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来源：数据分析1480


100 个 pandas 数据分析函数总结

经过一段时间的整理，本期将分享我认为比较常规的100个实用函数，这些函数大致可以分为六类，分别是统计汇总函数、数据清洗函数、数据筛选、绘图与元素级运算函数、时间序列函数和其他函数。

一、统计汇总函数
数据分析过程中，必然要做一些数据的统计汇总工作，那么对于这一块的数据运算有哪些可用的函数可以帮助到我们呢？具体看如下几张表。
100 个 pandas 数据分析函数总结

100 个 pandas 数据分析函数总结
<section><span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">import</span> pandas <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">as</span> pd<br  /><span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">import</span> numpy <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">as</span> np<br  />x = pd.Series(np.random.normal(<span style="line-height: 26px;">2</span>,<span style="line-height: 26px;">3</span>,<span style="line-height: 26px;">1000</span>))<br  />y = <span style="line-height: 26px;">3</span>*x + <span style="line-height: 26px;">10</span> + pd.Series(np.random.normal(<span style="line-height: 26px;">1</span>,<span style="line-height: 26px;">2</span>,<span style="line-height: 26px;">1000</span>))<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 计算x与y的相关系数</span><br  />print(x.corr(y))<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 计算y的偏度</span><br  />print(y.skew())<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 计算y的统计描述值</span><br  />print(x.describe())<br  /><br  />z = pd.Series([<span style="color: rgb(166, 226, 46);line-height: 26px;">'A'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'B'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'C'</span>]).sample(n = <span style="line-height: 26px;">1000</span>, replace = <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">True</span>)<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 重新修改z的行索引</span><br  />z.index = range(<span style="line-height: 26px;">1000</span>)<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 按照z分组，统计y的组内平均值</span><br  />y.groupby(by = z).aggregate(np.mean)</section>
100 个 pandas 数据分析函数总结
100 个 pandas 数据分析函数总结
<section><span style="color: rgb(117, 113, 94);line-height: 26px;"># 统计z中个元素的频次</span><br  />print(z.value_counts())<br  /><br  />a = pd.Series([<span style="line-height: 26px;">1</span>,<span style="line-height: 26px;">5</span>,<span style="line-height: 26px;">10</span>,<span style="line-height: 26px;">15</span>,<span style="line-height: 26px;">25</span>,<span style="line-height: 26px;">30</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 计算a中各元素的累计百分比</span><br  />print(a.cumsum() / a.cumsum()[a.size - <span style="line-height: 26px;">1</span>])</section>

100 个 pandas 数据分析函数总结


二、数据清洗函数
同样，数据清洗工作也是必不可少的工作，在如下表格中罗列了常有的数据清洗的函数。
100 个 pandas 数据分析函数总结
<section>x = pd.Series([<span style="line-height: 26px;">10</span>,<span style="line-height: 26px;">13</span>,np.nan,<span style="line-height: 26px;">17</span>,<span style="line-height: 26px;">28</span>,<span style="line-height: 26px;">19</span>,<span style="line-height: 26px;">33</span>,np.nan,<span style="line-height: 26px;">27</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;">#检验序列中是否存在缺失值</span><br  />print(x.hasnans)<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 将缺失值填充为平均值</span><br  />print(x.fillna(value = x.mean()))<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 前向填充缺失值</span><br  />print(x.ffill())</section>
100 个 pandas 数据分析函数总结
100 个 pandas 数据分析函数总结
<section>income = pd.Series([<span style="color: rgb(166, 226, 46);line-height: 26px;">'12500元'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'8000元'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'8500元'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'15000元'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'9000元'</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 将收入转换为整型</span><br  />print(income.str[:<span style="line-height: 26px;">-1</span>].astype(int))<br  /><br  />gender = pd.Series([<span style="color: rgb(166, 226, 46);line-height: 26px;">'男'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'女'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'女'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'女'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'男'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'女'</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 性别因子化处理</span><br  />print(gender.factorize())<br  /><br  />house = pd.Series([<span style="color: rgb(166, 226, 46);line-height: 26px;">'大宁金茂府 | 3室2厅 | 158.32平米 | 南 | 精装'</span>,<br  />                   <span style="color: rgb(166, 226, 46);line-height: 26px;">'昌里花园 | 2室2厅 | 104.73平米 | 南 | 精装'</span>,<br  />                   <span style="color: rgb(166, 226, 46);line-height: 26px;">'纺大小区 | 3室1厅 | 68.38平米 | 南 | 简装'</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 取出二手房的面积，并转换为浮点型</span><br  />house.str.split(<span style="color: rgb(166, 226, 46);line-height: 26px;">'|'</span>).str[<span style="line-height: 26px;">2</span>].str.strip().str[:<span style="line-height: 26px;">-2</span>].astype(float)</section>
100 个 pandas 数据分析函数总结

三、数据筛选
数据分析中如需对变量中的数值做子集筛选时，可以巧妙的使用下表中的几个函数，其中部分函数既可以使用在序列身上，也基本可以使用在数据框对象中。
100 个 pandas 数据分析函数总结
<section>np.random.seed(<span style="line-height: 26px;">1234</span>)<br  />x = pd.Series(np.random.randint(<span style="line-height: 26px;">10</span>,<span style="line-height: 26px;">20</span>,<span style="line-height: 26px;">10</span>))<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 筛选出16以上的元素</span><br  />print(x.loc[x > <span style="line-height: 26px;">16</span>])<br  /><br  />print(x.compress(x > <span style="line-height: 26px;">16</span>))<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 筛选出13~16之间的元素</span><br  />print(x[x.between(<span style="line-height: 26px;">13</span>,<span style="line-height: 26px;">16</span>)])<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 取出最大的三个元素</span><br  />print(x.nlargest(<span style="line-height: 26px;">3</span>))<br  /><br  />y = pd.Series([<span style="color: rgb(166, 226, 46);line-height: 26px;">'ID:1 name:张三 age:24 income:13500'</span>,<br  />               <span style="color: rgb(166, 226, 46);line-height: 26px;">'ID:2 name:李四 age:27 income:25000'</span>,<br  />               <span style="color: rgb(166, 226, 46);line-height: 26px;">'ID:3 name:王二 age:21 income:8000'</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 取出年龄，并转换为整数</span><br  />print(y.str.findall(<span style="color: rgb(166, 226, 46);line-height: 26px;">'age:(d+)'</span>).str[<span style="line-height: 26px;">0</span>].astype(int))</section>

100 个 pandas 数据分析函数总结


四、绘图与元素级函数
100 个 pandas 数据分析函数总结
<section>np.random.seed(<span style="line-height: 26px;">123</span>)<br  /><span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">import</span> matplotlib.pyplot <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">as</span> plt<br  />x = pd.Series(np.random.normal(<span style="line-height: 26px;">10</span>,<span style="line-height: 26px;">3</span>,<span style="line-height: 26px;">1000</span>))<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 绘制x直方图</span><br  />x.hist()<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 显示图形</span><br  />plt.show()<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 绘制x的箱线图</span><br  />x.plot(kind=<span style="color: rgb(166, 226, 46);line-height: 26px;">'box'</span>)<br  />plt.show()<br  /><br  />installs = pd.Series([<span style="color: rgb(166, 226, 46);line-height: 26px;">'1280万'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'6.7亿'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'2488万'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'1892万'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'9877'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'9877万'</span>,<span style="color: rgb(166, 226, 46);line-height: 26px;">'1.2亿'</span>])<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 将安装量统一更改为“万”的单位</span><br  /><span style="line-height: 26px;"><span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">def</span> <span style="color: rgb(166, 226, 46);font-weight: bold;line-height: 26px;">transform</span><span style="line-height: 26px;">(x)</span>:</span><br  />    <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">if</span> x.find(<span style="color: rgb(166, 226, 46);line-height: 26px;">'亿'</span>) != <span style="line-height: 26px;">-1</span>:<br  />        res = float(x[:<span style="line-height: 26px;">-1</span>])*<span style="line-height: 26px;">10000</span><br  />    <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">elif</span> x.find(<span style="color: rgb(166, 226, 46);line-height: 26px;">'万'</span>) != <span style="line-height: 26px;">-1</span>:<br  />        res = float(x[:<span style="line-height: 26px;">-1</span>])<br  />    <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">else</span>:<br  />        res = float(x)/<span style="line-height: 26px;">10000</span><br  />    <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">return</span> res<br  />installs.apply(transform)</section>
100 个 pandas 数据分析函数总结

100 个 pandas 数据分析函数总结

100 个 pandas 数据分析函数总结

五、时间序列函数

100 个 pandas 数据分析函数总结
100 个 pandas 数据分析函数总结
100 个 pandas 数据分析函数总结

六、其他函数

100 个 pandas 数据分析函数总结
<section><span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">import</span> numpy <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">as</span> np<br  /><span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">import</span> pandas <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">as</span> pd<br  /><br  />np.random.seed(<span style="line-height: 26px;">112</span>)<br  />x = pd.Series(np.random.randint(<span style="line-height: 26px;">8</span>,<span style="line-height: 26px;">18</span>,<span style="line-height: 26px;">6</span>))<br  />print(x)<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 对x中的元素做一阶差分</span><br  />print(x.diff())<br  /><br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 对x中的元素做降序处理</span><br  />print(x.sort_values(ascending = <span style="color: rgb(249, 38, 114);font-weight: bold;line-height: 26px;">False</span>))<br  /><br  />y = pd.Series(np.random.randint(<span style="line-height: 26px;">8</span>,<span style="line-height: 26px;">16</span>,<span style="line-height: 26px;">100</span>))<br  /><span style="color: rgb(117, 113, 94);line-height: 26px;"># 将y中的元素做排重处理，并转换为列表对象</span><br  />y.unique().tolist()</section>

100 个 pandas 数据分析函数总结

100 个 pandas 数据分析函数总结

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UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><strong style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"><span style="max-width: 100%;font-size: 15px;color: rgb(2, 30, 170);box-sizing: border-box !important;overflow-wrap: break-word !important;">推荐阅读:</span></strong></p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><span style="max-width: 100%;font-size: 15px;color: rgb(2, 30, 170);box-sizing: border-box !important;overflow-wrap: break-word 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!important;overflow-wrap: break-word !important;">|学Python就是这条捷径</span></p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><br style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"  /></p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><span style="max-width: 100%;font-size: 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underline;box-sizing: border-box !important;overflow-wrap: break-word !important;">用Python做个海量小姐姐素描图 ｜</span></p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><br style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"  /></p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><span style="max-width: 100%;font-size: 15px;color: rgb(2, 30, 170);box-sizing: border-box !important;overflow-wrap: break-word !important;">趣味:</span><span style="max-width: 100%;color: rgb(2, 30, 170);text-decoration: underline;font-size: 14px;box-sizing: border-box !important;overflow-wrap: break-word !important;">弹球游戏</span>  | <span style="max-width: 100%;text-decoration: underline;font-size: 14px;box-sizing: border-box !important;overflow-wrap: break-word !important;">九宫格</span>  | 漂亮的花 | 两百行Python《天天酷跑》游戏!</p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><br style="max-width: 100%;box-sizing: border-box !important;overflow-wrap: break-word !important;"  /></p><p style="max-width: 100%;min-height: 1em;caret-color: rgb(51, 51, 51);font-family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;letter-spacing: 0.544px;white-space: normal;text-size-adjust: auto;box-sizing: border-box !important;overflow-wrap: break-word !important;text-align: left;"><span style="max-width: 100%;color: rgb(2, 30, 170);box-sizing: border-box !important;overflow-wrap: break-word !important;">AI:</span> 会做诗的机器人 | 给图片上色 | 预测收入 | 碟中谍这么火，我用机器学习做个迷你推荐系统电影</p>


年度爆款文案

  • 1).卧槽！Pdf转Word用Python轻松搞定！

  • 2).学Python真香！我用100行代码做了个网站，帮人PS旅行图片，赚个鸡腿吃

  • 3).首播过亿，火爆全网，我分析了《乘风破浪的姐姐》，发现了这些秘密 

  • 4).80行代码！用Python做一个哆来A梦分身 

  • 5).你必须掌握的20个python代码，短小精悍，用处无穷 

  • 6).30个Python奇淫技巧集 

  • 7).我总结的80页《菜鸟学Python精选干货.pdf》,都是干货 

  • 8).再见Python！我要学Go了！2500字深度分析！

  • 9).发现一个舔狗福利！这个Python爬虫神器太爽了，自动下载妹子图片




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230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" style="max-width: 100%;display: inline-block;clear: both;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-brushtype="text" data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-style="padding: 18px 15px 20px 10px; color: rgb(86, 146, 214); text-align: center; letter-spacing: 1.5px; background-image: url('https://www.zkxjob.com/wp-content/uploads/2022/06/wxsync-2022-06-a2a8a5e1e58f30392066a170034ee027.png'); background-size: 100% 100%; background-repeat: no-repeat; overflow-wrap: break-word !important;" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="padding: 18px 15px 20px 10px;max-width: 100%;background-size: 100% 100%;background-image: url('https://www.zkxjob.com/wp-content/uploads/2022/06/wxsync-2022-06-a2a8a5e1e58f30392066a170034ee027.png');color: rgb(86, 146, 214);letter-spacing: 1.5px;background-repeat: no-repeat;box-sizing: border-box !important;overflow-wrap: break-word !important;"><section data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" 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data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="margin-left: 2px;max-width: 100%;width: 20px;box-sizing: border-box !important;overflow-wrap: break-word !important;"></section><section data-brushtype="text" data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(51, 51, 51)" data-darkmode-original-color-15860613985508="rgb(51, 51, 51)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(51, 51, 51)" data-darkmode-original-color-15870356070738="rgb(51, 51, 51)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(51, 51, 51)" data-darkmode-original-color-15870356071023="rgb(51, 51, 51)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(51, 51, 51)" data-darkmode-original-color-15882384789136="rgb(51, 51, 51)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(51, 51, 51)" data-darkmode-original-color-15882396318564="rgb(51, 51, 51)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(51, 51, 51)" data-darkmode-original-color-15900529136199="rgb(51, 51, 51)" data-darkmode-bgimage-15900529136199="1" style="max-width: 100%;font-size: 14px;color: rgb(51, 51, 51);text-align: left;box-sizing: border-box !important;overflow-wrap: break-word !important;"><span style="max-width: 100%;font-family: 楷体, 楷体_GB2312, SimKai;font-size: 15px;box-sizing: border-box !important;overflow-wrap: break-word !important;">点这里，直达菜鸟学PythonB站！！</span></section></section></section></section></section></section></section>
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