{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "power_eva = pd.read_csv('./发电测试结果.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "power_eva.columns = ['real', 'pred']\n",
    "power_eva['error'] = (power_eva.pred - power_eva.real).apply(abs) / power_eva.real"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "         real      pred     error\n222  0.517443  0.518051  0.001175\n54   0.701795  0.671254  0.043519\n201  0.539900  0.541033  0.002099\n30   0.532658  0.530621  0.003823\n124  0.410033  0.420981  0.026701\n37   0.390315  0.391309  0.002548\n7    0.571029  0.579793  0.015347\n232  0.580826  0.579876  0.001635\n165  0.352021  0.374194  0.062987\n139  0.584566  0.567410  0.029348",
      "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>real</th>\n      <th>pred</th>\n      <th>error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>222</th>\n      <td>0.517443</td>\n      <td>0.518051</td>\n      <td>0.001175</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>0.701795</td>\n      <td>0.671254</td>\n      <td>0.043519</td>\n    </tr>\n    <tr>\n      <th>201</th>\n      <td>0.539900</td>\n      <td>0.541033</td>\n      <td>0.002099</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>0.532658</td>\n      <td>0.530621</td>\n      <td>0.003823</td>\n    </tr>\n    <tr>\n      <th>124</th>\n      <td>0.410033</td>\n      <td>0.420981</td>\n      <td>0.026701</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>0.390315</td>\n      <td>0.391309</td>\n      <td>0.002548</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.571029</td>\n      <td>0.579793</td>\n      <td>0.015347</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>0.580826</td>\n      <td>0.579876</td>\n      <td>0.001635</td>\n    </tr>\n    <tr>\n      <th>165</th>\n      <td>0.352021</td>\n      <td>0.374194</td>\n      <td>0.062987</td>\n    </tr>\n    <tr>\n      <th>139</th>\n      <td>0.584566</td>\n      <td>0.567410</td>\n      <td>0.029348</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_eva.sample(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "heat_eva = pd.read_csv('./供热测试结果.csv')\n",
    "heat_eva.columns = ['real', 'pred']\n",
    "heat_eva['error'] = (heat_eva.pred - heat_eva.real).apply(abs) / heat_eva.real"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "         real      pred     error\n131  0.071626  0.071494  0.001839\n256  0.076446  0.069821  0.086672\n141  0.067995  0.068865  0.012802\n71   0.071438  0.071276  0.002270\n284  0.072052  0.071835  0.003018\n294  0.075010  0.074507  0.006716\n77   0.052603  0.055783  0.060461\n96   0.062181  0.063483  0.020932\n176  0.077847  0.077317  0.006807\n164  0.082962  0.082844  0.001420",
      "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>real</th>\n      <th>pred</th>\n      <th>error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>131</th>\n      <td>0.071626</td>\n      <td>0.071494</td>\n      <td>0.001839</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>0.076446</td>\n      <td>0.069821</td>\n      <td>0.086672</td>\n    </tr>\n    <tr>\n      <th>141</th>\n      <td>0.067995</td>\n      <td>0.068865</td>\n      <td>0.012802</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>0.071438</td>\n      <td>0.071276</td>\n      <td>0.002270</td>\n    </tr>\n    <tr>\n      <th>284</th>\n      <td>0.072052</td>\n      <td>0.071835</td>\n      <td>0.003018</td>\n    </tr>\n    <tr>\n      <th>294</th>\n      <td>0.075010</td>\n      <td>0.074507</td>\n      <td>0.006716</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>0.052603</td>\n      <td>0.055783</td>\n      <td>0.060461</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>0.062181</td>\n      <td>0.063483</td>\n      <td>0.020932</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>0.077847</td>\n      <td>0.077317</td>\n      <td>0.006807</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>0.082962</td>\n      <td>0.082844</td>\n      <td>0.001420</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heat_eva.sample(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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