError

Visual

words = ["cat","bat","cat","hat"] counts = {} for w in words: counts[w] = counts.get(w,0)+1 # {"cat":2,"bat":1,"hat":1}

Common mistakes

不存在检查就用dict[key]
不知道有Counter

Practice questions

从1到n+1的整数数组中找缺失数
按第一次和再次出现分类重复

Time

O(n)

Space

O(n)

复杂度

时间

O(n)

空间

O(k)

复杂度概览

O(1)

O(log n)

O(n)时间

O(n log n)

O(n²)

n = 8范围 1-20

当 N = 8 时

复杂度	操作
O(1)	1
O(log n)	3
O(n)	8
O(n log n)	24
O(n²)	64

挑战

给定一个整数列表，找出所有出现次数超过n/3的元素。

提示

先使用频率字典。然后过滤count > len(arr)//3的键。

代码工作室