{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "                 电厂  机组                 监测日期   流量 （m3/h）  含氧量（%）  温度（℃）  \\\n0        浙江秀舟热电有限公司   1  2018-03-23 00:00:00   244136.19     7.7   51.4   \n1        浙江秀舟热电有限公司   1  2018-03-23 01:00:00   234599.89     7.8   51.3   \n2        浙江秀舟热电有限公司   1  2018-03-23 02:00:00   249264.88     7.3   54.9   \n3        浙江秀舟热电有限公司   1  2018-03-23 03:00:00   229360.17     7.5   52.7   \n4        浙江秀舟热电有限公司   1  2018-03-23 04:00:00   236416.45     7.2   55.1   \n...             ...  ..                  ...         ...     ...    ...   \n38035  建投遵化热电有限责任公司   2  2022-05-31 19:00:00  1111320.00     4.8   47.5   \n38036  建投遵化热电有限责任公司   2  2022-05-31 20:00:00  1090440.00     4.7   47.6   \n38037  建投遵化热电有限责任公司   2  2022-05-31 21:00:00  1106640.00     4.5   46.5   \n38038  建投遵化热电有限责任公司   2  2022-05-31 22:00:00  1103040.00     4.6   46.2   \n38039  建投遵化热电有限责任公司   2  2022-05-31 23:00:00   912240.00     5.7   46.2   \n\n       NOx实测浓度(mg/m3)  SO2实测浓度(mg/m3)  烟尘实测浓度(mg/m3)  烟气湿度（%）  烟气压力（千帕）  \\\n0           24.212548        0.752494       1.014598      NaN       NaN   \n1           25.348257        0.642777       0.989539      NaN       NaN   \n2           17.562606        1.823513       1.137605      NaN       NaN   \n3           20.513299        1.650563       1.136867      NaN       NaN   \n4           15.354987        1.379341       1.128551      NaN       NaN   \n...               ...             ...            ...      ...       ...   \n38035       23.500000       18.540000       1.700000     10.4       NaN   \n38036       20.600000       19.750000       1.660000     10.4       NaN   \n38037       22.120000       18.550000       1.680000      8.6       NaN   \n38038       22.500000       18.040000       1.570000      9.4       NaN   \n38039       15.940000       12.920000       1.400000      9.5       NaN   \n\n       烟气流速（m/s）  \n0            NaN  \n1            NaN  \n2            NaN  \n3            NaN  \n4            NaN  \n...          ...  \n38035      13.60  \n38036      13.35  \n38037      13.24  \n38038      13.30  \n38039      11.03  \n\n[38040 rows x 12 columns]",
      "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>电厂</th>\n      <th>机组</th>\n      <th>监测日期</th>\n      <th>流量 （m3/h）</th>\n      <th>含氧量（%）</th>\n      <th>温度（℃）</th>\n      <th>NOx实测浓度(mg/m3)</th>\n      <th>SO2实测浓度(mg/m3)</th>\n      <th>烟尘实测浓度(mg/m3)</th>\n      <th>烟气湿度（%）</th>\n      <th>烟气压力（千帕）</th>\n      <th>烟气流速（m/s）</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>浙江秀舟热电有限公司</td>\n      <td>1</td>\n      <td>2018-03-23 00:00:00</td>\n      <td>244136.19</td>\n      <td>7.7</td>\n      <td>51.4</td>\n      <td>24.212548</td>\n      <td>0.752494</td>\n      <td>1.014598</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>浙江秀舟热电有限公司</td>\n      <td>1</td>\n      <td>2018-03-23 01:00:00</td>\n      <td>234599.89</td>\n      <td>7.8</td>\n      <td>51.3</td>\n      <td>25.348257</td>\n      <td>0.642777</td>\n      <td>0.989539</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>浙江秀舟热电有限公司</td>\n      <td>1</td>\n      <td>2018-03-23 02:00:00</td>\n      <td>249264.88</td>\n      <td>7.3</td>\n      <td>54.9</td>\n      <td>17.562606</td>\n      <td>1.823513</td>\n      <td>1.137605</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>浙江秀舟热电有限公司</td>\n      <td>1</td>\n      <td>2018-03-23 03:00:00</td>\n      <td>229360.17</td>\n      <td>7.5</td>\n      <td>52.7</td>\n      <td>20.513299</td>\n      <td>1.650563</td>\n      <td>1.136867</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>浙江秀舟热电有限公司</td>\n      <td>1</td>\n      <td>2018-03-23 04:00:00</td>\n      <td>236416.45</td>\n      <td>7.2</td>\n      <td>55.1</td>\n      <td>15.354987</td>\n      <td>1.379341</td>\n      <td>1.128551</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</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    </tr>\n    <tr>\n      <th>38035</th>\n      <td>建投遵化热电有限责任公司</td>\n      <td>2</td>\n      <td>2022-05-31 19:00:00</td>\n      <td>1111320.00</td>\n      <td>4.8</td>\n      <td>47.5</td>\n      <td>23.500000</td>\n      <td>18.540000</td>\n      <td>1.700000</td>\n      <td>10.4</td>\n      <td>NaN</td>\n      <td>13.60</td>\n    </tr>\n    <tr>\n      <th>38036</th>\n      <td>建投遵化热电有限责任公司</td>\n      <td>2</td>\n      <td>2022-05-31 20:00:00</td>\n      <td>1090440.00</td>\n      <td>4.7</td>\n      <td>47.6</td>\n      <td>20.600000</td>\n      <td>19.750000</td>\n      <td>1.660000</td>\n      <td>10.4</td>\n      <td>NaN</td>\n      <td>13.35</td>\n    </tr>\n    <tr>\n      <th>38037</th>\n      <td>建投遵化热电有限责任公司</td>\n      <td>2</td>\n      <td>2022-05-31 21:00:00</td>\n      <td>1106640.00</td>\n      <td>4.5</td>\n      <td>46.5</td>\n      <td>22.120000</td>\n      <td>18.550000</td>\n      <td>1.680000</td>\n      <td>8.6</td>\n      <td>NaN</td>\n      <td>13.24</td>\n    </tr>\n    <tr>\n      <th>38038</th>\n      <td>建投遵化热电有限责任公司</td>\n      <td>2</td>\n      <td>2022-05-31 22:00:00</td>\n      <td>1103040.00</td>\n      <td>4.6</td>\n      <td>46.2</td>\n      <td>22.500000</td>\n      <td>18.040000</td>\n      <td>1.570000</td>\n      <td>9.4</td>\n      <td>NaN</td>\n      <td>13.30</td>\n    </tr>\n    <tr>\n      <th>38039</th>\n      <td>建投遵化热电有限责任公司</td>\n      <td>2</td>\n      <td>2022-05-31 23:00:00</td>\n      <td>912240.00</td>\n      <td>5.7</td>\n      <td>46.2</td>\n      <td>15.940000</td>\n      <td>12.920000</td>\n      <td>1.400000</td>\n      <td>9.5</td>\n      <td>NaN</td>\n      <td>11.03</td>\n    </tr>\n  </tbody>\n</table>\n<p>38040 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_data = pd.read_excel('data/机组小时数据.xlsx')\n",
    "hourly_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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