使用Pandas进行数据探索（Python）

不念不忘少年蓝@ 2024-03-26 12:52 144阅读 0赞

[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/87480378  
[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/87480378

#### 实验一、使用Pandas进行数据探索 ####

介绍：
    	本次实验通过分析电信运营商的客户离网率数据集来熟悉Pandas数据探索的常用方法，并构建一个预测客户离网率的简单模型！
    
    知识点：
    	排列/索引/交叉表/透视表/数据探索

> Pandas的主要方法

Pandas是居于Numpy的一种工具，提供了大量数据探索的方法。Pandas可以使用类似SQL的方式对.csv/.tsv/.xlsx等格式的数据进行处理分析
    
    Pandas主要使用的数据结构是Series和DataFrame类。下面简要介绍这两个类：
    	Series是一种类似一维数组的对象，它由一组数据(各种Numpy数据类型)及一组与之相关的数据标签(即索引)组成。
    	DataFrame是一个二维数结构，即一张表格，其中每列数据的类型相同，你可以把它看成由Series实例构成的字典

下面开始此次实验，我们将通过分析电信运营商的客户离散率数据集来展示Pandas的主要方法！

首先导入必要的库，即`Numpy`和`Pandas`

import numpy as np
    import pandas as pd
    import warnings
    # python开发中经常遇到报错的情况，但是warning通常并不影响程序的运行，而且有时特别讨厌,使用此方法即可
    warnings.filterwarnings('ignore')

通过`read_csv()`方法读取数据，然后使用`head()`方法查看前五行数据！

df = pd.read_csv('../数据集/telecom_churn.csv')
    df.head()
    
    """
    	df = pd.read_csv('https://labfile.oss.aliyuncs.com/courses/1283/telecom_churn.csv')
    	可以直接在线解析！
    """

<table> 
 <thead> 
  <tr> 
   <th align="right"></th> 
   <th align="center">State</th> 
   <th align="center">Account length</th> 
   <th align="center">Area code</th> 
   <th align="center">International plan</th> 
   <th align="center">Voice mail plan</th> 
   <th align="center">Number vmail messages</th> 
   <th align="center">Total day minutes</th> 
   <th align="center">Total day calls</th> 
   <th align="center">Total day charge</th> 
   <th align="center">Total eve minutes</th> 
   <th align="center">Total eve calls</th> 
   <th align="center">Total eve charge</th> 
   <th align="center">Total night minutes</th> 
   <th align="center">Total night calls</th> 
   <th align="center">Total night charge</th> 
   <th align="center">Total intl minutes</th> 
   <th align="center">Total intl calls</th> 
   <th align="center">Total intl charge</th> 
   <th align="center">Customer service calls</th> 
   <th align="center">Churn</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="right">0</td> 
   <td align="center">KS</td> 
   <td align="center">128</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">Yes</td> 
   <td align="center">25</td> 
   <td align="center">265.1</td> 
   <td align="center">110</td> 
   <td align="center">45.07</td> 
   <td align="center">197.4</td> 
   <td align="center">99</td> 
   <td align="center">16.78</td> 
   <td align="center">244.7</td> 
   <td align="center">91</td> 
   <td align="center">11.01</td> 
   <td align="center">10.0</td> 
   <td align="center">3</td> 
   <td align="center">2.70</td> 
   <td align="center">1</td> 
   <td align="center">False</td> 
  </tr> 
  <tr> 
   <td align="right">1</td> 
   <td align="center">OH</td> 
   <td align="center">107</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">Yes</td> 
   <td align="center">26</td> 
   <td align="center">161.6</td> 
   <td align="center">123</td> 
   <td align="center">27.47</td> 
   <td align="center">195.5</td> 
   <td align="center">103</td> 
   <td align="center">16.62</td> 
   <td align="center">254.4</td> 
   <td align="center">103</td> 
   <td align="center">11.45</td> 
   <td align="center">13.7</td> 
   <td align="center">3</td> 
   <td align="center">3.70</td> 
   <td align="center">1</td> 
   <td align="center">False</td> 
  </tr> 
  <tr> 
   <td align="right">2</td> 
   <td align="center">NJ</td> 
   <td align="center">137</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">243.4</td> 
   <td align="center">114</td> 
   <td align="center">41.38</td> 
   <td align="center">121.2</td> 
   <td align="center">110</td> 
   <td align="center">10.30</td> 
   <td align="center">162.6</td> 
   <td align="center">104</td> 
   <td align="center">7.32</td> 
   <td align="center">12.2</td> 
   <td align="center">5</td> 
   <td align="center">3.29</td> 
   <td align="center">0</td> 
   <td align="center">False</td> 
  </tr> 
  <tr> 
   <td align="right">3</td> 
   <td align="center">OH</td> 
   <td align="center">84</td> 
   <td align="center">408</td> 
   <td align="center">Yes</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">299.4</td> 
   <td align="center">71</td> 
   <td align="center">50.90</td> 
   <td align="center">61.9</td> 
   <td align="center">88</td> 
   <td align="center">5.26</td> 
   <td align="center">196.9</td> 
   <td align="center">89</td> 
   <td align="center">8.86</td> 
   <td align="center">6.6</td> 
   <td align="center">7</td> 
   <td align="center">1.78</td> 
   <td align="center">2</td> 
   <td align="center">False</td> 
  </tr> 
  <tr> 
   <td align="right">4</td> 
   <td align="center">OK</td> 
   <td align="center">75</td> 
   <td align="center">415</td> 
   <td align="center">Yes</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">166.7</td> 
   <td align="center">113</td> 
   <td align="center">28.34</td> 
   <td align="center">148.3</td> 
   <td align="center">122</td> 
   <td align="center">12.61</td> 
   <td align="center">186.9</td> 
   <td align="center">121</td> 
   <td align="center">8.41</td> 
   <td align="center">10.1</td> 
   <td align="center">3</td> 
   <td align="center">2.73</td> 
   <td align="center">3</td> 
   <td align="center">False</td> 
  </tr> 
 </tbody> 
</table>

上图中每行对应一个客户，每列对应客户的一个特征！

让我们查看一下该数据库的维度、特征名称和特征类型：

`df.shape`

结果表明列表包含3333行和20列，下面我们来尝试打印列名：

`df.columns`

我们还可以使用`info()`方法输出DataFrame的一些总体信息：

`df.info()`

![724c39f03cd92611a4602154e551e75c.png][]

`bool、int64、float64和object`是该数据库特征的数据类型。这一方法同时也会显示是否有缺失值，上述结果表明在该数据集中不存在缺失值，因为每列都包含3333个观测，和我们之前使用`shape`方法得到的数字是一样的！

`astype()`方法可以更改列的类型，下列公式将Churn离网率特征修改魏int64类型！

`df['Churn']=df['Churn'].astype('int64')`

![eceea1d1aea8ddc2cd038e40fe21d3b7.png][]

`describe()`方法可以显示数值特征(`int64和float64`)的基本统计学特性，如未缺失值的数值、均值、标准差、范围、四分位数等！

`df.describe()`

<table> 
 <thead> 
  <tr> 
   <th align="center"></th> 
   <th align="center">Account length</th> 
   <th align="center">Area code</th> 
   <th align="center">Number vmail messages</th> 
   <th align="center">Total day minutes</th> 
   <th align="center">Total day calls</th> 
   <th align="center">Total day charge</th> 
   <th align="center">Total eve minutes</th> 
   <th align="center">Total eve calls</th> 
   <th align="center">Total eve charge</th> 
   <th align="center">Total night minutes</th> 
   <th align="center">Total night calls</th> 
   <th align="center">Total night charge</th> 
   <th align="center">Total intl minutes</th> 
   <th align="center">Total intl calls</th> 
   <th align="center">Total intl charge</th> 
   <th align="center">Customer service calls</th> 
   <th align="center">Churn</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="center">count</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
   <td align="center">3333.000000</td> 
  </tr> 
  <tr> 
   <td align="center">mean</td> 
   <td align="center">101.064806</td> 
   <td align="center">437.182418</td> 
   <td align="center">8.099010</td> 
   <td align="center">179.775098</td> 
   <td align="center">100.435644</td> 
   <td align="center">30.562307</td> 
   <td align="center">200.980348</td> 
   <td align="center">100.114311</td> 
   <td align="center">17.083540</td> 
   <td align="center">200.872037</td> 
   <td align="center">100.107711</td> 
   <td align="center">9.039325</td> 
   <td align="center">10.237294</td> 
   <td align="center">4.479448</td> 
   <td align="center">2.764581</td> 
   <td align="center">1.562856</td> 
   <td align="center">0.144914</td> 
  </tr> 
  <tr> 
   <td align="center">std</td> 
   <td align="center">39.822106</td> 
   <td align="center">42.371290</td> 
   <td align="center">13.688365</td> 
   <td align="center">54.467389</td> 
   <td align="center">20.069084</td> 
   <td align="center">9.259435</td> 
   <td align="center">50.713844</td> 
   <td align="center">19.922625</td> 
   <td align="center">4.310668</td> 
   <td align="center">50.573847</td> 
   <td align="center">19.568609</td> 
   <td align="center">2.275873</td> 
   <td align="center">2.791840</td> 
   <td align="center">2.461214</td> 
   <td align="center">0.753773</td> 
   <td align="center">1.315491</td> 
   <td align="center">0.352067</td> 
  </tr> 
  <tr> 
   <td align="center">min</td> 
   <td align="center">1.000000</td> 
   <td align="center">408.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">23.200000</td> 
   <td align="center">33.000000</td> 
   <td align="center">1.040000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">0.000000</td> 
  </tr> 
  <tr> 
   <td align="center">25%</td> 
   <td align="center">74.000000</td> 
   <td align="center">408.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">143.700000</td> 
   <td align="center">87.000000</td> 
   <td align="center">24.430000</td> 
   <td align="center">166.600000</td> 
   <td align="center">87.000000</td> 
   <td align="center">14.160000</td> 
   <td align="center">167.000000</td> 
   <td align="center">87.000000</td> 
   <td align="center">7.520000</td> 
   <td align="center">8.500000</td> 
   <td align="center">3.000000</td> 
   <td align="center">2.300000</td> 
   <td align="center">1.000000</td> 
   <td align="center">0.000000</td> 
  </tr> 
  <tr> 
   <td align="center">50%</td> 
   <td align="center">101.000000</td> 
   <td align="center">415.000000</td> 
   <td align="center">0.000000</td> 
   <td align="center">179.400000</td> 
   <td align="center">101.000000</td> 
   <td align="center">30.500000</td> 
   <td align="center">201.400000</td> 
   <td align="center">100.000000</td> 
   <td align="center">17.120000</td> 
   <td align="center">201.200000</td> 
   <td align="center">100.000000</td> 
   <td align="center">9.050000</td> 
   <td align="center">10.300000</td> 
   <td align="center">4.000000</td> 
   <td align="center">2.780000</td> 
   <td align="center">1.000000</td> 
   <td align="center">0.000000</td> 
  </tr> 
  <tr> 
   <td align="center">75%</td> 
   <td align="center">127.000000</td> 
   <td align="center">510.000000</td> 
   <td align="center">20.000000</td> 
   <td align="center">216.400000</td> 
   <td align="center">114.000000</td> 
   <td align="center">36.790000</td> 
   <td align="center">235.300000</td> 
   <td align="center">114.000000</td> 
   <td align="center">20.000000</td> 
   <td align="center">235.300000</td> 
   <td align="center">113.000000</td> 
   <td align="center">10.590000</td> 
   <td align="center">12.100000</td> 
   <td align="center">6.000000</td> 
   <td align="center">3.270000</td> 
   <td align="center">2.000000</td> 
   <td align="center">0.000000</td> 
  </tr> 
  <tr> 
   <td align="center">max</td> 
   <td align="center">243.000000</td> 
   <td align="center">510.000000</td> 
   <td align="center">51.000000</td> 
   <td align="center">350.800000</td> 
   <td align="center">165.000000</td> 
   <td align="center">59.640000</td> 
   <td align="center">363.700000</td> 
   <td align="center">170.000000</td> 
   <td align="center">30.910000</td> 
   <td align="center">395.000000</td> 
   <td align="center">175.000000</td> 
   <td align="center">17.770000</td> 
   <td align="center">20.000000</td> 
   <td align="center">20.000000</td> 
   <td align="center">5.400000</td> 
   <td align="center">9.000000</td> 
   <td align="center">1.000000</td> 
  </tr> 
 </tbody> 
</table>

通过`include`参数显式指定包含的数据类型，可以查看非数值特征的统计数据！

`df.describe(include=['object','bool'])`

![951a83fe143b97e85e9b37c70ebb7d02.png][]

`value_counts()`方法可以查看类别(类型为object)和布尔值(类型为bool)特征。让我们看下Churn离网率的分布！

`df['churn'].value_counts()`

![01c1de85cc37a742e271d1b248066583.png][]

上述结果表明，在3333位用户中，2850位是忠实客户，它们的`Churn`值为0.调用`value_counts()`函数时，加上`normalize=True`参数可以显示比例！

`df['Churn'].value_counts(normalize=True)`

![0e88134f8293525cf2a3a73f22807799.png][]

> 排序

DataFrame可以根据某个变量的值(也就是列)排序。eg：根据每日消费额排序(设置ascending=False倒序排列)

`df.sort_values(by='Total day charge', ascending=False).head()`

<table> 
 <thead> 
  <tr> 
   <th align="center"></th> 
   <th align="center">State</th> 
   <th align="center">Account length</th> 
   <th align="center">Area code</th> 
   <th align="center">International plan</th> 
   <th align="center">Voice mail plan</th> 
   <th align="center">Number vmail messages</th> 
   <th align="center">Total day minutes</th> 
   <th align="center">Total day calls</th> 
   <th align="center">Total day charge</th> 
   <th align="center">Total eve minutes</th> 
   <th align="center">Total eve calls</th> 
   <th align="center">Total eve charge</th> 
   <th align="center">Total night minutes</th> 
   <th align="center">Total night calls</th> 
   <th align="center">Total night charge</th> 
   <th align="center">Total intl minutes</th> 
   <th align="center">Total intl calls</th> 
   <th align="center">Total intl charge</th> 
   <th align="center">Customer service calls</th> 
   <th align="center">Churn</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="center">365</td> 
   <td align="center">CO</td> 
   <td align="center">154</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">350.8</td> 
   <td align="center">75</td> 
   <td align="center">59.64</td> 
   <td align="center">216.5</td> 
   <td align="center">94</td> 
   <td align="center">18.40</td> 
   <td align="center">253.9</td> 
   <td align="center">100</td> 
   <td align="center">11.43</td> 
   <td align="center">10.1</td> 
   <td align="center">9</td> 
   <td align="center">2.73</td> 
   <td align="center">1</td> 
   <td align="center">1</td> 
  </tr> 
  <tr> 
   <td align="center">985</td> 
   <td align="center">NY</td> 
   <td align="center">64</td> 
   <td align="center">415</td> 
   <td align="center">Yes</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">346.8</td> 
   <td align="center">55</td> 
   <td align="center">58.96</td> 
   <td align="center">249.5</td> 
   <td align="center">79</td> 
   <td align="center">21.21</td> 
   <td align="center">275.4</td> 
   <td align="center">102</td> 
   <td align="center">12.39</td> 
   <td align="center">13.3</td> 
   <td align="center">9</td> 
   <td align="center">3.59</td> 
   <td align="center">1</td> 
   <td align="center">1</td> 
  </tr> 
  <tr> 
   <td align="center">2594</td> 
   <td align="center">OH</td> 
   <td align="center">115</td> 
   <td align="center">510</td> 
   <td align="center">Yes</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">345.3</td> 
   <td align="center">81</td> 
   <td align="center">58.70</td> 
   <td align="center">203.4</td> 
   <td align="center">106</td> 
   <td align="center">17.29</td> 
   <td align="center">217.5</td> 
   <td align="center">107</td> 
   <td align="center">9.79</td> 
   <td align="center">11.8</td> 
   <td align="center">8</td> 
   <td align="center">3.19</td> 
   <td align="center">1</td> 
   <td align="center">1</td> 
  </tr> 
  <tr> 
   <td align="center">156</td> 
   <td align="center">OH</td> 
   <td align="center">83</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">337.4</td> 
   <td align="center">120</td> 
   <td align="center">57.36</td> 
   <td align="center">227.4</td> 
   <td align="center">116</td> 
   <td align="center">19.33</td> 
   <td align="center">153.9</td> 
   <td align="center">114</td> 
   <td align="center">6.93</td> 
   <td align="center">15.8</td> 
   <td align="center">7</td> 
   <td align="center">4.27</td> 
   <td align="center">0</td> 
   <td align="center">1</td> 
  </tr> 
  <tr> 
   <td align="center">605</td> 
   <td align="center">MO</td> 
   <td align="center">112</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">No</td> 
   <td align="center">0</td> 
   <td align="center">335.5</td> 
   <td align="center">77</td> 
   <td align="center">57.04</td> 
   <td align="center">212.5</td> 
   <td align="center">109</td> 
   <td align="center">18.06</td> 
   <td align="center">265.0</td> 
   <td align="center">132</td> 
   <td align="center">11.93</td> 
   <td align="center">12.7</td> 
   <td align="center">8</td> 
   <td align="center">3.43</td> 
   <td align="center">2</td> 
   <td align="center">1</td> 
  </tr> 
 </tbody> 
</table>

此外，还可以根据多个列的数值排序。下面函数实现的功能为：先按 Churn 离网率 升序排列，再按 `Total day charge` 每日总话费 降序排列，优先级 `Churn > Tatal day charge`。

`df.sort_values(by=['Churn','Total day charge'],ascending=[True,False]).head()`

<table> 
 <thead> 
  <tr> 
   <th align="center"></th> 
   <th align="center">State</th> 
   <th align="center">Account length</th> 
   <th align="center">Area code</th> 
   <th align="center">International plan</th> 
   <th align="center">Voice mail plan</th> 
   <th align="right">Number vmail messages</th> 
   <th align="center">Total day minutes</th> 
   <th align="center">Total day calls</th> 
   <th align="center">Total day charge</th> 
   <th align="center">Total eve minutes</th> 
   <th align="center">Total eve calls</th> 
   <th align="center">Total eve charge</th> 
   <th align="center">Total night minutes</th> 
   <th align="center">Total night calls</th> 
   <th align="center">Total night charge</th> 
   <th align="center">Total intl minutes</th> 
   <th align="center">Total intl calls</th> 
   <th align="center">Total intl charge</th> 
   <th align="center">Customer service calls</th> 
   <th align="center">Churn</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="center">688</td> 
   <td align="center">MN</td> 
   <td align="center">13</td> 
   <td align="center">510</td> 
   <td align="center">No</td> 
   <td align="center">Yes</td> 
   <td align="right">21</td> 
   <td align="center">315.6</td> 
   <td align="center">105</td> 
   <td align="center">53.65</td> 
   <td align="center">208.9</td> 
   <td align="center">71</td> 
   <td align="center">17.76</td> 
   <td align="center">260.1</td> 
   <td align="center">123</td> 
   <td align="center">11.70</td> 
   <td align="center">12.1</td> 
   <td align="center">3</td> 
   <td align="center">3.27</td> 
   <td align="center">3</td> 
   <td align="center">0</td> 
  </tr> 
  <tr> 
   <td align="center">2259</td> 
   <td align="center">NC</td> 
   <td align="center">210</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">Yes</td> 
   <td align="right">31</td> 
   <td align="center">313.8</td> 
   <td align="center">87</td> 
   <td align="center">53.35</td> 
   <td align="center">147.7</td> 
   <td align="center">103</td> 
   <td align="center">12.55</td> 
   <td align="center">192.7</td> 
   <td align="center">97</td> 
   <td align="center">8.67</td> 
   <td align="center">10.1</td> 
   <td align="center">7</td> 
   <td align="center">2.73</td> 
   <td align="center">3</td> 
   <td align="center">0</td> 
  </tr> 
  <tr> 
   <td align="center">534</td> 
   <td align="center">LA</td> 
   <td align="center">67</td> 
   <td align="center">510</td> 
   <td align="center">No</td> 
   <td align="center">No</td> 
   <td align="right">0</td> 
   <td align="center">310.4</td> 
   <td align="center">97</td> 
   <td align="center">52.77</td> 
   <td align="center">66.5</td> 
   <td align="center">123</td> 
   <td align="center">5.65</td> 
   <td align="center">246.5</td> 
   <td align="center">99</td> 
   <td align="center">11.09</td> 
   <td align="center">9.2</td> 
   <td align="center">10</td> 
   <td align="center">2.48</td> 
   <td align="center">4</td> 
   <td align="center">0</td> 
  </tr> 
  <tr> 
   <td align="center">575</td> 
   <td align="center">SD</td> 
   <td align="center">114</td> 
   <td align="center">415</td> 
   <td align="center">No</td> 
   <td align="center">Yes</td> 
   <td align="right">36</td> 
   <td align="center">309.9</td> 
   <td align="center">90</td> 
   <td align="center">52.68</td> 
   <td align="center">200.3</td> 
   <td align="center">89</td> 
   <td align="center">17.03</td> 
   <td align="center">183.5</td> 
   <td align="center">105</td> 
   <td align="center">8.26</td> 
   <td align="center">14.2</td> 
   <td align="center">2</td> 
   <td align="center">3.83</td> 
   <td align="center">1</td> 
   <td align="center">0</td> 
  </tr> 
  <tr> 
   <td align="center">2858</td> 
   <td align="center">AL</td> 
   <td align="center">141</td> 
   <td align="center">510</td> 
   <td align="center">No</td> 
   <td align="center">Yes</td> 
   <td align="right">28</td> 
   <td align="center">308.0</td> 
   <td align="center">123</td> 
   <td align="center">52.36</td> 
   <td align="center">247.8</td> 
   <td align="center">128</td> 
   <td align="center">21.06</td> 
   <td align="center">152.9</td> 
   <td align="center">103</td> 
   <td align="center">6.88</td> 
   <td align="center">7.4</td> 
   <td align="center">3</td> 
   <td align="center">2.00</td> 
   <td align="center">1</td> 
   <td align="center">0</td> 
  </tr> 
 </tbody> 
</table>

[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/87480378  
[资源下载地址][Link 1]：https://download.csdn.net/download/sheziqiong/87480378

[Link 1]: https://download.csdn.net/download/sheziqiong/87480378
[724c39f03cd92611a4602154e551e75c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/26/1bf2d790b07c4251909d585b2fd8b21a.png
[eceea1d1aea8ddc2cd038e40fe21d3b7.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/26/71a5bba27a5f4360958e0d36f10ca3b8.png
[951a83fe143b97e85e9b37c70ebb7d02.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/26/e74588b82f5941a7b7ceeeb9d1e51c80.png
[01c1de85cc37a742e271d1b248066583.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/26/90981d7145e744028c64b214507422a2.png
[0e88134f8293525cf2a3a73f22807799.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/26/3fcf0ecee3ef4aeeb1e91481e203c721.png