https://gb.yeadoc.com/tags/df/ Recent content in Df on Clarity Hugo -- gohugo.io zh Copyright © 2008–2018, Steve Francia and the Hugo Authors; all rights reserved. Thu, 26 Mar 2026 08:49:31 +0000 https://gb.yeadoc.com/20260326084931155502/ Thu, 26 Mar 2026 08:49:31 +0000 https://gb.yeadoc.com/20260326084931155502/ <p>考试情况： 1.题目形式没有较大的变化，填空题目数量有增加。如果填空题较多一定注意考试时间。 2.论述题上，目前几次去考试。题目都是集中在聚类和集成算法上。 3.z-scale(特征缩放),scale。推荐使用这种方式进行解答from sklearn.preprocessing import scale 4.能够掌握具体的方法的使用再去考试。 5.多去看sklearn的文档 数据挖掘流程： 读取数据： 数据预处理：缺失值处理(<em><strong>)、异常值处理、特征缩放(</strong></em>)、特征离散化、特征编码(<em><strong>)、特征选择(</strong></em>)、特征降维(***)。 拆分数据： 建模：分类、聚类 模型评估： 分类模型评估(交叉验证)、聚类模型评估 模型优化 过采样、网格搜索</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"><span class="kn">import</span> <span class="nn">warnings</span> </span></span><span class="line"><span class="ln">4</span><span class="cl"><span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span> </span></span></code></pre></div><p>1.读取除表头外的前五行\读取数据前五行 注意点： 1.数据读取不到，检查路径和文件名是否有误，记得同步到obs上 2.注意编码,gbk、utf8</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;./titanic_trains.csv&#39;</span><span class="p">,</span><span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf8&#39;</span><span class="p">)</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"><span class="n">df</span><span class="o">.</span><span class="n">shape</span> </span></span></code></pre></div><pre><code>(891, 12) </code></pre> <p>2.缺失值处理(没有要求就遵循80%原则) 注意点： 1.名义型变量(一般在题目中都会指定那几列)，没有指定就是值object类型。 2.有时题目中会指定处理那几列，但是一定要检查其他的列中是否还有缺失值。如果一定要处理掉。 3.数值型使用均值或者中位数,object类型使用众数。目标列如果有缺失值一定使用众数。 2.1.查看缺失值情况，输出缺失值比例、列名、缺失值数量</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"> <span class="n">zb</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"> <span class="n">sl</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> </span></span><span class="line"><span class="ln">4</span><span class="cl"> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;缺失值占比为:&#39;</span><span class="p">,</span><span class="nb">round</span><span class="p">(</span><span class="n">zb</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span><span class="s2">&#34;%,列名:&#34;</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="s1">&#39;,缺失值数量:&#39;</span><span class="p">,</span><span class="n">sl</span><span class="p">)</span> </span></span></code></pre></div><pre><code>缺失值占比为: 0.0 %,列名: PassengerId ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Survived ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Pclass ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Name ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Sex ,缺失值数量: 0 缺失值占比为: 19.87 %,列名: Age ,缺失值数量: 177 缺失值占比为: 0.0 %,列名: SibSp ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Parch ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Ticket ,缺失值数量: 0 缺失值占比为: 0.0 %,列名: Fare ,缺失值数量: 0 缺失值占比为: 77.1 %,列名: Cabin ,缺失值数量: 687 缺失值占比为: 0.22 %,列名: Embarked ,缺失值数量: 2 </code></pre> <p>2.2.对缺失值进行处理</p>