第一章：第三节探索性数据分析-课程.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"BW4iWbLWVJtp"},"source":["**复习：**在前面我们已经学习了Pandas基础，知道利用Pandas读取csv数据的增删查改，今天我们要学习的就是**探索性数据分析**，主要介绍如何利用Pandas进行排序、算术计算以及计算描述函数describe()的使用。"]},{"cell_type":"markdown","metadata":{"id":"hu_BVZ3IVJtr"},"source":["# 1 第一章：探索性数据分析"]},{"cell_type":"markdown","metadata":{"id":"1aMxN66XVJts"},"source":["#### 开始之前，导入numpy、pandas包和数据"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"_4oQvviSVJts","executionInfo":{"status":"ok","timestamp":1678532638777,"user_tz":-540,"elapsed":630,"user":{"displayName":"张婷","userId":"16421421487827652066"}}},"outputs":[],"source":["#加载所需的库\n","import numpy as np\n","import pandas as pd"]},{"cell_type":"code","execution_count":2,"metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":528},"id":"dRp8mNVVVJtt","executionInfo":{"status":"ok","timestamp":1678532668604,"user_tz":-540,"elapsed":243,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"14290508-7021-4c4d-de2f-5c189cab28ad"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   乘客ID  是否幸存  仓位等级                                                 姓名  \\\n","0     1     0     3                            Braund, Mr. Owen Harris   \n","1     2     1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n","2     3     1     3                             Heikkinen, Miss. Laina   \n","3     4     1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n","4     5     0     3                           Allen, Mr. William Henry   \n","\n","       性别    年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  \n","0    male  22.0       1       0         A/5 21171   7.2500   NaN    S  \n","1  female  38.0       1       0          PC 17599  71.2833   C85    C  \n","2  female  26.0       0       0  STON/O2. 3101282   7.9250   NaN    S  \n","3  female  35.0       1       0            113803  53.1000  C123    S  \n","4    male  35.0       0       0            373450   8.0500   NaN    S  "],"text/html":["\n","  <div id=\"df-b6eab463-084f-4f4f-9504-cc219e313ef4\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>乘客ID</th>\n","      <th>是否幸存</th>\n","      <th>仓位等级</th>\n","      <th>姓名</th>\n","      <th>性别</th>\n","      <th>年龄</th>\n","      <th>兄弟姐妹个数</th>\n","      <th>父母子女个数</th>\n","      <th>船票信息</th>\n","      <th>票价</th>\n","      <th>客舱</th>\n","      <th>登船港口</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6eab463-084f-4f4f-9504-cc219e313ef4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-b6eab463-084f-4f4f-9504-cc219e313ef4 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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4)),index=['2','1'],columns=['d', 'a', 'b', 'c'])\n","frame"]},{"cell_type":"markdown","metadata":{"id":"djIL-VJiVJtu"},"source":["【代码解析】\n","\n","pd.DataFrame() ：创建一个DataFrame对象 \n","\n","np.arange(8).reshape((2, 4)) : 生成一个二维数组（2*4）,第一列：0，1，2，3 第二列：4，5，6，7\n","\n","index=['2, 1] ：DataFrame 对象的索引列\n","\n","columns=['d', 'a', 'b', 'c'] ：DataFrame 对象的索引行"]},{"cell_type":"markdown","metadata":{"id":"F7buoTbwVJtu"},"source":["【问题】：大多数时候我们都是想根据列的值来排序,所以将你构建的DataFrame中的数据根据某一列，升序排列"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"wsxymhKbVJtu","executionInfo":{"status":"ok","timestamp":1678533198959,"user_tz":-540,"elapsed":255,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"9d027780-b8df-4905-d543-48f4a2f73f09"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   d  a  b  c\n","2  0  1  2  3\n","1  4  5  6  7"],"text/html":["\n","  <div 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":4}],"source":["#回答代码 sort_values这个函数中by参数指向要排列的列，ascending参数指向排序的方式（升序还是降序）\n","frame.sort_values(by='c', ascending=True)"]},{"cell_type":"markdown","metadata":{"id":"Vcd7lV5CVJtu"},"source":["【思考】通过书本你能说出Pandas对DataFrame数据的其他排序方式吗？"]},{"cell_type":"markdown","metadata":{"id":"uZkvzpO8VJtu"},"source":["【总结】下面将不同的排序方式做一个总结"]},{"cell_type":"markdown","metadata":{"id":"z7S_0e_9VJtv"},"source":["1.让行索引升序排序"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"tB_KBCWSVJtv","executionInfo":{"status":"ok","timestamp":1678533251226,"user_tz":-540,"elapsed":244,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"912c41cc-9b8a-4d92-9dd2-dc31696d5bdd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   d  a  b  c\n","1  4  5  6  7\n","2  0  1  2  3"],"text/html":["\n","  <div id=\"df-a69c0d6e-2b88-4deb-9263-ff20c52867c9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>d</th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a69c0d6e-2b88-4deb-9263-ff20c52867c9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-a69c0d6e-2b88-4deb-9263-ff20c52867c9 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Anna  female  35.0   \n","737   738     1     1              Lesurer, Mr. Gustave J    male  35.0   \n","\n","     兄弟姐妹个数  父母子女个数      船票信息        票价           客舱 登船港口  \n","679       0       1  PC 17755  512.3292  B51 B53 B55    C  \n","258       0       0  PC 17755  512.3292          NaN    C  \n","737       0       0  PC 17755  512.3292         B101    C  "],"text/html":["\n","  <div id=\"df-5046a9d7-f546-48da-9d53-ac349b41f2ef\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>乘客ID</th>\n","      <th>是否幸存</th>\n","      <th>仓位等级</th>\n","      <th>姓名</th>\n","      <th>性别</th>\n","      <th>年龄</th>\n","      <th>兄弟姐妹个数</th>\n","      <th>父母子女个数</th>\n","      <th>船票信息</th>\n","      <th>票价</th>\n","      <th>客舱</th>\n","      <th>登船港口</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>679</th>\n","      <td>680</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n","      <td>male</td>\n","      <td>36.0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>PC 17755</td>\n","      <td>512.3292</td>\n","      <td>B51 B53 B55</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>258</th>\n","      <td>259</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Ward, Miss. Anna</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>PC 17755</td>\n","      <td>512.3292</td>\n","      <td>NaN</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>737</th>\n","      <td>738</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Lesurer, Mr. Gustave J</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>PC 17755</td>\n","      <td>512.3292</td>\n","      <td>B101</td>\n","      <td>C</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5046a9d7-f546-48da-9d53-ac349b41f2ef')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-5046a9d7-f546-48da-9d53-ac349b41f2ef button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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ascending=False).head(3)"]},{"cell_type":"markdown","metadata":{"id":"HLvM99k0VJtw"},"source":["【思考】排序后，如果我们仅仅关注年龄和票价两列。根据常识我知道发现票价越高的应该客舱越好，所以我们会明显看出，票价前20的乘客中存活的有14人，这是相当高的一个比例，那么我们后面是不是可以进一步分析一下票价和存活之间的关系，年龄和存活之间的关系呢？当你开始发现数据之间的关系了，数据分析就开始了。\n","\n","当然，这只是我的想法，你还可以有更多想法，欢迎写在你的学习笔记中。"]},{"cell_type":"markdown","metadata":{"id":"XYWCqI5CVJtx"},"source":["**多做几个数据的排序**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tAJRwGZfVJtx"},"outputs":[],"source":["#代码\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D1N5T6WgVJtx"},"outputs":[],"source":["#写下你的思考\n"]},{"cell_type":"markdown","metadata":{"id":"nBOdM4wPVJtx"},"source":["#### 1.6.3 任务三：利用Pandas进行算术计算，计算两个DataFrame数据相加结果"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"66jDVEL4VJtx","executionInfo":{"status":"ok","timestamp":1678533699861,"user_tz":-540,"elapsed":258,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"c6bcc26e-69fd-4c9a-95e6-adb2b3320406"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["         a    b    c\n","one    0.0  1.0  2.0\n","two    3.0  4.0  5.0\n","three  6.0  7.0  8.0"],"text/html":["\n","  <div id=\"df-995badcc-8d64-429e-9a05-5e6138c79280\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":10}],"source":["# 具体请看《利用Python进行数据分析》第五章 算术运算与数据对齐 部分\n","\n","#自己构建两个都为数字的DataFrame数据\n","\n","#我们举了一个例子：\n","frame1_a = pd.DataFrame(np.arange(9.).reshape(3, 3),\n","                     columns=['a', 'b', 'c'],\n","                     index=['one', 'two', 'three'])\n","frame1_b = pd.DataFrame(np.arange(12.).reshape(4, 3),\n","                     columns=['a', 'e', 'c'],\n","                     index=['first', 'one', 'two', 'second'])\n","frame1_a"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"bQqy0dtkVJtx","executionInfo":{"status":"ok","timestamp":1678533721144,"user_tz":-540,"elapsed":252,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"cec2e5ba-d5c7-4d80-a73e-6b80c01b7b49"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["          a     e     c\n","first   0.0   1.0   2.0\n","one     3.0   4.0   5.0\n","two     6.0   7.0   8.0\n","second  9.0  10.0  11.0"],"text/html":["\n","  <div id=\"df-2e4aa93f-1b54-490f-b7a3-f0078a6d64be\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>a</th>\n","      <th>e</th>\n","      <th>c</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>first</th>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>one</th>\n","      <td>3.0</td>\n","      <td>4.0</td>\n","      <td>5.0</td>\n","    </tr>\n","    <tr>\n","      <th>two</th>\n","      <td>6.0</td>\n","      <td>7.0</td>\n","      <td>8.0</td>\n","    </tr>\n","    <tr>\n","      <th>second</th>\n","      <td>9.0</td>\n","      <td>10.0</td>\n","      <td>11.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e4aa93f-1b54-490f-b7a3-f0078a6d64be')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-2e4aa93f-1b54-490f-b7a3-f0078a6d64be button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":14}],"source":["#代码\n","frame1_a + frame1_b"]},{"cell_type":"markdown","metadata":{"id":"_5kdhg9IVJty"},"source":["【提醒】两个DataFrame相加后，会返回一个新的DataFrame，对应的行和列的值会相加，没有对应的会变成空值NaN。<br>\n","当然，DataFrame还有很多算术运算，如减法，除法等，有兴趣的同学可以看《利用Python进行数据分析》第五章 算术运算与数据对齐 部分，多在网络上查找相关学习资料。"]},{"cell_type":"markdown","metadata":{"id":"jun5rxvBVJty"},"source":["#### 1.6.4 任务四：通过泰坦尼克号数据如何计算出在船上最大的家族有多少人？"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bUhP5OAQVJty"},"outputs":[],"source":["'''\n","还是用之前导入的chinese_train.csv如果我们想看看在船上，最大的家族有多少人（‘兄弟姐妹个数’+‘父母子女个数’），我们该怎么做呢？\n","'''\n"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_v6ad_1gVJty","executionInfo":{"status":"ok","timestamp":1678533899418,"user_tz":-540,"elapsed":240,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"9e040499-2bfb-401f-c52b-fe5924be7ab8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["10"]},"metadata":{},"execution_count":15}],"source":["#代码\n","max(text['兄弟姐妹个数'] + text['父母子女个数'])"]},{"cell_type":"markdown","metadata":{"id":"xvHHdA9YVJtz"},"source":["【提醒】我们只需找出”兄弟姐妹个数“和”父母子女个数“之和最大的数，当然你还可以想出很多方法和思考角度，欢迎你来说出你的看法。"]},{"cell_type":"markdown","metadata":{"id":"Em0rDPpWVJtz"},"source":["**多做几个数据的相加，看看你能分析出什么？**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MMzN6mYmVJtz"},"outputs":[],"source":["#代码\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EbCKte8QVJtz"},"outputs":[],"source":["#写下你的其他分析\n"]},{"cell_type":"markdown","metadata":{"id":"6GL-FIYlVJt0"},"source":["#### 1.6.5 任务五：学会使用Pandas describe()函数查看数据基本统计信息"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"z0WjeRz_VJt0","executionInfo":{"status":"ok","timestamp":1678533955406,"user_tz":-540,"elapsed":232,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"727b2611-33b7-482a-f77f-ec18e4ae132d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    one  two\n","a  1.40  NaN\n","b  7.10 -4.5\n","c   NaN  NaN\n","d  0.75 -1.3"],"text/html":["\n","  <div id=\"df-2b14ac81-b003-42ed-82c8-b68a9b918b48\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>one</th>\n","      <th>two</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>a</th>\n","      <td>1.40</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>b</th>\n","      <td>7.10</td>\n","      <td>-4.5</td>\n","    </tr>\n","    <tr>\n","      <th>c</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>d</th>\n","      <td>0.75</td>\n","      <td>-1.3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b14ac81-b003-42ed-82c8-b68a9b918b48')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-2b14ac81-b003-42ed-82c8-b68a9b918b48');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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columns=['one', 'two'])\n","frame2\n"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"klx1oMSDVJt0","executionInfo":{"status":"ok","timestamp":1678533979321,"user_tz":-540,"elapsed":227,"user":{"displayName":"张婷","userId":"16421421487827652066"}},"outputId":"5785d1f8-28db-4607-b522-4ee815c6f67c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["            one       two\n","count  3.000000  2.000000\n","mean   3.083333 -2.900000\n","std    3.493685  2.262742\n","min    0.750000 -4.500000\n","25%    1.075000 -3.700000\n","50%    1.400000 -2.900000\n","75%    4.250000 -2.100000\n","max    7.100000 -1.300000"],"text/html":["\n","  <div id=\"df-25a5b0d3-dc31-4c74-9ff6-483d910dbf8c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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<th>75%</th>\n","      <td>4.250000</td>\n","      <td>-2.100000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>7.100000</td>\n","      <td>-1.300000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25a5b0d3-dc31-4c74-9ff6-483d910dbf8c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-25a5b0d3-dc31-4c74-9ff6-483d910dbf8c');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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