阶段五模块五作业.ipynb · 黄佳宝/阶段五模块五作业

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阶段五模块五作业.ipynb 123.76 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd, matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 载入数据\n",
    "data_m = pd.read_excel(r'/Users/jiabaohuang/Downloads/matplotlib作业/体测分数_男生.xls')\n",
    "data_f = pd.read_excel(r'/Users/jiabaohuang/Downloads/matplotlib作业/体测分数_女生.xls')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
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       "      <td>12</td>\n",
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       "      <td>2785</td>\n",
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       "      <td>72.599998</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
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       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
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       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
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       "   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  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0      0  2785      62  170  72.599998  25.120001  \n",
       "1     60  3133      68  174  52.700001  17.410000  \n",
       "2      0  3901      80  169  46.500000  16.280001  \n",
       "3      0  4946     100  183  79.699997  23.799999  \n",
       "4     68  3538      74  171  54.700001  18.709999  "
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
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       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
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       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
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      ],
      "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  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业\n",
    "1、对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比\n",
    "\n",
    "2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份\n",
    "\n",
    "3、使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖(男女生体重指数参考如下)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3.647, 5.47]        261\n",
      "(1.823, 3.647]       195\n",
      "(-0.00547, 1.823]     21\n",
      "Name: 男1000米跑, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男生1000米跑成绩')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rC4wDjrSWI4Ah9ir6DquBz9EjBa9VlpcttleOobcYcw8QXl9gF7SJI4b22iqo96xHDwe+DLxcWV4W7Ca/wWaMufsRy9AXoCPoToqCtwFvAf8B/mP67cmJMXeC8foCGcDx6KesHIvzg5ZhdG1+L7pGNze9JAnG3AnC6wvsgTb0eSRf/3mgqERPw32wsrxss81a0h5j7p3A6wt40M3uK4B9bJaTTLSi75S7t7K87B27xaQrxtx9wBp/Ph/4P/QYtKFrFgO/B/5ZWV4W71ZXQz9hzN0LvL6AC/2YpF+jbyIx9JzFwLWV5WUvdJvTkBCMuXuANcnkDLSpJ9ssJ9VZAFxTWV72od1CnI4xdzd4fYGTgZuAaTZLcRrPomvy5XYLcSrG3F3g9QW86OemHWuzFCfTDjwI+CvLy8zTXxOMMXcMVgT85+gXDuTaqyZt2AZcUVle9rjdQpyEMXcUXl9gOvqxxHvZLCVdeQJt8q12C3ECxtxsH9ryoV/9k2mznHRnPfCjyvKyeXYLSXXS3txeX2AS+sGH+9utxdCJ+4BfVJaXmZc39JG0NrfXFzgJeAT99FJD8vENcEFledkHdgtJRdL2AYleX+B69IMNjbGTlwnAAq8vcI3dQlKRtKu5vb5ALvB39KQUQ+rwD+DSyvKyFruFpAppZW6vLzAGmIuJhqcq7wOnmDvOekbamNvrCxwCPAMMtVuLYaf4Fji2srxsqd1Ckp206HN7fYEfAa9jjO0EdgXe9foCB9stJNlxvLm9vsAc9Ct9zPi1cygB5lvz/g1d4Ghze32By4G77dZh6BeygWe8vsAFdgtJVhzb5/b6ApcAf0W/99rgXELA6ZXlZXPtFpJsONLcXl/gInRT3Bg7PWgByirLy163W0gy4Thze32BWcBDOLzLYfgO9cBRleVlH9ktJFlwlLm9vsC56HnixtjpyTbge5XlZYvsFpIMOMYEVuT0HzjomAaCthpHPSNhEPCq1xcYb7eQZMARNbfXF5gMfAQU2K0lmpr3HiP47qOd0oacdgO5EzpuQAs11bHhwSsoPfFqssdM77bMb287Pm76LmffQvaY6TRVfs62+ffTtm0d7rwSig85h/zpR6OUomrurTSvXcKQk31kj55G89olhBuD5E48cOcONPmoBA6uLC9bb7cQO0n5t2F4fYFC9JTSpDI2QOv6ZQw68hLypx25PU0yczrl2frafYQatvW4zNFXdn5YScuG5VS9eCeZu0ygrWYjW567heLvnU/e7ofSuOw9qufdTdbI3VFK0bp5FcWHnEPdpwGyR0+j6ZuPKP7+rJ07yOTECwS8vsCBleVlzXaLsYuUbsJaTyX9FzDRbi2xKKVoWf8V2d69cGXnb1/E5d6ep3H5BzSv/gJXTmGPy40uy5WdT/D9Jyg+5BxcWbm0VX1L0UFnUrjPCbjziinYuwxXTgEtG74m3FSLO78ET8lIwk21tNVsxFO0CyIpfQrsiD2Bu2zWYCup/steD5xot4h4tFevJdzSSNXzt/Pt705h3d8uo2HJ29vXh5pqqX71Lww+5qdIRnaf9tG0YiGh+mryZ/wAgNwJB1B0wGkdGuq3Em6qI6NkJK6sPMLNdYSb6nBl5dGw6HXyph6xcweZ/Fzq9QXOsluEXaSsub2+wHHohxgmJa1bKskoGcmgIy9h5KUPUDDjf6h68fe0Va0BYOur95Izbj9yJxzQ530EP36Ggn1P6tQa6LT+3X+TNWJ3skZMIqN0DOLJojpwJznj90NcHlyZfbuopBj3e32BpGvZDQQpaW6vLzABeJQk1p83+VBGXHwvObvOwFMwmML9TyVr5GQalrxFw1fv0rJ+GSVHXtLn8ls3raB100rypx8dd33jNx/TsPhNSn5wBQDicjPsgt8zas4jhFsayBy5O+sfmsPmp29Etbf1WUcKUAA85fUF0uJKFk3SmqMrrIctPAcU2yyl17jzS2jdtIKt8++jtOwqXFl9f3Jy/ZevkTvxQFxxmvRt29ZTHfgDJUdfRuYQ7/Z0ERfiySTUWEPzqk/J9u5JuLmB5tVf9llHijAd+JPdIgaalDM3cDMp8PaPbW8+RO1/O16LpcIhWjYsJ3P4RMINQTY/ezOr7zqT1XedSah2C5uf/g3Vr97To7JVOETD0nfIm/z976wLNdSw+ekbyZt2RNxaveGrBeTtfiihxloyS8fgKd6FUFNt3w80dbjEmuSUNqTUUJjXFzgQ+JndOnpC5rDd2Dr/PjzFw3DnFlG38HnCTXXkTz+K/D2O7JR34yPXMOjwi8j27glAuKUR8WQg7oy4ZbesW4pqayZ7dOdrnGpvZfNTv8ZTOJTiQ84l3NoEgLg928tq27SS/KmH487Op61mE+11Vbiz0+Yxcvd5fYGPKsvLvrFbyECQMub2+gJZpNCc8bzJh9Jeu4nql/8I4TCZw3Zj2LnleArjPC/C5cadPwh3bhEA6x+aQ+H+p1C4zwlxy25a9SmZIyYhnoyY9M9o3bQCgDV3dTwirujgsyk+5FxaN68ka8weWt8eR7L56RvxFA7ZnpYG5KNvAU6LV0SlzAw1ry9wC3Ct3ToMjuCUdLhFNCXM7fUFpgCfA/HbqQZD76gEplSWlzXZLaQ/SYkmLvAXjLENicOLfn2Uo0n6mtuKcD5itw6D42gGplaWl620W0h/kdQ1t9cXKAB+Z7cOgyPJxuFzz5Pa3MBPgGF2izA4lhOsacyOJGmb5dZMtEpgiM1SDM7mG3RwzXFzcJO55r4cY2xD/zMBOM9uEf1BUtbc1iT/VZgmuWFgWA5MriwvC9stJJEka839Y4yxDQPHROCHdotINElnbmuaqXkfs2Gguc5uAYkm6cwNXASMtFuEIe2Y7vUF/sduEYkkqczt9QUySIOZQ4ak5Sq7BSSSpDI3cBIwxm4RhrTlGK8vMMluEYki2cx9vt0CDGmNkCLPC+gJSTMU5vUFSoH1mBtEDPZSBwx1wvPOk6nmPhNjbIP9FOCQhzkkk7lNk9yQLDhizDspmuXWc6WX2a3DYLCoB4aketM8WWpuR87tNaQs+TigaW67ua33fRlzG5KNlG+a225u4CBgrN0iDIYYTkj1t5Qkg7lTvvljcCQp3zRPBnN/97UZBkNycEb3WZIXW83t9QVygP3t1GAw7ICUvpHE7pp7JpBpswaDoStKvL7AbnaL6Ct2m9s0yQ3Jzky7BfQVY26DYccYc/cW64krKfvFGdKGlD1H7ay590c/GN5gSGamW4HflMNOc5smuSEV8AD72i2iL9hp7j1t3LfB0BtSsmlup7kd8zgbg+M5wG4BfcEWc3t9ARf6TQ8GQyqwh90C+oJdNfeumGCaIXVIyUdt22XuiTbt12DoC3leX2CQ3SJ6i13m9tq0X4Ohr6Rc7W2XuUfbtF+Doa+MsltAb7HL3ObFA4ZUw5i7hxhzG1IN0yzvIUNs2q/B0FdMzd1Dsmzar8HQV4y5e4h5QIMh1djFbgG9xdTcBkPPSLkKyZjbYOgZKfceO9MsNxh6hsduAb3F1NwGQ89IuZp7wK9GXl/Ajf3PbnMUe8o3y2d7Xt5wpOuzISsypfWxwsIt7+RlldS62BXzXScIVy2U2S2iV9jR1DC19k7iob3tB66FFbM8r9TvLV+P80h4ItbNODPaYEZ1E1TDZrd787MFeV+9mJ/nXu3xTFYixfYqT2XCdXYr6C12mNvUJH2gkPrgWe63Fp/lfpOxsmGaCHt3t83QUGjoZTW1Qy+rqSUEoQ9zsiueLMiv/jAne5dGkd0RkYHQbhet1a1kDMpAXAk5zFAiChlIbHk/t9cXaCUF+zADjVc2rJntnrfqePeHhSXUTRNJ3MW4yu3aMjc/f/kL+XmsyvBMViIliSo7ls0vbKZ+UT3jrh3Xq+3W/X0dql0x6pKO+SPVr1WzJbAFwlBaVkrpD0oBqF9az5p715A3MY8xc/Ts5o2Pb2TYWcMSdRiLK2ZVTEtUYQOBXRHAKmC4TftOYpQ60LVkyUXueVWHuL4ckSNtu9FPd9CVhsJDLg7WDrk4WEsYwp9kZy1+qiB/y7u5OUMaRCYjkpAWVvPaZrb8Zws543v3ANG6RXVse3sbxQcXb0+r+bCGjU9sZNQlo8gancWau9eQOTSTwr0K2frGVoaePJTqedW0rG9BhRRZoxPaA9yayMIGAmNum8mitfkE9wdfXuB+tWWqVE5yi5o60Bpc4DqguWXqAc0tAGxzubb+Jz/vq/8U5KkVGRkTlUif7gVQIcXaB9aSuUvvRj5DTSHWP7SerJGdzVn9SjXFhxRTdEARAENPGUr1a9UU7lVIqD5E1rAsPMUe2uvbqfuyjqEnD+2L7K7YlsjCBgK7zL3Fpv0mBaXUbDnPM3/Z6e53MkdSNU0kuV6GOCgcLrmwtu6gC2vrUKD+m5W19KnC/E0LcnIG17lkCiLunpSz5cUtiEso+Z8Sat6v6fH+N/x7AznjcsgamUVbVRsASimaVjcx5PiO60zO+ByaHmoCwJ3rJlQfIlQfItwaxpPnweVJaHinVzW3iLiUUuFECugtxtwDxGT5duVs98urj3EvLC2gcYoIh9itqScIyL4tLZP33dIyGSDoctW8kJ+3dG5+XujrzIyJYZG41WPzmmaq5lUx/obxNH7T2OP91X1ZR/0X9Uy4eQLV86u3p4dbwhCCzKEdrQB3rptwc5hQY4iimUWsvX8t2aOzaVrZRMkRCQ8h9LZZfrmInAScrJRqFJGxwOvAoWjfHaSUeizehiLiAQYBg9G3mo5Gj4bsCzQAp6oeBMuMufsJF+HQ4a7PFl3ofqXmANdXu2ZK+zigdxGlJKQoHC4+r7buwPOsWv3LrMxlTxTkb3gnN6ck6HJNQcSj2hVr/7aWXU7dhazhWT02d6gxxLqH1zFi9gg8BZ1PzUjEWzI6It+uLF0zh1vCFO1XRP60fBCoeqmK6nnV1HxYw/BzhlO4d2EiDr23zfITgE1KqcjBtwFj0eacATwoIsuUUp9GNhCRG4CfW3m2AdXol3c8BiwDnrHSioCa7gQYcyeQPJrqT3MvWHSue35oN1k7xSXMsFtTfyIgM1paJ81o2ToJoNYlwZfy8pbe8FLDCHe2a1jJUSW96mxveHQD+VPzKdzru2Z0ZbpwZbloD7aTNUz3xUONenRKPNrw7hw3W9/aSuE+hay6dRXDzx/Olhe3JMrcG3uaUUQmAkcDB0YlR4bSmpRSb4vIH4HpwKfWNjnArUqp38SUpYBrlFJro9JcIpKjlGrakQ5j7p1kJFs2zPK8+vVJ7vfyhlIzTSQ1306RCArDquisuvqZ135aR7heUXnZknArqPYQLhVWsuTyJUz47QQyB8f3fM17NbiyXCxZuAQA1aZAQe2ntUy5dwo5E3Jo/LqRvEl5ADRVNiGZgjtPhwCUUrRuaiV/j3zcuW7ydstjy38Sdqqt6kXeqwEFfCoiHqVUu/UZLJMrpa4VTaZSqhX4HBgtIq1Wvkw67sFYIyL1dFwgMoE1dPNiD7vMvdmm/SaEvWX5sos88zYe7vpsaJ60TMZE/juxYHYe7TqU5AJ4ekkbjy1uD11wXknF+5mhuiUN7ePJ84yI3W7iHZ2feF39ajVt29oYdqYeqy6eWcympzdRdGARngIPVS9WUTC9YHuTve7zOgpmFOjgWmOI5rXN242fACp7kklEdgcutD6eC9wrIm1ApD+xTUTygForbT0wWSk1KaqMXOAd4B/An4A/A7sBZyilansq2C5zL7Npv30ig/bWY1wfV8zyvNqwp3wzwSPhSZjXIXXJqMLOUerSXKEgA/eVOS17Fs1fx+uvtrDg+pIVTxXkr309L7dgq8s1DZHMzCGda3RXrgtXo4tIevHBxdQvqudr39dIhuDOdjP2R2O3529e08zQE4duz7v2gbWMuOA715C+0A58210m0TP+7gHeAg5XSv0DbVBE5FDgRfQE9UeUUt4uyvCi+9ifKqXuFpE/AbcBvwYWishlSqk3eiLarhlqHqCOJH7rSBH1NWe731hypvst8crGqSIkpONm+C6NIg2v5OUuebYgv2lRVubYdpEdTtxpqmyivaad3Em5uHMSVjPviOUVsyq6vZiLyMnAH9HBtE+VUp6odRcBVwI/IcbcIpKJ7qOfDJwNlAM3K6WU1eceDawDfok2+ZfAU8DdSqmWLvXYYW4Ary/wMbCfLTvvgrGyfvVF7nmrytwfFg+ifmoip3saes7XGRmrnirMX/1abm5elds1DRG7K4EXKmZVnNhdJqvmngi0AN/EmPt36Jdx3EWcmltE/g7ko5vp5wCt6H76SHQwLwQ8YC0+oF0pddWO9Nh58n6GzeYWwuGDXYuXzHbPqz7YtWhUtrSNxzx22XZ2a2sbe131trHXVW+jWaRpfm7OwqcL8hu+yM7ytovsaoOkip5kssael1lN61hOAG7fwbazYfsY9/8CvwOKotK/Bb6youZzeqLHbnMPONm0NJ3ofr/iAvdrrZPl20luUSl1M0C6ka1UzvENjfse36CHi1dmeFY/VZC/6tW83NzNbvdUdPCpv/l4ZzYWkb3QcxxeRAfGukQp1W61AA4H/mJtPxRd6fxBRMqBlUqpI7rbb1qYeyjbtpzneW3Zae4FmSOo3iPZpnsaes64tvYxv9xaM+aXW2toEZrfyM3979MF+fWfZ2eNbhXpr0lCH+3k9ncA9ymlNolIJ3OLiA94VCm1Jip5Nnp22qMASqnNWNF2EfkV9Gy41U5zf4nuR/RLRGSqrFpxkWfe2qNdCwcX0JQy0z0NPSdLkX1sQ+M+x1q1+mqPZ+1TBfkr5+XnZm/UtXpeAnaztmJWRY8nsFhsf9qQiFwFTAHOtNaFgHzRcYTRwC3AEvRY9mTgZ8As4DSlVEN0oSKSBRxED7sJtgXUALy+wGL0ge80bkLtR7o+W3She15wP9cyb4aE7OibGZKEVmh9Ozdn0VMF+XWfZmeNbHG5JvSxqGcrZlWc1psNRGQKsBg4DHgYOF4ptdhaNwhdsUXmRrwHHIGe+HIr8DLwS6VURUyZt1t5tgBlSqlPutVhs7kfQQ/094k8mup+6H570Tnu19UEWTfZJaTcO5QNA8Naj3vdMwX5K1/Oy8tY53FPRaSgh5teWzGroryv+xURiXeTh1Vzq8hQlug77SYqpZZ2UU4JMAwdVOvR3WZ2m/tS4L7ebDNaNq+b5X5lxUnu9/NLCU4TMY9JNvSONmh7NzdnyVMF+ds+yc4a0exyTdxB9oMqZlV8MGDiEojd47gv9yTTfvLVV7M98zYd5vpiWK60TCIF37hoSB4yIOPwxqYZhzfq+y42ut0bnynI/zqQn+tZ4/FMQaTIylrDTkbK7cTWmhvi97szaG891vVRxSzPqw0zZMVuHgmbuduGAaEd2j/IyV7yZEH+1qVZmcvm/2jpZXZr6it219yga+8pg6jdeo77jaVnuN9yj5HNU0XYx25hhvTDA55Dm5qnH9rUDPCI3Xp2BtvN/SP3S3PneOYeVUz9NBEOtluPwWChgIDdInYG25vl+Is86Lmzg+0VYjB04hP8wZSe7GT/CwL8wXbgBbtlGAwxPGO3gJ3FfnNrUv6LNDiKMCne34bkMfcrwCa7RRgMFvPxB9fZLWJnSQ5z+4Nt6Gl6BkMy8LDdAhJBcphb8wAdD5EzGOwiCMy1W0QiSB5z+4PfAG/aLcOQ9jyJP7jDRwanCsljbs3f7BZgSHv+breARJFs5n4W/ZJAg8EOPsIfTMmbROKRXOb2B1vRfW+DwQ5us1tAIkkuc2vuBHr+5jiDITF8hUMCaRGSz9z+4BZ6eY+3wZAA7sAfdNRoTfKZW3MH0Gy3CEPasA4HzEiLJTnN7Q9uxETODQPHH6x4j6NITnNrbkO/ucFg6E/W4dBuYPKaW8/tNbW3ob+5Dn+woftsqUfymlvjB7baLcLgWD4B/mW3iP4iuc3tD1YD19ktw+BYrnJahDya5Da35m/Af+0WYXAcT+APvm+3iP4k+c3tD4bR7zR27BXWMOA0o9917WiS39wA/uBHOGhCfyqxaluPXm6RatyEP/it3SL6m9Qwt8YHVNstor+4ZUELhz3cEbR9YlEb4/9Ux+Db6/DNbybcwwdZrqsNc9Q/Gyi4tZaS22qZNbeJupaObf/8cSu73lVH9m9rmXpPPS8ubwNAKcVpTzayy+/qeOfbdgDeW93OF5tCCTzKpOAjHDaHvCtSx9x6WurFdsvoDxZtDnHj2x1D+u+tbufcZ5v4+cwsPro4jw/Whvjzxz2bY3HR801MHeJm2Zx8Xr8gj0WbQ1z7up7s9/6adn77TgtPnJ7Dqivz+fHeGZz6RBPr68IsrQrzxcYQ/u9ncc8nel/PL2vnxEm2P/06kTQBs/AHHXfFikfqmBvAH5yLw+4aaw8rLpzbxG4lHT/FXR+1ctQ4N3P2z2RCiYs7js7m7o/belTeB2tC/HifDEYUuNhruJtTds/gqyrdtP5wbYhDd3Uzc5SH4QUufnaAfs3aym1hqhsVIwpcTCp1UdWoWLktjLfYhUsk8QdtH9fiDy6zW8RAkVrm1lwFLLdbRKK4dUErbhf8fGbH+ww/3RDimAkdNeY+w118WxOmurH7/u/ew9384cNWalsUy6pCPPJlG2W7ebave21FOx+ubaehVXH7e62U5gp7DXNTlC1sbVJUNyqKsoV/ftHKBTMyEn/A9vEW8Ce7RQwkqdfm8gcb8BedC7wPpPTZ9+WmEL//oGV70ztCsBnGD+q47rpdQkGWsLZWMTh3x2Xed3w2+9zfwIOf6Zr++Ime7TX0YV4Pp07O4MAH9R21WW549fxc8jKFqUNc5GTABXObuOe4bNbXKfIyHVNr1wGznTymHY9UrLnBH1wI/NpuGTtDW0gxa24TNx2ezaRSd6d1HhdkezobKzcD6lu7PzcveK6Jqw/KpNZXwJIr8thUH2bOS7rPHVjexoLVIT67NI+G6wq44+hsTn68keXVIdwu4aOL89j0iwJqmhUHjnYz4756TniskZb2lPaEQvezK+0WMtCkprk1twHz7BbRV25e0MLgHGHO/t9tfJTmChvrOzfBa1sUWZ4d16QVm0J8szWM/7BsCrKEyUPc3HR4Ng981kZ7WPHQ5238dP8M9hzmJjdD+OkBmUwqdfNYhY6Ou0TI8cDmBsUr37Rz1FgP25oUb1amdPzpZvzB5+wWYQepa249ueVMYIndUvrCP75o4/01IQbdVkdxeS1XBJp5d3WI4vJa9hvp5t3VHYZaXh2itgVGFe7Y3O1hqG2BxraOmnZTQ5iwAqX0+o31HevCSrGlIUwoapjtycXtnDE1g6pGxdShLsYNclHVg75+kvICcIPdIuwi9frc0fiDtfiLTkCPXZbaLac3LJidR3uUZ55e0sbTS9p5/PQcvtka5uTHG7ls30xmDHNx49st7DvCxbB8fS2ubVFkeyDT3dnsk0pdDM0Tzn+uiTOmZLC+Lsyt77Zy0iQPGW7hcK+bm95pZXi+i9Jc4ZmlbVTWKE7evaP18NnGEOdOz2BQjrByW5g1tWFKclLyNFkGnJdu/exoUvJX64Q/uBJ/0anAfCCzu+zJwqjCzo2m0lwh2wPeYhfeYheX75vJ/g80MChbCCmYd25HJG36vfX84qAs5uzf+XBzM4Tnz87lF682c+mLTbSH4bjdPNxTlg3AT/bLpKpRceeHLayrVYwqFB49NYe9h+s+/xcbQxzm1f/PmpHB8Y81MqbIxWHelDtNaoGT8Adr7RZiJ/a/wjdR+ItmAw/ZLSORfFUVYllVmIPHuCnNTd0e1ADTCpyIP/iK3ULsxjnmBvAX3YqepmpIT0LAWfiDT9stJBlwVnXgD15Lmk1UMGxHARcbY3fgLHMD+INXAvfYLcMw4FyJP/iw3SKSCeeZWzMH+KvdIgwDxvX4g3fbLSLZcKa59fDH5cCDdksx9Ds34g/ebLeIZMRZAbVY/EUudA3uyFtF0xz9hB5/0JGPJU4EzjZ3BH/R9cBNdsswJIxm4Jx0nVbaU9LD3AD+onPQj2pKmYkuhrjUoMexF9gtJNlJH3MD+Iu+BzwHlNgtxdAn1gHH4A8usltIKuDMgFpX+IPvAAcCK+yWYug1HwD7G2P3nPQyN4A/uBw4AHjZbimGHvMX4Pv4g+vtFpJKpFezPBp/kaCnqt4EuLvJbbCHeuAy/MFH7RaSiqSvuSP4iw5Bv5t5V7ulGDrxOXCm1dIy9IH0a5bH4g++C8wAHrdbigHQN3/8HphpjL1zmJo7Gn/RWcBdwC42K0lXvkDf/LHQbiFOwNTc0fiDjwOT0DeepOyzhVKQZvTbXPc1xk4cpubuCn/RfsB9wN52S3E47wCXmCZ44jE1d1f4g58A+wM/Qz+2x5BYVgLnAIcZY/cPpubuCf6iwcA16FtJu3ktgKEbNqGHH+/HH+zZO5IMfcKYuzf4i4aix8YvB7JtVpNq1AK/A+7EH2zoLrNh5zHm7gv+ouHoANAlQJbNapKdauB+tKmr7BaTThhz7wz+ohHApWiTD7dZTbKxGPgj8Aj+YJPdYtIRY+5E4C/yACejm+tH2CvGVhQQAP6IPzjfbjHpjjF3ovEX7Q5cBpxLir0FZSdYjJ7h92/8wZV2izFojLn7C3+RG/gecCpwCjDSXkEJZwXa0I+b2zCTE2PugUDfgXYA2ugnA7vZqqdvtAOfAG8Ac81MsuTHmNsO/EUjgUOs5VBgD5JvQlEY+Axt5jeBBfiD9fZKMvQGY+5kwF9UCBwEzAQmo+e3TwRyBkhBLbrfvChq+RR/sGaA9m/oB4y5kxXdlB+NNvokYBwwGP38t9ilq9dwhoBG9KywyLIx6v81wGL8wW/77TgMtmHM7QT8RTmARC3tQCv+oLmzLY0x5jYYHEqyBXEMSYKIFIhI3Ki+iJiHWaQAxtxphoh8T0SUiAzpJusvgOdFJN/azi0iWSJSCqwUkdP6XaxhpzDN8jRDRGainwFeopTa1kWeUcASYC0wFn3zRy16SMwN/BAdjNsV2IK+Q+5KpZR5vU8SYWru9CNyD3V7vJUiMhh4HnhaKTUF/QqmRegn0rwBHAZMV0rtib5IvKCUGmOMnXyYmtvBiMhBwHtAMCrZDeTHpAlQCEwDlgGzgKfQw2he9AMjt6Jr80KlVJ1V/hFos/uVUiYyn2R0NT5qcAYt1t9RSql6ABHZEz3zLDptFLqZ3ayUagceFJEbgCvoqOEjL25YKiKR8jOBPxtjJyfG3M6mt6YLi0gmoJRSvwF+E1khIhOAr4EJSqnmqHQRkRygzbowGJIE0+d2Nr3tcynAD9SISKcFWIhuym8UkTor4l5jpQUBEz1PMkyf28GIyN7Af+l5n3u8Uqrb+7FF5BBggVJKustrsA9TczubDOvvKKVUsVKqGH0XWmzaVCstCzqNae/QvFaT3CMipnuXhBhzO5vFwH7oqPeO2GTlq7Q+nwXUANtim+dWUzzy+uNaq+z/TbBuQwIwzfI0IypaXhCJlu8g70T044jPVEo1RaWbZnkKYGru9CPSVO/Jb78SKEI/mjheGYYkxvSV0o9i629mdxmVUu0ichGwJ4CIZKHfnzYDWN1P+gwJwjTLDb1CRK4FmoDnlFLmIQ9JjDG3weBQTJ/bYHAoxtwGg0Mx5jYYHIoxt8HgUIy5DQaHYsxtMDgUY26DwaEYcxsMDsWY22BwKMbcBoNDMeY2GBzK/weOqZ2bhxwywAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1km跑等宽分箱\n",
    "data_1km = pd.cut(data_m.男1000米跑,\n",
    "                  bins = 3)\n",
    "cnt_1km = data_1km.value_counts()\n",
    "print(cnt_1km)\n",
    "# 饼图\n",
    "plt.pie(cnt_1km,\n",
    "       labels = ['较差','中等','优秀'],\n",
    "       textprops = {'family':'SimHei','fontsize':15},\n",
    "       autopct='%.2f%%')\n",
    "plt.title(label = '男生1000米跑成绩',\n",
    "          fontdict = {'family':'SimHei','fontsize':18},\n",
    "          pad = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7.667, 15.333]    230\n",
      "(-0.023, 7.667]    227\n",
      "(15.333, 23.0]      20\n",
      "Name: 男引体, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 引体向上分箱\n",
    "data_chinning = pd.cut(data_m.男引体,bins=3)\n",
    "cnt_chinning = data_chinning.value_counts()\n",
    "print(cnt_chinning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男生1000米跑成绩')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 饼图\n",
    "plt.pie(cnt_chinning,\n",
    "       labels=['中等','不合格','优秀'],\n",
    "       autopct='%.2f%%',\n",
    "       textprops={'family':'SimHei','fontsize':15})\n",
    "plt.title(label = '男生1000米跑成绩',\n",
    "          fontdict = {'family':'SimHei','fontsize':18},\n",
    "          pad = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女生800米跑分数')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 女生800米\n",
    "plt.hist(data_f.女800米跑分数,\n",
    "        bins=4)\n",
    "plt.title(label='女生800米跑分数',\n",
    "         pad=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女跳远分数')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 女生跳远\n",
    "plt.hist(data_f.女跳远分数,\n",
    "        bins=4)\n",
    "plt.title(label='女跳远分数',\n",
    "         pad=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       超重\n",
       "1       正常\n",
       "2      低体重\n",
       "3       超重\n",
       "4       正常\n",
       "      ... \n",
       "472     正常\n",
       "473     超重\n",
       "474     正常\n",
       "475     正常\n",
       "476    低体重\n",
       "Name: BMI, Length: 477, dtype: category\n",
       "Categories (4, object): ['低体重' < '正常' < '超重' < '肥胖']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 男生BMI评分\n",
    "BMI_score_m = pd.cut(data_m.BMI,\n",
    "                    bins=[0,16.5,23.3,26.4,100],\n",
    "                    right=False,\n",
    "                    labels=['低体重','正常','超重','肥胖'])\n",
    "BMI_score_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      正常\n",
       "1      超重\n",
       "2      超重\n",
       "3      正常\n",
       "4      超重\n",
       "       ..\n",
       "588    正常\n",
       "589    正常\n",
       "590    正常\n",
       "591    正常\n",
       "592    正常\n",
       "Name: BMI, Length: 593, dtype: category\n",
       "Categories (4, object): ['低体重' < '正常' < '超重' < '肥胖']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 女生BMI评分\n",
    "BMI_score_f = pd.cut(data_f.BMI,\n",
    "                    bins=[0,16.5,22.5,25.3,100],\n",
    "                    right=False,\n",
    "                    labels=['低体重','正常','超重','肥胖'])\n",
    "BMI_score_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_BMI_m = BMI_score_m.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_BMI_f = BMI_score_f.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8698737520>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[5,5],dpi=100)\n",
    "# 外圈 —— 男生\n",
    "plt.pie(cnt_BMI_m,\n",
    "       autopct='%.1f%%',\n",
    "       radius=1,\n",
    "       pctdistance=0.85,\n",
    "       wedgeprops={'linewidth':3,'width':.4,'edgecolor':'w'},\n",
    "       labels=cnt_BMI_m.index)\n",
    "# 内圈 —— 女生\n",
    "plt.pie(cnt_BMI_f,\n",
    "        autopct='%.1f%%',\n",
    "       radius=.6,\n",
    "       pctdistance=.6,\n",
    "       wedgeprops={'linewidth':3,'width':.6,'edgecolor':'w'})\n",
    "plt.title(label = '男女生BMI评分',\n",
    "         fontdict = {'family':'SimHei','fontsize':18},\n",
    "          pad = 20)\n",
    "plt.legend(cnt_BMI_f.index,\n",
    "          title='BMI评分',\n",
    "          loc='lower right',\n",
    "          bbox_to_anchor=(1.3, 0.6))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

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提交于 2021-06-22 08:52 . 阶段五模块五作业1

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