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<?xml version="1.0" encoding="utf-8"?>
<feed xmlns="http://www.w3.org/2005/Atom">
<title>周卫东的博客</title>
<link href="/atom.xml" rel="self"/>
<link href="http://zhouweidong.ml/"/>
<updated>2021-04-29T14:49:05.379Z</updated>
<id>http://zhouweidong.ml/</id>
<author>
<name>周卫东</name>
</author>
<generator uri="http://hexo.io/">Hexo</generator>
<entry>
<title>二分查找</title>
<link href="http://zhouweidong.ml/2021/04/29/%E4%BA%8C%E5%88%86%E6%9F%A5%E6%89%BE/"/>
<id>http://zhouweidong.ml/2021/04/29/二分查找/</id>
<published>2021-04-29T14:05:25.000Z</published>
<updated>2021-04-29T14:49:05.379Z</updated>
<content type="html"><![CDATA[<p> 二分查找是最基本算法，它的复杂度是 <span class="math inline">\(O (log_{2} n)\)</span>。</p><a id="more"></a><h1 id="基本思想">基本思想</h1><p> 二分查找的基本问题是：给定一个 n 个元素有序的（升序）整型数组 nums 和一个目标值 target ，写一个函数搜索 nums 中的 target，如果目标值存在返回下标，否则返回 -1。</p><p> 二分查找一般由三个主要部分组成：</p><ul><li>*** 预处理 *** —— 如果集合未排序，则进行排序。</li><li>*** 二分查找 *** —— 使用循环或递归在每次比较后将查找空间划分为两半。</li><li>*** 后处理 *** —— 在剩余空间中确定可行的候选者。</li></ul><h1 id="模板-1">模板 1</h1><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></pre></td><td class="code"><pre><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">binarySearch</span><span class="params">(nums, target)</span>:</span></span><br><span class="line"> <span class="string">"""</span></span><br><span class="line"><span class="string"> :type nums: List [int]</span></span><br><span class="line"><span class="string"> :type target: int</span></span><br><span class="line"><span class="string"> :rtype: int</span></span><br><span class="line"><span class="string">"""</span></span><br><span class="line"> <span class="keyword">if</span> len (nums) == <span class="number">0</span>:</span><br><span class="line"> <span class="keyword">return</span> <span class="number">-1</span></span><br><span class="line"> left, right = <span class="number">0</span>, len (nums) - <span class="number">1</span></span><br><span class="line"> <span class="comment"># End Condition: left > right</span></span><br><span class="line"> <span class="keyword">while</span> left <= right:</span><br><span class="line"> mid = (left + right) // <span class="number">2</span></span><br><span class="line"> <span class="keyword">if</span> nums [mid] == target:</span><br><span class="line"> <span class="keyword">return</span> mid</span><br><span class="line"> <span class="keyword">elif</span> nums [mid] < target:</span><br><span class="line"> left = mid + <span class="number">1</span></span><br><span class="line"> <span class="keyword">else</span>:</span><br><span class="line"> right = mid - <span class="number">1</span></span><br><span class="line"> <span class="keyword">return</span> <span class="number">-1</span></span><br></pre></td></tr></table></figure><p> 实现 <code>int sqrt (int x)</code> 函数： 计算并返回 <em>x</em> 的平方根，其中 <em>x</em> 是非负整数。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 二分查找是最基本算法，它的复杂度是 <span class="math inline">\(O (log_{2} n)\)</span>。</p>
</summary>
<category term="算法" scheme="http://zhouweidong.ml/categories/%E7%AE%97%E6%B3%95/"/>
<category term="算法" scheme="http://zhouweidong.ml/tags/%E7%AE%97%E6%B3%95/"/>
</entry>
<entry>
<title>数据挖掘面试题</title>
<link href="http://zhouweidong.ml/2021/01/17/%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98%E9%9D%A2%E8%AF%95%E9%A2%98/"/>
<id>http://zhouweidong.ml/2021/01/17/数据挖掘面试题/</id>
<published>2021-01-17T12:59:57.000Z</published>
<updated>2021-01-18T11:15:03.730Z</updated>
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</div><script 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<title>面试题：你的平均薪水是多少？</title>
<link href="http://zhouweidong.ml/2021/01/12/%E9%9D%A2%E8%AF%95%E9%A2%98%EF%BC%9A%E4%BD%A0%E7%9A%84%E5%B9%B3%E5%9D%87%E8%96%AA%E6%B0%B4%E6%98%AF%E5%A4%9A%E5%B0%91%EF%BC%9F/"/>
<id>http://zhouweidong.ml/2021/01/12/面试题：你的平均薪水是多少？/</id>
<published>2021-01-12T13:38:28.000Z</published>
<updated>2021-01-12T14:51:55.617Z</updated>
<content type="html"><![CDATA[<p> 面试题：查询出每个部门出去最高和最低薪水后的平均薪水，并保留整数。</p><a id="more"></a><table><thead><tr class="header"><th style="text-align: center;">雇员编号</th><th style="text-align: center;">部门编号</th><th style="text-align: center;">薪水</th></tr></thead><tbody><tr class="odd"><td style="text-align: center;">10001</td><td style="text-align: center;">1</td><td style="text-align: center;">60117</td></tr><tr class="even"><td style="text-align: center;">10002</td><td style="text-align: center;">2</td><td style="text-align: center;">92102</td></tr><tr class="odd"><td style="text-align: center;">10003</td><td style="text-align: center;">2</td><td style="text-align: center;">86074</td></tr><tr class="even"><td style="text-align: center;">10004</td><td style="text-align: center;">1</td><td style="text-align: center;">66596</td></tr><tr class="odd"><td style="text-align: center;">10005</td><td style="text-align: center;">1</td><td style="text-align: center;">66961</td></tr><tr class="even"><td style="text-align: center;">10006</td><td style="text-align: center;">2</td><td style="text-align: center;">81046</td></tr><tr class="odd"><td style="text-align: center;">10007</td><td style="text-align: center;">2</td><td style="text-align: center;">94333</td></tr><tr class="even"><td style="text-align: center;">10008</td><td style="text-align: center;">1</td><td style="text-align: center;">75286</td></tr><tr class="odd"><td style="text-align: center;">10009</td><td style="text-align: center;">2</td><td style="text-align: center;">85994</td></tr><tr class="even"><td style="text-align: center;">10010</td><td style="text-align: center;">1</td><td style="text-align: center;">76884</td></tr></tbody></table><p> 解题思路：分部门查询最高薪水和最低薪水，然后去掉。</p><h1 id="查询最高薪水和最低薪水">查询最高薪水和最低薪水</h1><p> 查询最高和最低薪水可以利用窗口函数 rank ()：</p><figure class="highlight sql"><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">SELECT</span></span><br><span class="line">*,</span><br><span class="line"><span class="keyword">rank</span> () <span class="keyword">over</span> ( <span class="keyword">PARTITION</span> <span class="keyword">BY</span> department_id <span class="keyword">ORDER</span> <span class="keyword">BY</span> salary ) rank_1,</span><br><span class="line"><span class="keyword">rank</span> () <span class="keyword">over</span> ( <span class="keyword">PARTITION</span> <span class="keyword">BY</span> department_id <span class="keyword">ORDER</span> <span class="keyword">BY</span> salary <span class="keyword">DESC</span> ) rank_2 </span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">data_test.salary_table</span><br></pre></td></tr></table></figure><table><thead><tr class="header"><th style="text-align: center;">employee_id</th><th style="text-align: center;">department_id</th><th style="text-align: center;">salary</th><th style="text-align: center;">rank_1</th><th style="text-align: center;">rank_2</th></tr></thead><tbody><tr class="odd"><td style="text-align: center;">10010</td><td style="text-align: center;">1</td><td style="text-align: center;">76884</td><td style="text-align: center;">5</td><td style="text-align: center;">1</td></tr><tr class="even"><td style="text-align: center;">10008</td><td style="text-align: center;">1</td><td style="text-align: center;">75286</td><td style="text-align: center;">4</td><td style="text-align: center;">2</td></tr><tr class="odd"><td style="text-align: center;">10005</td><td style="text-align: center;">1</td><td style="text-align: center;">66961</td><td style="text-align: center;">3</td><td style="text-align: center;">3</td></tr><tr class="even"><td style="text-align: center;">10004</td><td style="text-align: center;">1</td><td style="text-align: center;">66596</td><td style="text-align: center;">2</td><td style="text-align: center;">4</td></tr><tr class="odd"><td style="text-align: center;">10001</td><td style="text-align: center;">1</td><td style="text-align: center;">60117</td><td style="text-align: center;">1</td><td style="text-align: center;">5</td></tr><tr class="even"><td style="text-align: center;">10007</td><td style="text-align: center;">2</td><td style="text-align: center;">94333</td><td style="text-align: center;">5</td><td style="text-align: center;">1</td></tr><tr class="odd"><td style="text-align: center;">10002</td><td style="text-align: center;">2</td><td style="text-align: center;">92102</td><td style="text-align: center;">4</td><td style="text-align: center;">2</td></tr><tr class="even"><td style="text-align: center;">10003</td><td style="text-align: center;">2</td><td style="text-align: center;">86074</td><td style="text-align: center;">3</td><td style="text-align: center;">3</td></tr><tr class="odd"><td style="text-align: center;">10009</td><td style="text-align: center;">2</td><td style="text-align: center;">85994</td><td style="text-align: center;">2</td><td style="text-align: center;">4</td></tr><tr class="even"><td style="text-align: center;">10006</td><td style="text-align: center;">2</td><td style="text-align: center;">81046</td><td style="text-align: center;">1</td><td style="text-align: center;">5</td></tr></tbody></table><h1 id="去除最高薪水和最低薪水">去除最高薪水和最低薪水</h1><p> 利用子查询 + where 语句：</p><figure class="highlight sql"><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">SELECT</span></span><br><span class="line">department_id,</span><br><span class="line"><span class="keyword">AVG</span>(salary) avg_salary</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">(<span class="keyword">SELECT</span></span><br><span class="line">*,</span><br><span class="line"><span class="keyword">rank</span> () <span class="keyword">over</span> ( <span class="keyword">PARTITION</span> <span class="keyword">BY</span> department_id <span class="keyword">ORDER</span> <span class="keyword">BY</span> salary ) rank_1,</span><br><span class="line"><span class="keyword">rank</span> () <span class="keyword">over</span> ( <span class="keyword">PARTITION</span> <span class="keyword">BY</span> department_id <span class="keyword">ORDER</span> <span class="keyword">BY</span> salary <span class="keyword">DESC</span> ) rank_2 </span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">data_test.salary_table) t</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line">t.rank_1 > <span class="number">1</span> <span class="keyword">and</span> t.rank_2 > <span class="number">1</span></span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line">department_id</span><br></pre></td></tr></table></figure><table><thead><tr class="header"><th style="text-align: center;">department_id</th><th style="text-align: center;">avg_salary</th></tr></thead><tbody><tr class="odd"><td style="text-align: center;">1</td><td style="text-align: center;">69614.3333</td></tr><tr class="even"><td style="text-align: center;">2</td><td style="text-align: center;">88056.6667</td></tr></tbody></table><p> 最后需要对结果进行格式化处理：format 函数</p><figure class="highlight sql"><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">SELECT</span></span><br><span class="line">department_id,</span><br><span class="line"><span class="keyword">FORMAT</span>(<span class="keyword">AVG</span>(salary), <span class="number">0</span>) avg_salary</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">(<span class="keyword">SELECT</span></span><br><span class="line">*,</span><br><span class="line"><span class="keyword">rank</span> () <span class="keyword">over</span> ( <span class="keyword">PARTITION</span> <span class="keyword">BY</span> department_id <span class="keyword">ORDER</span> <span class="keyword">BY</span> salary ) rank_1,</span><br><span class="line"><span class="keyword">rank</span> () <span class="keyword">over</span> ( <span class="keyword">PARTITION</span> <span class="keyword">BY</span> department_id <span class="keyword">ORDER</span> <span class="keyword">BY</span> salary <span class="keyword">DESC</span> ) rank_2 </span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">data_test.salary_table) t</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line">t.rank_1 > <span class="number">1</span> <span class="keyword">and</span> t.rank_2 > <span class="number">1</span></span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line">department_id</span><br></pre></td></tr></table></figure><table><thead><tr class="header"><th style="text-align: center;">department_id</th><th style="text-align: center;">avg_salary</th></tr></thead><tbody><tr class="odd"><td style="text-align: center;">1</td><td style="text-align: center;">69614</td></tr><tr class="even"><td style="text-align: center;">2</td><td style="text-align: center;">88057</td></tr></tbody></table><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 面试题：查询出每个部门出去最高和最低薪水后的平均薪水，并保留整数。</p>
</summary>
<category term="sql面试题" scheme="http://zhouweidong.ml/categories/sql%E9%9D%A2%E8%AF%95%E9%A2%98/"/>
<category term="面试题" scheme="http://zhouweidong.ml/tags/%E9%9D%A2%E8%AF%95%E9%A2%98/"/>
<category term="sql" scheme="http://zhouweidong.ml/tags/sql/"/>
</entry>
<entry>
<title>如何分析交易量增加的原因？</title>
<link href="http://zhouweidong.ml/2021/01/11/%E5%A6%82%E4%BD%95%E5%88%86%E6%9E%90%E4%BA%A4%E6%98%93%E9%87%8F%E5%A2%9E%E5%8A%A0%E7%9A%84%E5%8E%9F%E5%9B%A0%EF%BC%9F/"/>
<id>http://zhouweidong.ml/2021/01/11/如何分析交易量增加的原因？/</id>
<published>2021-01-11T14:10:33.000Z</published>
<updated>2021-01-11T15:05:52.805Z</updated>
<content type="html"><![CDATA[<p> 面试题：根据下面的业务数据，进行分析并得出观点。</p><table><thead><tr class="header"><th style="text-align: center;">日期</th><th style="text-align: center;">交易量</th><th style="text-align: center;">交易笔数</th><th style="text-align: center;">客户数</th><th style="text-align: center;">新客户数</th><th style="text-align: center;">新客户交易笔数</th><th style="text-align: center;">新客户交易量</th></tr></thead><tbody><tr class="odd"><td style="text-align: center;">2020/4/1</td><td style="text-align: center;">594.7</td><td style="text-align: center;">16.8</td><td style="text-align: center;">13.5</td><td style="text-align: center;">1.9</td><td style="text-align: center;">2.2</td><td style="text-align: center;">65.9</td></tr><tr class="even"><td style="text-align: center;">2020/4/2</td><td style="text-align: center;">601.9</td><td style="text-align: center;">17.0</td><td style="text-align: center;">13.5</td><td style="text-align: center;">4.0</td><td style="text-align: center;">4.7</td><td style="text-align: center;">133.8</td></tr><tr class="odd"><td style="text-align: center;">2020/4/3</td><td style="text-align: center;">607.2</td><td style="text-align: center;">17.4</td><td style="text-align: center;">13.8</td><td style="text-align: center;">4.4</td><td style="text-align: center;">4.4</td><td style="text-align: center;">132.8</td></tr><tr class="even"><td style="text-align: center;">2020/4/4</td><td style="text-align: center;">632.1</td><td style="text-align: center;">17.9</td><td style="text-align: center;">14.1</td><td style="text-align: center;">4.8</td><td style="text-align: center;">4.8</td><td style="text-align: center;">162.5</td></tr><tr class="odd"><td style="text-align: center;">2020/4/5</td><td style="text-align: center;">685.4</td><td style="text-align: center;">19.1</td><td style="text-align: center;">15.0</td><td style="text-align: center;">6.1</td><td style="text-align: center;">6.1</td><td style="text-align: center;">192.8</td></tr><tr class="even"><td style="text-align: center;">2020/4/6</td><td style="text-align: center;">756.6</td><td style="text-align: center;">18.7</td><td style="text-align: center;">14.9</td><td style="text-align: center;">5.3</td><td style="text-align: center;">5.3</td><td style="text-align: center;">217.5</td></tr><tr class="odd"><td style="text-align: center;">2020/4/7</td><td style="text-align: center;">753.4</td><td style="text-align: center;">18.2</td><td style="text-align: center;">14.5</td><td style="text-align: center;">4.1</td><td style="text-align: center;">4.1</td><td style="text-align: center;">164.7</td></tr><tr class="even"><td style="text-align: center;">2020/4/8</td><td style="text-align: center;">640.3</td><td style="text-align: center;">18.8</td><td style="text-align: center;">14.6</td><td style="text-align: center;">4.7</td><td style="text-align: center;">4.7</td><td style="text-align: center;">164.8</td></tr><tr class="odd"><td style="text-align: center;">2020/4/9</td><td style="text-align: center;">1236.2</td><td style="text-align: center;">39.6</td><td style="text-align: center;">23.9</td><td style="text-align: center;">18.8</td><td style="text-align: center;">18.8</td><td style="text-align: center;">412.2</td></tr><tr class="even"><td style="text-align: center;">2020/4/10</td><td style="text-align: center;">664.6</td><td style="text-align: center;">19.7</td><td style="text-align: center;">15.3</td><td style="text-align: center;">4.4</td><td style="text-align: center;">4.4</td><td style="text-align: center;">145.8</td></tr></tbody></table><a id="more"></a><h1 id="明确要解决的问题">明确要解决的问题</h1><h2 id="第一步明确数据的来源和验证其准确性">第一步：明确数据的来源和验证其准确性</h2><p> 可以从时间、地点、数据来源三个维度验证数据的准确性：</p><ul><li>时间：2020.4.1~2020.4.10</li><li>地点：全网销售</li><li>数据来源：后台导出，数据准确。</li></ul><h2 id="第二步可视化相关数据">第二步：可视化相关数据</h2><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210111222241.png"></p><p> 从折线图可知：9 号交易量骤然增加，其余日期交易量相差不大，比较平稳。</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210111222620.png"></p><p> 排除数据异常问题，可以进一步获取上月同期和上年同期的交易量：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210111222931.png"></p><h1 id="分析问题出现的原因">分析问题出现的原因</h1><p> 分析问题出现的原因，可以对指标进行多维度拆解：</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><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">graph LR</span><br><span class="line">A (交易量) --> B (交易笔数)</span><br><span class="line">A (交易量) --> C (单均交易金额)</span><br><span class="line">B (交易笔数) --> D1 (新客户交易笔数)</span><br><span class="line">B (交易笔数) --> D2 (老客户交易笔数)</span><br></pre></td></tr></table></figure><p> 根据指标的拆解，可以做出如下假设：</p><ul><li>假设 1：单均交易金额增加导致 9 号交易量增加。</li><li>假设 2：新客户交易笔数增加导致 9 号交易量增加。</li><li>假设 3：老客户交易笔数增加导致 9 号交易量增加。</li></ul><p> 单均交易金额 = 交易量 / 交易笔数</p><figure><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210111225431.png" alt="20210111225431"><figcaption aria-hidden="true">20210111225431</figcaption></figure><p> 可以看到：9 号单均交易金额骤然下降，与交易量骤增的形势正好相反，假设 1 不成立。</p><p> 交易笔数 = 新客户交易笔数 + 老客户交易笔数</p><figure><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210111230048.png" alt="20210111230048"><figcaption aria-hidden="true">20210111230048</figcaption></figure><p> 可以看到：新客户交易笔数和老客户交易笔数在 9 号同时骤然增加，与交易量的走势相符，表明假设 2 和假设 3 正确。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 面试题：根据下面的业务数据，进行分析并得出观点。</p>
<table>
<thead>
<tr class="header">
<th style="text-align: center;">日期</th>
<th style="text-align: center;">交易量</th>
<th style="text-align: center;">交易笔数</th>
<th style="text-align: center;">客户数</th>
<th style="text-align: center;">新客户数</th>
<th style="text-align: center;">新客户交易笔数</th>
<th style="text-align: center;">新客户交易量</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: center;">2020/4/1</td>
<td style="text-align: center;">594.7</td>
<td style="text-align: center;">16.8</td>
<td style="text-align: center;">13.5</td>
<td style="text-align: center;">1.9</td>
<td style="text-align: center;">2.2</td>
<td style="text-align: center;">65.9</td>
</tr>
<tr class="even">
<td style="text-align: center;">2020/4/2</td>
<td style="text-align: center;">601.9</td>
<td style="text-align: center;">17.0</td>
<td style="text-align: center;">13.5</td>
<td style="text-align: center;">4.0</td>
<td style="text-align: center;">4.7</td>
<td style="text-align: center;">133.8</td>
</tr>
<tr class="odd">
<td style="text-align: center;">2020/4/3</td>
<td style="text-align: center;">607.2</td>
<td style="text-align: center;">17.4</td>
<td style="text-align: center;">13.8</td>
<td style="text-align: center;">4.4</td>
<td style="text-align: center;">4.4</td>
<td style="text-align: center;">132.8</td>
</tr>
<tr class="even">
<td style="text-align: center;">2020/4/4</td>
<td style="text-align: center;">632.1</td>
<td style="text-align: center;">17.9</td>
<td style="text-align: center;">14.1</td>
<td style="text-align: center;">4.8</td>
<td style="text-align: center;">4.8</td>
<td style="text-align: center;">162.5</td>
</tr>
<tr class="odd">
<td style="text-align: center;">2020/4/5</td>
<td style="text-align: center;">685.4</td>
<td style="text-align: center;">19.1</td>
<td style="text-align: center;">15.0</td>
<td style="text-align: center;">6.1</td>
<td style="text-align: center;">6.1</td>
<td style="text-align: center;">192.8</td>
</tr>
<tr class="even">
<td style="text-align: center;">2020/4/6</td>
<td style="text-align: center;">756.6</td>
<td style="text-align: center;">18.7</td>
<td style="text-align: center;">14.9</td>
<td style="text-align: center;">5.3</td>
<td style="text-align: center;">5.3</td>
<td style="text-align: center;">217.5</td>
</tr>
<tr class="odd">
<td style="text-align: center;">2020/4/7</td>
<td style="text-align: center;">753.4</td>
<td style="text-align: center;">18.2</td>
<td style="text-align: center;">14.5</td>
<td style="text-align: center;">4.1</td>
<td style="text-align: center;">4.1</td>
<td style="text-align: center;">164.7</td>
</tr>
<tr class="even">
<td style="text-align: center;">2020/4/8</td>
<td style="text-align: center;">640.3</td>
<td style="text-align: center;">18.8</td>
<td style="text-align: center;">14.6</td>
<td style="text-align: center;">4.7</td>
<td style="text-align: center;">4.7</td>
<td style="text-align: center;">164.8</td>
</tr>
<tr class="odd">
<td style="text-align: center;">2020/4/9</td>
<td style="text-align: center;">1236.2</td>
<td style="text-align: center;">39.6</td>
<td style="text-align: center;">23.9</td>
<td style="text-align: center;">18.8</td>
<td style="text-align: center;">18.8</td>
<td style="text-align: center;">412.2</td>
</tr>
<tr class="even">
<td style="text-align: center;">2020/4/10</td>
<td style="text-align: center;">664.6</td>
<td style="text-align: center;">19.7</td>
<td style="text-align: center;">15.3</td>
<td style="text-align: center;">4.4</td>
<td style="text-align: center;">4.4</td>
<td style="text-align: center;">145.8</td>
</tr>
</tbody>
</table>
</summary>
<category term="数据分析" scheme="http://zhouweidong.ml/categories/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/"/>
<category term="面试题" scheme="http://zhouweidong.ml/tags/%E9%9D%A2%E8%AF%95%E9%A2%98/"/>
<category term="数据分析" scheme="http://zhouweidong.ml/tags/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/"/>
</entry>
<entry>
<title>Excel 复杂业务查询</title>
<link href="http://zhouweidong.ml/2021/01/09/Excel-%E5%A4%8D%E6%9D%82%E4%B8%9A%E5%8A%A1%E6%9F%A5%E8%AF%A2/"/>
<id>http://zhouweidong.ml/2021/01/09/Excel-复杂业务查询/</id>
<published>2021-01-09T14:50:06.000Z</published>
<updated>2021-01-09T15:51:51.959Z</updated>
<content type="html"><![CDATA[<p> 面试题：利用下拉框选择机构名称、利率档、期限等条件，查询出相应的费用。</p><a id="more"></a><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109225303.png"></p><h1 id="制作联动下拉列表">制作联动下拉列表</h1><p> 制作下拉列表可以借助数据有效性，具体步骤见下图：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109225732.png"></p><p> 具体到本案例中，首先需要找到机构名称和利率档的非重复值，可以利用高级筛选，步骤见下图：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109230109.png"></p><p> 筛选结果见下图：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109230409.png"></p><p> 需要转换成下图的格式：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109230729.png"></p><p> 然后利用公式 - 定义的名称，对区域进行定义，具体步骤见下图：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109233052.png"></p><p> 同理，可以分别将各机构的利率档定义为 A 机构、B 机构和 C 机构。</p><p> 为了实现 <a href="https://jingyan.baidu.com/article/7c6fb42802525a80642c9034.html" target="_blank" rel="noopener"></a>，需要将序列的名称用公式定义：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109233319.png"></p><h1 id="查询费用">查询费用</h1><p> 查询费用即根据条件在区域内匹配值，可以利用 index+match，index 的作用是在区域内选择指定位置的数，具体参数见下图：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109233534.png"></p><p> 区域是确定的，变化的是行号和列号，需要借助 match 进行匹配：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210109233654.png"></p><p> 首先是列号，比较简单：MATCH (I3&"期费用",A1:E1,0)。</p><p> 然后是行号，稍微复杂，有两个条件，对应两个区域，条件和条件间、区域和区域间用 & 进行连接：MATCH (I1&I2, A:A&B:B,0)。</p><p> 总结可得公式为：INDEX (A:E, MATCH (I1&I2, A:A&B:B,0), MATCH (I3&"期费用",A1:E1,0))，值得注意的是，这是一个数组公式，输入公式后，按住 ctrl_shift+enter 公式才会生效。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 面试题：利用下拉框选择机构名称、利率档、期限等条件，查询出相应的费用。</p>
</summary>
<category term="Excel" scheme="http://zhouweidong.ml/categories/Excel/"/>
<category term="Excel" scheme="http://zhouweidong.ml/tags/Excel/"/>
<category term="面试题" scheme="http://zhouweidong.ml/tags/%E9%9D%A2%E8%AF%95%E9%A2%98/"/>
</entry>
<entry>
<title>会员价值分析</title>
<link href="http://zhouweidong.ml/2021/01/03/%E4%BC%9A%E5%91%98%E4%BB%B7%E5%80%BC%E5%88%86%E6%9E%90/"/>
<id>http://zhouweidong.ml/2021/01/03/会员价值分析/</id>
<published>2021-01-03T10:16:15.000Z</published>
<updated>2021-01-03T15:45:50.995Z</updated>
<content type="html"><![CDATA[<p> 目前有如下数据：会员卡信息（会员 Id、卡状态、性别、年龄、HYKTYPE（等同于会员卡类型编码）等）、会员卡类型（卡折扣率、卡类型编码和卡类型名称）、门店品牌表（品牌代码、品牌名、品类代码、品类名、店代码、店名等）和销售明细表（会员卡类型编码、销售金额、销售数量、会员卡类型、会员卡 Id 等）。</p><a id="more"></a><h1 id="漏斗图分析会员结构的合理性">漏斗图分析会员结构的合理性</h1><p> 如何进行会员价值分析呢？一个方法是漏斗图，漏斗图可以用于分析流程中每一步的转化和流失：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103224153.png"></p><p> 可以看出，会员转化不符合倒金字塔形状，贵宾卡用户数大于积分卡用户，原因可能是前期快速扩张导致的，如果是此原因后期需控制积分卡升级比率，保障会员卡的价值性。</p><p> 同时，与会员结构息息相关的是会员流失情况，明显会员流失情况较为严重，大于一般行业平均水平 10%。</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103225037.png"></p><p> 最后，会员总数也是十分重要的观察指标。</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103225922.png"></p><h1 id="rfm-模型用户价值分类">RFM 模型：用户价值分类</h1><p> RFM 模型可以给用户进行分类，衡量各分类用户的价值和获利能力：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103231359.png"></p><p> 进一步，分析每类用户的消费金额：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103231735.png"></p><p> 具体，可以分会员类型进行 RFM 分类：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103232122.png"></p><p> 具体结论：</p><ul><li><p>低价值的一般挽留客户人数过多，反映出前期会员人数快速扩张但后期维护未及时跟上导致的后遗症；</p></li><li><p>最高等级的钻石和黑钻会员中出现了较高比例的价值一般性用户，可能存在一卡多用或将会员卡权益进行倒卖的情况，需重点排查。</p></li></ul><h1 id="会员消费偏好">会员消费偏好</h1><p> 分类会员消费偏好，从品类到品牌：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103233314.png"></p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103233346.png"></p><p> 也可以分析每类用户的消费区间，看用户类型是否合理：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20210103233545.png"></p><p> 具体结论： - 根据不同会员的消费偏好，定向推送活动信息。比如针对积分卡用户重点以化妆品类中的兰蔻、香奈儿等品牌作为大促活动的主推点； - 积分卡和贵宾卡会员的消费区间主要落在 0-300 和 500-1000 的区间，建议每期大促可针对该二类会员定向推送 300-xx 元和 500-xx 元的满减优惠券；钻石卡会员建议大促推送 300 元和 2000 元的满减优惠券；黑钻卡会员建议大促推送 1000 元和 2000 元的满减优惠券。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 目前有如下数据：会员卡信息（会员 Id、卡状态、性别、年龄、HYKTYPE（等同于会员卡类型编码）等）、会员卡类型（卡折扣率、卡类型编码和卡类型名称）、门店品牌表（品牌代码、品牌名、品类代码、品类名、店代码、店名等）和销售明细表（会员卡类型编码、销售金额、销售数量、会员卡类型、会员卡 Id 等）。</p>
</summary>
</entry>
<entry>
<title>7 周成为数据分析师：SQL 练习题</title>
<link href="http://zhouweidong.ml/2020/12/29/7%E5%91%A8%E7%A7%B0%E4%B8%BA%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E5%B8%88%EF%BC%9ASQL-%E7%BB%83%E4%B9%A0%E9%A2%98/"/>
<id>http://zhouweidong.ml/2020/12/29/7周称为数据分析师：SQL-练习题/</id>
<published>2020-12-29T13:32:36.000Z</published>
<updated>2020-12-30T11:19:22.973Z</updated>
<content type="html"><![CDATA[<p> 在秦路老师的《7 周成为数据分析师》的 SQL 教程中，提出了几个问题：</p><ul><li>统计不同月份的下单人数</li><li>统计用户三月份的回购率和复购率</li><li>统计男女用户的消费频次是否有差异？</li><li>统计多次消费的用户，第一次和最后一次消费间隔是多少？</li><li>统计不同年龄段，用户的消费金额是否有差异？</li><li>统计消费的二八法则，消费的 top 20% 用户，贡献了多少额度？</li></ul><a id="more"></a><h1 id="统计不同月份的下单人数">统计不同月份的下单人数</h1><p> 首先，需要剔除未下单的用户；其次需要按年和月份进行分组统计；然后，需要对不同用户进行统计；最后，需要剔除脏数据（年份为 0000 的数据）。</p><figure class="highlight sql"><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></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span> </span><br><span class="line"> <span class="keyword">YEAR</span>(paid_time) <span class="keyword">year</span>,</span><br><span class="line"> <span class="keyword">MONTH</span>(paid_time) <span class="keyword">month</span>,</span><br><span class="line"> <span class="keyword">count</span>( <span class="keyword">distinct</span> userId ) <span class="keyword">num</span></span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line"> data_test.order_info </span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line"> is_paid = <span class="string">' 已支付 '</span> <span class="keyword">AND</span></span><br><span class="line"> <span class="keyword">year</span>(paid_time) > <span class="number">0</span></span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> <span class="keyword">YEAR</span>(paid_time) , </span><br><span class="line"> <span class="keyword">MONTH</span> (paid_time)</span><br></pre></td></tr></table></figure><h1 id="统计用户三月份的回购率和复购率">统计用户三月份的回购率和复购率</h1><p> 回购率是指回购用户占总用户的比例，回购用户是指购买产品一段时间后再次购买该产品的用户。对于不同属性的产品，间隔时间的设定应该是不一样的，这里暂设为一个月。</p><p> 用 SQL 计算回购率，最便捷的方式是自连接，即两个同样的表进行连接，但连接键设置为相隔一个月，即 3 月和 2 月连接。</p><figure class="highlight sql"><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><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span></span><br><span class="line"> <span class="keyword">DATE_FORMAT</span>(t1.m, <span class="string">"% Y-% m"</span>) <span class="keyword">month</span>,</span><br><span class="line"> <span class="keyword">count</span>(t2.m)/<span class="keyword">count</span>(t1.m) repurchase_rate</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line"> (<span class="keyword">SELECT</span> </span><br><span class="line"> userId, </span><br><span class="line"> <span class="keyword">DATE_FORMAT</span>(paid_time, <span class="string">"% Y-% m-01"</span>) m</span><br><span class="line"> <span class="keyword">FROM</span> </span><br><span class="line"> data_test.order_info</span><br><span class="line"> <span class="keyword">WHERE</span></span><br><span class="line"> is_paid = <span class="string">' 已支付 '</span></span><br><span class="line"> <span class="keyword">GROUP</span> <span class="keyword">BY</span> </span><br><span class="line"> userId, </span><br><span class="line"> <span class="keyword">DATE_FORMAT</span>(paid_time, <span class="string">"% Y-% m-01"</span>)) t1</span><br><span class="line"><span class="keyword">LEFT</span> <span class="keyword">JOIN</span> (<span class="keyword">SELECT</span></span><br><span class="line"> userId,</span><br><span class="line"> <span class="keyword">DATE_FORMAT</span>(paid_time, <span class="string">"% Y-% m-01"</span>) m</span><br><span class="line"> <span class="keyword">FROM</span></span><br><span class="line"> data_test.order_info</span><br><span class="line"> <span class="keyword">WHERE</span></span><br><span class="line"> is_paid = <span class="string">' 已支付 '</span></span><br><span class="line"> <span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> userId,</span><br><span class="line"> <span class="keyword">DATE_FORMAT</span>(paid_time, <span class="string">"% Y-% m-01"</span>)) t2</span><br><span class="line"><span class="keyword">ON</span></span><br><span class="line"> t1.userId = t2.userId <span class="keyword">AND</span></span><br><span class="line"> t1.m = <span class="keyword">DATE_SUB</span>(t2.m,<span class="built_in">INTERVAL</span> <span class="number">1</span> <span class="keyword">MONTH</span>)</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line"> t1.m > <span class="number">0</span> <span class="keyword">or</span></span><br><span class="line"> t2.m > <span class="number">0</span></span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> t1.m</span><br></pre></td></tr></table></figure><p> 复购率是指购买次数大于 1 的用户数占总用户数的比例，这里可以用 count 嵌套 if 函数进行处理：</p><figure class="highlight sql"><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">SELECT</span> </span><br><span class="line"> d.order_month <span class="keyword">month</span>,</span><br><span class="line"> <span class="keyword">count</span>(<span class="keyword">if</span>(d.c > <span class="number">1</span>, <span class="number">1</span>, <span class="literal">null</span>))/<span class="keyword">count</span>(d.c) repurchase_rate</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line"> (<span class="keyword">SELECT</span> </span><br><span class="line"> userId,</span><br><span class="line"> <span class="keyword">DATE_FORMAT</span>(paid_time, <span class="string">"% Y-% m"</span>) order_month,</span><br><span class="line"> <span class="keyword">COUNT</span>(userId) c</span><br><span class="line"> <span class="keyword">FROM</span></span><br><span class="line"> data_test.order_info</span><br><span class="line"> <span class="keyword">WHERE</span></span><br><span class="line"> is_paid = <span class="string">' 已支付 '</span></span><br><span class="line"> <span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> userId,</span><br><span class="line"> order_month</span><br><span class="line"> ) d</span><br><span class="line"> <span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> d.order_month</span><br></pre></td></tr></table></figure><h1 id="统计男女用户的消费频次是否有差异">统计男女用户的消费频次是否有差异</h1><p> 首先需要通过多表连接将订单表和用户表拼接在一起，获取相应信息；其次需要分性别和用户统计订单数；最后，利用平均数计算消费频次。</p><figure class="highlight sql"><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><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span></span><br><span class="line"> sex,</span><br><span class="line"> <span class="keyword">avg</span>(<span class="keyword">num</span>) fre</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line"> (<span class="keyword">SELECT</span></span><br><span class="line"> o.userId, sex, <span class="keyword">count</span>(<span class="number">1</span>) <span class="keyword">num</span></span><br><span class="line"> <span class="keyword">FROM</span></span><br><span class="line"> data_test.order_info o</span><br><span class="line"> <span class="keyword">inner</span> <span class="keyword">JOIN</span> (</span><br><span class="line"> <span class="keyword">SELECT</span></span><br><span class="line"> *</span><br><span class="line"> <span class="keyword">FROM</span></span><br><span class="line"> data_test.user_info</span><br><span class="line"> <span class="keyword">WHERE</span></span><br><span class="line"> sex <> <span class="string">''</span> <span class="keyword">AND</span></span><br><span class="line"> is_paid = <span class="string">' 已支付 '</span>) u</span><br><span class="line"> <span class="keyword">ON</span></span><br><span class="line"> o.userId = u.userId</span><br><span class="line"> <span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> userId,</span><br><span class="line"> sex) t</span><br><span class="line"> <span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> sex</span><br></pre></td></tr></table></figure><h1 id="统计多次消费的用户第一次和最后一次消费间隔是多少">统计多次消费的用户，第一次和最后一次消费间隔是多少？</h1><p> 分组统计消费间隔，筛选出消费次数大于 1 的用户。</p><figure class="highlight sql"><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">SELECT</span></span><br><span class="line"> userId,</span><br><span class="line"> <span class="keyword">DATEDIFF</span>(<span class="keyword">max</span>(paid_time), <span class="keyword">min</span>(paid_time)) intervals</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line"> data_test.order_info</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line"> is_paid = <span class="string">' 已支付 '</span></span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line"> userId</span><br><span class="line"><span class="keyword">HAVING</span></span><br><span class="line"> <span class="keyword">count</span>(<span class="number">1</span>) > <span class="number">1</span></span><br></pre></td></tr></table></figure><h1 id="统计不同年龄段用户的消费金额是否有差异">统计不同年龄段，用户的消费金额是否有差异？</h1><p> 如果只是想看一下各个年龄段的消费金额是否有差异，可以用年龄除以区间间隔，向上取整，然后求消费金额的平均数：</p><figure class="highlight sql"><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></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span></span><br><span class="line">age,</span><br><span class="line"><span class="keyword">AVG</span>(price) consumes</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">(<span class="keyword">SELECT</span></span><br><span class="line">o.userId,</span><br><span class="line"><span class="keyword">CEIL</span>((<span class="keyword">year</span>(<span class="keyword">now</span>()) - <span class="keyword">year</span>(birth_date))/<span class="number">10</span>) age,</span><br><span class="line">price</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">data_test.order_info o</span><br><span class="line"><span class="keyword">INNER</span> <span class="keyword">JOIN</span></span><br><span class="line">data_test.user_info u</span><br><span class="line"><span class="keyword">ON</span></span><br><span class="line">o.userId = u.userId</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line">is_paid = <span class="string">' 已支付 '</span> <span class="keyword">AND</span></span><br><span class="line">birth_date > <span class="built_in">date</span>(<span class="string">'1901-00-00'</span>)) t</span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line">age</span><br></pre></td></tr></table></figure><p> 如果想看一下哪个年龄段的消费金额最高或最低，需要利用 case when :</p><figure class="highlight sql"><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><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span></span><br><span class="line">(<span class="keyword">CASE</span> </span><br><span class="line"> <span class="keyword">WHEN</span> age < <span class="number">10</span> <span class="keyword">THEN</span> <span class="string">'10 岁以内 '</span></span><br><span class="line"> <span class="keyword">WHEN</span> age <span class="keyword">BETWEEN</span> <span class="number">10</span> <span class="keyword">AND</span> <span class="number">19</span> <span class="keyword">THEN</span> <span class="string">'10~19 岁 '</span></span><br><span class="line"> <span class="keyword">WHEN</span> age <span class="keyword">BETWEEN</span> <span class="number">20</span> <span class="keyword">AND</span> <span class="number">29</span> <span class="keyword">THEN</span> <span class="string">'20~29 岁 '</span></span><br><span class="line"> <span class="keyword">WHEN</span> age <span class="keyword">BETWEEN</span> <span class="number">30</span> <span class="keyword">AND</span> <span class="number">39</span> <span class="keyword">THEN</span> <span class="string">'30~39 岁 '</span></span><br><span class="line"> <span class="keyword">WHEN</span> age <span class="keyword">BETWEEN</span> <span class="number">40</span> <span class="keyword">AND</span> <span class="number">49</span> <span class="keyword">THEN</span> <span class="string">'40~49 岁 '</span></span><br><span class="line"> <span class="keyword">WHEN</span> age <span class="keyword">BETWEEN</span> <span class="number">50</span> <span class="keyword">AND</span> <span class="number">59</span> <span class="keyword">THEN</span> <span class="string">'50~59 岁 '</span></span><br><span class="line"> <span class="keyword">ELSE</span> <span class="string">'60 岁及以上 '</span></span><br><span class="line"><span class="keyword">END</span>) <span class="keyword">as</span> ages,</span><br><span class="line"><span class="keyword">AVG</span>(price) price_avg</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">(<span class="keyword">SELECT</span></span><br><span class="line">o.userId,</span><br><span class="line">(<span class="keyword">year</span>(<span class="keyword">now</span>()) - <span class="keyword">year</span>(birth_date)) age,</span><br><span class="line">price</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">data_test.order_info o</span><br><span class="line"><span class="keyword">INNER</span> <span class="keyword">JOIN</span></span><br><span class="line">data_test.user_info u</span><br><span class="line"><span class="keyword">ON</span></span><br><span class="line">o.userId = u.userId</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line">is_paid = <span class="string">' 已支付 '</span> <span class="keyword">AND</span></span><br><span class="line">birth_date > <span class="built_in">date</span>(<span class="string">'1901-00-00'</span>)) t</span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line">ages</span><br></pre></td></tr></table></figure><h1 id="统计消费的二八法则消费的-top-20-用户贡献了多少额度">统计消费的二八法则，消费的 top 20% 用户，贡献了多少额度？</h1><p> 由于 MySQL 没有分组排名的函数 row_number，因此需借助 limit 限制条数来达到分组排名统计的功能：</p><figure class="highlight sql"><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="keyword">SELECT</span></span><br><span class="line"><span class="keyword">COUNT</span>(userId) user_num,</span><br><span class="line"><span class="keyword">SUM</span>(total) total_num</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">(<span class="keyword">SELECT</span></span><br><span class="line">userId,</span><br><span class="line"><span class="keyword">sum</span>(price) <span class="keyword">as</span> total</span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">data_test.order_info</span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line">is_paid = <span class="string">' 已支付 '</span></span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span></span><br><span class="line">userId</span><br><span class="line"><span class="keyword">ORDER</span> <span class="keyword">BY</span></span><br><span class="line">total <span class="keyword">DESC</span></span><br><span class="line"><span class="keyword">LIMIT</span></span><br><span class="line"><span class="number">17000</span>) t</span><br></pre></td></tr></table></figure><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 在秦路老师的《7 周成为数据分析师》的 SQL 教程中，提出了几个问题：</p>
<ul>
<li>统计不同月份的下单人数</li>
<li>统计用户三月份的回购率和复购率</li>
<li>统计男女用户的消费频次是否有差异？</li>
<li>统计多次消费的用户，第一次和最后一次消费间隔是多少？</li>
<li>统计不同年龄段，用户的消费金额是否有差异？</li>
<li>统计消费的二八法则，消费的 top 20% 用户，贡献了多少额度？</li>
</ul>
</summary>
<category term="数据分析" scheme="http://zhouweidong.ml/categories/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/"/>
<category term="SQL" scheme="http://zhouweidong.ml/categories/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/SQL/"/>
</entry>
<entry>
<title>win10 安装 superset</title>
<link href="http://zhouweidong.ml/2020/12/18/win10-%E5%AE%89%E8%A3%85-superset/"/>
<id>http://zhouweidong.ml/2020/12/18/win10-安装-superset/</id>
<published>2020-12-18T02:59:50.000Z</published>
<updated>2020-12-18T05:18:45.180Z</updated>
<content type="html"><![CDATA[<p> 在安装 superset 之前，为了避免包版本的混乱，需要先创立一个虚拟的环境：</p><a id="more"></a><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">conda create -n superset python=<span class="number">3.7</span> <span class="comment"># 指定 python 版本为 3.7</span></span><br></pre></td></tr></table></figure><p> 然后，进入虚拟环境安装相应的包：</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">conda activate superset</span><br></pre></td></tr></table></figure><p> 在正式安装 superset 之前，需要先安装 sasl 和 python-geohash：</p><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">conda install -c conda-forge python-geohash -y</span><br><span class="line">conda install -c conda-forge sasl -y</span><br></pre></td></tr></table></figure><p> 最后，就可以安装 superset：</p><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">pip install apache-superset -i https://pypi.douban.com/simple</span><br><span class="line">pip install Pillow -i https://pypi.douban.com/simple</span><br></pre></td></tr></table></figure><p> 之后，可以对 superset 进行初始化：</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></pre></td><td class="code"><pre><span class="line"><span class="comment"># 创建管理员账户 </span></span><br><span class="line">set FLASK_APP=superset</span><br><span class="line">flask fab create-admin</span><br><span class="line"></span><br><span class="line"><span class="comment"># 初始化数据库 </span></span><br><span class="line">superset db upgrade</span><br><span class="line"></span><br><span class="line"><span class="comment"># 载入案例数据 </span></span><br><span class="line">superset load_examples</span><br><span class="line"></span><br><span class="line"><span class="comment"># 初始化角色和权限 </span></span><br><span class="line">superset init</span><br><span class="line"></span><br><span class="line"><span class="comment"># 启动服务，端口号 8001，使用 -p 更改端口号 </span></span><br><span class="line">superset run -p <span class="number">8001</span> --<span class="keyword">with</span>-threads --reload --debugger</span><br></pre></td></tr></table></figure><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 在安装 superset 之前，为了避免包版本的混乱，需要先创立一个虚拟的环境：</p>
</summary>
</entry>
<entry>
<title>AB test</title>
<link href="http://zhouweidong.ml/2020/12/16/AB-test/"/>
<id>http://zhouweidong.ml/2020/12/16/AB-test/</id>
<published>2020-12-16T12:49:41.000Z</published>
<updated>2020-12-29T04:51:16.672Z</updated>
<content type="html"><![CDATA[<p> AB test 是一种对照实验，目的是为了验证一个模型 / 版本是不是比另外一个模型 / 版本更好。 AB test</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> AB test 是一种对照实验，目的是为了验证一个模型 / 版本是不是比另外一个模型 / 版本更好。 AB test</p>
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity
</summary>
</entry>
<entry>
<title>数据分析：异常值处理</title>
<link href="http://zhouweidong.ml/2020/12/11/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%EF%BC%9A%E5%BC%82%E5%B8%B8%E5%80%BC%E5%A4%84%E7%90%86/"/>
<id>http://zhouweidong.ml/2020/12/11/数据分析：异常值处理/</id>
<published>2020-12-11T02:15:48.000Z</published>
<updated>2020-12-11T07:29:11.221Z</updated>
<content type="html"><![CDATA[<p> 异常值处理是数据分析中的一个基本步骤，异常值是指不符合整体样本一般性性质的数值。</p><a id="more"></a><h1 id="异常值的检测方法">异常值的检测方法</h1><p> 常用的检测异常值的方法主要是：3<span class="math inline">\(\sigma\)</span> 原则、箱型图、格拉布斯准则和多维度异常检测。</p><h2 id="sigma-原则">3<span class="math inline">\(\sigma\)</span> 原则</h2><p> 3<span class="math inline">\(\sigma\)</span> 原则的必要条件是：数据需要服从正态分布。在 3<span class="math inline">\(\sigma\)</span> 原则下，异常值是指超过 3 倍标准差的值；如果数据不服从正态分布，也可以用近似值的 3 倍标准差作为替代。</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></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"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">ThreeSigmod</span><span class="params">(value)</span>:</span></span><br><span class="line"> avg = np.mean (value)</span><br><span class="line"> std = np.std (value)</span><br><span class="line"> threshold_up = avg + <span class="number">3</span>*std</span><br><span class="line"> threshold_down = avg - <span class="number">3</span>*std</span><br><span class="line"> <span class="keyword">return</span> [float (threshold_down), float (threshold_up)]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">identify_outliers</span><span class="params">(value)</span>:</span></span><br><span class="line"> threshold = ThreeSigmod (value)</span><br><span class="line"> outliers = list (filter (<span class="keyword">lambda</span> x: (s<threshold [<span class="number">0</span>])|(s>threshold [<span class="number">1</span>]), value))</span><br><span class="line"> <span class="keyword">return</span> outliers</span><br></pre></td></tr></table></figure><h2 id="箱型图">箱型图</h2><p> </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="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">fig, axes = plt.subplots ()</span><br><span class="line">data.boxplot (column = <span class="string">''</span>, by=[<span class="string">''</span>, <span class="string">''</span>], ax=axes)</span><br></pre></td></tr></table></figure><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></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"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">threshold_value</span><span class="params">(value)</span>:</span></span><br><span class="line"> normal_value = np.quantile (value,<span class="number">0.75</span>) - np.quantile (value,<span class="number">0.25</span>)</span><br><span class="line"> quan_down = np.quantile (value,<span class="number">0.25</span>)<span class="number">-1.5</span>*normal_value</span><br><span class="line"> quan_up = np.quantile (value,<span class="number">0.75</span>)+<span class="number">1.5</span>*normal_value</span><br><span class="line"> <span class="keyword">return</span> [float (quan_down),float (quan_up)]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">identity_outliers</span><span class="params">(value)</span>:</span></span><br><span class="line"> threshold = threshold_value (value)</span><br><span class="line"> outliers = list (filter (<span class="keyword">lambda</span> x: (s<threshold [<span class="number">0</span>])|(s>threshold [<span class="number">1</span>]), value))</span><br><span class="line"> <span class="keyword">return</span> outliers</span><br></pre></td></tr></table></figure><h2 id="格拉布斯法则">格拉布斯法则</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="keyword">from</span> outliers <span class="keyword">import</span> smirnov_grubbs <span class="keyword">as</span> grubbs</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">identity_outliers</span><span class="params">(value)</span>:</span></span><br><span class="line"> outliers = set (value) - set (list (grubbs.test (value, alpha = <span class="number">0.05</span>)))</span><br></pre></td></tr></table></figure><h2 id="多维度异常检测">多维度异常检测</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="keyword">from</span> scipy.spatial <span class="keyword">import</span> distance</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">identity_outliers</span><span class="params">(data, num)</span>:</span></span><br><span class="line"> m_dist_order = Series ([float (distance.mahalanobis (data.iloc [i], data.mean (), np.mat (data.cov ().as_matrix ()).I) ** <span class="number">2</span>) <span class="keyword">for</span> i <span class="keyword">in</span> range (len (df))]).sort_values (ascending=<span class="keyword">False</span>).index.tolist ()</span><br><span class="line"> outliers_index = m_dist_order [:num]</span><br><span class="line"> outliers = data.iloc [outliers_index]</span><br><span class="line"> <span class="keyword">return</span> outliers</span><br></pre></td></tr></table></figure><h1 id="异常值的处理方法">异常值的处理方法</h1><p> 处理异常值一般采用移动平均值和直接删除。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 异常值处理是数据分析中的一个基本步骤，异常值是指不符合整体样本一般性性质的数值。</p>
</summary>
</entry>
<entry>
<title>Power BI 学习 Day1</title>
<link href="http://zhouweidong.ml/2020/12/08/Power-BI-%E5%AD%A6%E4%B9%A0-Day1/"/>
<id>http://zhouweidong.ml/2020/12/08/Power-BI-学习-Day1/</id>
<published>2020-12-08T13:16:58.000Z</published>
<updated>2020-12-08T13:58:54.872Z</updated>
<content type="html"><![CDATA[<p> 在 Power BI 中，可以很方便地为报表插入链接。</p><a id="more"></a><h1 id="向表中插入超链接"><a href="https://docs.microsoft.com/zh-cn/power-bi/create-reports/power-bi-hyperlinks-in-tables" target="_blank" rel="noopener">向表中插入超链接</a></h1><p> 如果数据已经包含 URL，可以通过将数据类别转换为 “Web URL” 插入超链接，具体操作如下图所示：</p><figure><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20201208213456.png" alt="20201208213456"><figcaption aria-hidden="true">20201208213456</figcaption></figure><p> 如果数据尚未包含 URL， 可以通过建立自定义列的方式插入超链接。</p><p> 对于长链接，可以在表中显示超链接图表，具体操作如下图所示：</p><figure><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20201208214252.png" alt="20201208214252"><figcaption aria-hidden="true">20201208214252</figcaption></figure><h1 id="向文本框插入超链接"><a href="https://docs.microsoft.com/zh-cn/power-bi/create-reports/service-add-hyperlink-to-text-box" target="_blank" rel="noopener">向文本框插入超链接</a></h1><p> 插入文本框并输入一段文字后，可以选中部分文字插入超链接，具体操作如下：</p><figure><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20201208214504.png" alt="20201208214504"><figcaption aria-hidden="true">20201208214504</figcaption></figure><figure><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20201208214513.png" alt="20201208214513"><figcaption aria-hidden="true">20201208214513</figcaption></figure><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 在 Power BI 中，可以很方便地为报表插入链接。</p>
</summary>
<category term="Power BI" scheme="http://zhouweidong.ml/tags/Power-BI/"/>
<category term="数据分析" scheme="http://zhouweidong.ml/tags/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/"/>
<category term="可视化" scheme="http://zhouweidong.ml/tags/%E5%8F%AF%E8%A7%86%E5%8C%96/"/>
</entry>
<entry>
<title>LeetCode MySQL Day9</title>
<link href="http://zhouweidong.ml/2020/09/07/%E2%80%9CLeetCode-MySQL-Day9/"/>
<id>http://zhouweidong.ml/2020/09/07/“LeetCode-MySQL-Day9/</id>
<published>2020-09-07T14:34:41.000Z</published>
<updated>2020-09-07T14:49:43.376Z</updated>
<content type="html"><![CDATA[<p> 第 9 天：<br> <a id="more"></a> Write a SQL query to find all numbers that appear at least three times consecutively.</p><table><thead><tr class="header"><th>Id</th><th>Num</th></tr></thead><tbody><tr class="odd"><td>1</td><td>1</td></tr><tr class="even"><td>2</td><td>1</td></tr><tr class="odd"><td>3</td><td>1</td></tr><tr class="even"><td>4</td><td>2</td></tr><tr class="odd"><td>5</td><td>1</td></tr><tr class="even"><td>6</td><td>2</td></tr><tr class="odd"><td>7</td><td>2</td></tr><tr class="even"><td>-</td><td>--</td></tr></tbody></table><p>For example, given the above Logs table, 1 is the only number that appears consecutively for at least three times.</p><table><thead><tr class="header"><th>ConsecutiveNums</th></tr></thead><tbody><tr class="odd"><td>1</td></tr></tbody></table><p> 解决思路：前后相同则表示连续三个数均相同。<br></p><figure class="highlight sql"><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></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span> <span class="keyword">DISTINCT</span></span><br><span class="line">( a.Num ) <span class="keyword">AS</span> ConsecutiveNums </span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line">(</span><br><span class="line"><span class="keyword">SELECT</span></span><br><span class="line"><span class="keyword">id</span>,</span><br><span class="line"><span class="keyword">num</span>,</span><br><span class="line">lag ( <span class="keyword">num</span>, <span class="number">1</span>, <span class="number">0</span> ) <span class="keyword">over</span> () <span class="keyword">AS</span> pre,</span><br><span class="line"><span class="keyword">lead</span> ( <span class="keyword">num</span>, <span class="number">1</span>, <span class="number">0</span> ) <span class="keyword">over</span> () <span class="keyword">AS</span> nxt </span><br><span class="line"><span class="keyword">FROM</span></span><br><span class="line"><span class="keyword">LOGS</span> </span><br><span class="line">) a </span><br><span class="line"><span class="keyword">WHERE</span></span><br><span class="line">a.Num = a.pre </span><br><span class="line"><span class="keyword">AND</span> a.Num = a.nxt</span><br></pre></td></tr></table></figure><p> 知识点：窗口函数 <a href="https://www.jianshu.com/p/e0d73f8b71ec" target="_blank" rel="noopener">lag () 和 lead ()</a>。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第 9 天：<br>
</summary>
</entry>
<entry>
<title>LeetCode MySQL Day8</title>
<link href="http://zhouweidong.ml/2020/09/06/LeetCode-MySQL-Day8/"/>
<id>http://zhouweidong.ml/2020/09/06/LeetCode-MySQL-Day8/</id>
<published>2020-09-06T14:16:34.000Z</published>
<updated>2020-09-06T15:07:07.672Z</updated>
<content type="html"><![CDATA[<p> 第 8 天：</p><a id="more"></a><p> Write a SQL query to rank scores. If there is a tie between two scores, both should have the same ranking. Note that after a tie, the next ranking number should be the next consecutive integer value. In other words, there should be no "holes" between ranks.</p><table><thead><tr class="header"><th>Id</th><th>Score</th></tr></thead><tbody><tr class="odd"><td>1</td><td>3.50</td></tr><tr class="even"><td>2</td><td>3.65</td></tr><tr class="odd"><td>3</td><td>4.00</td></tr><tr class="even"><td>4</td><td>3.85</td></tr><tr class="odd"><td>5</td><td>4.00</td></tr><tr class="even"><td>6</td><td>3.65</td></tr></tbody></table><p> For example, given the above Scores table, your query should generate the following report (order by highest score):</p><table><thead><tr class="header"><th>score</th><th>Rank</th></tr></thead><tbody><tr class="odd"><td>4.00</td><td>1</td></tr><tr class="even"><td>4.00</td><td>1</td></tr><tr class="odd"><td>3.85</td><td>2</td></tr><tr class="even"><td>3.65</td><td>3</td></tr><tr class="odd"><td>3.65</td><td>3</td></tr><tr class="even"><td>3.50</td><td>4</td></tr></tbody></table><p> Important Note: For MySQL solutions, to escape reserved words used as column names, you can use an apostrophe before and after the keyword. For example <code>Rank</code>.</p><p> 解决方案：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> score, <span class="keyword">dense_rank</span>() <span class="keyword">over</span>(<span class="keyword">order</span> <span class="keyword">by</span> score <span class="keyword">desc</span>) <span class="keyword">as</span> <span class="string">'Rank'</span> <span class="keyword">from</span> Scores</span><br></pre></td></tr></table></figure><p> 知识点：</p><p><img src="https://picgo-1258437747.cos.ap-nanjing.myqcloud.com/20200906224250.png"></p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第 8 天：</p>
</summary>
</entry>
<entry>
<title>从瑟尔沃法则看内外双循环</title>
<link href="http://zhouweidong.ml/2020/09/04/%E4%BB%8E%E7%91%9F%E5%B0%94%E6%B2%83%E6%B3%95%E5%88%99%E7%9C%8B%E5%86%85%E5%A4%96%E5%8F%8C%E5%BE%AA%E7%8E%AF/"/>
<id>http://zhouweidong.ml/2020/09/04/从瑟尔沃法则看内外双循环/</id>
<published>2020-09-04T13:58:37.000Z</published>
<updated>2020-09-04T14:46:51.400Z</updated>
<content type="html"><![CDATA[<blockquote><p> 在收支均衡约束下的哈罗德模型中，经济增长对外部需求扩张的依赖关系，被称为 “瑟尔沃法则”。<br> 令 M 为进口，X 为出口，Y 为实际 GDP，<span class="math inline">\(\mu\)</span> 为进口倾向，则在内部均衡的前提下，外部均衡为 <span class="math inline">\(X=M=\mu Y\)</span>，则有 <span class="math inline">\(Y=\frac {X}{\mu}\)</span>，得 <span class="math inline">\(g = \frac {\Delta Y}{Y} = \frac {\frac {\Delta X}{X}}{\epsilon}\)</span>。<br></p></blockquote><p> 从分子来看：外部需求的增长与经济增长存在着对应关系。<br> 从分母来看：如果要实现 “内循环”，必须降低国内循环的对外需求，即实现消费品和生产资料的进口替代。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<blockquote>
<p> 在收支均衡约束下的哈罗德模型中，经济增长对外部需求扩张的依赖关系，被称为 “瑟尔沃法则”。<br> 令 M 为进口，X 为出口，Y 为实际 GDP，<span class="math inline">\(\mu\)</span> 为进
</summary>
<category term="宏观经济" scheme="http://zhouweidong.ml/categories/%E5%AE%8F%E8%A7%82%E7%BB%8F%E6%B5%8E/"/>
</entry>
<entry>
<title>LeetCode MySql Day07</title>
<link href="http://zhouweidong.ml/2020/09/02/LeetCode-MySql-Day07/"/>
<id>http://zhouweidong.ml/2020/09/02/LeetCode-MySql-Day07/</id>
<published>2020-09-02T15:50:21.000Z</published>
<updated>2020-09-02T15:54:31.192Z</updated>
<content type="html"><![CDATA[<p> 第 7 天 <br> <a id="more"></a> Write a SQL query to get the nth highest salary from the Employee table.</p><table><thead><tr class="header"><th>Id</th><th>Salary</th></tr></thead><tbody><tr class="odd"><td>1</td><td>100</td></tr><tr class="even"><td>2</td><td>200</td></tr><tr class="odd"><td>3</td><td>300</td></tr></tbody></table><p>For example, given the above Employee table, the nth highest salary where n = 2 is 200. If there is no nth highest salary, then the query should return null.</p><table><thead><tr class="header"><th>getNthHighestSalary (2)</th></tr></thead><tbody><tr class="odd"><td>200</td></tr></tbody></table><p> 解决方法如下：<br></p><figure class="highlight sql"><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></pre></td><td class="code"><pre><span class="line"><span class="keyword">CREATE</span> <span class="keyword">FUNCTION</span> getNthHighestSalary (N <span class="built_in">INT</span>) <span class="keyword">RETURNS</span> <span class="built_in">INT</span></span><br><span class="line"><span class="keyword">BEGIN</span></span><br><span class="line"><span class="keyword">DECLARE</span> M <span class="built_in">INT</span>;</span><br><span class="line"><span class="keyword">SET</span> M = N<span class="number">-1</span>;</span><br><span class="line"> RETURN (</span><br><span class="line"> # Write your MySQL query statement below.</span><br><span class="line"> <span class="keyword">SELECT</span> <span class="keyword">DISTINCT</span> Salary</span><br><span class="line"> <span class="keyword">FROM</span> Employee</span><br><span class="line"> <span class="keyword">ORDER</span> <span class="keyword">BY</span> Salary <span class="keyword">DESC</span></span><br><span class="line"> <span class="keyword">LIMIT</span> <span class="number">1</span> <span class="keyword">OFFSET</span> M</span><br><span class="line"> );</span><br><span class="line"><span class="keyword">END</span></span><br></pre></td></tr></table></figure><p> 这里涉及到自定义函数，基本的语法格式如下：<br> <figure class="highlight sql"><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">CREATE</span> <span class="keyword">FUNCTION</span> < 函数名 & gt; ( [ < 参数 < span class="number">1</span>> < 类型 < span class="number">1> [ , < 参数 < span class="number">2> < 类型 < span class="number">2>] ] … )<br><span class="line"> <span class="keyword">RETURNS</span> < 类型 & gt;</span><br><span class="line"> < 函数主体 & gt;</span><br></pre></td></tr></table></figure></p><p> 调用自定义函数的基本语法格式如下：<br> <figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span> < 自定义函数名 & gt; ([< 参数 & gt; [,...]])</span><br></pre></td></tr></table></figure></p><p> 删除自定义函数的基本语法格式如下：<br> <figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">DROP</span> <span class="keyword">FUNCTION</span> [ <span class="keyword">IF</span> <span class="keyword">EXISTS</span> ] < 自定义函数名 & gt;</span><br></pre></td></tr></table></figure></p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第 7 天 <br>
</summary>
<category term="LeetCode" scheme="http://zhouweidong.ml/categories/LeetCode/"/>
<category term="MySQL" scheme="http://zhouweidong.ml/tags/MySQL/"/>
<category term="Function" scheme="http://zhouweidong.ml/tags/Function/"/>
</entry>
<entry>
<title>灰色预测模型</title>
<link href="http://zhouweidong.ml/2020/09/02/%E7%81%B0%E8%89%B2%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B/"/>
<id>http://zhouweidong.ml/2020/09/02/灰色预测模型/</id>
<published>2020-09-02T14:44:02.000Z</published>
<updated>2020-09-02T15:01:40.129Z</updated>
<content type="html"><![CDATA[<p> 灰色预测模型是在样本较小、信息不完全的情况下，通过挖掘数据的特征进行预测的方法。<br> <a id="more"></a> 首先需要由原始数据序列计算一次累加序列：<br></p><figure class="highlight r"><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">x0 <- seq (<span class="number">1</span>, <span class="number">9</span>)</span><br><span class="line">x1 <- cumsum (x)</span><br></pre></td></tr></table></figure><p> 然后，需要建立矩阵 <span class="math display">\[\pmb {B}=\left [\begin {array}{cc} -\frac {1}{2}[x^{(1)}(2)+x^{(1)}(1)]&1\\ \vdots&\vdots\\ -\frac {1}{2}[x^{(1)}(k)+x^{(1)}(k-1)]&1 \end {array}\right]\]</span> 和 <span class="math display">\[\pmb {y}=\left [\begin {array}{c} x^{0}_{2}\\ \vdots \\ x^{0}_{k}\end {array}\right]\]</span></p><figure class="highlight r"><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">B = matrix (data = <span class="number">1</span>, nrow = (length (x1) - <span class="number">1</span>), ncol = <span class="number">2</span>)</span><br><span class="line"><span class="keyword">for</span> (i <span class="keyword">in</span> <span class="number">1</span>:(length (x1) - <span class="number">1</span>)) {</span><br><span class="line"> B [i, <span class="number">1</span>] = (x1 [i] + x1 [i+<span class="number">1</span>])*(-<span class="number">1.0</span>)/<span class="number">2</span></span><br><span class="line">}</span><br><span class="line">y = as.matrix (x0 [<span class="number">2</span>:length (x0)])</span><br></pre></td></tr></table></figure><p> 之后，可以由 <span class="math inline">\(\hat {U} = (\pmb {B}^{T}\pmb {B})^{-1}\pmb {B}^{T}\pmb {y}=\left [\begin {array}{c} \hat {a} \\ \hat {u} \end {array}\right]\)</span>, 求出 <span class="math inline">\(\hat {a}\)</span> 和 <span class="math inline">\(\hat {u}\)</span>：</p><figure class="highlight r"><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">BT = t (B)</span><br><span class="line">a = sovle (BT %*% B) %*% BT %*% y</span><br></pre></td></tr></table></figure><p> 最后，把 <span class="math inline">\(\hat {a}\)</span> 和 <span class="math inline">\(\hat {u}\)</span> 代入时间响应方程：</p><p><span class="math display">\[x^{(1)}(k+1) = [x^{(1)}_{1} - \frac {\hat {u}}{\hat {a}}] e^{-\hat {a} k} + \frac {\hat {u}}{\hat {a}}\]</span></p><figure class="highlight r"><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">k <- rep (<span class="number">1</span>, length)</span><br><span class="line">xk <- rep (<span class="number">1</span>,(length (x0)+n))</span><br><span class="line"><span class="keyword">for</span> (i <span class="keyword">in</span> <span class="number">1</span>:length (x0)+n) {</span><br><span class="line"> u = a [<span class="number">2</span>]/a [<span class="number">1</span>]</span><br><span class="line"> xk [i] = (x1 [<span class="number">1</span>] - u)*exp ((-<span class="number">1</span>)*a [<span class="number">1</span>]*(k [i])) + u</span><br><span class="line">}</span><br></pre></td></tr></table></figure><p> 此时得到的结果是拟合值 <span class="math inline">\(\hat {x}^{1}_{i}\)</span> ，需要利用后减运算还原为模型的拟合值 <span class="math inline">\(\hat {x}^{0}_{i}\)</span>：</p><figure class="highlight r"><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">xhat <- rep (<span class="number">1</span>, length (xk))</span><br><span class="line"><span class="keyword">for</span> (i <span class="keyword">in</span> <span class="number">1</span>:legth (xk)) {</span><br><span class="line"> <span class="keyword">if</span> (i == <span class="number">1</span>) {</span><br><span class="line"> xhat [i] = x0 [<span class="number">1</span>]</span><br><span class="line"> } <span class="keyword">else</span> {</span><br><span class="line"> xhat [i] = xk [i] - xk [i-<span class="number">1</span>]</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line">}</span><br></pre></td></tr></table></figure><p> 得出拟合值后，需要评价一下模型的精度，主要评价指标有：</p><ul><li>残差：<span class="math inline">\(e (k)=x^{(0)}_{k}-\hat {x}^{(0)}_{k}\)</span>。</li><li>相对残差：<span class="math inline">\(e (k)=\frac {x^{(0)}_{k}-\hat {x}^{(0)}_{k}}{x^{(0)}_{k}}\)</span>。</li><li>均方差比（后残差比值）：<span class="math inline">\(C=\frac {S_{2}}{S_{1}}=\frac {\sqrt {\frac {1}{N}\sum_{k=1}^{N}[x^{(0)}_k-\bar {X}]^{2}}}{\sqrt {\frac {1}{N-1}\sum_{k=2}^{N}[E (k)-\bar {E}]^{2}}}\)</span>，小于 0.35 表示模型精度相对比较好。</li><li>小误差概率：<span class="math inline">\(P = P {|E (k)-\bar {E}|<0.6745S_{1}}\)</span>，大于 0.95 表示模型精度相对比较好。</li></ul><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 灰色预测模型是在样本较小、信息不完全的情况下，通过挖掘数据的特征进行预测的方法。<br>
</summary>
</entry>
<entry>
<title>LeetCode MySql Day6</title>
<link href="http://zhouweidong.ml/2020/05/13/LeetCode-MySql-Day06/"/>
<id>http://zhouweidong.ml/2020/05/13/LeetCode-MySql-Day06/</id>
<published>2020-05-13T11:36:38.000Z</published>
<updated>2020-05-13T13:54:21.050Z</updated>
<content type="html"><![CDATA[<p> 第六天：</p><a id="more"></a><h2 id="swap-salary">627. Swap Salary</h2><p> Given a table <code>salary</code>, such as the one below, that has m=male and f=female values. Swap all f and m values (i.e., change all f values to m and vice versa) with a <strong>single update statement</strong> and no intermediate temp table.</p><p> Note that you must write a single update statement, <strong>DO NOT</strong> write any select statement for this problem.</p><p> <strong>Example:</strong></p><table><thead><tr class="header"><th>id</th><th>name</th><th>sex</th><th>salary</th></tr></thead><tbody><tr class="odd"><td>1</td><td>A</td><td>m</td><td>2500</td></tr><tr class="even"><td>2</td><td>B</td><td>f</td><td>1500</td></tr><tr class="odd"><td>3</td><td>C</td><td>m</td><td>5500</td></tr><tr class="even"><td>4</td><td>D</td><td>f</td><td>500</td></tr></tbody></table><p> After running your <strong>update</strong> statement, the above salary table should have the following rows:</p><table><thead><tr class="header"><th>id</th><th>name</th><th>sex</th><th>salary</th></tr></thead><tbody><tr class="odd"><td>1</td><td>A</td><td>f</td><td>2500</td></tr><tr class="even"><td>2</td><td>B</td><td>m</td><td>1500</td></tr><tr class="odd"><td>3</td><td>C</td><td>f</td><td>5500</td></tr><tr class="even"><td>4</td><td>D</td><td>m</td><td>500</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">update</span> salary <span class="keyword">set</span> sex = (<span class="keyword">case</span> <span class="keyword">when</span> sex = <span class="string">"f"</span> <span class="keyword">then</span> <span class="string">"m"</span> <span class="keyword">else</span> <span class="string">"f"</span> <span class="keyword">end</span>);</span><br></pre></td></tr></table></figure><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">update</span> salary <span class="keyword">set</span> sex = <span class="built_in">char</span>(<span class="keyword">ASCII</span>(<span class="string">'f'</span>) ^ <span class="keyword">ASCII</span>(<span class="string">'m'</span>) ^ <span class="keyword">ASCII</span>(sex));</span><br></pre></td></tr></table></figure><p> 解法一：利用条件表达式函数 case when … … then … … else … … end，相当于 if … … else 。</p><p> 解法二：利用 XOR (异或) 运算符和 ASCII 编码进行转换：f 和 m 的 ASCII 编码值分别为 102 和 109 ，XOR 运算符 (^) 的作用是舍去二进制的进位，具体而言：首先将十进制转换为二进制，即 bin (102)=0b1100110，bin (109)=0b1101101，易知 0b1100110^0b110110 = 0b1011 ，再将二进制转换为十进制，即 int (str (1011), 2) = 11，同理可知 11^102=109 和 11^109 = 102 。</p><h2 id="reformat-department-table">1179. Reformat Department Table</h2><p> Table: <code>Department</code></p><table><thead><tr class="header"><th>Column Name</th><th>Type</th></tr></thead><tbody><tr class="odd"><td>id</td><td>int</td></tr><tr class="even"><td>revenue</td><td>int</td></tr><tr class="odd"><td>month</td><td>varchar</td></tr></tbody></table><p> (id, month) is the primary key of this table.</p><p> The table has information about the revenue of each department per month.</p><p> The month has values in ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"].</p><p> Write an SQL query to reformat the table such that there is a department id column and a revenue column <strong>for each month</strong>.</p><p> The query result format is in the following example:</p><p> Department table:</p><table><thead><tr class="header"><th>id</th><th>revenue</th><th>month</th></tr></thead><tbody><tr class="odd"><td>1</td><td>8000</td><td>Jan</td></tr><tr class="even"><td>2</td><td>9000</td><td>Jan</td></tr><tr class="odd"><td>3</td><td>10000</td><td>Feb</td></tr><tr class="even"><td>1</td><td>7000</td><td>Feb</td></tr><tr class="odd"><td>1</td><td>6000</td><td>Mar</td></tr></tbody></table><p> Result table:</p><table><thead><tr class="header"><th>id</th><th>Jan_Revenue</th><th>Feb_Revenue</th><th>Mar_Revenue</th><th>...</th><th>Dec_Revenue</th></tr></thead><tbody><tr class="odd"><td>1</td><td>8000</td><td>7000</td><td>6000</td><td>...</td><td>null</td></tr><tr class="even"><td>2</td><td>9000</td><td>null</td><td>null</td><td>...</td><td>null</td></tr><tr class="odd"><td>3</td><td>null</td><td>10000</td><td>null</td><td>...</td><td>null</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><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="keyword">SELECT</span></span><br><span class="line"> <span class="keyword">id</span>,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Jan'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Jan_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Feb'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Feb_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Mar'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Mar_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Apr'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Apr_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'May'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> May_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Jun'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Jun_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Jul'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Jul_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Aug'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Aug_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Sep'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Sep_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Oct'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Oct_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Nov'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Nov_Revenue,</span><br><span class="line"> <span class="keyword">MAX</span>(<span class="keyword">IF</span>(<span class="keyword">month</span> = <span class="string">'Dec'</span>, revenue, <span class="literal">null</span>)) <span class="keyword">AS</span> Dec_Revenue</span><br><span class="line"><span class="keyword">FROM</span> Department</span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span> <span class="keyword">id</span></span><br><span class="line"><span class="keyword">ORDER</span> <span class="keyword">BY</span> <span class="keyword">id</span>;</span><br></pre></td></tr></table></figure><p> 知识点：if (expr1,expr2,expr3)，如果 expr1 是 TRUE (expr1 <> 0 and expr1 <> NULL)，则 IF () 的返回值为 expr2; 否则返回值则为 expr3。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第六天：</p>
</summary>
<category term="LeetCode" scheme="http://zhouweidong.ml/categories/LeetCode/"/>
<category term="LeetCode" scheme="http://zhouweidong.ml/tags/LeetCode/"/>
<category term="MySql" scheme="http://zhouweidong.ml/tags/MySql/"/>
</entry>
<entry>
<title>LeetCode MySql Day5</title>
<link href="http://zhouweidong.ml/2020/05/12/LeetCode-MySql-Day5/"/>
<id>http://zhouweidong.ml/2020/05/12/LeetCode-MySql-Day5/</id>
<published>2020-05-12T12:38:05.000Z</published>
<updated>2020-05-12T13:28:06.264Z</updated>
<content type="html"><![CDATA[<p> 第五天：</p><a id="more"></a><h2 id="classes-more-than-5-students">596. Classes More Than 5 Students</h2><p> There is a table <code>courses</code> with columns: <strong>student</strong> and <strong>class</strong></p><p> Please list out all classes which have more than or equal to 5 students.</p><p> For example, the table:</p><table><thead><tr class="header"><th>student</th><th>class</th></tr></thead><tbody><tr class="odd"><td>A</td><td>Math</td></tr><tr class="even"><td>B</td><td>English</td></tr><tr class="odd"><td>C</td><td>Math</td></tr><tr class="even"><td>D</td><td>Biology</td></tr><tr class="odd"><td>E</td><td>Math</td></tr><tr class="even"><td>F</td><td>Computer</td></tr><tr class="odd"><td>G</td><td>Math</td></tr><tr class="even"><td>H</td><td>Math</td></tr><tr class="odd"><td>I</td><td>Math</td></tr></tbody></table><p> Should output:</p><table><thead><tr class="header"><th>class</th></tr></thead><tbody><tr class="odd"><td>Math</td></tr></tbody></table><p> <strong>Note:</strong></p><p> The students should not be counted duplicate in each course.</p><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> <span class="keyword">class</span> <span class="keyword">from</span> courses <span class="keyword">group</span> <span class="keyword">by</span> <span class="keyword">class</span> <span class="keyword">having</span> <span class="keyword">count</span>(<span class="keyword">distinct</span> student) >= <span class="number">5</span>;</span><br></pre></td></tr></table></figure><h2 id="not-boring-movies">620. Not Boring Movies</h2><p> X city opened a new cinema, many people would like to go to this cinema. The cinema also gives out a poster indicating the movies’ ratings and descriptions.</p><p> Please write a SQL query to output movies with an odd numbered ID and a description that is not 'boring'. Order the result by rating.</p><p> For example, table <code>cinema</code>:</p><table><thead><tr class="header"><th>id</th><th>movie</th><th>description</th><th>rating</th></tr></thead><tbody><tr class="odd"><td>1</td><td>War</td><td>great 3D</td><td>8.9</td></tr><tr class="even"><td>2</td><td>Science</td><td>fiction</td><td>8.5</td></tr><tr class="odd"><td>3</td><td>irish</td><td>boring</td><td>6.2</td></tr><tr class="even"><td>4</td><td>Ice song</td><td>Fantacy</td><td>8.6</td></tr><tr class="odd"><td>5</td><td>House card</td><td>Interesting</td><td>9.1</td></tr></tbody></table><p> For the example above, the output should be:</p><table><thead><tr class="header"><th>id</th><th>movie</th><th>description</th><th>rating</th></tr></thead><tbody><tr class="odd"><td>5</td><td>House card</td><td>Interesting</td><td>9.1</td></tr><tr class="even"><td>1</td><td>War</td><td>great 3D</td><td>8.9</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> * <span class="keyword">from</span> cinema <span class="keyword">where</span> <span class="keyword">mod</span>(<span class="keyword">id</span>, <span class="number">2</span>) != <span class="number">0</span> <span class="keyword">and</span> description <span class="keyword">not</span> <span class="keyword">like</span> <span class="string">'boring'</span> <span class="keyword">order</span> <span class="keyword">by</span> rating <span class="keyword">DESC</span>;</span><br></pre></td></tr></table></figure><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> * <span class="keyword">from</span> cinema <span class="keyword">where</span> description <> <span class="string">'boring'</span> <span class="keyword">and</span> <span class="keyword">id</span> & <span class="number">1</span> = <span class="number">1</span> <span class="keyword">order</span> <span class="keyword">by</span> rating <span class="keyword">DESC</span></span><br></pre></td></tr></table></figure><p> 两个知识点：对于字符串 not like 是模糊查询，<> 则是完整查询，二者的区别是前者要求数据里面不包含某个字段，后者要求数据不是某个字段；& 是位运算符，可以用于判断对 <span class="math inline">\(2^n\)</span> 取余，是一个取巧的办法，加快了运算速度。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第五天：</p>
</summary>
<category term="LeetCode" scheme="http://zhouweidong.ml/categories/LeetCode/"/>
<category term="LeetCode" scheme="http://zhouweidong.ml/tags/LeetCode/"/>
<category term="MySql" scheme="http://zhouweidong.ml/tags/MySql/"/>
</entry>
<entry>
<title>LeetCode MySql Day4</title>
<link href="http://zhouweidong.ml/2020/05/08/LeetCode-MySql-Day4/"/>
<id>http://zhouweidong.ml/2020/05/08/LeetCode-MySql-Day4/</id>
<published>2020-05-08T04:57:53.000Z</published>
<updated>2020-05-08T05:40:29.680Z</updated>
<content type="html"><![CDATA[<p> 第四天：</p><a id="more"></a><h2 id="rising-temperature">197. Rising Temperature</h2><p> Given a Weather table, write a SQL query to find all dates' Ids with higher temperature compared to its previous (yesterday's) dates.</p><table><thead><tr class="header"><th>Id (INT)</th><th>RecordDate (DATE)</th><th>Temperature (INT)</th></tr></thead><tbody><tr class="odd"><td>1</td><td>2015-01-01</td><td>10</td></tr><tr class="even"><td>2</td><td>2015-01-02</td><td>25</td></tr><tr class="odd"><td>3</td><td>2015-01-03</td><td>20</td></tr><tr class="even"><td>4</td><td>2015-01-04</td><td>30</td></tr></tbody></table><p> For example, return the following Ids for the above Weather table:title: LeetCode MySql Day4</p><table><thead><tr class="header"><th style="text-align: left;">Id</th></tr></thead><tbody><tr class="odd"><td style="text-align: left;">2</td></tr><tr class="even"><td style="text-align: left;">4</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> w1.Id <span class="keyword">from</span> Weather w1, Weather w2 <span class="keyword">where</span> w1.Temperature > w2.Temperature <span class="keyword">and</span> <span class="keyword">DateDiff</span>(w1. RecordDate, w2.RecordDate) = <span class="number">1</span>;</span><br></pre></td></tr></table></figure><p> 知识点：时间类型的处理 - DataDiff (d1, d2)：计算日期 d1->d2 之间相隔的天数。</p><h2 id="big-countries">595. Big Countries</h2><p> There is a table World：</p><table><thead><tr class="header"><th>name</th><th>continent</th><th>area</th><th>population</th><th>gdp</th></tr></thead><tbody><tr class="odd"><td>Afghanistan</td><td>Asia</td><td>652230</td><td>25500100</td><td>20343000</td></tr><tr class="even"><td>Albania</td><td>Europe</td><td>28748</td><td>2831741</td><td>12960000</td></tr><tr class="odd"><td>Algeria</td><td>Africa</td><td>2381741</td><td>37100000</td><td>1.89E+08</td></tr><tr class="even"><td>Andorra</td><td>Europe</td><td>468</td><td>78115</td><td>3712000</td></tr><tr class="odd"><td>Angola</td><td>Africa</td><td>1246700</td><td>20609294</td><td>1.01E+08</td></tr></tbody></table><p> A country is big if it has an area of bigger than 3 million square km or a population of more than 25 million.</p><p> Write a SQL solution to output big countries' name, population and area.</p><p> For example, according to the above table, we should output:</p><table><thead><tr class="header"><th>name</th><th>population</th><th>area</th></tr></thead><tbody><tr class="odd"><td>Afghanistan</td><td>25500100</td><td>652230</td></tr><tr class="even"><td>Algeria</td><td>37100000</td><td>2381741</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> <span class="keyword">name</span>, population, area <span class="keyword">from</span> World <span class="keyword">where</span> population > <span class="number">25000000</span> <span class="keyword">or</span> area > <span class="number">3000000</span>;</span><br></pre></td></tr></table></figure><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> <span class="keyword">name</span>, population, area <span class="keyword">from</span> World <span class="keyword">where</span> population > <span class="number">25000000</span> <span class="keyword">union</span> <span class="keyword">select</span> <span class="keyword">name</span>, population, area <span class="keyword">from</span> World <span class="keyword">where</span> area > <span class="number">3000000</span>;</span><br></pre></td></tr></table></figure><p> union 替代 or 虽然看起来比较繁琐，但会加快运算速度。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第四天：</p>
</summary>
<category term="LeetCode" scheme="http://zhouweidong.ml/categories/LeetCode/"/>
<category term="LeetCode" scheme="http://zhouweidong.ml/tags/LeetCode/"/>
<category term="MySql" scheme="http://zhouweidong.ml/tags/MySql/"/>
</entry>
<entry>
<title>LeetCode MySql Day3</title>
<link href="http://zhouweidong.ml/2020/05/07/LeetCode-MySql-Day3/"/>
<id>http://zhouweidong.ml/2020/05/07/LeetCode-MySql-Day3/</id>
<published>2020-05-07T04:48:04.000Z</published>
<updated>2020-09-03T12:52:27.461Z</updated>
<content type="html"><![CDATA[<p> 第三天：</p><a id="more"></a><h2 id="customers-who-never-order">183. Customers Who Never Order</h2><p> Suppose that a website contains two tables, the Customers table and the Orders table. Write a SQL query to find all customers who never order anything.</p><p> Table: Customers.</p><table><thead><tr class="header"><th>Id</th><th>Name</th></tr></thead><tbody><tr class="odd"><td>1</td><td>Joe</td></tr><tr class="even"><td>2</td><td>Henry</td></tr><tr class="odd"><td>3</td><td>Sam</td></tr><tr class="even"><td>4</td><td>Max</td></tr></tbody></table><p> Table: Orders.</p><table><thead><tr class="header"><th>Id</th><th>CustomerId</th></tr></thead><tbody><tr class="odd"><td>1</td><td>3</td></tr><tr class="even"><td>2</td><td>1</td></tr></tbody></table><p>Using the above tables as example, return the following:</p><table><thead><tr class="header"><th>Customers</th></tr></thead><tbody><tr class="odd"><td>Henry</td></tr><tr class="even"><td>Max</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> <span class="keyword">Name</span> <span class="keyword">as</span> Customers <span class="keyword">from</span> Customers <span class="keyword">where</span> <span class="keyword">Id</span> <span class="keyword">not</span> <span class="keyword">in</span> (<span class="keyword">select</span> CustimerId <span class="keyword">from</span> Orders);</span><br></pre></td></tr></table></figure><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">select</span> c.Name <span class="keyword">from</span> Customers c <span class="keyword">left</span> <span class="keyword">join</span> Orders o <span class="keyword">on</span> c.Id = o.CustormerId <span class="keyword">where</span> o.CustomerId <span class="keyword">is</span> <span class="literal">NULL</span>;</span><br></pre></td></tr></table></figure><p> 前者的逻辑清晰，但对于大数据而言运算速度慢于后者。</p><h2 id="delete-duplicate-emails">196. Delete Duplicate Emails</h2><p> Write a SQL query to delete all duplicate email entries in a table named Person, keeping only unique emails based on its smallest Id.</p><table><thead><tr class="header"><th>Id</th><th>Email</th></tr></thead><tbody><tr class="odd"><td>1</td><td>john@example.com</td></tr><tr class="even"><td>2</td><td>bob@example.com</td></tr><tr class="odd"><td>3</td><td>john@example.com</td></tr></tbody></table><p> Id is the primary key column for this table.</p><p> For example, after running your query, the above Person table should have the following rows:</p><table><thead><tr class="header"><th>Id</th><th>Email</th></tr></thead><tbody><tr class="odd"><td>1</td><td>john@example.com</td></tr><tr class="even"><td>2</td><td>bob@example.com</td></tr></tbody></table><p> 解决方法：</p><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">delete</span> p1 <span class="keyword">from</span> Person p1, Person p2 <span class="keyword">where</span> p1.Email = p2.Email <span class="keyword">and</span> p1.Id > p2.Id;</span><br></pre></td></tr></table></figure><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">delete</span> <span class="keyword">from</span> Person <span class="keyword">where</span> <span class="keyword">Id</span> <span class="keyword">not</span> <span class="keyword">in</span> (<span class="keyword">select</span> * <span class="keyword">from</span> (<span class="keyword">select</span> <span class="keyword">min</span>(<span class="keyword">id</span>) <span class="keyword">from</span> Person <span class="keyword">group</span> <span class="keyword">by</span> Email) <span class="keyword">as</span> p);</span><br></pre></td></tr></table></figure><p> 前者的逻辑相对比较清晰；后者的逻辑是：无论是否重复，利用 group by 分组 求 Id 的最小值，即需要保留的数据，然后利用 not in 删除剩余部分。</p><p> 值得注意的是：利用 delete 语句删除数据前，务必先使用 select 语句确定删除的数据是否正确，避免误删。</p><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kity@2.0.4/dist/kity.min.js"></script><script type="text/javascript" src="https://cdn.jsdelivr.net/npm/kityminder-core@1.4.50/dist/kityminder.core.min.js"></script><script defer="true" type="text/javascript" src="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.js"></script><link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/hexo-simple-mindmap@0.2.0/dist/mindmap.min.css">]]></content>
<summary type="html">
<p> 第三天：</p>
</summary>
<category term="LeetCode" scheme="http://zhouweidong.ml/categories/LeetCode/"/>
<category term="LeetCode" scheme="http://zhouweidong.ml/tags/LeetCode/"/>
<category term="MySql" scheme="http://zhouweidong.ml/tags/MySql/"/>
</entry>
</feed>