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1021 lines
58 KiB
1021 lines
58 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"__根据我们提供的数据表，从中抽取了一些游泳的数据组成了新的数据集“dataset.csv”,数据表如下所示__"
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]
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},
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{
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"cell_type": "code",
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" <td>7:40</td>\n",
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" <td>7:50</td>\n",
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" <td>8:00</td>\n",
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" <th>10</th>\n",
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" <td>8:10</td>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>8:20</td>\n",
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" <td>B1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>8:30</td>\n",
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" <td>7</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>8:40</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>7</td>\n",
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" <td>NaN</td>\n",
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" <td>8:40</td>\n",
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" <td>B2</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>8:50</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>10</td>\n",
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" <td>0.40</td>\n",
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" <td>NaN</td>\n",
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" <td>8:50</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>9:00</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>10</td>\n",
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" <td>0.40</td>\n",
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" <td>NaN</td>\n",
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" <td>9:00</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <td>9:10</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>12</td>\n",
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" <td>0.48</td>\n",
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" <td>NaN</td>\n",
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" <td>9:10</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>9:20</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>12</td>\n",
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" <td>0.48</td>\n",
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" <td>NaN</td>\n",
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" <td>9:20</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>18</th>\n",
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" <td>9:30</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>9:30</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>19</th>\n",
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" <td>9:40</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>9:40</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20</th>\n",
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" <td>9:50</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>9:50</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>21</th>\n",
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" <td>10:00</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>10:00</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>2.0</td>\n",
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" <td>0.07</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>22</th>\n",
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" <td>10:10</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>15</td>\n",
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" <td>0.60</td>\n",
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" <td>NaN</td>\n",
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" <td>10:10</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>3.0</td>\n",
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" <td>0.10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>23</th>\n",
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" <td>10:20</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>10:20</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>8.0</td>\n",
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" <td>0.27</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>24</th>\n",
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" <td>10:30</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>10:30</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>10.0</td>\n",
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" <td>0.33</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25</th>\n",
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" <td>10:40</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>10:40</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>10.0</td>\n",
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" <td>0.33</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>26</th>\n",
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" <td>10:50</td>\n",
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" <td>B1</td>\n",
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" <td>A</td>\n",
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>10:50</td>\n",
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" <td>B2</td>\n",
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" <td>A</td>\n",
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" <td>12.0</td>\n",
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" <td>0.40</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>27</th>\n",
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" <td>11:00</td>\n",
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" <td>B1</td>\n",
|
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" <td>A</td>\n",
|
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>11:00</td>\n",
|
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" <td>B2</td>\n",
|
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" <td>A</td>\n",
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" <td>12.0</td>\n",
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" <td>0.40</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>28</th>\n",
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" <td>11:10</td>\n",
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" <td>B1</td>\n",
|
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" <td>A</td>\n",
|
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" <td>13</td>\n",
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" <td>0.52</td>\n",
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" <td>NaN</td>\n",
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" <td>11:10</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>A</td>\n",
|
|
" <td>16.0</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>11:20</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>A</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>0.52</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>11:20</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>A</td>\n",
|
|
" <td>18.0</td>\n",
|
|
" <td>0.60</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",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>440</th>\n",
|
|
" <td>17:10</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>19</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>17:10</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>441</th>\n",
|
|
" <td>17:20</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>17:20</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>442</th>\n",
|
|
" <td>17:30</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>17:30</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>443</th>\n",
|
|
" <td>17:40</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>17:40</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>444</th>\n",
|
|
" <td>17:50</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>0.84</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>17:50</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>445</th>\n",
|
|
" <td>18:00</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>18:00</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>446</th>\n",
|
|
" <td>18:10</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>18:10</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>447</th>\n",
|
|
" <td>18:20</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>16</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>18:20</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>448</th>\n",
|
|
" <td>18:30</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>18</td>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>18:30</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>449</th>\n",
|
|
" <td>18:40</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>18:40</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>450</th>\n",
|
|
" <td>18:50</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>18:50</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>451</th>\n",
|
|
" <td>19:00</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>19:00</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>452</th>\n",
|
|
" <td>19:10</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>19:10</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>453</th>\n",
|
|
" <td>19:20</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>22</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>19:20</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>454</th>\n",
|
|
" <td>19:30</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>19:30</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>455</th>\n",
|
|
" <td>19:40</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>17</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>19:40</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>456</th>\n",
|
|
" <td>19:50</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>14</td>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>19:50</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>457</th>\n",
|
|
" <td>20:00</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>0.40</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>20:00</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>458</th>\n",
|
|
" <td>20:10</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>0.32</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>20:10</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>459</th>\n",
|
|
" <td>20:20</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>0.20</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>20:20</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>460</th>\n",
|
|
" <td>20:30</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>0.24</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>20:30</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>461</th>\n",
|
|
" <td>20:40</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>0.24</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>20:40</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>462</th>\n",
|
|
" <td>20:50</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>0.20</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>20:50</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>463</th>\n",
|
|
" <td>21:00</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>21:00</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>464</th>\n",
|
|
" <td>21:10</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>21:10</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>465</th>\n",
|
|
" <td>21:20</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>21:20</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>466</th>\n",
|
|
" <td>21:30</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>21:30</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>467</th>\n",
|
|
" <td>21:40</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>21:40</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>468</th>\n",
|
|
" <td>21:50</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>21:50</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>469</th>\n",
|
|
" <td>22:00</td>\n",
|
|
" <td>B1</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>22:00</td>\n",
|
|
" <td>B2</td>\n",
|
|
" <td>E</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"<p>470 rows × 11 columns</p>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 时间 层 区 占位数 占位率 Unnamed: 5 时间.1 层.1 区.1 占位数.1 占位率.1\n",
|
|
"0 6:30 B1 A 0 0.00 NaN 6:30 B2 A 0.0 0.0\n",
|
|
"1 6:40 B1 A 0 0.00 NaN 6:40 B2 A 0.0 0.0\n",
|
|
"2 6:50 B1 A 0 0.00 NaN 6:50 B2 A 0.0 0.0\n",
|
|
"3 7:00 B1 A 2 0.08 NaN 7:00 B2 A 0.0 0.0\n",
|
|
"4 7:10 B1 A 4 0.16 NaN 7:10 B2 A 0.0 0.0\n",
|
|
".. ... .. .. ... ... ... ... .. .. ... ...\n",
|
|
"465 21:20 B1 E 0 0.00 NaN 21:20 B2 E 0.0 0.0\n",
|
|
"466 21:30 B1 E 1 0.04 NaN 21:30 B2 E 0.0 0.0\n",
|
|
"467 21:40 B1 E 0 0.00 NaN 21:40 B2 E 0.0 0.0\n",
|
|
"468 21:50 B1 E 0 0.00 NaN 21:50 B2 E 0.0 0.0\n",
|
|
"469 22:00 B1 E 0 0.00 NaN 22:00 B2 E 0.0 0.0\n",
|
|
"\n",
|
|
"[470 rows x 11 columns]"
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"data = pd.read_csv(\"dataset.csv\")\n",
|
|
"data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"__针对此任务，数据分析人员从停车场数据库中导出 类似的某天 6:30 到 22:00 的停车位使用实时数据表，筛选出 以 10 分钟为间隔的记录，并按时间、层、区域分类统计已占 用车位数，再计算出车位占用率。__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"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": [
|
|
"data1 = data.values[:,0:5]\n",
|
|
"b1_X=data1[0:94,0]\n",
|
|
"b1_A=data1[0:94,4]\n",
|
|
"b1_B=data1[94:188,4]\n",
|
|
"b1_C=data1[188:282,4]\n",
|
|
"x=len(b1_A)\n",
|
|
"b1_X=range(x)\n",
|
|
"#sub_axix = filter(lambda x:x%200 == 0, x_axix)\n",
|
|
"plt.title('Result Analysis')\n",
|
|
"plt.plot(b1_X, b1_A, color='green', label='B1_A')\n",
|
|
"plt.plot(b1_X, b1_B, color='red', label='B1_B')\n",
|
|
"plt.plot(b1_X, b1_C, color='skyblue', label='B1_C')\n",
|
|
"plt.legend() # 显示图例\n",
|
|
"plt.ylim(0,1.1)\n",
|
|
"plt.xlim(0)\n",
|
|
"plt.xlabel('iteration times')\n",
|
|
"plt.ylabel('rate')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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