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<h4 id="1）功能">1）功能</h4>
<ul>
<li>
<p><strong>功能</strong>：对数据中每一列数进行统计分析；（以“列”为单位进行统计分析）</p>
</li>
<li>
<p>默认只先对**“number”**的列进行统计分析；</p>
</li>
</ul>
<p></p>
<ul>
<li><strong>一列数据全是“number”</strong></li>
</ul>
<ol>
<li><strong>count</strong>：一列的元素个数；</li>
<li><strong>mean</strong>：一列数据的平均值；</li>
<li><strong>std</strong>：一列数据的均方差；（方差的算术平方根，反映一个数据集的离散程度：越大，数据间的差异越大，数据集中数据的离散程度越高；越小，数据间的大小差异越小，数据集中的数据离散程度越低）</li>
<li><strong>min</strong>：一列数据中的最小值；</li>
<li><strong>max</strong>：一列数中的最大值；</li>
<li><strong>25%</strong>：一列数据中，前 25% 的数据的平均值；</li>
<li><strong>50%</strong>：一列数据中，前 50% 的数据的平均值；</li>
<li><strong>75%</strong>：一列数据中，前 75% 的数据的平均值；</li>
</ol>
<p></p>
<ul>
<li><strong>一列数据： “categorical”、“categorical” + “number”：</strong></li>
</ul>
<ol>
<li><strong>count</strong>：一列数据的元素个数；</li>
<li><strong>unique</strong>：一列数据中元素的种类；</li>
<li><strong>top</strong>：一列数据中出现频率最高的元素；</li>
<li><strong>freq</strong>：一列数据中出现频率最高的元素的个数；</li>
</ol>
<p></p>
<ul>
<li><strong>一列数据：object（如时间序列）</strong></li>
</ul>
<ol>
<li><strong>first</strong>：开始时间；</li>
<li><strong>last</strong>：结束时间；</li>
</ol>
<p></p>
<h4 id="2）实例及参数使用：Series-数据类型">2）实例及参数使用：Series 数据类型</h4>
<ul>
<li>
<p>number</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-0.png" alt="Pandas.md-fig-0.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>categorical</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-1.png" alt="Pandas.md-fig-1.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>object（统称为 “string” 类）</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-2.png" alt="Pandas.md-fig-2.png"></p>
</li>
</ul>
<h4 id=""></h4>
<h4 id="-2"></h4>
<h4 id="3）实例及参数使用：DataFrame-数据类型">3）实例及参数使用：DataFrame 数据类型</h4>
<ul>
<li>
<p>（一）默认只处理 number</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-3.png" alt="Pandas.md-fig-3.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>**（二）**分析整个 DataFrame 数据：include = ‘all’</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-4.png" alt="Pandas.md-fig-4.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>（三）指定统计分析 DataFrame 中的某一列</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-5.png" alt="Pandas.md-fig-5.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>（四）只分析所有的 “number” 列</p>
</li>
<li>
<p>也可以是：<strong>df.describe(include=[‘number’])</strong></p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-6.png" alt="Pandas.md-fig-6.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>（五）只分析所有 “category” 列</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-7.png" alt="Pandas.md-fig-7.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>（六）只统计所有 “object” 列</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-8.png" alt="Pandas.md-fig-8.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>（七）分析除了 “number” 列的所有列</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-9.png" alt="Pandas.md-fig-9.png"></p>
</li>
</ul>
<p></p>
<ul>
<li>
<p>（八）分析除了 “object” 列的所有列</p>
</li>
<li>
<p><img src="https://imgs-1302910354.cos.ap-shanghai.myqcloud.com/images/Pandas.md-fig-10.png" alt="Pandas.md-fig-10.png"></p>
</li>
</ul>
<p></p>
<h4 id="4）与-loc、sort-的配合使用">4）与 loc、sort 的配合使用</h4>
<ul>
<li><strong>df.describe(include=[‘number’]).loc[[‘min’, ‘max’, ‘mean’, ‘std’]].T.sort_values(‘max’)</strong></li>
<li>只对数据的“min”、“max”、“mean”、“std”进行分析，并将分析的结果转置后，以“max”的大小对每行进行排序；（默认从小到大）</li>
</ul>
</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta">文章作者: </span><span class="post-copyright-info"><a href="https://ismaxu.com">Ruoneo</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta">文章链接: </span><span class="post-copyright-info"><a href="https://ismaxu.com/2020/06/23/Pandas/">https://ismaxu.com/2020/06/23/Pandas/</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta">版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://ismaxu.com" target="_blank">水榭听泉</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"></div><div class="post_share"><div class="social-share" data-image="https://i.loli.net/2020/05/01/gkihqEjXxJ5UZ1C.jpg" data-sites="facebook,twitter,wechat,weibo,qq"></div><link rel="stylesheet" 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class="toc-item toc-level-3"><a class="toc-link" href="#%E4%B8%80%E3%80%81Pandas-%E5%92%8C-Series-%E7%9A%84-describe-%E6%96%B9%E6%B3%95"><span class="toc-text">一、Pandas 和 Series 的 describe() 方法</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#1%EF%BC%89%E5%8A%9F%E8%83%BD"><span class="toc-text">1）功能</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#2%EF%BC%89%E5%AE%9E%E4%BE%8B%E5%8F%8A%E5%8F%82%E6%95%B0%E4%BD%BF%E7%94%A8%EF%BC%9ASeries-%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B"><span class="toc-text">2）实例及参数使用：Series 数据类型</span></a></li><li class="toc-item toc-level-4"><a class="toc-link"><span class="toc-text"></span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#-2"><span class="toc-text"></span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#3%EF%BC%89%E5%AE%9E%E4%BE%8B%E5%8F%8A%E5%8F%82%E6%95%B0%E4%BD%BF%E7%94%A8%EF%BC%9ADataFrame-%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B"><span 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