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<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Python 3 进阶复习手册</title>
<link href="https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;700&family=Noto+Serif+SC:wght@400;600;700&family=Space+Grotesk:wght@300;400;600;700&display=swap" rel="stylesheet">
<style>
:root {
--bg: #0d0f14;
--surface: #151820;
--surface2: #1c2030;
--border: #252a38;
--accent: #4ade80;
--accent2: #38bdf8;
--accent3: #f59e0b;
--accent4: #e879f9;
--text: #e2e8f0;
--text-muted: #64748b;
--text-dim: #94a3b8;
--code-bg: #0a0c10;
--keyword: #c792ea;
--string: #c3e88d;
--comment: #546e7a;
--func: #82aaff;
--number: #f78c6c;
--builtin: #ffcb6b;
--operator: #89ddff;
}
* { box-sizing: border-box; margin: 0; padding: 0; }
body {
background: var(--bg);
color: var(--text);
font-family: 'Space Grotesk', 'Noto Serif SC', sans-serif;
line-height: 1.7;
min-height: 100vh;
}
/* Noise texture overlay */
body::before {
content: '';
position: fixed;
inset: 0;
background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 256 256' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='noise'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='4' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23noise)' opacity='0.04'/%3E%3C/svg%3E");
pointer-events: none;
z-index: 0;
opacity: 0.4;
}
.layout {
display: flex;
min-height: 100vh;
position: relative;
z-index: 1;
}
/* ── Sidebar ── */
.sidebar {
width: 280px;
flex-shrink: 0;
background: var(--surface);
border-right: 1px solid var(--border);
position: sticky;
top: 0;
height: 100vh;
overflow-y: auto;
padding: 0 0 2rem;
}
.sidebar::-webkit-scrollbar { width: 4px; }
.sidebar::-webkit-scrollbar-track { background: transparent; }
.sidebar::-webkit-scrollbar-thumb { background: var(--border); border-radius: 2px; }
.sidebar-header {
padding: 1.8rem 1.5rem 1.2rem;
border-bottom: 1px solid var(--border);
position: sticky;
top: 0;
background: var(--surface);
z-index: 10;
}
.sidebar-logo {
display: flex;
align-items: center;
gap: 0.6rem;
margin-bottom: 0.4rem;
}
.logo-badge {
background: linear-gradient(135deg, #3b82f6, #8b5cf6);
color: white;
font-family: 'JetBrains Mono', monospace;
font-size: 0.65rem;
font-weight: 700;
padding: 0.15rem 0.4rem;
border-radius: 4px;
letter-spacing: 0.05em;
}
.sidebar-title {
font-size: 0.9rem;
font-weight: 700;
color: var(--text);
letter-spacing: -0.02em;
}
.sidebar-subtitle {
font-size: 0.72rem;
color: var(--text-muted);
}
.nav-section {
padding: 1rem 1rem 0.4rem;
}
.nav-section-label {
font-size: 0.65rem;
font-weight: 700;
text-transform: uppercase;
letter-spacing: 0.12em;
color: var(--text-muted);
padding: 0 0.5rem;
margin-bottom: 0.5rem;
}
.nav-item {
display: flex;
align-items: flex-start;
gap: 0.5rem;
padding: 0.45rem 0.7rem;
border-radius: 6px;
cursor: pointer;
transition: background 0.15s, color 0.15s;
text-decoration: none;
color: var(--text-dim);
font-size: 0.8rem;
line-height: 1.4;
margin-bottom: 0.15rem;
}
.nav-item:hover { background: var(--surface2); color: var(--text); }
.nav-item.active { background: rgba(74,222,128,0.08); color: var(--accent); }
.nav-num {
font-family: 'JetBrains Mono', monospace;
font-size: 0.65rem;
color: var(--text-muted);
min-width: 1.4rem;
padding-top: 0.1rem;
}
/* ── Main Content ── */
.main {
flex: 1;
padding: 3rem 3.5rem;
max-width: 960px;
}
.page-header {
margin-bottom: 3.5rem;
padding-bottom: 2rem;
border-bottom: 1px solid var(--border);
}
.page-eyebrow {
font-family: 'JetBrains Mono', monospace;
font-size: 0.7rem;
color: var(--accent);
letter-spacing: 0.15em;
text-transform: uppercase;
margin-bottom: 0.8rem;
}
.page-title {
font-family: 'Noto Serif SC', serif;
font-size: 2.6rem;
font-weight: 700;
letter-spacing: -0.03em;
line-height: 1.15;
background: linear-gradient(135deg, #e2e8f0 30%, #64748b);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
margin-bottom: 1rem;
}
.page-meta {
display: flex;
gap: 1.5rem;
font-size: 0.78rem;
color: var(--text-muted);
}
.meta-chip {
display: flex;
align-items: center;
gap: 0.35rem;
}
.meta-dot {
width: 6px; height: 6px;
border-radius: 50%;
background: var(--accent);
}
/* ── Chapter ── */
.chapter {
margin-bottom: 4rem;
}
.chapter-header {
display: flex;
align-items: center;
gap: 1rem;
margin-bottom: 2.5rem;
padding-bottom: 1rem;
border-bottom: 1px solid var(--border);
}
.chapter-num {
font-family: 'JetBrains Mono', monospace;
font-size: 0.65rem;
color: var(--accent);
background: rgba(74,222,128,0.08);
border: 1px solid rgba(74,222,128,0.2);
padding: 0.25rem 0.6rem;
border-radius: 4px;
letter-spacing: 0.08em;
white-space: nowrap;
}
.chapter-title {
font-family: 'Noto Serif SC', serif;
font-size: 1.35rem;
font-weight: 700;
color: var(--text);
}
/* ── Section ── */
.section {
margin-bottom: 3rem;
scroll-margin-top: 2rem;
}
.section-title {
font-size: 1rem;
font-weight: 700;
color: var(--accent2);
margin-bottom: 0.8rem;
display: flex;
align-items: center;
gap: 0.6rem;
}
.section-num {
font-family: 'JetBrains Mono', monospace;
font-size: 0.72rem;
color: var(--text-muted);
background: var(--surface2);
padding: 0.1rem 0.45rem;
border-radius: 3px;
}
.section-body {
color: var(--text-dim);
font-size: 0.9rem;
margin-bottom: 1.2rem;
line-height: 1.8;
}
/* ── Code Block ── */
.code-wrap {
background: var(--code-bg);
border: 1px solid var(--border);
border-radius: 8px;
overflow: hidden;
margin: 1.2rem 0;
font-family: 'JetBrains Mono', monospace;
}
.code-header {
display: flex;
align-items: center;
justify-content: space-between;
padding: 0.5rem 1rem;
background: var(--surface);
border-bottom: 1px solid var(--border);
font-size: 0.7rem;
}
.code-lang {
color: var(--text-muted);
font-size: 0.65rem;
text-transform: uppercase;
letter-spacing: 0.1em;
}
.code-dots { display: flex; gap: 5px; }
.code-dot { width: 10px; height: 10px; border-radius: 50%; }
.dot-r { background: #ff5f56; }
.dot-y { background: #ffbd2e; }
.dot-g { background: #27c93f; }
pre {
padding: 1.2rem 1.4rem;
overflow-x: auto;
font-size: 0.82rem;
line-height: 1.75;
tab-size: 4;
}
pre::-webkit-scrollbar { height: 4px; }
pre::-webkit-scrollbar-track { background: transparent; }
pre::-webkit-scrollbar-thumb { background: var(--border); border-radius: 2px; }
/* Syntax highlighting */
.kw { color: var(--keyword); font-style: italic; }
.st { color: var(--string); }
.cm { color: var(--comment); font-style: italic; }
.fn { color: var(--func); }
.nb { color: var(--builtin); }
.nm { color: var(--number); }
.op { color: var(--operator); }
.cls { color: #ffcb6b; }
.dec { color: var(--accent4); }
.var { color: var(--text); }
.pn { color: #89ddff; } /* punctuation */
/* ── Info Boxes ── */
.info-box {
border-radius: 8px;
padding: 1rem 1.2rem;
margin: 1.2rem 0;
font-size: 0.85rem;
border-left: 3px solid;
line-height: 1.7;
}
.info-tip {
background: rgba(74,222,128,0.06);
border-color: var(--accent);
color: #a7f3d0;
}
.info-warn {
background: rgba(245,158,11,0.07);
border-color: var(--accent3);
color: #fde68a;
}
.info-note {
background: rgba(56,189,248,0.07);
border-color: var(--accent2);
color: #bae6fd;
}
.box-label {
font-family: 'JetBrains Mono', monospace;
font-size: 0.65rem;
font-weight: 700;
letter-spacing: 0.1em;
text-transform: uppercase;
margin-bottom: 0.3rem;
opacity: 0.8;
}
/* ── Method Table ── */
.method-table {
width: 100%;
border-collapse: collapse;
margin: 1.2rem 0;
font-size: 0.82rem;
}
.method-table th {
text-align: left;
padding: 0.6rem 1rem;
background: var(--surface2);
color: var(--text-muted);
font-weight: 600;
font-size: 0.72rem;
text-transform: uppercase;
letter-spacing: 0.08em;
border-bottom: 1px solid var(--border);
}
.method-table td {
padding: 0.7rem 1rem;
border-bottom: 1px solid var(--border);
color: var(--text-dim);
vertical-align: top;
}
.method-table tr:last-child td { border-bottom: none; }
.method-table tr:hover td { background: rgba(255,255,255,0.02); }
.method-table code {
font-family: 'JetBrains Mono', monospace;
font-size: 0.78rem;
color: var(--func);
background: var(--surface2);
padding: 0.1rem 0.35rem;
border-radius: 3px;
}
/* ── Inline Code ── */
p code, li code, td code {
font-family: 'JetBrains Mono', monospace;
font-size: 0.82em;
color: var(--accent2);
background: rgba(56,189,248,0.08);
padding: 0.1rem 0.35rem;
border-radius: 3px;
}
/* ── Summary Banner ── */
.summary {
background: linear-gradient(135deg, rgba(74,222,128,0.05), rgba(56,189,248,0.05));
border: 1px solid var(--border);
border-radius: 10px;
padding: 1.2rem 1.5rem;
margin: 1.5rem 0;
}
.summary-title {
font-size: 0.72rem;
font-weight: 700;
text-transform: uppercase;
letter-spacing: 0.1em;
color: var(--accent);
margin-bottom: 0.6rem;
}
.summary ul {
list-style: none;
display: flex;
flex-wrap: wrap;
gap: 0.5rem;
}
.summary ul li {
font-family: 'JetBrains Mono', monospace;
font-size: 0.75rem;
color: var(--text-dim);
background: var(--surface2);
padding: 0.25rem 0.7rem;
border-radius: 20px;
border: 1px solid var(--border);
}
/* ── Comparison grid ── */
.compare-grid {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 1rem;
margin: 1.2rem 0;
}
.compare-card {
background: var(--surface2);
border: 1px solid var(--border);
border-radius: 8px;
overflow: hidden;
}
.compare-card-header {
padding: 0.5rem 1rem;
font-size: 0.72rem;
font-weight: 700;
letter-spacing: 0.08em;
text-transform: uppercase;
border-bottom: 1px solid var(--border);
}
.compare-card.good .compare-card-header { color: var(--accent); background: rgba(74,222,128,0.05); }
.compare-card.bad .compare-card-header { color: #f87171; background: rgba(248,113,113,0.05); }
.compare-card pre { padding: 0.9rem 1rem; font-size: 0.78rem; }
/* divider */
.divider {
border: none;
border-top: 1px solid var(--border);
margin: 2.5rem 0;
}
</style>
</head>
<body>
<div class="layout">
<!-- ── Sidebar Navigation ── -->
<nav class="sidebar">
<div class="sidebar-header">
<div class="sidebar-logo">
<span class="logo-badge">Py3</span>
<span class="sidebar-title">进阶复习手册</span>
</div>
<div class="sidebar-subtitle">数据结构 · 字符串处理</div>
</div>
<div class="nav-section">
<div class="nav-section-label">Chapter I — 数据结构与算法</div>
<a class="nav-item" href="#s1-1"><span class="nav-num">1.1</span>条件筛选数据</a>
<a class="nav-item" href="#s1-2"><span class="nav-num">1.2</span>元组元素命名</a>
<a class="nav-item" href="#s1-3"><span class="nav-num">1.3</span>字典按值排序</a>
<a class="nav-item" href="#s1-4"><span class="nav-num">1.4</span>统计元素频度</a>
<a class="nav-item" href="#s1-5"><span class="nav-num">1.5</span>多字典公共键</a>
<a class="nav-item" href="#s1-6"><span class="nav-num">1.6</span>字典保持有序</a>
<a class="nav-item" href="#s1-7"><span class="nav-num">1.7</span>历史记录功能</a>
</div>
<div class="nav-section">
<div class="nav-section-label">Chapter II — 字符串处理</div>
<a class="nav-item" href="#s2-1"><span class="nav-num">2.1</span>多分隔符拆分</a>
<a class="nav-item" href="#s2-2"><span class="nav-num">2.2</span>前缀/后缀判断</a>
<a class="nav-item" href="#s2-3"><span class="nav-num">2.3</span>文本格式调整</a>
<a class="nav-item" href="#s2-4"><span class="nav-num">2.4</span>字符串拼接</a>
<a class="nav-item" href="#s2-5"><span class="nav-num">2.5</span>对齐处理</a>
<a class="nav-item" href="#s2-6"><span class="nav-num">2.6</span>去除无用字符</a>
</div>
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<!-- ── Main Content ── -->
<main class="main">
<header class="page-header">
<div class="page-eyebrow">Python 3 · Senior Review</div>
<h1 class="page-title">数据结构 & 字符串<br>处理技巧精要</h1>
<div class="page-meta">
<span class="meta-chip"><span class="meta-dot"></span>Python 3.10+</span>
<span class="meta-chip"><span class="meta-dot" style="background:var(--accent2)"></span>13 个核心技巧</span>
<span class="meta-chip"><span class="meta-dot" style="background:var(--accent3)"></span>标准库 · 内置工具</span>
</div>
</header>
<!-- ═══════════════════════════════════════════════
CHAPTER I
════════════════════════════════════════════════ -->
<section class="chapter" id="chapter1">
<div class="chapter-header">
<span class="chapter-num">CHAPTER I</span>
<h2 class="chapter-title">数据结构与算法相关问题与解决技巧</h2>
</div>
<!-- 1.1 -->
<div class="section" id="s1-1">
<h3 class="section-title">
<span class="section-num">1.1</span>
如何在列表、字典、集合中根据条件筛选数据
</h3>
<p class="section-body">
Python 提供了三种核心筛选方式：<strong>列表/字典/集合推导式</strong>、内置函数 <code>filter()</code>
以及 <code>itertools.compress()</code>（配合掩码使用）。推导式是最 Pythonic 的方式，同时兼顾可读性与性能。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots">
<div class="code-dot dot-r"></div>
<div class="code-dot dot-y"></div>
<div class="code-dot dot-g"></div>
</div>
<span class="code-lang">python</span>
</div>
<pre><span class="cm"># ── 列表筛选 ──────────────────────────────────────</span>
data <span class="op">=</span> [<span class="nm">-3</span>, <span class="nm">5</span>, <span class="nm">-1</span>, <span class="nm">8</span>, <span class="nm">-2</span>, <span class="nm">6</span>, <span class="nm">0</span>]
<span class="cm"># 方式1: 列表推导式 (推荐)</span>
pos <span class="op">=</span> [x <span class="kw">for</span> x <span class="kw">in</span> data <span class="kw">if</span> x <span class="op">></span> <span class="nm">0</span>] <span class="cm"># [5, 8, 6]</span>
<span class="cm"># 方式2: filter() —— 返回惰性迭代器，大数据更省内存</span>
pos <span class="op">=</span> <span class="nb">list</span>(<span class="fn">filter</span>(<span class="kw">lambda</span> x<span class="op">:</span> x <span class="op">></span> <span class="nm">0</span>, data))
<span class="cm"># 方式3: itertools.compress (掩码筛选)</span>
<span class="kw">from</span> itertools <span class="kw">import</span> compress
mask <span class="op">=</span> [x <span class="op">></span> <span class="nm">0</span> <span class="kw">for</span> x <span class="kw">in</span> data]
pos <span class="op">=</span> <span class="nb">list</span>(<span class="fn">compress</span>(data, mask))
<span class="cm"># ── 字典筛选 ──────────────────────────────────────</span>
prices <span class="op">=</span> {<span class="st">'AAPL'</span>: <span class="nm">182</span>, <span class="st">'BABA'</span>: <span class="nm">78</span>, <span class="st">'TSLA'</span>: <span class="nm">245</span>, <span class="st">'NIO'</span>: <span class="nm">6</span>}
<span class="cm"># 字典推导式: 筛选价格 > 100 的股票</span>
expensive <span class="op">=</span> {k<span class="op">:</span> v <span class="kw">for</span> k, v <span class="kw">in</span> prices.<span class="fn">items</span>() <span class="kw">if</span> v <span class="op">></span> <span class="nm">100</span>}
<span class="cm"># {'AAPL': 182, 'TSLA': 245}</span>
<span class="cm"># ── 集合筛选 ──────────────────────────────────────</span>
nums <span class="op">=</span> {<span class="nm">1</span>, <span class="nm">2</span>, <span class="nm">3</span>, <span class="nm">4</span>, <span class="nm">5</span>, <span class="nm">6</span>}
evens <span class="op">=</span> {x <span class="kw">for</span> x <span class="kw">in</span> nums <span class="kw">if</span> x <span class="op">%</span> <span class="nm">2</span> <span class="op">==</span> <span class="nm">0</span>} <span class="cm"># {2, 4, 6}</span></pre>
</div>
<div class="info-box info-tip">
<div class="box-label">💡 性能建议</div>
数据量巨大时，优先用 <code>filter()</code> 或生成器表达式（<code>(x for x in data if ...)</code>），避免一次性构建完整列表占用内存。
</div>
</div>
<!-- 1.2 -->
<div class="section" id="s1-2">
<h3 class="section-title">
<span class="section-num">1.2</span>
如何为元组中的每个元素命名，提高程序可读性
</h3>
<p class="section-body">
原始元组用下标访问（<code>t[0]</code>、<code>t[2]</code>）可读性极差。有两种命名方案：
<strong>collections.namedtuple</strong>（轻量，兼容元组所有操作）和
<strong>typing.NamedTuple</strong>（支持类型注解，更现代）。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre><span class="kw">from</span> collections <span class="kw">import</span> namedtuple
<span class="kw">from</span> typing <span class="kw">import</span> NamedTuple
<span class="cm"># ── 方式1: namedtuple (经典) ───────────────────────</span>
Student <span class="op">=</span> <span class="fn">namedtuple</span>(<span class="st">'Student'</span>, [<span class="st">'name'</span>, <span class="st">'age'</span>, <span class="st">'score'</span>])
s <span class="op">=</span> <span class="cls">Student</span>(<span class="st">'Alice'</span>, <span class="nm">22</span>, <span class="nm">95.5</span>)
<span class="cm"># 通过名称访问，清晰易读</span>
<span class="nb">print</span>(s.name, s.score) <span class="cm"># Alice 95.5</span>
<span class="cm"># 仍然兼容元组下标和解包</span>
name, age, score <span class="op">=</span> s
<span class="nb">print</span>(s[<span class="nm">0</span>]) <span class="cm"># Alice</span>
<span class="cm"># _replace 返回新实例（namedtuple 不可变）</span>
s2 <span class="op">=</span> s.<span class="fn">_replace</span>(score<span class="op">=</span><span class="nm">98.0</span>)
<span class="cm"># ── 方式2: typing.NamedTuple (推荐，支持类型注解) ──</span>
<span class="kw">class</span> <span class="cls">Trade</span>(<span class="cls">NamedTuple</span>):
symbol<span class="op">:</span> <span class="nb">str</span>
price<span class="op">:</span> <span class="nb">float</span>
volume<span class="op">:</span> <span class="nb">int</span>
direction<span class="op">:</span> <span class="nb">str</span> <span class="op">=</span> <span class="st">'BUY'</span> <span class="cm"># 支持默认值</span>
t <span class="op">=</span> <span class="cls">Trade</span>(<span class="st">'600519'</span>, <span class="nm">1680.0</span>, <span class="nm">100</span>)
<span class="nb">print</span>(t.symbol, t.direction) <span class="cm"># 600519 BUY</span>
<span class="nb">print</span>(t.<span class="fn">_asdict</span>()) <span class="cm"># 转 dict</span></pre>
</div>
<div class="info-box info-note">
<div class="box-label">📝 Note</div>
<code>NamedTuple</code> 是 <code>namedtuple</code> 的类语法糖，两者底层相同。如需可变字段，考虑 <code>dataclasses.dataclass</code>。
</div>
</div>
<!-- 1.3 -->
<div class="section" id="s1-3">
<h3 class="section-title">
<span class="section-num">1.3</span>
如何根据字典中值的大小，对字典中的项排序
</h3>
<p class="section-body">
字典本身无序排序概念（Python 3.7+ 插入有序）。对值排序的核心是 <code>sorted()</code>
配合 <code>key</code> 参数，常用 <code>operator.itemgetter</code> 或 <code>lambda</code>。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre><span class="kw">from</span> operator <span class="kw">import</span> itemgetter
scores <span class="op">=</span> {<span class="st">'Alice'</span>: <span class="nm">88</span>, <span class="st">'Bob'</span>: <span class="nm">73</span>, <span class="st">'Charlie'</span>: <span class="nm">95</span>, <span class="st">'Diana'</span>: <span class="nm">81</span>}
<span class="cm"># ── 按值升序 ───────────────────────────────────────</span>
asc <span class="op">=</span> <span class="fn">sorted</span>(scores.<span class="fn">items</span>(), key<span class="op">=</span><span class="fn">itemgetter</span>(<span class="nm">1</span>))
<span class="cm"># [('Bob', 73), ('Diana', 81), ('Alice', 88), ('Charlie', 95)]</span>
<span class="cm"># ── 按值降序 ───────────────────────────────────────</span>
desc <span class="op">=</span> <span class="fn">sorted</span>(scores.<span class="fn">items</span>(), key<span class="op">=</span><span class="fn">itemgetter</span>(<span class="nm">1</span>), reverse<span class="op">=</span><span class="kw">True</span>)
<span class="cm"># ── lambda 等价写法 ────────────────────────────────</span>
desc2 <span class="op">=</span> <span class="fn">sorted</span>(scores.<span class="fn">items</span>(), key<span class="op">=</span><span class="kw">lambda</span> kv<span class="op">:</span> kv[<span class="nm">1</span>], reverse<span class="op">=</span><span class="kw">True</span>)
<span class="cm"># ── 只取 Top-N：用 heapq 更高效 ────────────────────</span>
<span class="kw">import</span> heapq
top3 <span class="op">=</span> heapq.<span class="fn">nlargest</span>(<span class="nm">3</span>, scores.<span class="fn">items</span>(), key<span class="op">=</span><span class="fn">itemgetter</span>(<span class="nm">1</span>))
<span class="cm"># [('Charlie', 95), ('Alice', 88), ('Diana', 81)]</span>
<span class="cm"># ── 多字段排序：先按值降序，值相同则按键升序 ─────────</span>
multi <span class="op">=</span> <span class="fn">sorted</span>(scores.<span class="fn">items</span>(), key<span class="op">=</span><span class="kw">lambda</span> kv<span class="op">:</span> (<span class="op">-</span>kv[<span class="nm">1</span>], kv[<span class="nm">0</span>]))</pre>
</div>
<div class="info-box info-tip">
<div class="box-label">💡 性能对比</div>
<code>itemgetter</code> 是 C 实现，比等效的 <code>lambda</code> 快约 30%；<code>heapq.nlargest/nsmallest</code> 在 n 远小于数据量时，时间复杂度优于全量排序。
</div>
</div>
<!-- 1.4 -->
<div class="section" id="s1-4">
<h3 class="section-title">
<span class="section-num">1.4</span>
如何统计序列中元素的频度
</h3>
<p class="section-body">
<code>collections.Counter</code> 是专为频度统计设计的子类，支持加减运算、
<code>most_common(n)</code> 等实用方法，是最优解。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre><span class="kw">from</span> collections <span class="kw">import</span> Counter
words <span class="op">=</span> [<span class="st">'apple'</span>, <span class="st">'banana'</span>, <span class="st">'apple'</span>, <span class="st">'cherry'</span>, <span class="st">'banana'</span>, <span class="st">'apple'</span>]
<span class="cm"># ── 基本用法 ───────────────────────────────────────</span>
c <span class="op">=</span> <span class="cls">Counter</span>(words)
<span class="nb">print</span>(c) <span class="cm"># Counter({'apple': 3, 'banana': 2, 'cherry': 1})</span>
<span class="nb">print</span>(c[<span class="st">'apple'</span>]) <span class="cm"># 3 (不存在的 key 返回 0 而非 KeyError)</span>
<span class="cm"># ── Top-N 最高频 ───────────────────────────────────</span>
<span class="nb">print</span>(c.<span class="fn">most_common</span>(<span class="nm">2</span>)) <span class="cm"># [('apple', 3), ('banana', 2)]</span>
<span class="cm"># ── 统计字符串字符频度 ─────────────────────────────</span>
char_freq <span class="op">=</span> <span class="cls">Counter</span>(<span class="st">"programming"</span>)
<span class="cm"># ── Counter 支持集合运算 ───────────────────────────</span>
c1 <span class="op">=</span> <span class="cls">Counter</span>(<span class="st">'aababc'</span>)
c2 <span class="op">=</span> <span class="cls">Counter</span>(<span class="st">'abc'</span>)
combined <span class="op">=</span> c1 <span class="op">+</span> c2 <span class="cm"># 合并计数</span>
diff <span class="op">=</span> c1 <span class="op">-</span> c2 <span class="cm"># 差集计数 (只保留正数)</span>
inter <span class="op">=</span> c1 <span class="op">&</span> c2 <span class="cm"># 取最小 (交集)</span>
union <span class="op">=</span> c1 <span class="op">|</span> c2 <span class="cm"># 取最大 (并集)</span>
<span class="cm"># ── 统计词频 + 按频排名（工程常用）──────────────────</span>
text <span class="op">=</span> <span class="st">"the cat sat on the mat the cat"</span>
word_freq <span class="op">=</span> <span class="cls">Counter</span>(text.<span class="fn">split</span>())
<span class="kw">for</span> word, freq <span class="kw">in</span> word_freq.<span class="fn">most_common</span>():
<span class="nb">print</span>(<span class="st">f"</span>{word<span class="op">:</span><span class="nm">10</span>}<span class="st"> </span>{<span class="st">'█'</span> <span class="op">*</span> freq<span class="st">}"</span>)</pre>
</div>
<div class="summary">
<div class="summary-title">Counter 常用 API</div>
<ul>
<li><code>most_common(n)</code></li>
<li><code>elements()</code></li>
<li><code>update(iterable)</code></li>
<li><code>subtract(iterable)</code></li>
<li><code>total()</code> <small style="color:var(--text-muted)">3.10+</small></li>
</ul>
</div>
</div>
<!-- 1.5 -->
<div class="section" id="s1-5">
<h3 class="section-title">
<span class="section-num">1.5</span>
如何快速找到多个字典中的公共键 (key)
</h3>
<p class="section-body">
字典的 <code>.keys()</code> 返回 <strong>视图对象</strong>，支持集合运算（交集 <code>&</code>、并集 <code>|</code>、差集 <code>-</code>）。
利用这一特性，可以用一行代码完成多字典公共键查找。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre>d1 <span class="op">=</span> {<span class="st">'a'</span><span class="op">:</span> <span class="nm">1</span>, <span class="st">'b'</span><span class="op">:</span> <span class="nm">2</span>, <span class="st">'c'</span><span class="op">:</span> <span class="nm">3</span>}
d2 <span class="op">=</span> {<span class="st">'b'</span><span class="op">:</span> <span class="nm">4</span>, <span class="st">'c'</span><span class="op">:</span> <span class="nm">5</span>, <span class="st">'d'</span><span class="op">:</span> <span class="nm">6</span>}
d3 <span class="op">=</span> {<span class="st">'c'</span><span class="op">:</span> <span class="nm">7</span>, <span class="st">'d'</span><span class="op">:</span> <span class="nm">8</span>, <span class="st">'e'</span><span class="op">:</span> <span class="nm">9</span>}
<span class="cm"># ── 两个字典 ───────────────────────────────────────</span>
common_2 <span class="op">=</span> d1.<span class="fn">keys</span>() <span class="op">&</span> d2.<span class="fn">keys</span>() <span class="cm"># {'b', 'c'}</span>
<span class="cm"># ── 多个字典: reduce + & ───────────────────────────</span>
<span class="kw">from</span> functools <span class="kw">import</span> reduce
dicts <span class="op">=</span> [d1, d2, d3]
common_all <span class="op">=</span> <span class="fn">reduce</span>(<span class="kw">lambda</span> a, b<span class="op">:</span> a <span class="op">&</span> b, (d.<span class="fn">keys</span>() <span class="kw">for</span> d <span class="kw">in</span> dicts))
<span class="cm"># {'c'}</span>
<span class="cm"># ── 等价写法: map + set.intersection ──────────────</span>
common_all2 <span class="op">=</span> <span class="fn">set</span>.<span class="fn">intersection</span>(<span class="op">*</span><span class="fn">map</span>(<span class="fn">set</span>, [d.<span class="fn">keys</span>() <span class="kw">for</span> d <span class="kw">in</span> dicts]))
<span class="cm"># ── 找公共键并取各字典对应的值 ─────────────────────</span>
<span class="kw">for</span> key <span class="kw">in</span> common_all:
values <span class="op">=</span> [d[key] <span class="kw">for</span> d <span class="kw">in</span> dicts <span class="kw">if</span> key <span class="kw">in</span> d]
<span class="nb">print</span>(<span class="st">f"key=</span>{key}<span class="st">, values=</span>{values}<span class="st">"</span>) <span class="cm"># key=c, values=[3, 5, 7]</span></pre>
</div>
<div class="info-box info-note">
<div class="box-label">📝 原理</div>
<code>dict.keys()</code> 返回 <code>dict_keys</code> 视图，实现了集合协议（<code>__and__</code>、<code>__or__</code> 等），无需转换 <code>set</code> 即可直接做集合运算，且是动态视图，字典变更后视图自动更新。
</div>
</div>
<!-- 1.6 -->
<div class="section" id="s1-6">
<h3 class="section-title">
<span class="section-num">1.6</span>
如何让字典保持有序
</h3>
<p class="section-body">
Python 3.7+ 中普通 <code>dict</code> 已按<strong>插入顺序</strong>有序。但若需要
<strong>访问顺序</strong>排序（LRU 语义）或需要兼容旧代码，使用
<code>collections.OrderedDict</code>。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre><span class="kw">from</span> collections <span class="kw">import</span> OrderedDict
<span class="cm"># ── Python 3.7+ dict 已按插入顺序有序 ────────────</span>
d <span class="op">=</span> {}
d[<span class="st">'first'</span>] <span class="op">=</span> <span class="nm">1</span>
d[<span class="st">'second'</span>] <span class="op">=</span> <span class="nm">2</span>
d[<span class="st">'third'</span>] <span class="op">=</span> <span class="nm">3</span>
<span class="nb">list</span>(d) <span class="cm"># ['first', 'second', 'third'] ✓</span>
<span class="cm"># ── OrderedDict 的独特能力: move_to_end ───────────</span>
od <span class="op">=</span> <span class="cls">OrderedDict</span>([(<span class="st">'a'</span>, <span class="nm">1</span>), (<span class="st">'b'</span>, <span class="nm">2</span>), (<span class="st">'c'</span>, <span class="nm">3</span>)])
od.<span class="fn">move_to_end</span>(<span class="st">'a'</span>) <span class="cm"># 'a' 移到末尾: b, c, a</span>
od.<span class="fn">move_to_end</span>(<span class="st">'c'</span>, last<span class="op">=</span><span class="kw">False</span>) <span class="cm"># 'c' 移到首位: c, b, a</span>
od.<span class="fn">popitem</span>(last<span class="op">=</span><span class="kw">False</span>) <span class="cm"># 弹出首项 (FIFO)</span>
od.<span class="fn">popitem</span>(last<span class="op">=</span><span class="kw">True</span>) <span class="cm"># 弹出末项 (LIFO/Stack)</span>
<span class="cm"># ── OrderedDict 等值比较关注顺序 ──────────────────</span>
od1 <span class="op">=</span> <span class="cls">OrderedDict</span>([(<span class="st">'a'</span>, <span class="nm">1</span>), (<span class="st">'b'</span>, <span class="nm">2</span>)])
od2 <span class="op">=</span> <span class="cls">OrderedDict</span>([(<span class="st">'b'</span>, <span class="nm">2</span>), (<span class="st">'a'</span>, <span class="nm">1</span>)])
od1 <span class="op">==</span> od2 <span class="cm"># False (dict == dict 则 True)</span>
<span class="cm"># ── 实现 LRU Cache 概念 (手动版) ──────────────────</span>
<span class="kw">class</span> <span class="cls">LRUCache</span>:
<span class="kw">def</span> <span class="fn">__init__</span>(<span class="var">self</span>, capacity<span class="op">:</span> <span class="nb">int</span>):
<span class="var">self</span>.cache <span class="op">=</span> <span class="cls">OrderedDict</span>()
<span class="var">self</span>.capacity <span class="op">=</span> capacity
<span class="kw">def</span> <span class="fn">get</span>(<span class="var">self</span>, key):
<span class="kw">if</span> key <span class="kw">not in</span> <span class="var">self</span>.cache: <span class="kw">return</span> <span class="op">-</span><span class="nm">1</span>
<span class="var">self</span>.cache.<span class="fn">move_to_end</span>(key) <span class="cm"># 标记为最近使用</span>
<span class="kw">return</span> <span class="var">self</span>.cache[key]
<span class="kw">def</span> <span class="fn">put</span>(<span class="var">self</span>, key, value):
<span class="var">self</span>.cache[key] <span class="op">=</span> value
<span class="var">self</span>.cache.<span class="fn">move_to_end</span>(key)
<span class="kw">if</span> <span class="fn">len</span>(<span class="var">self</span>.cache) <span class="op">></span> <span class="var">self</span>.capacity:
<span class="var">self</span>.cache.<span class="fn">popitem</span>(last<span class="op">=</span><span class="kw">False</span>) <span class="cm"># 淘汰最旧</span></pre>
</div>
<div class="info-box info-warn">
<div class="box-label">⚠️ 注意</div>
Python 3.7+ 的普通 <code>dict</code> 在大多数场景已足够，<code>OrderedDict</code> 的主要使用场景是：需要 <code>move_to_end</code>、顺序敏感的相等比较、或需要 <code>popitem(last=False)</code> 实现队列语义。
</div>
</div>
<!-- 1.7 -->
<div class="section" id="s1-7">
<h3 class="section-title">
<span class="section-num">1.7</span>
如何实现用户的历史记录功能 (最多 n 条)
</h3>
<p class="section-body">
历史记录需要<strong>固定长度的 FIFO 队列</strong>——<code>collections.deque(maxlen=n)</code>
是最优数据结构，超出容量时自动淘汰最旧的记录，时间复杂度 O(1)。
</p>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre><span class="kw">from</span> collections <span class="kw">import</span> deque
<span class="kw">from</span> dataclasses <span class="kw">import</span> dataclass, field
<span class="kw">from</span> datetime <span class="kw">import</span> datetime
<span class="cm"># ── 基础用法 ───────────────────────────────────────</span>
history <span class="op">=</span> <span class="fn">deque</span>(maxlen<span class="op">=</span><span class="nm">5</span>)
<span class="kw">for</span> cmd <span class="kw">in</span> [<span class="st">'ls'</span>, <span class="st">'cd /home'</span>, <span class="st">'pwd'</span>, <span class="st">'cat a.txt'</span>, <span class="st">'vim b.py'</span>, <span class="st">'git log'</span>]:
history.<span class="fn">appendleft</span>(cmd) <span class="cm"># 新记录插入头部</span>
<span class="nb">print</span>(<span class="nb">list</span>(history))
<span class="cm"># ['git log', 'vim b.py', 'cat a.txt', 'pwd', 'cd /home'] (ls 已被淘汰)</span>
<span class="cm"># ── 封装为历史记录类 ───────────────────────────────</span>
<span class="kw">class</span> <span class="cls">SearchHistory</span>:
<span class="kw">def</span> <span class="fn">__init__</span>(<span class="var">self</span>, max_size<span class="op">:</span> <span class="nb">int</span> <span class="op">=</span> <span class="nm">10</span>):
<span class="var">self</span>._history: deque <span class="op">=</span> <span class="fn">deque</span>(maxlen<span class="op">=</span>max_size)
<span class="kw">def</span> <span class="fn">add</span>(<span class="var">self</span>, query: <span class="nb">str</span>) <span class="op">-></span> <span class="kw">None</span>:
<span class="cm"># 去重：如果已存在则移到最前</span>
<span class="kw">if</span> query <span class="kw">in</span> <span class="var">self</span>._history:
<span class="var">self</span>._history.<span class="fn">remove</span>(query)
<span class="var">self</span>._history.<span class="fn">appendleft</span>(query)
<span class="kw">def</span> <span class="fn">clear</span>(<span class="var">self</span>) <span class="op">-></span> <span class="kw">None</span>:
<span class="var">self</span>._history.<span class="fn">clear</span>()
<span class="kw">def</span> <span class="fn">__iter__</span>(<span class="var">self</span>):
<span class="kw">return</span> <span class="fn">iter</span>(<span class="var">self</span>._history)
<span class="kw">def</span> <span class="fn">__len__</span>(<span class="var">self</span>):
<span class="kw">return</span> <span class="fn">len</span>(<span class="var">self</span>._history)
<span class="cm"># 使用</span>
sh <span class="op">=</span> <span class="cls">SearchHistory</span>(max_size<span class="op">=</span><span class="nm">3</span>)
sh.<span class="fn">add</span>(<span class="st">"RAG pipeline"</span>)
sh.<span class="fn">add</span>(<span class="st">"LangChain"</span>)
sh.<span class="fn">add</span>(<span class="st">"vector db"</span>)
sh.<span class="fn">add</span>(<span class="st">"RAG pipeline"</span>) <span class="cm"># 重复，移到最前</span>
<span class="nb">print</span>(<span class="nb">list</span>(sh))
<span class="cm"># ['RAG pipeline', 'vector db', 'LangChain']</span></pre>
</div>
<div class="info-box info-tip">
<div class="box-label">💡 deque vs list</div>
<code>list</code> 头部插入/删除是 O(n)；<code>deque</code> 两端操作均为 O(1)，<code>maxlen</code> 参数让容量管理变成零代码。
</div>
</div>
</section>
<hr class="divider">
<!-- ═══════════════════════════════════════════════
CHAPTER II
════════════════════════════════════════════════ -->
<section class="chapter" id="chapter2">
<div class="chapter-header">
<span class="chapter-num" style="color:var(--accent2);background:rgba(56,189,248,0.08);border-color:rgba(56,189,248,0.2)">CHAPTER II</span>
<h2 class="chapter-title">复杂场景下字符串处理相关问题与解决技巧</h2>
</div>
<!-- 2.1 -->
<div class="section" id="s2-1">
<h3 class="section-title">
<span class="section-num">2.1</span>
如何拆分含有多种分隔符的字符串
</h3>
<p class="section-body">
<code>str.split()</code> 只支持单一分隔符。处理多种分隔符（如 CSV 变体、日志、自由文本）
应使用 <code>re.split()</code>，支持正则表达式模式，灵活且高效。
</p>
<div class="compare-grid">
<div class="compare-card bad">
<div class="compare-card-header">✗ 局限写法</div>
<pre><span class="cm"># 只能处理单一分隔符</span>
s <span class="op">=</span> <span class="st">"a,b;c|d e"</span>
s.<span class="fn">split</span>(<span class="st">','</span>)
<span class="cm"># ['a', 'b;c|d e'] ✗</span></pre>
</div>
<div class="compare-card good">
<div class="compare-card-header">✓ 推荐写法</div>
<pre><span class="kw">import</span> re
<span class="fn">re.split</span>(<span class="st">r'[,;|\s]+'</span>, s)
<span class="cm"># ['a', 'b', 'c', 'd', 'e'] ✓</span></pre>
</div>
</div>
<div class="code-wrap">
<div class="code-header">
<div class="code-dots"><div class="code-dot dot-r"></div><div class="code-dot dot-y"></div><div class="code-dot dot-g"></div></div>
<span class="code-lang">python</span>
</div>
<pre><span class="kw">import</span> re
s <span class="op">=</span> <span class="st">"one,two;three|four five\tsix"</span>
<span class="cm"># 以逗号、分号、竖线、空白字符为分隔符</span>
result <span class="op">=</span> re.<span class="fn">split</span>(<span class="st">r'[,;|\s]+'</span>, s)
<span class="cm"># ['one', 'two', 'three', 'four', 'five', 'six']</span>
<span class="cm"># ── 保留分隔符（用捕获组）────────────────────────</span>
result2 <span class="op">=</span> re.<span class="fn">split</span>(<span class="st">r'([,;|])'</span>, <span class="st">"a,b;c"</span>)
<span class="cm"># ['a', ',', 'b', ';', 'c']</span>
<span class="cm"># ── 预编译正则: 同一模式多次使用时提升性能 ─────────</span>
SEPARATOR <span class="op">=</span> re.<span class="fn">compile</span>(<span class="st">r'[,;|\s]+'</span>)
lines <span class="op">=</span> [<span class="st">"a,b,c"</span>, <span class="st">"d;e;f"</span>, <span class="st">"g|h|i"</span>]
parsed <span class="op">=</span> [SEPARATOR.<span class="fn">split</span>(line) <span class="kw">for</span> line <span class="kw">in</span> lines]
<span class="cm"># ── 过滤空字符串 (首尾分隔符可能产生空串) ──────────</span>
raw <span class="op">=</span> <span class="st">",a,,b,c,"</span>
clean <span class="op">=</span> [x <span class="kw">for</span> x <span class="kw">in</span> re.<span class="fn">split</span>(<span class="st">r','</span>, raw) <span class="kw">if</span> x]
<span class="cm"># ['a', 'b', 'c']</span></pre>
</div>
</div>
<!-- 2.2 -->
<div class="section" id="s2-2">
<h3 class="section-title">
<span class="section-num">2.2</span>