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<a href="/2019/07/17/fluent-python/chapter3-dictionaries-and-sets/" class="post-title-link" itemprop="url">3. Dictionaries and Sets</a>
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<h1 id="字典和集合"><a href="#字典和集合" class="headerlink" title="字典和集合"></a>字典和集合</h1><p>本章内容大纲：</p>
<ul>
<li>常见的字典方法</li>
<li>如何处理查找不到的键</li>
<li>标准库中dict类型的变种</li>
<li>set和frozenset类型</li>
<li>散列表的工作原理</li>
<li>散列表带来的潜在影响</li>
</ul>
<h2 id="3-1-泛映射类型"><a href="#3-1-泛映射类型" class="headerlink" title="3.1 泛映射类型"></a>3.1 泛映射类型</h2><p>collections.abc模块中有Mapping和MutableMapping抽象基类，用于dict和其他类dict类型定义形式接口</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> abc</span><br><span class="line">my_dict={}</span><br><span class="line">isinstance(my_dict, abc.Mapping)</span><br></pre></td></tr></table></figure>
<pre><code>True</code></pre><h2 id="3-2-字典推导"><a href="#3-2-字典推导" class="headerlink" title="3.2 字典推导"></a>3.2 字典推导</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 字典推导的应用</span></span><br><span class="line">DIAL_CODES=[</span><br><span class="line"> (<span class="number">86</span>, <span class="string">'China'</span>),</span><br><span class="line"> (<span class="number">91</span>, <span class="string">'India'</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="string">'United States'</span>)</span><br><span class="line">]</span><br><span class="line">country_code={country:code <span class="keyword">for</span> code, country <span class="keyword">in</span> DIAL_CODES}</span><br><span class="line">country_code</span><br></pre></td></tr></table></figure>
<pre><code>{'China': 86, 'India': 91, 'United States': 1}</code></pre><h2 id="3-3-常见的映射方法"><a href="#3-3-常见的映射方法" class="headerlink" title="3.3 常见的映射方法"></a>3.3 常见的映射方法</h2><p>用setdefault处理找不到的键</p>
<p>A: <code>my_dict.setdefault(key, []).append(new_value)</code></p>
<p>B:</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">if key not in my_dict:</span><br><span class="line"> my_dict[key]=[]</span><br><span class="line">my_dict[key].append(new_value)</span><br></pre></td></tr></table></figure>
<p>二者的效果是一样的，只是B至少进行了两次键查询，如果键不存在则需要三次，而A只需要一次就可以完成整个操作</p>
<h2 id="3-4-映射的弹性键查询"><a href="#3-4-映射的弹性键查询" class="headerlink" title="3.4 映射的弹性键查询"></a>3.4 映射的弹性键查询</h2><h3 id="3-4-1-defaultdict-处理找不到的键的一个选择"><a href="#3-4-1-defaultdict-处理找不到的键的一个选择" class="headerlink" title="3.4.1 defaultdict: 处理找不到的键的一个选择"></a>3.4.1 defaultdict: 处理找不到的键的一个选择</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> defaultdict</span><br><span class="line">s = [(<span class="string">'yellow'</span>, <span class="number">1</span>), (<span class="string">'blue'</span>, <span class="number">2</span>), (<span class="string">'yellow'</span>, <span class="number">3</span>), (<span class="string">'blue'</span>, <span class="number">4</span>), (<span class="string">'red'</span>, <span class="number">1</span>)]</span><br><span class="line">d = defaultdict(list)</span><br><span class="line"><span class="keyword">for</span> k,v <span class="keyword">in</span> s:</span><br><span class="line"> d[k].append(v)</span><br><span class="line">d</span><br></pre></td></tr></table></figure>
<pre><code>defaultdict(list, {'yellow': [1, 3], 'blue': [2, 4], 'red': [1]})</code></pre><p>defaultdict的default_factory(即list)只会在<code>__getitem__</code>里被调用，其他方法中不会发挥作用
d[k]: 调用default_factory创造默认值
d.get(k)则返回None</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">d.get(<span class="string">'black'</span>)</span><br></pre></td></tr></table></figure>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">d[<span class="string">'black'</span>]</span><br></pre></td></tr></table></figure>
<pre><code>[]</code></pre><h3 id="3-4-2-特殊方法-missing"><a href="#3-4-2-特殊方法-missing" class="headerlink" title="3.4.2 特殊方法__missing__"></a>3.4.2 特殊方法<code>__missing__</code></h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line"><span class="class"><span class="keyword">class</span> <span class="title">StrKeyDict0</span><span class="params">(dict)</span>:</span></span><br><span class="line"> </span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">__missing__</span><span class="params">(self, key)</span>:</span></span><br><span class="line"> <span class="comment"># isinstance判断是必需的，否则引起无限递归</span></span><br><span class="line"> <span class="keyword">if</span> isinstance(key, str):</span><br><span class="line"> <span class="keyword">raise</span> KeyError(key)</span><br><span class="line"> <span class="keyword">return</span> self[str(key)]</span><br><span class="line"> </span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">get</span><span class="params">(self, key, default=None)</span>:</span></span><br><span class="line"> <span class="keyword">try</span>:</span><br><span class="line"> <span class="keyword">return</span> self[key]</span><br><span class="line"> <span class="keyword">except</span> KeyError:</span><br><span class="line"> <span class="keyword">return</span> default</span><br><span class="line"> </span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">__contains__</span><span class="params">(self, key)</span>:</span></span><br><span class="line"> <span class="comment"># 这里不能使用key in self来检查键是否存在，会导致__contains__被递归调用</span></span><br><span class="line"> <span class="keyword">return</span> key <span class="keyword">in</span> self.keys() <span class="keyword">or</span> str(key) <span class="keyword">in</span> self.keys()</span><br></pre></td></tr></table></figure>
<h2 id="3-5-字典的变种"><a href="#3-5-字典的变种" class="headerlink" title="3.5 字典的变种"></a>3.5 字典的变种</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># collections.OrderedDict</span></span><br><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> OrderedDict</span><br><span class="line">d = OrderedDict.fromkeys(<span class="string">'abcde'</span>)</span><br><span class="line">d.keys()</span><br></pre></td></tr></table></figure>
<pre><code>odict_keys(['a', 'b', 'c', 'd', 'e'])</code></pre><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># collections.ChainMap</span></span><br><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> ChainMap</span><br><span class="line">baseline = {<span class="string">'music'</span>: <span class="string">'bach'</span>, <span class="string">'art'</span>: <span class="string">'rembrandt'</span>}</span><br><span class="line">adjustments = {<span class="string">'art'</span>: <span class="string">'van gogh'</span>, <span class="string">'opera'</span>: <span class="string">'carmen'</span>}</span><br><span class="line">list(ChainMap(adjustments, baseline))</span><br></pre></td></tr></table></figure>
<pre><code>['music', 'art', 'opera']</code></pre><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># collections.Counter</span></span><br><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> Counter</span><br><span class="line">ct=Counter(<span class="string">'abracadabra'</span>)</span><br><span class="line">ct</span><br></pre></td></tr></table></figure>
<pre><code>Counter({'a': 5, 'b': 2, 'r': 2, 'c': 1, 'd': 1})</code></pre><h2 id="3-6-子类化UserDict"><a href="#3-6-子类化UserDict" class="headerlink" title="3.6 子类化UserDict"></a>3.6 子类化UserDict</h2><p>与OrderedDict, ChainMap, Counter开箱即用类型不同，UserDict是让用户继承写子类</p>
<p>与dict继承相比，使用UserDict继承更加合适，因为dict继承在某些方法走一些捷径，导致在子类中不得不重写这些方法，UserDict就不会有这种问题</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> collections</span><br><span class="line"></span><br><span class="line"><span class="class"><span class="keyword">class</span> <span class="title">StrKeyDict</span><span class="params">(collections.UserDict)</span>:</span></span><br><span class="line"> </span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">__missing__</span><span class="params">(self, key)</span>:</span></span><br><span class="line"> <span class="keyword">if</span> isinstance(key, str):</span><br><span class="line"> <span class="keyword">raise</span> KeyError(key)</span><br><span class="line"> <span class="keyword">return</span> self[str(key)]</span><br><span class="line"> </span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">__contains__</span><span class="params">(self, key)</span>:</span></span><br><span class="line"> <span class="keyword">return</span> str(key) <span class="keyword">in</span> self.data</span><br><span class="line"> </span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">__setitem__</span><span class="params">(self, key, item)</span>:</span></span><br><span class="line"> self.data[str(key)]=item</span><br></pre></td></tr></table></figure>
<p>在这个类中没有定义3.4.2中的get方法</p>
<p>UserDict–>MutableMapping–>Mapping</p>
<p><a href="https://hg.python.org/cpython/file/3.4/Lib/_collections_abc.py#l422" target="_blank" rel="noopener">Mapping.get</a>: 与3.4.2中定义的get方法一致</p>
<h2 id="3-7-不可变映射类型"><a href="#3-7-不可变映射类型" class="headerlink" title="3.7 不可变映射类型"></a>3.7 不可变映射类型</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 用types.MappingProxyType来获取字典的只读实例mappingproxy</span></span><br><span class="line"><span class="keyword">from</span> types <span class="keyword">import</span> MappingProxyType</span><br><span class="line">d={<span class="number">1</span>:<span class="string">'A'</span>}</span><br><span class="line">d_proxy=MappingProxyType(d)</span><br><span class="line">d_proxy</span><br></pre></td></tr></table></figure>
<pre><code>mappingproxy({1: 'A'})</code></pre><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">d_proxy[<span class="number">1</span>]</span><br></pre></td></tr></table></figure>
<pre><code>'A'</code></pre><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">d_proxy[<span class="number">2</span>]=<span class="string">'x'</span></span><br></pre></td></tr></table></figure>
<pre><code>---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-31-cb84dba8e1f4> in <module>()
----> 1 d_proxy[2]='x'
TypeError: 'mappingproxy' object does not support item assignment</code></pre><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">d[<span class="number">2</span>]=<span class="string">'B'</span></span><br><span class="line">d_proxy</span><br></pre></td></tr></table></figure>
<pre><code>mappingproxy({1: 'A', 2: 'B'})</code></pre><p>设计时，把d_proxy作为公共属性暴露给api用户，这样就不能修改d这个映射了</p>
<h2 id="3-8-集合论"><a href="#3-8-集合论" class="headerlink" title="3.8 集合论"></a>3.8 集合论</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 集合唯一性</span></span><br><span class="line">l = [<span class="string">'spam'</span>, <span class="string">'spam'</span>, <span class="string">'eggs'</span>, <span class="string">'spam'</span>]</span><br><span class="line">set(l)</span><br></pre></td></tr></table></figure>
<pre><code>{'eggs', 'spam'}</code></pre><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 集合实现了很多基础中缀运算符: a|b, a&b, a-b</span></span><br><span class="line"><span class="comment"># needles 的元素在 haystack 里出现的次数，两个变量都是 set 类型</span></span><br><span class="line"><span class="comment"># found=len(needles & haystack)</span></span><br></pre></td></tr></table></figure>
<h3 id="3-8-1-集合字面量"><a href="#3-8-1-集合字面量" class="headerlink" title="3.8.1 集合字面量"></a>3.8.1 集合字面量</h3><p>set:
如果是空集，必须写成set()形式
其他的类似{1}, {1,2}等</p>
<p><code>{1,2,3} vs set([1,2,3])</code>
后者速度慢，因为后者先从set查询构造方法，然后新建列表，最后传入构造方法中。而前者，python会使用专门的BUILD_SET字节码来创建集合</p>
<p>frozenset:只能采用构造方法，因为没有提供特殊字面量语法</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">frozenset(range(<span class="number">10</span>))</span><br></pre></td></tr></table></figure>
<pre><code>frozenset({0, 1, 2, 3, 4, 5, 6, 7, 8, 9})</code></pre><h3 id="3-8-2-集合推导"><a href="#3-8-2-集合推导" class="headerlink" title="3.8.2 集合推导"></a>3.8.2 集合推导</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> unicodedata <span class="keyword">import</span> name</span><br><span class="line">{chr(i) <span class="keyword">for</span> i <span class="keyword">in</span> range(<span class="number">32</span>, <span class="number">256</span>) <span class="keyword">if</span> <span class="string">'SIGN'</span> <span class="keyword">in</span> name(chr(i), <span class="string">''</span>)}</span><br></pre></td></tr></table></figure>
<pre><code>{'#',
'$',
'%',
'+',
'<',
'=',
'>',
'¢',
'£',
'¤',
'¥',
'§',
'©',
'¬',
'®',
'°',
'±',
'µ',
'¶',
'×',
'÷'}</code></pre><h3 id="3-8-3-集合的操作"><a href="#3-8-3-集合的操作" class="headerlink" title="3.8.3 集合的操作"></a>3.8.3 集合的操作</h3><h2 id="3-9-dict和set的背后"><a href="#3-9-dict和set的背后" class="headerlink" title="3.9 dict和set的背后"></a>3.9 dict和set的背后</h2><h3 id="3-9-1-一个关于效率的实验"><a href="#3-9-1-一个关于效率的实验" class="headerlink" title="3.9.1 一个关于效率的实验"></a>3.9.1 一个关于效率的实验</h3><p>从haystack查找needles元素
dict: O(1)
set: O(1)
list: O(n)</p>
<h3 id="3-9-2-字典中的散列表"><a href="#3-9-2-字典中的散列表" class="headerlink" title="3.9.2 字典中的散列表"></a>3.9.2 字典中的散列表</h3><p>python使用散列表实现dict类型</p>
<p>散列表：稀疏数组
python设法保证大概三分之一的表元是空的，当快到达到阈值时，原来的散列表被复制到更大的空间里面</p>
<h3 id="3-9-3-dict的实现及其导致的结果"><a href="#3-9-3-dict的实现及其导致的结果" class="headerlink" title="3.9.3 dict的实现及其导致的结果"></a>3.9.3 dict的实现及其导致的结果</h3><ol>
<li><p>键必须是可散列的</p>
<ul>
<li>支持hash()函数</li>
<li>支持<strong>eq</strong>()方法检测相等性</li>
<li>a==b, 则hash(a)==hash(b)</li>
</ul>
</li>
<li><p>字典在内存中的开销很大
因为使用了散列表，稀疏数组</p>
</li>
<li><p>键查询很快
dict实现是典型的空间换时间</p>
</li>
<li><p>键的次序取决于添加顺序</p>
</li>
</ol>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># dialcodes.py 将同样的数据以不同的顺序添加到 3 个字典里</span></span><br><span class="line">DIAL_CODES = [</span><br><span class="line"> (<span class="number">86</span>, <span class="string">'China'</span>),</span><br><span class="line"> (<span class="number">91</span>, <span class="string">'India'</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="string">'United States'</span>),</span><br><span class="line"> (<span class="number">62</span>, <span class="string">'Indonesia'</span>),</span><br><span class="line"> (<span class="number">55</span>, <span class="string">'Brazil'</span>),</span><br><span class="line"> (<span class="number">92</span>, <span class="string">'Pakistan'</span>),</span><br><span class="line"> (<span class="number">880</span>, <span class="string">'Bangladesh'</span>),</span><br><span class="line"> (<span class="number">234</span>, <span class="string">'Nigeria'</span>),</span><br><span class="line"> (<span class="number">7</span>, <span class="string">'Russia'</span>),</span><br><span class="line"> (<span class="number">81</span>, <span class="string">'Japan'</span>),</span><br><span class="line">]</span><br><span class="line">d1=dict(DIAL_CODES)</span><br><span class="line">print(<span class="string">'d1:'</span>, d1.keys())</span><br><span class="line">d2=dict(sorted(DIAL_CODES))</span><br><span class="line">print(<span class="string">'d2:'</span>, d2.keys())</span><br><span class="line">d3=dict(sorted(DIAL_CODES, key=<span class="keyword">lambda</span> x:x[<span class="number">1</span>]))</span><br><span class="line">print(<span class="string">'d3:'</span>, d3.keys())</span><br><span class="line"><span class="keyword">assert</span> d1==d2 <span class="keyword">and</span> d2==d3</span><br></pre></td></tr></table></figure>
<pre><code>d1: dict_keys([86, 91, 1, 62, 55, 92, 880, 234, 7, 81])
d2: dict_keys([1, 7, 55, 62, 81, 86, 91, 92, 234, 880])
d3: dict_keys([880, 55, 86, 91, 62, 81, 234, 92, 7, 1])</code></pre><ol start="5">
<li>往字典里添加新建可能会改变已有键的顺序</li>
</ol>
<p>添加新键，可能字典扩容，导致新建更大的散列表，并把字典已有的元素添加到新表中。可能产生散列冲突，导致新散列表中键的次序变化。所以在迭代字典过程中同时对字典进行修改，可能会跳过一些键。所以不要在<strong>字典迭代时进行修改</strong></p>
<h3 id="3-9-4-set实现以及导致的结果"><a href="#3-9-4-set实现以及导致的结果" class="headerlink" title="3.9.4 set实现以及导致的结果"></a>3.9.4 set实现以及导致的结果</h3><p>set和frozenset的实现也依赖散列表</p>
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<p>本章讨论的内容是序列类型</p>
<h2 id="2-1-内置序列类型概览"><a href="#2-1-内置序列类型概览" class="headerlink" title="2.1 内置序列类型概览"></a>2.1 内置序列类型概览</h2><h3 id="根据存储数据类型分类"><a href="#根据存储数据类型分类" class="headerlink" title="根据存储数据类型分类"></a>根据存储数据类型分类</h3><p>容器序列: 能存放不同类型的数据</p>
<p> list, tuple, collections.deque</p>
<p>扁平序列：只能容纳一种类型</p>
<p> str, bytes, byte array, memoryview, array.array</p>
<h3 id="根据是否被修改分类"><a href="#根据是否被修改分类" class="headerlink" title="根据是否被修改分类"></a>根据是否被修改分类</h3><p>可变类型</p>
<p> list, byte array, array.array, collections.deque, memoryview</p>
<p>不可变类型</p>
<p> tuple, str, bytes</p>
<h2 id="2-2-列表推导和生成器表达式"><a href="#2-2-列表推导和生成器表达式" class="headerlink" title="2.2 列表推导和生成器表达式"></a>2.2 列表推导和生成器表达式</h2><h3 id="2-2-1-列表推导和可读性"><a href="#2-2-1-列表推导和可读性" class="headerlink" title="2.2.1 列表推导和可读性"></a>2.2.1 列表推导和可读性</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 字符串变成unicode码</span></span><br><span class="line">symbols = <span class="string">'$¢£¥€¤'</span></span><br><span class="line">codes = []</span><br><span class="line"><span class="keyword">for</span> symbol <span class="keyword">in</span> symbols:</span><br><span class="line"> codes.append(ord(symbol))</span><br><span class="line"> </span><br><span class="line"> </span><br><span class="line"><span class="comment"># 列表推导式写法</span></span><br><span class="line">codes = [ord(symbol) <span class="keyword">for</span> symbol <span class="keyword">in</span> symbols]</span><br></pre></td></tr></table></figure>
<p>NOTE：列表推导不会再有变量泄漏的问题</p>
<img src="/2019/07/17/fluent-python/chapter2-an-array-of-sequences/2-variable-leakage-list-derivation">
<h3 id="2-2-2-列表推导同filter和map的比较"><a href="#2-2-2-列表推导同filter和map的比较" class="headerlink" title="2.2.2 列表推导同filter和map的比较"></a>2.2.2 列表推导同filter和map的比较</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">symbols = <span class="string">'$¢£¥€¤'</span></span><br><span class="line"><span class="comment"># 列表推导</span></span><br><span class="line">beyond_ascii = [ord(s) <span class="keyword">for</span> s <span class="keyword">in</span> symbols <span class="keyword">if</span> ord(s) > <span class="number">127</span>]</span><br><span class="line"><span class="comment"># map/filter</span></span><br><span class="line">beyond_ascii = list(filter(<span class="keyword">lambda</span> c: c><span class="number">127</span>, map(ord, symbols)))</span><br></pre></td></tr></table></figure>
<h3 id="2-2-3-笛卡尔积"><a href="#2-2-3-笛卡尔积" class="headerlink" title="2.2.3 笛卡尔积"></a>2.2.3 笛卡尔积</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">colors = [<span class="string">'black'</span>, <span class="string">'white'</span>]</span><br><span class="line">sizes = [<span class="string">'S'</span>, <span class="string">'M'</span>, <span class="string">'L'</span>]</span><br><span class="line">tshirts = [(color, size) <span class="keyword">for</span> color <span class="keyword">in</span> colors <span class="keyword">for</span> size <span class="keyword">in</span> sizes]</span><br></pre></td></tr></table></figure>
<h3 id="2-2-4-生成器表达式"><a href="#2-2-4-生成器表达式" class="headerlink" title="2.2.4 生成器表达式"></a>2.2.4 生成器表达式</h3><p>生成器表达式的语法跟列表推导差不多，只不过是方括号换成圆括号而已</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">symbols = <span class="string">'$¢£¥€¤'</span></span><br><span class="line"><span class="comment"># 生成器推导</span></span><br><span class="line">beyond_ascii = (ord(s) <span class="keyword">for</span> s <span class="keyword">in</span> symbols <span class="keyword">if</span> ord(s) > <span class="number">127</span>)</span><br><span class="line"></span><br><span class="line">colors = [<span class="string">'black'</span>, <span class="string">'white'</span>]</span><br><span class="line">sizes = [<span class="string">'S'</span>, <span class="string">'M'</span>, <span class="string">'L'</span>]</span><br><span class="line">tshirts = ((color, size) <span class="keyword">for</span> color <span class="keyword">in</span> colors <span class="keyword">for</span> size <span class="keyword">in</span> sizes)</span><br></pre></td></tr></table></figure>
<h2 id="2-3-元组不仅仅是不可变的列表"><a href="#2-3-元组不仅仅是不可变的列表" class="headerlink" title="2.3 元组不仅仅是不可变的列表"></a>2.3 元组不仅仅是不可变的列表</h2><h3 id="2-3-1-元组和记录"><a href="#2-3-1-元组和记录" class="headerlink" title="2.3.1 元组和记录"></a>2.3.1 元组和记录</h3><p>元组可以对数据记录，每个元素记录了字段的数据</p>
<p><code>city, year, pop, chg, area = ('Tokyo', 2003, 32450, 0.66, 8014)</code></p>
<h3 id="2-3-2-元组拆包"><a href="#2-3-2-元组拆包" class="headerlink" title="2.3.2 元组拆包"></a>2.3.2 元组拆包</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">lax_coordinates = (<span class="number">33.9425</span>, <span class="number">-118.408056</span>)</span><br><span class="line">latitude, longitude = lax_coordinates <span class="comment"># 元组拆包</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 用*来处理剩下的元素</span></span><br><span class="line">a, b, *rest=range(<span class="number">5</span>)</span><br></pre></td></tr></table></figure>
<p>关于拆包，可以参考<a href="https://www.python.org/dev/peps/pep-3132/" target="_blank" rel="noopener">可迭代元素拆包</a></p>
<h3 id="2-3-3-嵌套元组拆包"><a href="#2-3-3-嵌套元组拆包" class="headerlink" title="2.3.3 嵌套元组拆包"></a>2.3.3 嵌套元组拆包</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line">metro_areas = [</span><br><span class="line"> (<span class="string">'Tokyo'</span>,<span class="string">'JP'</span>,<span class="number">36.933</span>,(<span class="number">35.689722</span>,<span class="number">139.691667</span>)), </span><br><span class="line"> (<span class="string">'Delhi NCR'</span>, <span class="string">'IN'</span>, <span class="number">21.935</span>, (<span class="number">28.613889</span>, <span class="number">77.208889</span>)), </span><br><span class="line"> (<span class="string">'Mexico City'</span>, <span class="string">'MX'</span>, <span class="number">20.142</span>, (<span class="number">19.433333</span>, <span class="number">-99.133333</span>))</span><br><span class="line">]</span><br><span class="line"><span class="comment"># 嵌套元组拆包</span></span><br><span class="line"><span class="keyword">for</span> name, cc, pop, (latitude, longitude) <span class="keyword">in</span> metro_areas:</span><br><span class="line"> <span class="keyword">if</span> longitude <= <span class="number">0</span>:</span><br><span class="line"> print(fmt.format(name, latitude, longitude))</span><br></pre></td></tr></table></figure>
<h3 id="2-3-4-具名元组"><a href="#2-3-4-具名元组" class="headerlink" title="2.3.4 具名元组"></a>2.3.4 具名元组</h3><p><code>collections.namedtuple</code>: 用来构建只有少数属性但是没有方法的对象</p>
<p><code>_fields</code>属性：包含这个类所有字段名称的元组</p>
<p><code>_make</code> : 接收可迭代对象生成类实例，作用类似<code>City(*delhi_data)</code></p>
<p><code>_asdict</code>: 具名元组以<code>collections.OrderedDict</code>形式返回</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span><span class="keyword">from</span> collections <span class="keyword">import</span> namedtuple</span><br><span class="line"><span class="meta">>>> </span>City = namedtuple(<span class="string">'City'</span>, <span class="string">'name country population coordinates'</span>)</span><br><span class="line"><span class="meta">>>> </span>City._fields</span><br><span class="line"><span class="meta">>>> </span>LatLong=namedtuple(<span class="string">'LatLong'</span>, <span class="string">'lat long'</span>)</span><br><span class="line"><span class="meta">>>> </span>delhi_data = (<span class="string">'Delhi NCR'</span>, <span class="string">'IN'</span>, <span class="number">21.935</span>, LatLong(<span class="number">28.613889</span>, <span class="number">77.208889</span>))</span><br><span class="line"><span class="meta">>>> </span>delhi = City._make(delhi_data)</span><br><span class="line"><span class="meta">>>> </span>delhi._asdict()</span><br><span class="line">OrderedDict([(<span class="string">'name'</span>, <span class="string">'Delhi NCR'</span>), (<span class="string">'country'</span>, <span class="string">'IN'</span>), (<span class="string">'population'</span>, <span class="number">21.935</span>), (<span class="string">'coordinates'</span>, LatLong(lat=<span class="number">28.613889</span>, long=<span class="number">77.208889</span>))])</span><br></pre></td></tr></table></figure>
<h3 id="2-3-5-作为不可变列表的元组"><a href="#2-3-5-作为不可变列表的元组" class="headerlink" title="2.3.5 作为不可变列表的元组"></a>2.3.5 作为不可变列表的元组</h3><p>较之list， 除了增减元素相关的方法以外，元组支持列表的其他所有方法</p>
<h2 id="2-4-切片"><a href="#2-4-切片" class="headerlink" title="2.4 切片"></a>2.4 切片</h2><h3 id="2-4-1-为什么切片和区间会忽略最后一个元素"><a href="#2-4-1-为什么切片和区间会忽略最后一个元素" class="headerlink" title="2.4.1 为什么切片和区间会忽略最后一个元素"></a>2.4.1 为什么切片和区间会忽略最后一个元素</h3><ol>
<li>通过最后一个位置信息，快速看出切片和区间有几个元素：range(3)和my_list[:3]都返回3个元素</li>
<li>当起止位置信息都可见时，快速计算切片和区间的长度，下标(stop-start)即可</li>
<li>my_list[:x]和my_list[x:] 分割成不重叠的两部分</li>
</ol>
<h3 id="2-4-2-对对象进行切片"><a href="#2-4-2-对对象进行切片" class="headerlink" title="2.4.2 对对象进行切片"></a>2.4.2 对对象进行切片</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span>s = <span class="string">'bicycle'</span></span><br><span class="line"><span class="meta">>>> </span>s[::<span class="number">3</span>]</span><br><span class="line"><span class="string">'bye'</span></span><br></pre></td></tr></table></figure>
<h3 id="2-4-3-多维切片和省略"><a href="#2-4-3-多维切片和省略" class="headerlink" title="2.4.3 多维切片和省略"></a>2.4.3 多维切片和省略</h3><p>多维切片：见2.9.3 Numpy</p>
<p>省略：Ellipsis</p>
<h3 id="2-4-4-给切片赋值"><a href="#2-4-4-给切片赋值" class="headerlink" title="2.4.4 给切片赋值"></a>2.4.4 给切片赋值</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre></td><td class="code"><pre><span class="line"></span><br><span class="line"><span class="meta">>>> </span>l = list(range(<span class="number">10</span>))</span><br><span class="line"><span class="meta">>>> </span>l</span><br><span class="line">[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">4</span>, <span class="number">5</span>, <span class="number">6</span>, <span class="number">7</span>, <span class="number">8</span>, <span class="number">9</span>]</span><br><span class="line"><span class="meta">>>> </span>l[<span class="number">2</span>:<span class="number">5</span>] = [<span class="number">20</span>, <span class="number">30</span>]</span><br><span class="line"><span class="meta">>>> </span>l</span><br><span class="line">[<span class="number">0</span>, <span class="number">1</span>, <span class="number">20</span>, <span class="number">30</span>, <span class="number">5</span>, <span class="number">6</span>, <span class="number">7</span>, <span class="number">8</span>, <span class="number">9</span>]</span><br><span class="line"><span class="meta">>>> </span><span class="keyword">del</span> l[<span class="number">5</span>:<span class="number">7</span>]</span><br><span class="line"><span class="meta">>>> </span>l</span><br><span class="line">[<span class="number">0</span>, <span class="number">1</span>, <span class="number">20</span>, <span class="number">30</span>, <span class="number">5</span>, <span class="number">8</span>, <span class="number">9</span>]</span><br><span class="line"><span class="meta">>>> </span>l[<span class="number">3</span>::<span class="number">2</span>] = [<span class="number">11</span>, <span class="number">22</span>]</span><br><span class="line"><span class="meta">>>> </span>l</span><br><span class="line">[<span class="number">0</span>, <span class="number">1</span>, <span class="number">20</span>, <span class="number">11</span>, <span class="number">5</span>, <span class="number">22</span>, <span class="number">9</span>]</span><br><span class="line"><span class="comment"># 如果赋值的对象时一个切片，那么赋值语句右侧必须是可迭代对象</span></span><br><span class="line"><span class="meta">>>> </span>l[<span class="number">2</span>:<span class="number">5</span>] = <span class="number">100</span> </span><br><span class="line">Traceback (most recent call last):</span><br><span class="line"> File <span class="string">"<stdin>"</span>, line <span class="number">1</span>, <span class="keyword">in</span> <module></span><br><span class="line"> TypeError: can only assign an iterable</span><br><span class="line"><span class="meta">>>> </span>l[<span class="number">2</span>:<span class="number">5</span>] = [<span class="number">100</span>]</span><br><span class="line"><span class="meta">>>> </span>l</span><br><span class="line">[<span class="number">0</span>, <span class="number">1</span>, <span class="number">100</span>, <span class="number">22</span>, <span class="number">9</span>]</span><br></pre></td></tr></table></figure>
<h2 id="2-5-对序列使用-和"><a href="#2-5-对序列使用-和" class="headerlink" title="2.5 对序列使用+和*"></a>2.5 对序列使用+和*</h2><p>+和* 不修改原有的操作对象，而是构建一个全新的序列</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span>board = [[<span class="string">'_'</span>] * <span class="number">3</span> <span class="keyword">for</span> i <span class="keyword">in</span> range(<span class="number">3</span>)]</span><br><span class="line"><span class="meta">>>> </span>board</span><br><span class="line">[[<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>]] >>> board[<span class="number">1</span>][<span class="number">2</span>] = <span class="string">'X'</span> </span><br><span class="line"><span class="meta">>>> </span>board</span><br><span class="line">[[<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'X'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>]]</span><br></pre></td></tr></table></figure>
<p>下列是经常遇到的错误，对同一对象的引用的列表</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span>weird_board = [[<span class="string">'_'</span>] * <span class="number">3</span>] * <span class="number">3</span> </span><br><span class="line"><span class="meta">>>> </span>weird_board</span><br><span class="line">[[<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'_'</span>]] >>> weird_board[<span class="number">1</span>][<span class="number">2</span>] = <span class="string">'O'</span></span><br><span class="line"><span class="meta">>>> </span>weird_board</span><br><span class="line">[[<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'O'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'O'</span>], [<span class="string">'_'</span>, <span class="string">'_'</span>, <span class="string">'O'</span>]]</span><br></pre></td></tr></table></figure>
<h2 id="2-6-序列的增量赋值"><a href="#2-6-序列的增量赋值" class="headerlink" title="2.6 序列的增量赋值"></a>2.6 序列的增量赋值</h2><p><code>>>> a += b</code> 在这个表达式中，变量名会不会关联到新的对象，取决于<code>__iadd__</code>方法是否实现</p>
<ul>
<li>如果a实现了<code>__iadd__</code>方法，就调用这个方法。对可变序列（例如list, bytearray, array.array）来说, a会就地改动，就像调用了a.extend(b)</li>
<li>如果没有实现<code>__iadd__</code>方法，a+=b这个表达式的效果就等效a=a+b；首先计算a+b,得到新的对象，然后赋值给a</li>
</ul>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span>l = [<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>] </span><br><span class="line"><span class="meta">>>> </span>id(l) <span class="number">4311953800</span></span><br><span class="line"><span class="meta">>>> </span>l *= <span class="number">2</span></span><br><span class="line"><span class="meta">>>> </span>l</span><br><span class="line">[<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>] </span><br><span class="line"><span class="meta">>>> </span>id(l) <span class="number">4311953800</span></span><br><span class="line"><span class="meta">>>> </span>t = (<span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>) </span><br><span class="line"><span class="meta">>>> </span>id(t) <span class="number">4312681568</span></span><br><span class="line"><span class="meta">>>> </span>t *= <span class="number">2</span></span><br><span class="line"><span class="meta">>>> </span>id(t) <span class="number">4301348296</span></span><br></pre></td></tr></table></figure>
<p>str 不适用上述规则，因为对字符串+=太普遍，CPython对它做了优化，为str初始化内存的时候，程序预留了额外了可扩展空间，增量操作，不会涉及复制原有字符到新位置的操作。</p>
<h2 id="2-7-list-sort方法和内置函数sorted"><a href="#2-7-list-sort方法和内置函数sorted" class="headerlink" title="2.7 list.sort方法和内置函数sorted"></a>2.7 list.sort方法和内置函数sorted</h2><p><strong>Python 的一个惯例:如果一个函数或者方法对对象进行的是就地改动，那它就应该返回None，好让调用者知道传入的参数发生了变动，而且并未产生新的对象</strong></p>
<p>list.sort: 就地排序列表，不会新建列表</p>
<p>sorted：新建列表作为返回值</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line"><span class="meta">>>> </span>fruits = [<span class="string">'grape'</span>, <span class="string">'raspberry'</span>, <span class="string">'apple'</span>, <span class="string">'banana'</span>] </span><br><span class="line"><span class="meta">>>> </span>sorted(fruits)</span><br><span class="line">[<span class="string">'apple'</span>, <span class="string">'banana'</span>, <span class="string">'grape'</span>, <span class="string">'raspberry'</span>] </span><br><span class="line"><span class="meta">>>> </span>fruits</span><br><span class="line">[<span class="string">'grape'</span>, <span class="string">'raspberry'</span>, <span class="string">'apple'</span>, <span class="string">'banana'</span>] </span><br><span class="line"><span class="meta">>>> </span>fruits.sort() </span><br><span class="line"><span class="meta">>>> </span>fruits</span><br><span class="line">[<span class="string">'apple'</span>, <span class="string">'banana'</span>, <span class="string">'grape'</span>, <span class="string">'raspberry'</span>]</span><br></pre></td></tr></table></figure>
<h2 id="2-8-用bisect来管理已排序的序列"><a href="#2-8-用bisect来管理已排序的序列" class="headerlink" title="2.8 用bisect来管理已排序的序列"></a>2.8 用bisect来管理已排序的序列</h2><h3 id="2-8-1-用bisect来搜索"><a href="#2-8-1-用bisect来搜索" class="headerlink" title="2.8.1 用bisect来搜索"></a>2.8.1 用bisect来搜索</h3><p>分为bisect.bisect (bisect_right别名), bisect.bisect_left</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> bisect</span><br><span class="line"><span class="keyword">import</span> sys</span><br><span class="line"></span><br><span class="line">HAYSTACK=[<span class="number">1</span>,<span class="number">4</span>,<span class="number">5</span>,<span class="number">23</span>,<span class="number">23</span>,<span class="number">26</span>,<span class="number">29</span>,<span class="number">30</span>]</span><br><span class="line">postion=bisect.bisect(HAYSTACK, <span class="number">23</span>)</span><br><span class="line">print(postion)</span><br><span class="line">postion=bisect.bisect_left(HAYSTACK, <span class="number">23</span>)</span><br><span class="line">print(postion)</span><br><span class="line"><span class="comment">#结果:</span></span><br><span class="line"><span class="number">5</span></span><br><span class="line"><span class="number">3</span></span><br></pre></td></tr></table></figure>
<h3 id="2-8-2-用bisect-insort插入新元素"><a href="#2-8-2-用bisect-insort插入新元素" class="headerlink" title="2.8.2 用bisect.insort插入新元素"></a>2.8.2 用bisect.insort插入新元素</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> bisect</span><br><span class="line"><span class="keyword">import</span> sys</span><br><span class="line"></span><br><span class="line">my_list=[<span class="number">1</span>,<span class="number">4</span>,<span class="number">5</span>,<span class="number">26</span>,<span class="number">29</span>,<span class="number">30</span>]</span><br><span class="line">bisect.insort(my_list, <span class="number">23</span>)</span><br><span class="line">print(my_list)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 结果</span></span><br><span class="line">[<span class="number">1</span>, <span class="number">4</span>, <span class="number">5</span>, <span class="number">23</span>, <span class="number">26</span>, <span class="number">29</span>, <span class="number">30</span>]</span><br></pre></td></tr></table></figure>
<h2 id="2-9-当列表不是首选时"><a href="#2-9-当列表不是首选时" class="headerlink" title="2.9 当列表不是首选时"></a>2.9 当列表不是首选时</h2><h3 id="2-9-1-数组"><a href="#2-9-1-数组" class="headerlink" title="2.9.1 数组"></a>2.9.1 数组</h3><p>一个浮点型数据的创建，存入文件和从文件读取的过程</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> array <span class="keyword">import</span> array</span><br><span class="line"><span class="keyword">from</span> random <span class="keyword">import</span> random </span><br><span class="line">floats=array(<span class="string">'d'</span>, (random() <span class="keyword">for</span> i <span class="keyword">in</span> range(<span class="number">10</span>**<span class="number">7</span>)))</span><br><span class="line">floats[<span class="number">-1</span>]</span><br><span class="line"><span class="comment"># 0.10839796787230571</span></span><br><span class="line"></span><br><span class="line">fp=open(<span class="string">'floats.bin'</span>, <span class="string">'wb'</span>)</span><br><span class="line">floats.tofile(fp)</span><br><span class="line">fp.close()</span><br><span class="line">floats2=array(<span class="string">'d'</span>)</span><br><span class="line">fp=open(<span class="string">'floats.bin'</span>, <span class="string">'rb'</span>)</span><br><span class="line">floats2.fromfile(fp, <span class="number">10</span>**<span class="number">7</span>)</span><br><span class="line">fp.close()</span><br><span class="line">floats2[<span class="number">-1</span>]</span><br><span class="line"><span class="comment"># 0.10839796787230571</span></span><br><span class="line"></span><br><span class="line">floats2==floats</span><br><span class="line"><span class="comment"># True</span></span><br></pre></td></tr></table></figure>
<h3 id="2-9-2-内存视图"><a href="#2-9-2-内存视图" class="headerlink" title="2.9.2 内存视图"></a>2.9.2 内存视图</h3><p>通过改变数组中的一个字节来更新数组里某个元素的值</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> array</span><br><span class="line">numbers=array.array(<span class="string">'h'</span>, [<span class="number">-2</span>, <span class="number">-1</span>, <span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>])</span><br><span class="line">memv=memoryview(numbers)</span><br><span class="line">len(memv)</span><br><span class="line"><span class="comment"># 5</span></span><br><span class="line">memv[<span class="number">0</span>]</span><br><span class="line"><span class="comment"># -2</span></span><br><span class="line">memv_oct=memv.cast(<span class="string">'B'</span>)</span><br><span class="line">memv_oct.tolist()</span><br><span class="line"><span class="comment"># [254, 255, 255, 255, 0, 0, 1, 0, 2, 0]</span></span><br></pre></td></tr></table></figure>
<h3 id="2-9-3-NumPy和SciPy"><a href="#2-9-3-NumPy和SciPy" class="headerlink" title="2.9.3 NumPy和SciPy"></a>2.9.3 NumPy和SciPy</h3><p>对numpy.ndarray的行和列进行基本操作</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy</span><br><span class="line">a=numpy.arrange(<span class="number">12</span>)</span><br><span class="line"><span class="comment"># array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])</span></span><br><span class="line">a.shape=<span class="number">3</span>,<span class="number">4</span></span><br><span class="line">array([[ <span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>, <span class="number">3</span>],</span><br><span class="line"> [ <span class="number">4</span>, <span class="number">5</span>, <span class="number">6</span>, <span class="number">7</span>],</span><br><span class="line"> [ <span class="number">8</span>, <span class="number">9</span>, <span class="number">10</span>, <span class="number">11</span>]])</span><br></pre></td></tr></table></figure>
<h3 id="2-9-4-双向队列和其他形式的队列"><a href="#2-9-4-双向队列和其他形式的队列" class="headerlink" title="2.9.4 双向队列和其他形式的队列"></a>2.9.4 双向队列和其他形式的队列</h3><ul>
<li><p>collections.deque线程安全</p>
<p> 快速从两端添加或删除的数据类型</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> collections <span class="keyword">import</span> deque</span><br><span class="line">dq=deque(range(<span class="number">10</span>), maxlen=<span class="number">10</span>)</span><br><span class="line">dq</span><br><span class="line"><span class="comment"># deque([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], maxlen=10)</span></span><br><span class="line">dq.rotate(<span class="number">3</span>)</span><br><span class="line">dq</span><br><span class="line"><span class="comment"># deque([7, 8, 9, 0, 1, 2, 3, 4, 5, 6], maxlen=10)</span></span><br><span class="line">dq.extend([<span class="number">11</span>, <span class="number">22</span>, <span class="number">33</span>])</span><br><span class="line">dq</span><br><span class="line"><span class="comment"># deque([0, 1, 2, 3, 4, 5, 6, 11, 22, 33], maxlen=10)</span></span><br></pre></td></tr></table></figure>
</li>
</ul>
<ul>
<li><p>queue:</p>
<p> 线程安全类Queue, LifoQueue, PriorityQueue</p>
</li>
<li><p>multiprocessing:</p>
<p> Queue设计给进程通信用的</p>
<p> multiprocessing.JoinableQueue类型为任务管理提供便利</p>
</li>
<li><p>asyncio:</p>
<p> Queue, LifoQueue, PriorityQueue, JoinableQueue，受queue和multiprocessing模块影响，为异步编程的任务管理提供便利</p>
</li>
<li><p>heapq:</p>
<p> 没有队列类，提供了heappush和heappop方法，可以把可变队列当作堆队列或优先队列使用</p>
</li>
</ul>
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