模块四Pandas作业.ipynb · 罗诗羽/阶段五模块四作业

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模块四Pandas作业.ipynb 103.63 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "noticed-disease",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supreme-territory",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 体侧成绩加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "quick-contemporary",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <th>班级</th>\n",
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       "     班级 性别 男1000米跑  男50米跑    男跳远  男体前屈  男引体  男肺活量     身高    体重  BMI\n",
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       "1     1  男    4'16   7.70  225.0    11    7  3133  174.0  52.7    0\n",
       "2     1  男    4'09   8.45  218.0    14    1  3901  169.0  46.5    0\n",
       "3     1  男    4'21   8.05  206.0    13    1  4946  183.0  79.7    0\n",
       "4     1  男    3'44   7.52  210.0    13    9  3538  171.0  54.7    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "472  17  男    4'23   8.27  208.0    10    0  4647  176.0  69.5    0\n",
       "473  17  男    5'19   9.55  210.0    15    6  7042  177.0  76.0    0\n",
       "474  17  男    3'25   7.50  252.0    13   13  5755  181.0  65.0    0\n",
       "475  17  男    4'39   7.81  208.0    14   11  5688  172.0  51.7    0\n",
       "476  17  男       0   0.00    0.0     0    0     0    0.0   0.0    0\n",
       "\n",
       "[477 rows x 11 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_excel('./18级高一体测成绩汇总.xls')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "wicked-fusion",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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      "text/plain": [
       "     班级 性别  女800米跑  女50米跑    女跳远  女体前屈  女仰卧  女肺活量     身高    体重  BMI\n",
       "0     1  女    3.22   9.32  185.0    16   48  3775  163.0  51.3    0\n",
       "1     1  女    4.59  11.44  148.0     9   29  3683  163.0  66.6    0\n",
       "2     1  女    3.46  13.40  150.0     7   40  3331  157.0  60.0    0\n",
       "3     1  女    3.39   9.52  172.0    21   46  3701  160.0  50.7    0\n",
       "4     1  女    3.43   9.79  145.0     8   34  3592  167.0  63.9    0\n",
       "..   .. ..     ...    ...    ...   ...  ...   ...    ...   ...  ...\n",
       "588  17  女    3.51   9.60  150.0    24   41  2255  158.0  49.0    0\n",
       "589  17  女    4.00  10.18  150.0    13   36  2937  161.0  55.7    0\n",
       "590  17  女    3.45  10.18  152.0    15   35  2592  165.0  48.6    0\n",
       "591  17  女    4.01   9.67  165.0    10   41  1829  154.0  43.6    0\n",
       "592  17  女    4.48   9.09  180.0    10   46  2962  162.0  55.3    0\n",
       "\n",
       "[593 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_excel('./18级高一体测成绩汇总.xls', sheet_name = '女')\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "right-brooks",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 评分标准加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "sealed-halloween",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>4540</td>\n",
       "      <td>100</td>\n",
       "      <td>3150</td>\n",
       "      <td>100</td>\n",
       "      <td>7.1</td>\n",
       "      <td>100</td>\n",
       "      <td>7.8</td>\n",
       "      <td>100</td>\n",
       "      <td>23.6</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>100</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100</td>\n",
       "      <td>53</td>\n",
       "      <td>100</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>100</td>\n",
       "      <td>3'24\"</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4420</td>\n",
       "      <td>95</td>\n",
       "      <td>3100</td>\n",
       "      <td>95</td>\n",
       "      <td>7.2</td>\n",
       "      <td>95</td>\n",
       "      <td>7.9</td>\n",
       "      <td>95</td>\n",
       "      <td>21.5</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>198</td>\n",
       "      <td>95</td>\n",
       "      <td>15.0</td>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>3'35\"</td>\n",
       "      <td>95</td>\n",
       "      <td>3'30\"</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4300</td>\n",
       "      <td>90</td>\n",
       "      <td>3050</td>\n",
       "      <td>90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>90</td>\n",
       "      <td>8.0</td>\n",
       "      <td>90</td>\n",
       "      <td>19.4</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>90</td>\n",
       "      <td>14.0</td>\n",
       "      <td>90</td>\n",
       "      <td>49</td>\n",
       "      <td>90</td>\n",
       "      <td>3'40\"</td>\n",
       "      <td>90</td>\n",
       "      <td>3'36\"</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4050</td>\n",
       "      <td>85</td>\n",
       "      <td>2900</td>\n",
       "      <td>85</td>\n",
       "      <td>7.4</td>\n",
       "      <td>85</td>\n",
       "      <td>8.3</td>\n",
       "      <td>85</td>\n",
       "      <td>17.2</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3'47\"</td>\n",
       "      <td>85</td>\n",
       "      <td>3'43\"</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3800</td>\n",
       "      <td>80</td>\n",
       "      <td>2750</td>\n",
       "      <td>80</td>\n",
       "      <td>7.5</td>\n",
       "      <td>80</td>\n",
       "      <td>8.6</td>\n",
       "      <td>80</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>178</td>\n",
       "      <td>80</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80</td>\n",
       "      <td>43</td>\n",
       "      <td>80</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>80</td>\n",
       "      <td>3'50\"</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3680</td>\n",
       "      <td>78</td>\n",
       "      <td>2650</td>\n",
       "      <td>78</td>\n",
       "      <td>7.7</td>\n",
       "      <td>78</td>\n",
       "      <td>8.8</td>\n",
       "      <td>78</td>\n",
       "      <td>13.6</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>175</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>78</td>\n",
       "      <td>3'55\"</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3560</td>\n",
       "      <td>76</td>\n",
       "      <td>2550</td>\n",
       "      <td>76</td>\n",
       "      <td>7.9</td>\n",
       "      <td>76</td>\n",
       "      <td>9.0</td>\n",
       "      <td>76</td>\n",
       "      <td>12.2</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>76</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>76</td>\n",
       "      <td>4'00\"</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3440</td>\n",
       "      <td>74</td>\n",
       "      <td>2450</td>\n",
       "      <td>74</td>\n",
       "      <td>8.1</td>\n",
       "      <td>74</td>\n",
       "      <td>9.2</td>\n",
       "      <td>74</td>\n",
       "      <td>10.8</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>169</td>\n",
       "      <td>74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>37</td>\n",
       "      <td>74</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>74</td>\n",
       "      <td>4'05\"</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3320</td>\n",
       "      <td>72</td>\n",
       "      <td>2350</td>\n",
       "      <td>72</td>\n",
       "      <td>8.3</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>9.4</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>166</td>\n",
       "      <td>72</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>72</td>\n",
       "      <td>4'10\"</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3200</td>\n",
       "      <td>70</td>\n",
       "      <td>2250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>9.6</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>163</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>70</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>70</td>\n",
       "      <td>4'15\"</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3080</td>\n",
       "      <td>68</td>\n",
       "      <td>2150</td>\n",
       "      <td>68</td>\n",
       "      <td>8.7</td>\n",
       "      <td>68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>68</td>\n",
       "      <td>6.6</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>68</td>\n",
       "      <td>4'20\"</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2960</td>\n",
       "      <td>66</td>\n",
       "      <td>2050</td>\n",
       "      <td>66</td>\n",
       "      <td>8.9</td>\n",
       "      <td>66</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>157</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>66</td>\n",
       "      <td>4'25\"</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2840</td>\n",
       "      <td>64</td>\n",
       "      <td>1950</td>\n",
       "      <td>64</td>\n",
       "      <td>9.1</td>\n",
       "      <td>64</td>\n",
       "      <td>10.2</td>\n",
       "      <td>64</td>\n",
       "      <td>3.8</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>64</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>64</td>\n",
       "      <td>4'30\"</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2720</td>\n",
       "      <td>62</td>\n",
       "      <td>1850</td>\n",
       "      <td>62</td>\n",
       "      <td>9.3</td>\n",
       "      <td>62</td>\n",
       "      <td>10.4</td>\n",
       "      <td>62</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62</td>\n",
       "      <td>...</td>\n",
       "      <td>151</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>62</td>\n",
       "      <td>4'35\"</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2600</td>\n",
       "      <td>60</td>\n",
       "      <td>1750</td>\n",
       "      <td>60</td>\n",
       "      <td>9.5</td>\n",
       "      <td>60</td>\n",
       "      <td>10.6</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>60</td>\n",
       "      <td>4'45\"</td>\n",
       "      <td>60</td>\n",
       "      <td>4'40\"</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2470</td>\n",
       "      <td>50</td>\n",
       "      <td>1710</td>\n",
       "      <td>50</td>\n",
       "      <td>9.7</td>\n",
       "      <td>50</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>143</td>\n",
       "      <td>50</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>5'05\"</td>\n",
       "      <td>50</td>\n",
       "      <td>4'50\"</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2340</td>\n",
       "      <td>40</td>\n",
       "      <td>1670</td>\n",
       "      <td>40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>40</td>\n",
       "      <td>11.0</td>\n",
       "      <td>40</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40</td>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>5'25\"</td>\n",
       "      <td>40</td>\n",
       "      <td>5'00\"</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2210</td>\n",
       "      <td>30</td>\n",
       "      <td>1630</td>\n",
       "      <td>30</td>\n",
       "      <td>10.1</td>\n",
       "      <td>30</td>\n",
       "      <td>11.2</td>\n",
       "      <td>30</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>5'45\"</td>\n",
       "      <td>30</td>\n",
       "      <td>5'10\"</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080</td>\n",
       "      <td>20</td>\n",
       "      <td>1590</td>\n",
       "      <td>20</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20</td>\n",
       "      <td>11.4</td>\n",
       "      <td>20</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>6'05\"</td>\n",
       "      <td>20</td>\n",
       "      <td>5'20\"</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1950</td>\n",
       "      <td>10</td>\n",
       "      <td>1550</td>\n",
       "      <td>10</td>\n",
       "      <td>10.5</td>\n",
       "      <td>10</td>\n",
       "      <td>11.6</td>\n",
       "      <td>10</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>6'25\"</td>\n",
       "      <td>10</td>\n",
       "      <td>5'30\"</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    男肺活量       女肺活量      男50米跑      女50米跑       男体前屈       ...  女跳远       \\\n",
       "      成绩   分数    成绩   分数    成绩   分数    成绩   分数    成绩   分数  ...   成绩   分数   \n",
       "0   4540  100  3150  100   7.1  100   7.8  100  23.6  100  ...  204  100   \n",
       "1   4420   95  3100   95   7.2   95   7.9   95  21.5   95  ...  198   95   \n",
       "2   4300   90  3050   90   7.3   90   8.0   90  19.4   90  ...  192   90   \n",
       "3   4050   85  2900   85   7.4   85   8.3   85  17.2   85  ...  185   85   \n",
       "4   3800   80  2750   80   7.5   80   8.6   80  15.0   80  ...  178   80   \n",
       "5   3680   78  2650   78   7.7   78   8.8   78  13.6   78  ...  175   78   \n",
       "6   3560   76  2550   76   7.9   76   9.0   76  12.2   76  ...  172   76   \n",
       "7   3440   74  2450   74   8.1   74   9.2   74  10.8   74  ...  169   74   \n",
       "8   3320   72  2350   72   8.3   72   9.4   72   9.4   72  ...  166   72   \n",
       "9   3200   70  2250   70   8.5   70   9.6   70   8.0   70  ...  163   70   \n",
       "10  3080   68  2150   68   8.7   68   9.8   68   6.6   68  ...  160   68   \n",
       "11  2960   66  2050   66   8.9   66  10.0   66   5.2   66  ...  157   66   \n",
       "12  2840   64  1950   64   9.1   64  10.2   64   3.8   64  ...  154   64   \n",
       "13  2720   62  1850   62   9.3   62  10.4   62   2.4   62  ...  151   62   \n",
       "14  2600   60  1750   60   9.5   60  10.6   60   1.0   60  ...  148   60   \n",
       "15  2470   50  1710   50   9.7   50  10.8   50   0.0   50  ...  143   50   \n",
       "16  2340   40  1670   40   9.9   40  11.0   40  -1.0   40  ...  138   40   \n",
       "17  2210   30  1630   30  10.1   30  11.2   30  -2.0   30  ...  133   30   \n",
       "18  2080   20  1590   20  10.3   20  11.4   20  -3.0   20  ...  128   20   \n",
       "19  1950   10  1550   10  10.5   10  11.6   10  -4.0   10  ...  123   10   \n",
       "\n",
       "     男引体      女仰卧      男1000米跑      女800米跑       \n",
       "      成绩   分数  成绩   分数      成绩   分数     成绩   分数  \n",
       "0   16.0  100  53  100   3'30\"  100  3'24\"  100  \n",
       "1   15.0   95  51   95   3'35\"   95  3'30\"   95  \n",
       "2   14.0   90  49   90   3'40\"   90  3'36\"   90  \n",
       "3   13.0   85  46   85   3'47\"   85  3'43\"   85  \n",
       "4   12.0   80  43   80   3'55\"   80  3'50\"   80  \n",
       "5    NaN   78  41   78   4'00\"   78  3'55\"   78  \n",
       "6   11.0   76  39   76   4'05\"   76  4'00\"   76  \n",
       "7    NaN   74  37   74   4'10\"   74  4'05\"   74  \n",
       "8   10.0   72  35   72   4'15\"   72  4'10\"   72  \n",
       "9    NaN   70  33   70   4'20\"   70  4'15\"   70  \n",
       "10   9.0   68  31   68   4'25\"   68  4'20\"   68  \n",
       "11   NaN   66  29   66   4'30\"   66  4'25\"   66  \n",
       "12   8.0   64  27   64   4'35\"   64  4'30\"   64  \n",
       "13   NaN   62  25   62   4'40\"   62  4'35\"   62  \n",
       "14   7.0   60  23   60   4'45\"   60  4'40\"   60  \n",
       "15   6.0   50  21   50   5'05\"   50  4'50\"   50  \n",
       "16   5.0   40  19   40   5'25\"   40  5'00\"   40  \n",
       "17   4.0   30  17   30   5'45\"   30  5'10\"   30  \n",
       "18   3.0   20  15   20   6'05\"   20  5'20\"   20  \n",
       "19   2.0   10  13   10   6'25\"   10  5'30\"   10  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stander = pd.read_excel('./体侧成绩评分表.xls', header = [0, 1])\n",
    "stander"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulated-momentum",
   "metadata": {},
   "source": [
    "### 数据类型转换"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retained-butter",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### 男体测成绩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "solid-airport",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "班级           int64\n",
       "性别          object\n",
       "男1000米跑     object\n",
       "男50米跑      float64\n",
       "男跳远        float64\n",
       "男体前屈         int64\n",
       "男引体          int64\n",
       "男肺活量         int64\n",
       "身高         float64\n",
       "体重         float64\n",
       "BMI          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "streaming-elements",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>4.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2\n",
       "0    4.0  13.0  4.22\n",
       "1    4.0  16.0  4.27\n",
       "2    4.0   9.0  4.15\n",
       "3    4.0  21.0  4.35\n",
       "4    3.0  44.0  3.73\n",
       "..   ...   ...   ...\n",
       "472  4.0  23.0  4.38\n",
       "473  5.0  19.0  5.32\n",
       "474  3.0  25.0  3.42\n",
       "475  4.0  39.0  4.65\n",
       "476  0.0   0.0  0.00\n",
       "\n",
       "[477 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4'13形式无法直接转换，replace直接替换\"'\"为\".\"无法实现分秒转换\n",
    "\n",
    "# 将 男1000米跑 中’前后数字分列，字符串转化为浮点型，并将NaN变更为0\n",
    "field = df1['男1000米跑'] .str.extract(r\"(\\d+)'(\\d+)\").applymap(lambda x :float(x)).replace(np.NaN, 0)\n",
    "# 将秒转化为分，两列合并\n",
    "field[2] = (field[0] + (field[1]/60)).round(2)\n",
    "\n",
    "field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "uniform-pursuit",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 男1000米跑结果变更为数值型填入\n",
    "df1['男1000米跑'] = field[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "vital-asthma",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 将其他非float类型数据转换为float\n",
    "df1[['男体前屈','男引体','男肺活量','BMI']] = df1[['男体前屈','男引体','男肺活量','BMI']].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "transparent-parking",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "班级           int64\n",
       "性别          object\n",
       "男1000米跑    float64\n",
       "男50米跑      float64\n",
       "男跳远        float64\n",
       "男体前屈       float64\n",
       "男引体        float64\n",
       "男肺活量       float64\n",
       "身高         float64\n",
       "体重         float64\n",
       "BMI        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "considerable-messaging",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### 女体测成绩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "adult-magnitude",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "班级          int64\n",
       "性别         object\n",
       "女800米跑    float64\n",
       "女50米跑     float64\n",
       "女跳远       float64\n",
       "女体前屈        int64\n",
       "女仰卧         int64\n",
       "女肺活量        int64\n",
       "身高        float64\n",
       "体重        float64\n",
       "BMI         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "interesting-internet",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "班级          int64\n",
       "性别         object\n",
       "女800米跑    float64\n",
       "女50米跑     float64\n",
       "女跳远       float64\n",
       "女体前屈      float64\n",
       "女仰卧       float64\n",
       "女肺活量      float64\n",
       "身高        float64\n",
       "体重        float64\n",
       "BMI       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[['女体前屈','女仰卧','女肺活量','BMI']] = df2[['女体前屈','女仰卧','女肺活量','BMI']].astype('float')\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assisted-probe",
   "metadata": {},
   "source": [
    "#### 评分表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "synthetic-coalition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男肺活量     成绩      int64\n",
       "         分数      int64\n",
       "女肺活量     成绩      int64\n",
       "         分数      int64\n",
       "男50米跑    成绩    float64\n",
       "         分数      int64\n",
       "女50米跑    成绩    float64\n",
       "         分数      int64\n",
       "男体前屈     成绩    float64\n",
       "         分数      int64\n",
       "女体前屈     成绩    float64\n",
       "         分数      int64\n",
       "男跳远      成绩      int64\n",
       "         分数      int64\n",
       "女跳远      成绩      int64\n",
       "         分数      int64\n",
       "男引体      成绩    float64\n",
       "         分数      int64\n",
       "女仰卧      成绩      int64\n",
       "         分数      int64\n",
       "男1000米跑  成绩     object\n",
       "         分数      int64\n",
       "女800米跑   成绩     object\n",
       "         分数      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stander.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fresh-domestic",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 男1000米跑成绩转换\n",
    "field_st1 = stander['男1000米跑', '成绩'] .str.extract(r\"(\\d+)'(\\d+)\").applymap(lambda x :float(x)).replace(np.NaN, 0)\n",
    "field_st1[2] = (field_st1[0] + (field_st1[1]/60)).round(2)\n",
    "stander['男1000米跑', '成绩'] = field_st1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "compressed-standing",
   "metadata": {},
   "outputs": [],
   "source": [
    "#女800米跑成绩转换\n",
    "field_st2 = stander['女800米跑', '成绩'] .str.extract(r\"(\\d+)'(\\d+)\").applymap(lambda x :float(x)).replace(np.NaN, 0)\n",
    "field_st2[2] = (field_st2[0] + (field_st2[1]/60)).round(2)\n",
    "stander['女800米跑', '成绩'] = field_st2[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "musical-location",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 其他非float类型数据转换\n",
    "stander['男肺活量','成绩'] = stander['男肺活量','成绩'].astype('float')\n",
    "stander['男肺活量','分数'] = stander['男肺活量','分数'].astype('float')\n",
    "stander['女肺活量','成绩'] = stander['女肺活量','成绩'].astype('float')\n",
    "stander['女肺活量','成绩'] = stander['女肺活量','成绩'].astype('float')\n",
    "stander['男50米跑','分数'] = stander['男50米跑','分数'].astype('float')\n",
    "stander['女50米跑','分数'] = stander['女50米跑','分数'].astype('float')\n",
    "stander['男体前屈','分数'] = stander['男体前屈','分数'].astype('float')\n",
    "stander['女体前屈','分数'] = stander['女体前屈','分数'].astype('float')\n",
    "stander['男跳远','成绩'] = stander['男跳远','成绩'].astype('float')\n",
    "stander['男跳远','分数'] = stander['男跳远','分数'].astype('float')\n",
    "stander['女跳远','成绩'] = stander['女跳远','成绩'].astype('float')\n",
    "stander['女跳远','分数'] = stander['女跳远','分数'].astype('float')\n",
    "stander['男引体','分数'] = stander['男引体','分数'].astype('float')\n",
    "stander['女仰卧','成绩'] = stander['女仰卧','成绩'].astype('float')\n",
    "stander['女仰卧','分数'] = stander['女仰卧','分数'].astype('float')\n",
    "stander['男1000米跑','分数'] = stander['男1000米跑','分数'].astype('float')\n",
    "stander['女800米跑','分数'] = stander['女800米跑','分数'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "transparent-wings",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男肺活量     成绩    float64\n",
       "         分数    float64\n",
       "女肺活量     成绩    float64\n",
       "         分数      int64\n",
       "男50米跑    成绩    float64\n",
       "         分数    float64\n",
       "女50米跑    成绩    float64\n",
       "         分数    float64\n",
       "男体前屈     成绩    float64\n",
       "         分数    float64\n",
       "女体前屈     成绩    float64\n",
       "         分数    float64\n",
       "男跳远      成绩    float64\n",
       "         分数    float64\n",
       "女跳远      成绩    float64\n",
       "         分数    float64\n",
       "男引体      成绩    float64\n",
       "         分数    float64\n",
       "女仰卧      成绩    float64\n",
       "         分数    float64\n",
       "男1000米跑  成绩    float64\n",
       "         分数    float64\n",
       "女800米跑   成绩    float64\n",
       "         分数    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stander.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neural-andorra",
   "metadata": {},
   "source": [
    "### 体测成绩分数转换"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wicked-olive",
   "metadata": {},
   "source": [
    "#### 男生成绩转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "extreme-indication",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算BMI并填入\n",
    "df1['BMI'] = round(df1['体重']/np.square((df1['身高']/100)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "interested-collection",
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义跑步得分方法，分为两种大区间四种情况划分得分情况：\n",
    "#1）不在区间内：跑太慢没分；跑太快满分；\n",
    "#2）处于各得分区间内\n",
    "\n",
    "def convert_run(x):\n",
    "    for i in range(len(run)):\n",
    "        if x > run['成绩'].iloc[-1]: # 跑太慢没分\n",
    "            return 0\n",
    "        elif x <= run['成绩'].iloc[0]:\n",
    "            if x == 0:\n",
    "                return 0 # 未参加跑步\n",
    "            else:\n",
    "                return 100 # 跑太快满分\n",
    "        elif (x <= run['成绩'].iloc[i]) and (x > run['成绩'].iloc[i-1]): # 在得分区间内\n",
    "            return run['分数'].iloc[i]\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "colored-tonight",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       72.0\n",
       "1       70.0\n",
       "2       74.0\n",
       "3       68.0\n",
       "4       85.0\n",
       "       ...  \n",
       "472     68.0\n",
       "473     40.0\n",
       "474    100.0\n",
       "475     62.0\n",
       "476      0.0\n",
       "Name: 男1000米跑分数, Length: 477, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      66.0\n",
       "1      78.0\n",
       "2      70.0\n",
       "3      74.0\n",
       "4      78.0\n",
       "       ... \n",
       "472    72.0\n",
       "473    50.0\n",
       "474    80.0\n",
       "475    76.0\n",
       "476     0.0\n",
       "Name: 男50米跑分数, Length: 477, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in ['男1000米跑', '男50米跑']:  # 循环遍历得出跑步成绩\n",
    "    run = stander[k]\n",
    "    df1[k + '分数'] = df1[k].map(convert_run)\n",
    "    display(df1[k + '分数'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "alone-ethernet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义其他项目得分方法:\n",
    "# 成绩从大到小开始遍历，分数从大到小分依次开始筛选，直到低于最低标准0分\n",
    "def convert_other(x):\n",
    "    for i in range(len(other)):\n",
    "        if x >= other['成绩'].iloc[i]:\n",
    "            return other['分数'].iloc[i]\n",
    "    return 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "statutory-drove",
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in ['男跳远', '男体前屈', '男引体', '男肺活量']: # 遍历其他项目成绩结果\n",
    "    other = stander[j]\n",
    "    df1[j + '分数'] = df1[j].map(convert_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "virgin-kansas",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量分数</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>195.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>25.12</td>\n",
       "      <td>72.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.27</td>\n",
       "      <td>7.70</td>\n",
       "      <td>225.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>17.41</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.15</td>\n",
       "      <td>8.45</td>\n",
       "      <td>218.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>16.28</td>\n",
       "      <td>74.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.35</td>\n",
       "      <td>8.05</td>\n",
       "      <td>206.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>23.80</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.73</td>\n",
       "      <td>7.52</td>\n",
       "      <td>210.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.71</td>\n",
       "      <td>85.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.38</td>\n",
       "      <td>8.27</td>\n",
       "      <td>208.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>22.44</td>\n",
       "      <td>68.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.32</td>\n",
       "      <td>9.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.26</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.42</td>\n",
       "      <td>7.50</td>\n",
       "      <td>252.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.84</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.65</td>\n",
       "      <td>7.81</td>\n",
       "      <td>208.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5688.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>17.48</td>\n",
       "      <td>62.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男50米跑    男跳远  男体前屈   男引体    男肺活量     身高    体重    BMI  \\\n",
       "0     1  男     4.22   8.88  195.0  12.0   1.0  2785.0  170.0  72.6  25.12   \n",
       "1     1  男     4.27   7.70  225.0  11.0   7.0  3133.0  174.0  52.7  17.41   \n",
       "2     1  男     4.15   8.45  218.0  14.0   1.0  3901.0  169.0  46.5  16.28   \n",
       "3     1  男     4.35   8.05  206.0  13.0   1.0  4946.0  183.0  79.7  23.80   \n",
       "4     1  男     3.73   7.52  210.0  13.0   9.0  3538.0  171.0  54.7  18.71   \n",
       "..   .. ..      ...    ...    ...   ...   ...     ...    ...   ...    ...   \n",
       "472  17  男     4.38   8.27  208.0  10.0   0.0  4647.0  176.0  69.5  22.44   \n",
       "473  17  男     5.32   9.55  210.0  15.0   6.0  7042.0  177.0  76.0  24.26   \n",
       "474  17  男     3.42   7.50  252.0  13.0  13.0  5755.0  181.0  65.0  19.84   \n",
       "475  17  男     4.65   7.81  208.0  14.0  11.0  5688.0  172.0  51.7  17.48   \n",
       "476  17  男     0.00   0.00    0.0   0.0   0.0     0.0    0.0   0.0    NaN   \n",
       "\n",
       "     男1000米跑分数  男50米跑分数  男跳远分数  男体前屈分数  男引体分数  男肺活量分数  \n",
       "0         72.0     66.0   60.0    74.0    0.0    62.0  \n",
       "1         70.0     78.0   74.0    74.0   60.0    68.0  \n",
       "2         74.0     70.0   70.0    78.0    0.0    80.0  \n",
       "3         68.0     74.0   64.0    76.0    0.0   100.0  \n",
       "4         85.0     78.0   66.0    76.0   68.0    74.0  \n",
       "..         ...      ...    ...     ...    ...     ...  \n",
       "472       68.0     72.0   66.0    72.0    0.0   100.0  \n",
       "473       40.0     50.0   66.0    80.0   50.0   100.0  \n",
       "474      100.0     80.0   90.0    76.0   85.0   100.0  \n",
       "475       62.0     76.0   66.0    78.0   76.0   100.0  \n",
       "476        0.0      0.0    0.0    50.0    0.0     0.0  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chemical-andorra",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### 女生成绩转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "published-livestock",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
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       "      <th>女仰卧</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3775.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>9.60</td>\n",
       "      <td>150.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2937.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>10.18</td>\n",
       "      <td>152.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2592.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>9.67</td>\n",
       "      <td>165.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>18.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>9.09</td>\n",
       "      <td>180.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女50米跑    女跳远  女体前屈   女仰卧    女肺活量     身高    体重    BMI\n",
       "0     1  女    3.22   9.32  185.0  16.0  48.0  3775.0  163.0  51.3  19.31\n",
       "1     1  女    4.59  11.44  148.0   9.0  29.0  3683.0  163.0  66.6  25.07\n",
       "2     1  女    3.46  13.40  150.0   7.0  40.0  3331.0  157.0  60.0  24.34\n",
       "3     1  女    3.39   9.52  172.0  21.0  46.0  3701.0  160.0  50.7  19.80\n",
       "4     1  女    3.43   9.79  145.0   8.0  34.0  3592.0  167.0  63.9  22.91\n",
       "..   .. ..     ...    ...    ...   ...   ...     ...    ...   ...    ...\n",
       "588  17  女    3.51   9.60  150.0  24.0  41.0  2255.0  158.0  49.0  19.63\n",
       "589  17  女    4.00  10.18  150.0  13.0  36.0  2937.0  161.0  55.7  21.49\n",
       "590  17  女    3.45  10.18  152.0  15.0  35.0  2592.0  165.0  48.6  17.85\n",
       "591  17  女    4.01   9.67  165.0  10.0  41.0  1829.0  154.0  43.6  18.38\n",
       "592  17  女    4.48   9.09  180.0  10.0  46.0  2962.0  162.0  55.3  21.07\n",
       "\n",
       "[593 rows x 11 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算BMI并填入\n",
    "df2['BMI'] = round(df2['体重']/np.square((df2['身高']/100)), 2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "economic-prize",
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      100.0\n",
       "1       60.0\n",
       "2       95.0\n",
       "3      100.0\n",
       "4       95.0\n",
       "       ...  \n",
       "588     90.0\n",
       "589     76.0\n",
       "590     95.0\n",
       "591     74.0\n",
       "592     64.0\n",
       "Name: 女800米跑分数, Length: 593, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      72.0\n",
       "1      10.0\n",
       "2       0.0\n",
       "3      70.0\n",
       "4      68.0\n",
       "       ... \n",
       "588    70.0\n",
       "589    64.0\n",
       "590    64.0\n",
       "591    68.0\n",
       "592    74.0\n",
       "Name: 女50米跑分数, Length: 593, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in ['女800米跑', '女50米跑']:  # 循环遍历得出女生跑步成绩\n",
    "    run = stander[k]\n",
    "    df2[k + '分数'] = df2[k].map(convert_run)\n",
    "    display(df2[k + '分数'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "grand-hepatitis",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for j in ['女跳远', '女体前屈', '女仰卧', '女肺活量']: # 遍历其他项目成绩结果\n",
    "    other = stander[j]\n",
    "    df2[j + '分数'] = df2[j].map(convert_other)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "assigned-picture",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量分数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>9.32</td>\n",
       "      <td>185.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3775.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.31</td>\n",
       "      <td>100.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>11.44</td>\n",
       "      <td>148.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.07</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>13.40</td>\n",
       "      <td>150.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.34</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>9.52</td>\n",
       "      <td>172.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.80</td>\n",
       "      <td>100.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>9.79</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.91</td>\n",
       "      <td>95.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>9.60</td>\n",
       "      <td>150.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.63</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>150.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2937.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.49</td>\n",
       "      <td>76.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>10.18</td>\n",
       "      <td>152.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2592.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>17.85</td>\n",
       "      <td>95.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>9.67</td>\n",
       "      <td>165.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>18.38</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>9.09</td>\n",
       "      <td>180.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>21.07</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女50米跑    女跳远  女体前屈   女仰卧    女肺活量     身高    体重    BMI  \\\n",
       "0     1  女    3.22   9.32  185.0  16.0  48.0  3775.0  163.0  51.3  19.31   \n",
       "1     1  女    4.59  11.44  148.0   9.0  29.0  3683.0  163.0  66.6  25.07   \n",
       "2     1  女    3.46  13.40  150.0   7.0  40.0  3331.0  157.0  60.0  24.34   \n",
       "3     1  女    3.39   9.52  172.0  21.0  46.0  3701.0  160.0  50.7  19.80   \n",
       "4     1  女    3.43   9.79  145.0   8.0  34.0  3592.0  167.0  63.9  22.91   \n",
       "..   .. ..     ...    ...    ...   ...   ...     ...    ...   ...    ...   \n",
       "588  17  女    3.51   9.60  150.0  24.0  41.0  2255.0  158.0  49.0  19.63   \n",
       "589  17  女    4.00  10.18  150.0  13.0  36.0  2937.0  161.0  55.7  21.49   \n",
       "590  17  女    3.45  10.18  152.0  15.0  35.0  2592.0  165.0  48.6  17.85   \n",
       "591  17  女    4.01   9.67  165.0  10.0  41.0  1829.0  154.0  43.6  18.38   \n",
       "592  17  女    4.48   9.09  180.0  10.0  46.0  2962.0  162.0  55.3  21.07   \n",
       "\n",
       "     女800米跑分数  女50米跑分数  女跳远分数  女体前屈分数  女仰卧分数  女肺活量分数  \n",
       "0       100.0     72.0   85.0    76.0   85.0     100  \n",
       "1        60.0     10.0   60.0    66.0   66.0     100  \n",
       "2        95.0      0.0   60.0    64.0   76.0     100  \n",
       "3       100.0     70.0   76.0    90.0   85.0     100  \n",
       "4        95.0     68.0   50.0    64.0   70.0     100  \n",
       "..        ...      ...    ...     ...    ...     ...  \n",
       "588      90.0     70.0   60.0    95.0   78.0      70  \n",
       "589      76.0     64.0   60.0    72.0   72.0      85  \n",
       "590      95.0     64.0   62.0    76.0   72.0      76  \n",
       "591      74.0     68.0   70.0    68.0   78.0      60  \n",
       "592      64.0     74.0   80.0    68.0   85.0      85  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "national-humanitarian",
   "metadata": {},
   "source": [
    "#### 列索引重排"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "structured-dominican",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
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       "      <td>8.88</td>\n",
       "      <td>66.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>72.6</td>\n",
       "      <td>25.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.27</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>17.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.15</td>\n",
       "      <td>74.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>16.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.35</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>79.7</td>\n",
       "      <td>23.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.73</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>54.7</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.38</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.32</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.42</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5755.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.65</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5688.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>51.7</td>\n",
       "      <td>17.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数    男跳远  男跳远分数  男体前屈  男体前屈分数  \\\n",
       "0     1  男     4.22       72.0   8.88     66.0  195.0   60.0  12.0    74.0   \n",
       "1     1  男     4.27       70.0   7.70     78.0  225.0   74.0  11.0    74.0   \n",
       "2     1  男     4.15       74.0   8.45     70.0  218.0   70.0  14.0    78.0   \n",
       "3     1  男     4.35       68.0   8.05     74.0  206.0   64.0  13.0    76.0   \n",
       "4     1  男     3.73       85.0   7.52     78.0  210.0   66.0  13.0    76.0   \n",
       "..   .. ..      ...        ...    ...      ...    ...    ...   ...     ...   \n",
       "472  17  男     4.38       68.0   8.27     72.0  208.0   66.0  10.0    72.0   \n",
       "473  17  男     5.32       40.0   9.55     50.0  210.0   66.0  15.0    80.0   \n",
       "474  17  男     3.42      100.0   7.50     80.0  252.0   90.0  13.0    76.0   \n",
       "475  17  男     4.65       62.0   7.81     76.0  208.0   66.0  14.0    78.0   \n",
       "476  17  男     0.00        0.0   0.00      0.0    0.0    0.0   0.0    50.0   \n",
       "\n",
       "      男引体  男引体分数    男肺活量  男肺活量分数     身高    体重    BMI  \n",
       "0     1.0    0.0  2785.0    62.0  170.0  72.6  25.12  \n",
       "1     7.0   60.0  3133.0    68.0  174.0  52.7  17.41  \n",
       "2     1.0    0.0  3901.0    80.0  169.0  46.5  16.28  \n",
       "3     1.0    0.0  4946.0   100.0  183.0  79.7  23.80  \n",
       "4     9.0   68.0  3538.0    74.0  171.0  54.7  18.71  \n",
       "..    ...    ...     ...     ...    ...   ...    ...  \n",
       "472   0.0    0.0  4647.0   100.0  176.0  69.5  22.44  \n",
       "473   6.0   50.0  7042.0   100.0  177.0  76.0  24.26  \n",
       "474  13.0   85.0  5755.0   100.0  181.0  65.0  19.84  \n",
       "475  11.0   76.0  5688.0   100.0  172.0  51.7  17.48  \n",
       "476   0.0    0.0     0.0     0.0    0.0   0.0    NaN  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resort_male = ['班级', '性别', '男1000米跑', '男1000米跑分数', '男50米跑', '男50米跑分数', '男跳远', '男跳远分数',\\\n",
    "               '男体前屈', '男体前屈分数', '男引体', '男引体分数', '男肺活量', '男肺活量分数', '身高', '体重', 'BMI']\n",
    "df1 = df1[resort_male]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "little-raising",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3775.0</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>19.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3683.0</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.6</td>\n",
       "      <td>25.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.7</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>95.0</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3592.0</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>22.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2255.0</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>19.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2937.0</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.7</td>\n",
       "      <td>21.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2592.0</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.6</td>\n",
       "      <td>17.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>18.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>64.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.3</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数    女跳远  女跳远分数  女体前屈  女体前屈分数  \\\n",
       "0     1  女    3.22     100.0   9.32     72.0  185.0   85.0  16.0    76.0   \n",
       "1     1  女    4.59      60.0  11.44     10.0  148.0   60.0   9.0    66.0   \n",
       "2     1  女    3.46      95.0  13.40      0.0  150.0   60.0   7.0    64.0   \n",
       "3     1  女    3.39     100.0   9.52     70.0  172.0   76.0  21.0    90.0   \n",
       "4     1  女    3.43      95.0   9.79     68.0  145.0   50.0   8.0    64.0   \n",
       "..   .. ..     ...       ...    ...      ...    ...    ...   ...     ...   \n",
       "588  17  女    3.51      90.0   9.60     70.0  150.0   60.0  24.0    95.0   \n",
       "589  17  女    4.00      76.0  10.18     64.0  150.0   60.0  13.0    72.0   \n",
       "590  17  女    3.45      95.0  10.18     64.0  152.0   62.0  15.0    76.0   \n",
       "591  17  女    4.01      74.0   9.67     68.0  165.0   70.0  10.0    68.0   \n",
       "592  17  女    4.48      64.0   9.09     74.0  180.0   80.0  10.0    68.0   \n",
       "\n",
       "      女仰卧  女仰卧分数    女肺活量  女肺活量分数     身高    体重    BMI  \n",
       "0    48.0   85.0  3775.0     100  163.0  51.3  19.31  \n",
       "1    29.0   66.0  3683.0     100  163.0  66.6  25.07  \n",
       "2    40.0   76.0  3331.0     100  157.0  60.0  24.34  \n",
       "3    46.0   85.0  3701.0     100  160.0  50.7  19.80  \n",
       "4    34.0   70.0  3592.0     100  167.0  63.9  22.91  \n",
       "..    ...    ...     ...     ...    ...   ...    ...  \n",
       "588  41.0   78.0  2255.0      70  158.0  49.0  19.63  \n",
       "589  36.0   72.0  2937.0      85  161.0  55.7  21.49  \n",
       "590  35.0   72.0  2592.0      76  165.0  48.6  17.85  \n",
       "591  41.0   78.0  1829.0      60  154.0  43.6  18.38  \n",
       "592  46.0   85.0  2962.0      85  162.0  55.3  21.07  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resort_female = ['班级', '性别', '女800米跑', '女800米跑分数', '女50米跑', '女50米跑分数', '女跳远', '女跳远分数',\\\n",
    "                 '女体前屈', '女体前屈分数', '女仰卧', '女仰卧分数', '女肺活量', '女肺活量分数', '身高', '体重', 'BMI']\n",
    "df2 = df2[resort_female]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "affiliated-purse",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
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   "toc_position": {},
   "toc_section_display": true,
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

一键复制编辑原始数据按行查看历史

提交于 2021-04-17 22:14 . 阶段五模块四Pandas作业

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