{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>所处地区</th>\n",
       "      <th>机组类型</th>\n",
       "      <th>参数分类</th>\n",
       "      <th>冷凝器型式</th>\n",
       "      <th>铭牌容量 (MW)</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>power_co2_factor</th>\n",
       "      <th>heat_co2_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海市</td>\n",
       "      <td>供热式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.707110</td>\n",
       "      <td>4.807875</td>\n",
       "      <td>3.467769</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.574332</td>\n",
       "      <td>0.072680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海市</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.707110</td>\n",
       "      <td>4.807875</td>\n",
       "      <td>3.467769</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.582164</td>\n",
       "      <td>0.072391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海市</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.771441</td>\n",
       "      <td>4.808939</td>\n",
       "      <td>3.476886</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.569281</td>\n",
       "      <td>0.071041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海市</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>超超临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>4.807356</td>\n",
       "      <td>3.458373</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.506250</td>\n",
       "      <td>0.070460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海市</td>\n",
       "      <td>纯凝式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>4.807839</td>\n",
       "      <td>3.478627</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>0.565226</td>\n",
       "      <td>0.073717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  所处地区 机组类型  参数分类 冷凝器型式  铭牌容量 (MW)  longitude  latitude  altitude  \\\n",
       "0  上海市  供热式   亚临界    水冷   5.707110   4.807875  3.467769  1.386294   \n",
       "1  上海市  凝气式   亚临界    水冷   5.707110   4.807875  3.467769  1.386294   \n",
       "2  上海市  凝气式   亚临界    水冷   5.771441   4.808939  3.476886  1.098612   \n",
       "3  上海市  凝气式  超超临界    水冷   6.908755   4.807356  3.458373  1.609438   \n",
       "4  上海市  纯凝式   亚临界    水冷   5.860786   4.807839  3.478627  2.833213   \n",
       "\n",
       "   power_co2_factor  heat_co2_factor  \n",
       "0          0.574332         0.072680  \n",
       "1          0.582164         0.072391  \n",
       "2          0.569281         0.071041  \n",
       "3          0.506250         0.070460  \n",
       "4          0.565226         0.073717  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./results/去煤种化数据.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['所处地区', '机组类型', '参数分类', '冷凝器型式'],\n",
       " Index(['铭牌容量 (MW)', 'longitude', 'latitude', 'altitude'], dtype='object'))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_cols = data.columns[:4].tolist()\n",
    "num_cols = data.columns[4:8]\n",
    "object_cols, num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "test_data = pd.read_excel('./data/煤电机组情况（含企业名称）.xlsx',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_geo_info = pd.read_excel('./data/电厂地理信息.xlsx')\n",
    "test_geo_info.rename(columns={'name':'企业名称'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.merge(test_geo_info, how='left', on='企业名称').drop(columns='address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_cp = test_data.copy()\n",
    "test_data = test_data[['地区', '汽轮机类型', '压力参数', '冷却方式', '单机容量（MW）', 'lat', 'lng', 'altitude']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "test_data.columns = data.columns[:8].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "test_data['na_cols'] = test_data.isna().sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110838.446"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[test_data.na_cols <= 1]['铭牌容量 (MW)'].sum() /10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_test_data = test_data[test_data.na_cols <= 1].drop(columns='na_cols').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "水冷    413\n",
       "空冷    110\n",
       "其他      1\n",
       "Name: 冷凝器型式, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['冷凝器型式'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "水冷-闭式循环    2125\n",
       "其他         1076\n",
       "水冷-开式循环     972\n",
       "空冷-直接空冷     586\n",
       "空冷-间接空冷     264\n",
       "水冷           52\n",
       "空冷           14\n",
       "间接空冷          4\n",
       "直接空冷          2\n",
       "Name: 冷凝器型式, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test_data['冷凝器型式'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def change_type(x:str):\n",
    "    if '水冷' in x:\n",
    "        return '水冷'\n",
    "    elif '空冷' in x:\n",
    "        return \"空冷\"\n",
    "    else:\n",
    "        return '其他'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_test_data.fillna('其他', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_test_data['冷凝器型式'] = new_test_data['冷凝器型式'].apply(change_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "亚临界     265\n",
       "超临界     156\n",
       "超超临界     69\n",
       "超高压      32\n",
       "高压        2\n",
       "Name: 参数分类, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['参数分类'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "高压      1538\n",
       "亚临界     1075\n",
       "中压      1069\n",
       "超临界      608\n",
       "超高压      447\n",
       "超超临界     358\n",
       "Name: 参数分类, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test_data['参数分类'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_test_data['机组类型'] = new_test_data['机组类型'].apply(lambda x: x if x.endswith('式') else x + '式')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "for col in num_cols:\n",
    "    new_test_data[col] = new_test_data[col].apply(lambda x: 0 if x<0 else x)\n",
    "    new_test_data[col] = np.log1p(new_test_data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>所处地区</th>\n",
       "      <th>机组类型</th>\n",
       "      <th>参数分类</th>\n",
       "      <th>冷凝器型式</th>\n",
       "      <th>铭牌容量 (MW)</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.771441</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.771441</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>超超临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>超超临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5090</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.912023</td>\n",
       "      <td>3.427489</td>\n",
       "      <td>4.682353</td>\n",
       "      <td>5.645447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5091</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.258097</td>\n",
       "      <td>3.427666</td>\n",
       "      <td>4.682306</td>\n",
       "      <td>5.627621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽背式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.258097</td>\n",
       "      <td>3.427666</td>\n",
       "      <td>4.682306</td>\n",
       "      <td>5.627621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5093</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>背压式</td>\n",
       "      <td>高压</td>\n",
       "      <td>其他</td>\n",
       "      <td>3.433987</td>\n",
       "      <td>3.428715</td>\n",
       "      <td>4.682208</td>\n",
       "      <td>5.690359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>4.836282</td>\n",
       "      <td>3.428715</td>\n",
       "      <td>4.682208</td>\n",
       "      <td>5.690359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5095 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     所处地区 机组类型  参数分类 冷凝器型式  铭牌容量 (MW)  longitude  latitude  altitude\n",
       "0     安徽省  凝气式   亚临界    水冷   5.771441   3.451583  4.772094  2.397895\n",
       "1     安徽省  凝气式   亚临界    水冷   5.771441   3.451583  4.772094  2.397895\n",
       "2     安徽省  凝气式  超超临界    水冷   6.908755   3.451583  4.772094  2.397895\n",
       "3     安徽省  凝气式  超超临界    水冷   6.908755   3.451583  4.772094  2.397895\n",
       "4     安徽省  抽凝式    高压    水冷   3.713572   3.451583  4.772094  2.397895\n",
       "...   ...  ...   ...   ...        ...        ...       ...       ...\n",
       "5090  重庆市  抽凝式    高压    水冷   3.912023   3.427489  4.682353  5.645447\n",
       "5091  重庆市  抽凝式    高压    水冷   3.258097   3.427666  4.682306  5.627621\n",
       "5092  重庆市  抽背式    高压    水冷   3.258097   3.427666  4.682306  5.627621\n",
       "5093  重庆市  背压式    高压    其他   3.433987   3.428715  4.682208  5.690359\n",
       "5094  重庆市  抽凝式    高压    水冷   4.836282   3.428715  4.682208  5.690359\n",
       "\n",
       "[5095 rows x 8 columns]"
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  {
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       "     所处地区 机组类型  参数分类 冷凝器型式  铭牌容量 (MW)  longitude  latitude  altitude  \\\n",
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       "4     上海市  纯凝式   亚临界    水冷   5.860786   4.807839  3.478627  2.833213   \n",
       "...   ...  ...   ...   ...        ...        ...       ...       ...   \n",
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       "5093  重庆市  背压式    高压    其他   3.433987   3.428715  4.682208  5.690359   \n",
       "5094  重庆市  抽凝式    高压    水冷   4.836282   3.428715  4.682208  5.690359   \n",
       "\n",
       "      power_co2_factor  heat_co2_factor  \n",
       "0             0.574332         0.072680  \n",
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       "2             0.569281         0.071041  \n",
       "3             0.506250         0.070460  \n",
       "4             0.565226         0.073717  \n",
       "...                ...              ...  \n",
       "5090               NaN              NaN  \n",
       "5091               NaN              NaN  \n",
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       "5094               NaN              NaN  \n",
       "\n",
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    "merge_data = pd.concat([data, new_test_data], axis=0)\n",
    "merge_data"
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5093</th>\n",
       "      <td>3.433987</td>\n",
       "      <td>3.428715</td>\n",
       "      <td>4.682208</td>\n",
       "      <td>5.690359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>4.836282</td>\n",
       "      <td>3.428715</td>\n",
       "      <td>4.682208</td>\n",
       "      <td>5.690359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5619 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      铭牌容量 (MW)  longitude  latitude  altitude  power_co2_factor  \\\n",
       "0      5.707110   4.807875  3.467769  1.386294          0.574332   \n",
       "1      5.707110   4.807875  3.467769  1.386294          0.582164   \n",
       "2      5.771441   4.808939  3.476886  1.098612          0.569281   \n",
       "3      6.908755   4.807356  3.458373  1.609438          0.506250   \n",
       "4      5.860786   4.807839  3.478627  2.833213          0.565226   \n",
       "...         ...        ...       ...       ...               ...   \n",
       "5090   3.912023   3.427489  4.682353  5.645447               NaN   \n",
       "5091   3.258097   3.427666  4.682306  5.627621               NaN   \n",
       "5092   3.258097   3.427666  4.682306  5.627621               NaN   \n",
       "5093   3.433987   3.428715  4.682208  5.690359               NaN   \n",
       "5094   4.836282   3.428715  4.682208  5.690359               NaN   \n",
       "\n",
       "      heat_co2_factor  所处地区_上海市  所处地区_云南省  所处地区_内蒙古  所处地区_内蒙古自治区  ...  \\\n",
       "0            0.072680         1         0         0            0  ...   \n",
       "1            0.072391         1         0         0            0  ...   \n",
       "2            0.071041         1         0         0            0  ...   \n",
       "3            0.070460         1         0         0            0  ...   \n",
       "4            0.073717         1         0         0            0  ...   \n",
       "...               ...       ...       ...       ...          ...  ...   \n",
       "5090              NaN         0         0         0            0  ...   \n",
       "5091              NaN         0         0         0            0  ...   \n",
       "5092              NaN         0         0         0            0  ...   \n",
       "5093              NaN         0         0         0            0  ...   \n",
       "5094              NaN         0         0         0            0  ...   \n",
       "\n",
       "      机组类型_背压式  参数分类_中压  参数分类_亚临界  参数分类_超临界  参数分类_超超临界  参数分类_超高压  参数分类_高压  \\\n",
       "0            0        0         1         0          0         0        0   \n",
       "1            0        0         1         0          0         0        0   \n",
       "2            0        0         1         0          0         0        0   \n",
       "3            0        0         0         0          1         0        0   \n",
       "4            0        0         1         0          0         0        0   \n",
       "...        ...      ...       ...       ...        ...       ...      ...   \n",
       "5090         0        0         0         0          0         0        1   \n",
       "5091         0        0         0         0          0         0        1   \n",
       "5092         0        0         0         0          0         0        1   \n",
       "5093         1        0         0         0          0         0        1   \n",
       "5094         0        0         0         0          0         0        1   \n",
       "\n",
       "      冷凝器型式_其他  冷凝器型式_水冷  冷凝器型式_空冷  \n",
       "0            0         1         0  \n",
       "1            0         1         0  \n",
       "2            0         1         0  \n",
       "3            0         1         0  \n",
       "4            0         1         0  \n",
       "...        ...       ...       ...  \n",
       "5090         0         1         0  \n",
       "5091         0         1         0  \n",
       "5092         0         1         0  \n",
       "5093         1         0         0  \n",
       "5094         0         1         0  \n",
       "\n",
       "[5619 rows x 63 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_data = pd.get_dummies(merge_data, columns=object_cols)\n",
    "use_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "use_data.to_csv('./去煤种化后的训练数据.csv', encoding='utf-8-sig', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_set = use_data[~use_data.power_co2_factor.isna()].copy()\n",
    "test_set = use_data[use_data.power_co2_factor.isna()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "feature_cols = [x for x in train_set.columns if 'factor' not in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_data = train_set.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, valid = train_test_split(train_data.dropna(), test_size=0.1, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtest = xgb.DMatrix(test_set[feature_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "params_xgb = {'objective': 'reg:squarederror',\n",
    "              'booster': 'gbtree',\n",
    "              'eta': 0.005,\n",
    "              'max_depth': 15,\n",
    "              'subsample': 0.9,\n",
    "              'colsample_bytree': 0.9,\n",
    "              'min_child_weight': 1,\n",
    "              'seed': 42}\n",
    "\n",
    "num_boost_round = 1200\n",
    "\n",
    "dtrain = xgb.DMatrix(train[feature_cols], train['power_co2_factor'].values)\n",
    "dvalid = xgb.DMatrix(valid[feature_cols], valid['power_co2_factor'].values)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
    "\n",
    "gb_model_power = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
    "                early_stopping_rounds=100, verbose_eval=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new_test_data['power_co2_factor'] = gb_model_power.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "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>所处地区</th>\n",
       "      <th>机组类型</th>\n",
       "      <th>参数分类</th>\n",
       "      <th>冷凝器型式</th>\n",
       "      <th>铭牌容量 (MW)</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>altitude</th>\n",
       "      <th>prediction</th>\n",
       "      <th>power_co2_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.771441</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>0.563267</td>\n",
       "      <td>0.513529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>亚临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>5.771441</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>0.563267</td>\n",
       "      <td>0.513529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>超超临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>0.558872</td>\n",
       "      <td>0.478943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>凝气式</td>\n",
       "      <td>超超临界</td>\n",
       "      <td>水冷</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>0.558872</td>\n",
       "      <td>0.478943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>安徽省</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>3.451583</td>\n",
       "      <td>4.772094</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>0.563501</td>\n",
       "      <td>0.510681</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5090</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.912023</td>\n",
       "      <td>3.427489</td>\n",
       "      <td>4.682353</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>0.562492</td>\n",
       "      <td>0.512501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5091</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.258097</td>\n",
       "      <td>3.427666</td>\n",
       "      <td>4.682306</td>\n",
       "      <td>5.627621</td>\n",
       "      <td>0.562492</td>\n",
       "      <td>0.512513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽背式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>3.258097</td>\n",
       "      <td>3.427666</td>\n",
       "      <td>4.682306</td>\n",
       "      <td>5.627621</td>\n",
       "      <td>0.562597</td>\n",
       "      <td>0.514091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5093</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>背压式</td>\n",
       "      <td>高压</td>\n",
       "      <td>其他</td>\n",
       "      <td>3.433987</td>\n",
       "      <td>3.428715</td>\n",
       "      <td>4.682208</td>\n",
       "      <td>5.690359</td>\n",
       "      <td>0.560515</td>\n",
       "      <td>0.509951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>抽凝式</td>\n",
       "      <td>高压</td>\n",
       "      <td>水冷</td>\n",
       "      <td>4.836282</td>\n",
       "      <td>3.428715</td>\n",
       "      <td>4.682208</td>\n",
       "      <td>5.690359</td>\n",
       "      <td>0.561920</td>\n",
       "      <td>0.511886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5095 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     所处地区 机组类型  参数分类 冷凝器型式  铭牌容量 (MW)  longitude  latitude  altitude  \\\n",
       "0     安徽省  凝气式   亚临界    水冷   5.771441   3.451583  4.772094  2.397895   \n",
       "1     安徽省  凝气式   亚临界    水冷   5.771441   3.451583  4.772094  2.397895   \n",
       "2     安徽省  凝气式  超超临界    水冷   6.908755   3.451583  4.772094  2.397895   \n",
       "3     安徽省  凝气式  超超临界    水冷   6.908755   3.451583  4.772094  2.397895   \n",
       "4     安徽省  抽凝式    高压    水冷   3.713572   3.451583  4.772094  2.397895   \n",
       "...   ...  ...   ...   ...        ...        ...       ...       ...   \n",
       "5090  重庆市  抽凝式    高压    水冷   3.912023   3.427489  4.682353  5.645447   \n",
       "5091  重庆市  抽凝式    高压    水冷   3.258097   3.427666  4.682306  5.627621   \n",
       "5092  重庆市  抽背式    高压    水冷   3.258097   3.427666  4.682306  5.627621   \n",
       "5093  重庆市  背压式    高压    其他   3.433987   3.428715  4.682208  5.690359   \n",
       "5094  重庆市  抽凝式    高压    水冷   4.836282   3.428715  4.682208  5.690359   \n",
       "\n",
       "      prediction  power_co2_factor  \n",
       "0       0.563267          0.513529  \n",
       "1       0.563267          0.513529  \n",
       "2       0.558872          0.478943  \n",
       "3       0.558872          0.478943  \n",
       "4       0.563501          0.510681  \n",
       "...          ...               ...  \n",
       "5090    0.562492          0.512501  \n",
       "5091    0.562492          0.512513  \n",
       "5092    0.562597          0.514091  \n",
       "5093    0.560515          0.509951  \n",
       "5094    0.561920          0.511886  \n",
       "\n",
       "[5095 rows x 10 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_xgb = {'objective': 'reg:squarederror',\n",
    "              'booster': 'gbtree',\n",
    "              'eta': 0.01,\n",
    "              'max_depth': 30,\n",
    "              'subsample': 0.8,\n",
    "              'colsample_bytree': 0.9,\n",
    "              'min_child_weight': 10,\n",
    "              'seed': 108}\n",
    "\n",
    "num_boost_round = 1200\n",
    "\n",
    "dtrain = xgb.DMatrix(train[feature_cols], train['heat_co2_factor'].values)\n",
    "dvalid = xgb.DMatrix(valid[feature_cols], valid['heat_co2_factor'].values)\n",
    "watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
    "\n",
    "gb_model_heat = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
    "                early_stopping_rounds=100, verbose_eval=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test_data['heat_co2_factor'] = gb_model_heat.predict(dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plot_data = new_test_data[['地区', 'prediction']].copy()\n",
    "plot_data['地区'] = plot_data['地区'].apply(lambda x: jieba.lcut(x.strip(), cut_all=True)[0] if not pd.isna(x) else pd.NA)\n",
    "plot_data.columns = ['省份', 'CO2排放强度(kg/MJ)']\n",
    "total_plot_data = pd.concat([plot_data, add_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "total_plot_data['CO2排放强度(kg/MJ)'] = total_plot_data['CO2排放强度(kg/MJ)'].astype(float)\n",
    "total_plot_data['省份'] = total_plot_data['省份'].apply(lambda x: x if x != '内蒙' else '内蒙古')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>省份</th>\n",
       "      <th>CO2排放强度(kg/MJ)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>安徽</td>\n",
       "      <td>0.224686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>安徽</td>\n",
       "      <td>0.198733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>安徽</td>\n",
       "      <td>0.198733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>安徽</td>\n",
       "      <td>0.224686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>安徽</td>\n",
       "      <td>0.224686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>新疆</td>\n",
       "      <td>0.196452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>辽宁</td>\n",
       "      <td>0.185688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>内蒙古</td>\n",
       "      <td>0.181214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>山东</td>\n",
       "      <td>0.347570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>浙江</td>\n",
       "      <td>0.251777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3156 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      省份  CO2排放强度(kg/MJ)\n",
       "0     安徽        0.224686\n",
       "1     安徽        0.198733\n",
       "2     安徽        0.198733\n",
       "3     安徽        0.224686\n",
       "4     安徽        0.224686\n",
       "..   ...             ...\n",
       "848   新疆        0.196452\n",
       "849   辽宁        0.185688\n",
       "850  内蒙古        0.181214\n",
       "851   山东        0.347570\n",
       "852   浙江        0.251777\n",
       "\n",
       "[3156 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_plot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.violinplot(x='省份',\n",
    "               y='CO2排放强度(kg/MJ)',\n",
    "               data=total_plot_data,\n",
    "               scale='width',\n",
    "               palette='Set2',\n",
    "               inner='quartile')\n",
    "\n",
    "# Decoration\n",
    "plt.title('各省(市、自治区)燃煤发电CO2排放强度预测', fontsize=18)\n",
    "plt.savefig('./figure/各省预测值.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from pyecharts.charts import *\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.commons.utils import JsCode\n",
    "from pyecharts.globals import ThemeType, ChartType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_excel('././././data/机组预测结果.xlsx', sheet_name=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28918716"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['prediction'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "地区\n",
       "海南省         0.232931\n",
       "贵州省         0.234717\n",
       "广东省         0.236436\n",
       "上海市         0.237024\n",
       "甘肃省         0.238403\n",
       "湖北省         0.241863\n",
       "福建省         0.242241\n",
       "湖南省         0.243759\n",
       "宁夏回族自治区     0.245063\n",
       "云南省         0.245078\n",
       "山西省         0.245322\n",
       "新疆维吾尔自治区    0.246324\n",
       "安徽省         0.246587\n",
       "河南省         0.246775\n",
       "陕西省         0.248594\n",
       "天津市         0.248690\n",
       "内蒙古自治区      0.250336\n",
       "江西省         0.250423\n",
       "广西壮族自治区     0.251057\n",
       "河北省         0.251093\n",
       "重庆市         0.254725\n",
       "辽宁省         0.258904\n",
       "四川省         0.259836\n",
       "江苏省         0.261171\n",
       "吉林省         0.263193\n",
       "青海省         0.265025\n",
       "山东省         0.265427\n",
       "浙江省         0.269908\n",
       "黑龙江省        0.272978\n",
       "Name: prediction, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('地区')['prediction'].mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "map_dict = data.groupby('地区')['机组容量'].mean().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'上海市': 379.20512820512783,\n",
       " '云南省': 278.30434782608654,\n",
       " '内蒙古自治区': 230.6120649651969,\n",
       " '吉林省': 135.31451612903209,\n",
       " '四川省': 185.37333333333314,\n",
       " '天津市': 253.24468085106344,\n",
       " '宁夏回族自治区': 279.0265486725658,\n",
       " '安徽省': 316.693939393939,\n",
       " '山东省': 125.49526066350698,\n",
       " '山西省': 263.3278985507243,\n",
       " '广东省': 387.3771428571424,\n",
       " '广西壮族自治区': 246.94999999999965,\n",
       " '新疆维吾尔自治区': 240.52545454545407,\n",
       " '江苏省': 198.3687817258881,\n",
       " '江西省': 270.88586956521675,\n",
       " '河北省': 225.52138248847896,\n",
       " '河南省': 286.94827586206867,\n",
       " '浙江省': 143.89827272727254,\n",
       " '海南省': 256.1538461538457,\n",
       " '湖北省': 315.7307692307689,\n",
       " '湖南省': 292.77333333333297,\n",
       " '甘肃省': 287.76923076923043,\n",
       " '福建省': 335.7640449438199,\n",
       " '贵州省': 359.5219780219774,\n",
       " '辽宁省': 176.5326203208554,\n",
       " '重庆市': 215.15079365079336,\n",
       " '陕西省': 260.96775862068927,\n",
       " '青海省': 122.01612903225785,\n",
       " '黑龙江省': 84.8353658536584}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from pyecharts.faker import Faker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "map_co2 = map_dict = data.groupby('地区')['prediction'].mean().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 需要引用的库\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "\n",
    "# 设置不同的系列，和系列中区域对应的数量值\n",
    "pair_data1 = [[x, float(y)] for x, y in zip(map_co2.keys(), map_co2.values())]\n",
    "\n",
    "def create_map():\n",
    "    '''\n",
    "     作用：生成地图\n",
    "    '''\n",
    "    (  # 大小设置\n",
    "        Map()\n",
    "        .add(\n",
    "            series_name=\"各地区机组平均碳排放强度\",\n",
    "            data_pair=[[x[0], x[1]*1000] for x in pair_data1],\n",
    "            maptype=\"china\"\n",
    "        )\n",
    "\n",
    "        # 全局配置项\n",
    "        .set_global_opts(\n",
    "            # 设置标题\n",
    "            title_opts=opts.TitleOpts(title=\"各地区机组平均碳排放强度(单位：g/MJ)\", subtitle='港、澳、台、西藏数据暂缺'),\n",
    "            # 设置标准显示\n",
    "            visualmap_opts=opts.VisualMapOpts(max_=220, min_=280, is_piecewise=False)\n",
    "        )\n",
    "        # 系列配置项\n",
    "        .set_series_opts(\n",
    "            # 标签名称显示，默认为True\n",
    "            label_opts=opts.LabelOpts(is_show=True, color=\"blue\")\n",
    "        )\n",
    "        # 生成本地html文件\n",
    "        .render(\"co2.html\")\n",
    "    )\n",
    "\n",
    "\n",
    "create_map()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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