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<p>都是关于python3的内容</p>
</blockquote>
<h1 id="Python知识点"><a href="#Python知识点" class="headerlink" title="Python知识点"></a>Python知识点</h1><p>这里主要是记录下我平常编程中用到，但是不太清楚地知识点，仅仅作为一个记录使用</p>
<h2 id="python基本类型"><a href="#python基本类型" class="headerlink" title="python基本类型"></a>python基本类型</h2><ol>
<li>Number(数字)</li>
<li>String(字符串)</li>
<li>List(列表)</li>
<li>Tuple(元组)</li>
<li>Set(集合)</li>
<li>Dictionary(字典)</li>
</ol>
<p>不可变数据：Number（数字）、String（字符串）、Tuple（元组）；<br>可变数据：List（列表）、Dictionary（字典）、Set（集合）。<br>使用type()可以查看变量的类型，用isinstance()也可以查看。区别：</p>
<ul>
<li>type()不会认为子类是一种父类类型</li>
<li>isinstance()认为子类是一种父类类型</li>
</ul>
<p>如下代码所示：</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">class</span> <span class="title class_">A</span>:</span><br><span class="line"> <span class="keyword">pass</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">class</span> <span class="title class_">B</span>(<span class="title class_ inherited__">A</span>):</span><br><span class="line"> <span class="keyword">pass</span></span><br><span class="line"></span><br><span class="line"><span class="built_in">isinstance</span>(A(), A) <span class="comment"># returns True</span></span><br><span class="line"><span class="built_in">type</span>(A()) == A <span class="comment"># returns True</span></span><br><span class="line"><span class="built_in">isinstance</span>(B(), A) <span class="comment"># returns True</span></span><br><span class="line"><span class="built_in">type</span>(B()) == A <span class="comment"># returns False</span></span><br></pre></td></tr></table></figure>
<h3 id="Number"><a href="#Number" class="headerlink" title="Number"></a>Number</h3><p>Python3中Number包括：int,float,bool,complex.注意只有一种整数类型int，表示长整型。</p>
<h2 id="numpy相关"><a href="#numpy相关" class="headerlink" title="numpy相关"></a>numpy相关</h2><h3 id="维度问题"><a href="#维度问题" class="headerlink" title="维度问题"></a>维度问题</h3><p>注意下面的col_r1和col_r2的输出以及对应维度</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">a = np.array([[<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 class="number">10</span>,<span class="number">11</span>,<span class="number">12</span>]])</span><br><span class="line"><span class="built_in">print</span>(a)</span><br><span class="line">col_r1 = a[:, <span class="number">1</span>]</span><br><span class="line">col_r2 = a[:, <span class="number">1</span>:<span class="number">2</span>]</span><br><span class="line"><span class="built_in">print</span>(col_r1)</span><br><span class="line"><span class="built_in">print</span>(col_r1.shape)</span><br><span class="line"><span class="built_in">print</span>(col_r2)</span><br><span class="line"><span class="built_in">print</span>(col_r2.shape)</span><br></pre></td></tr></table></figure>
<h3 id="numpy数组访问"><a href="#numpy数组访问" class="headerlink" title="numpy数组访问"></a>numpy数组访问</h3><p>注意这里使用数组索引来访问numpy数组。这里可以看成[[[0, 1, 2], [0, 1, 0]]]中(0,0),(1,1),(2,0)看成三对<br><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">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line">a = np.array([[<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><br><span class="line"><span class="built_in">print</span>(a[[<span class="number">0</span>, <span class="number">1</span>, <span class="number">2</span>], [<span class="number">0</span>, <span class="number">1</span>, <span class="number">0</span>]]) <span class="comment"># Prints "[1 4 5]"</span></span><br></pre></td></tr></table></figure></p>
<ol>
<li>numpy中有mat和array，都可以用来表示多维数据。如果一个</li>
</ol>
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