Data_grouping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling (grouping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['suject_id', 'timestamp', 'season', 'city', 'climate', 'building_type',\n",
       "       'heating_type', 'gender', 'age', 'ht', 'wt', 'thermal_sensation',\n",
       "       'thermal_comfort', 'thermal_acceptability', 'clo', 'met', 'ta', 'rh',\n",
       "       'vel', 'tg', 'top', 'tr', 't_out_min', 't_out_monthly', 'latitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('CN_DB.csv')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by city, using mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>city</th>\n",
       "      <th>records</th>\n",
       "      <th>thermal_sensation</th>\n",
       "      <th>ta</th>\n",
       "      <th>top</th>\n",
       "      <th>rh</th>\n",
       "      <th>vel</th>\n",
       "      <th>met</th>\n",
       "      <th>clo</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baotou</td>\n",
       "      <td>303</td>\n",
       "      <td>0.016502</td>\n",
       "      <td>19.766997</td>\n",
       "      <td>19.277228</td>\n",
       "      <td>36.356436</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.069967</td>\n",
       "      <td>0.341782</td>\n",
       "      <td>40.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>651</td>\n",
       "      <td>-0.027957</td>\n",
       "      <td>22.948541</td>\n",
       "      <td>22.066052</td>\n",
       "      <td>28.614439</td>\n",
       "      <td>0.072442</td>\n",
       "      <td>1.056989</td>\n",
       "      <td>0.920568</td>\n",
       "      <td>39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FujianFuzhou</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>67.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.351429</td>\n",
       "      <td>26.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FujianNanping</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>60.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.417500</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FujianZhenjiang</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>26.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              city  records  thermal_sensation         ta        top  \\\n",
       "0           Baotou      303           0.016502  19.766997  19.277228   \n",
       "1          Beijing      651          -0.027957  22.948541  22.066052   \n",
       "2     FujianFuzhou        7          -0.714286  18.100000  18.100000   \n",
       "3    FujianNanping        4           0.000000  17.900000  17.900000   \n",
       "4  FujianZhenjiang        2          -0.500000  22.900000  22.900000   \n",
       "\n",
       "          rh       vel       met       clo  latitude  \n",
       "0  36.356436  0.050000  1.069967  0.341782     40.65  \n",
       "1  28.614439  0.072442  1.056989  0.920568     39.90  \n",
       "2  67.428571  0.000000  0.885714  0.351429     26.08  \n",
       "3  60.250000  0.000000  0.875000  0.417500     26.64  \n",
       "4  74.500000  0.000000  0.900000  0.305000     26.10  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city = df.groupby('city').agg(\n",
    "    records=('city', 'size'),\n",
    "    thermal_sensation=('thermal_sensation', 'mean'),\n",
    "    ta=('ta', 'mean'),\n",
    "    top=('top', 'mean'),\n",
    "    rh=('rh', 'mean'),\n",
    "    vel=('vel', 'mean'),\n",
    "    met=('met', 'mean'),\n",
    "    clo=('clo', 'mean'),\n",
    "    latitude=('latitude', 'mean')\n",
    ").reset_index()\n",
    "df_city.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>city</th>\n",
       "      <th>records</th>\n",
       "      <th>thermal_sensation</th>\n",
       "      <th>ta</th>\n",
       "      <th>top</th>\n",
       "      <th>rh</th>\n",
       "      <th>vel</th>\n",
       "      <th>met</th>\n",
       "      <th>clo</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HainanSanya</td>\n",
       "      <td>5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>24.880000</td>\n",
       "      <td>24.880000</td>\n",
       "      <td>70.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.332000</td>\n",
       "      <td>18.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GuangdongGuangzhou</td>\n",
       "      <td>997</td>\n",
       "      <td>-0.013039</td>\n",
       "      <td>23.850150</td>\n",
       "      <td>23.876128</td>\n",
       "      <td>52.936810</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.859910</td>\n",
       "      <td>23.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>YunnanKunming</td>\n",
       "      <td>415</td>\n",
       "      <td>-1.154217</td>\n",
       "      <td>13.617108</td>\n",
       "      <td>13.617108</td>\n",
       "      <td>16.946988</td>\n",
       "      <td>0.275325</td>\n",
       "      <td>1.084337</td>\n",
       "      <td>0.647133</td>\n",
       "      <td>24.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FujianFuzhou</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>67.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.351429</td>\n",
       "      <td>26.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FujianZhenjiang</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>26.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FujianNanping</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>60.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.417500</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HunanHengyang</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>26.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HunanChangsha</td>\n",
       "      <td>1108</td>\n",
       "      <td>-0.396029</td>\n",
       "      <td>17.222563</td>\n",
       "      <td>17.413267</td>\n",
       "      <td>48.351083</td>\n",
       "      <td>0.029856</td>\n",
       "      <td>1.020217</td>\n",
       "      <td>0.947500</td>\n",
       "      <td>28.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SichuanZigong</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.217391</td>\n",
       "      <td>11.026087</td>\n",
       "      <td>11.026087</td>\n",
       "      <td>66.521739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.586522</td>\n",
       "      <td>29.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Lhasa</td>\n",
       "      <td>355</td>\n",
       "      <td>-1.095775</td>\n",
       "      <td>10.640845</td>\n",
       "      <td>10.640845</td>\n",
       "      <td>71.191549</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.253380</td>\n",
       "      <td>29.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HubeiWuhan</td>\n",
       "      <td>526</td>\n",
       "      <td>-1.551331</td>\n",
       "      <td>12.633080</td>\n",
       "      <td>12.913118</td>\n",
       "      <td>47.975285</td>\n",
       "      <td>0.085894</td>\n",
       "      <td>1.070342</td>\n",
       "      <td>1.880342</td>\n",
       "      <td>30.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>2131</td>\n",
       "      <td>-0.051713</td>\n",
       "      <td>20.578461</td>\n",
       "      <td>20.837682</td>\n",
       "      <td>52.263726</td>\n",
       "      <td>0.032952</td>\n",
       "      <td>1.097982</td>\n",
       "      <td>0.881595</td>\n",
       "      <td>31.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>JiangsuChangzhou</td>\n",
       "      <td>144</td>\n",
       "      <td>0.347222</td>\n",
       "      <td>21.397222</td>\n",
       "      <td>20.514583</td>\n",
       "      <td>41.180556</td>\n",
       "      <td>0.012708</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>1.147847</td>\n",
       "      <td>31.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HenanNanyang</td>\n",
       "      <td>251</td>\n",
       "      <td>-0.406375</td>\n",
       "      <td>12.221116</td>\n",
       "      <td>12.221116</td>\n",
       "      <td>66.370518</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.307530</td>\n",
       "      <td>32.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HenanPingdingshan</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.800000</td>\n",
       "      <td>17.560000</td>\n",
       "      <td>17.560000</td>\n",
       "      <td>57.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.792000</td>\n",
       "      <td>33.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ShaanxiXi'an</td>\n",
       "      <td>1686</td>\n",
       "      <td>0.520996</td>\n",
       "      <td>23.028351</td>\n",
       "      <td>23.036833</td>\n",
       "      <td>21.916370</td>\n",
       "      <td>0.038488</td>\n",
       "      <td>1.143832</td>\n",
       "      <td>0.730522</td>\n",
       "      <td>34.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HenanLuoyang</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.177778</td>\n",
       "      <td>20.160000</td>\n",
       "      <td>19.040000</td>\n",
       "      <td>30.622222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997778</td>\n",
       "      <td>1.054444</td>\n",
       "      <td>34.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HenanZhengzhou</td>\n",
       "      <td>96</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>23.519792</td>\n",
       "      <td>22.296875</td>\n",
       "      <td>28.895833</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.012500</td>\n",
       "      <td>1.193229</td>\n",
       "      <td>34.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HenanKaifeng</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>34.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HenanJiaozuo</td>\n",
       "      <td>888</td>\n",
       "      <td>-0.255631</td>\n",
       "      <td>16.798874</td>\n",
       "      <td>16.798874</td>\n",
       "      <td>49.043919</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.188874</td>\n",
       "      <td>35.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ShandongXintai</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>22.833333</td>\n",
       "      <td>22.833333</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.686667</td>\n",
       "      <td>35.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ShandongQingdao</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>36.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HebeiHandan</td>\n",
       "      <td>26</td>\n",
       "      <td>-0.384615</td>\n",
       "      <td>19.576923</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>35.807692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980769</td>\n",
       "      <td>1.447692</td>\n",
       "      <td>36.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ShanxiLvliangfenyang</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.222222</td>\n",
       "      <td>21.133333</td>\n",
       "      <td>21.133333</td>\n",
       "      <td>40.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.411111</td>\n",
       "      <td>37.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ShandongYantai</td>\n",
       "      <td>7</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>25.671429</td>\n",
       "      <td>25.671429</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.804286</td>\n",
       "      <td>37.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LiaoningDalian</td>\n",
       "      <td>1703</td>\n",
       "      <td>0.293834</td>\n",
       "      <td>22.487551</td>\n",
       "      <td>22.459190</td>\n",
       "      <td>36.866119</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.994304</td>\n",
       "      <td>0.977962</td>\n",
       "      <td>38.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>730</td>\n",
       "      <td>0.220548</td>\n",
       "      <td>24.564521</td>\n",
       "      <td>24.495068</td>\n",
       "      <td>27.410959</td>\n",
       "      <td>0.018890</td>\n",
       "      <td>1.160137</td>\n",
       "      <td>0.813616</td>\n",
       "      <td>39.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>651</td>\n",
       "      <td>-0.027957</td>\n",
       "      <td>22.948541</td>\n",
       "      <td>22.066052</td>\n",
       "      <td>28.614439</td>\n",
       "      <td>0.072442</td>\n",
       "      <td>1.056989</td>\n",
       "      <td>0.920568</td>\n",
       "      <td>39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baotou</td>\n",
       "      <td>303</td>\n",
       "      <td>0.016502</td>\n",
       "      <td>19.766997</td>\n",
       "      <td>19.277228</td>\n",
       "      <td>36.356436</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.069967</td>\n",
       "      <td>0.341782</td>\n",
       "      <td>40.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hohhot</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>20.733333</td>\n",
       "      <td>20.733333</td>\n",
       "      <td>36.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>40.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LiaoningBenxi</td>\n",
       "      <td>22</td>\n",
       "      <td>-0.136364</td>\n",
       "      <td>19.736364</td>\n",
       "      <td>19.736364</td>\n",
       "      <td>41.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>1.035455</td>\n",
       "      <td>41.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Turpan</td>\n",
       "      <td>283</td>\n",
       "      <td>-0.031802</td>\n",
       "      <td>18.206714</td>\n",
       "      <td>19.441343</td>\n",
       "      <td>33.904594</td>\n",
       "      <td>0.023569</td>\n",
       "      <td>1.191166</td>\n",
       "      <td>1.422756</td>\n",
       "      <td>42.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HeilongjiangHarbin</td>\n",
       "      <td>3552</td>\n",
       "      <td>0.293300</td>\n",
       "      <td>23.085107</td>\n",
       "      <td>22.707995</td>\n",
       "      <td>32.613457</td>\n",
       "      <td>0.040315</td>\n",
       "      <td>1.006447</td>\n",
       "      <td>0.923361</td>\n",
       "      <td>45.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    city  records  thermal_sensation         ta        top  \\\n",
       "6            HainanSanya        5           0.600000  24.880000  24.880000   \n",
       "5     GuangdongGuangzhou      997          -0.013039  23.850150  23.876128   \n",
       "32         YunnanKunming      415          -1.154217  13.617108  13.617108   \n",
       "2           FujianFuzhou        7          -0.714286  18.100000  18.100000   \n",
       "4        FujianZhenjiang        2          -0.500000  22.900000  22.900000   \n",
       "3          FujianNanping        4           0.000000  17.900000  17.900000   \n",
       "18         HunanHengyang        1          -1.000000  16.900000  16.900000   \n",
       "17         HunanChangsha     1108          -0.396029  17.222563  17.413267   \n",
       "29         SichuanZigong       23          -0.217391  11.026087  11.026087   \n",
       "20                 Lhasa      355          -1.095775  10.640845  10.640845   \n",
       "16            HubeiWuhan      526          -1.551331  12.633080  12.913118   \n",
       "27              Shanghai     2131          -0.051713  20.578461  20.837682   \n",
       "19      JiangsuChangzhou      144           0.347222  21.397222  20.514583   \n",
       "12          HenanNanyang      251          -0.406375  12.221116  12.221116   \n",
       "13     HenanPingdingshan        5          -0.800000  17.560000  17.560000   \n",
       "23          ShaanxiXi'an     1686           0.520996  23.028351  23.036833   \n",
       "11          HenanLuoyang       45          -0.177778  20.160000  19.040000   \n",
       "14        HenanZhengzhou       96           0.656250  23.519792  22.296875   \n",
       "10          HenanKaifeng        1           0.000000  13.000000  13.000000   \n",
       "9           HenanJiaozuo      888          -0.255631  16.798874  16.798874   \n",
       "25        ShandongXintai        3           0.333333  22.833333  22.833333   \n",
       "24       ShandongQingdao        1           1.000000  23.000000  23.000000   \n",
       "7            HebeiHandan       26          -0.384615  19.576923  19.300000   \n",
       "28  ShanxiLvliangfenyang        9          -0.222222  21.133333  21.133333   \n",
       "26        ShandongYantai        7           0.857143  25.671429  25.671429   \n",
       "22        LiaoningDalian     1703           0.293834  22.487551  22.459190   \n",
       "30               Tianjin      730           0.220548  24.564521  24.495068   \n",
       "1                Beijing      651          -0.027957  22.948541  22.066052   \n",
       "0                 Baotou      303           0.016502  19.766997  19.277228   \n",
       "15                Hohhot       12          -0.333333  20.733333  20.733333   \n",
       "21         LiaoningBenxi       22          -0.136364  19.736364  19.736364   \n",
       "31                Turpan      283          -0.031802  18.206714  19.441343   \n",
       "8     HeilongjiangHarbin     3552           0.293300  23.085107  22.707995   \n",
       "\n",
       "           rh       vel       met       clo  latitude  \n",
       "6   70.200000  0.000000  0.980000  0.332000     18.25  \n",
       "5   52.936810  0.100000  1.000000  0.859910     23.13  \n",
       "32  16.946988  0.275325  1.084337  0.647133     24.87  \n",
       "2   67.428571  0.000000  0.885714  0.351429     26.08  \n",
       "4   74.500000  0.000000  0.900000  0.305000     26.10  \n",
       "3   60.250000  0.000000  0.875000  0.417500     26.64  \n",
       "18  66.000000  0.000000  0.900000  0.660000     26.89  \n",
       "17  48.351083  0.029856  1.020217  0.947500     28.23  \n",
       "29  66.521739  0.000000  1.000000  1.586522     29.34  \n",
       "20  71.191549  0.020000  1.100000  1.253380     29.65  \n",
       "16  47.975285  0.085894  1.070342  1.880342     30.58  \n",
       "27  52.263726  0.032952  1.097982  0.881595     31.23  \n",
       "19  41.180556  0.012708  1.016667  1.147847     31.78  \n",
       "12  66.370518  0.000000  1.000000  1.307530     32.99  \n",
       "13  57.800000  0.000000  0.940000  0.792000     33.74  \n",
       "23  21.916370  0.038488  1.143832  0.730522     34.26  \n",
       "11  30.622222  0.000000  0.997778  1.054444     34.62  \n",
       "14  28.895833  0.000000  1.012500  1.193229     34.75  \n",
       "10  50.000000  0.000000  0.800000  0.420000     34.79  \n",
       "9   49.043919  0.020000  1.100000  1.188874     35.24  \n",
       "25  31.000000  0.000000  0.900000  0.686667     35.90  \n",
       "24  27.000000  0.000000  1.000000  0.500000     36.07  \n",
       "7   35.807692  0.000000  0.980769  1.447692     36.62  \n",
       "28  40.555556  0.000000  0.966667  0.411111     37.51  \n",
       "26  34.000000  0.000000  1.100000  0.804286     37.53  \n",
       "22  36.866119  0.000969  0.994304  0.977962     38.92  \n",
       "30  27.410959  0.018890  1.160137  0.813616     39.12  \n",
       "1   28.614439  0.072442  1.056989  0.920568     39.90  \n",
       "0   36.356436  0.050000  1.069967  0.341782     40.65  \n",
       "15  36.833333  0.000000  0.958333  0.506667     40.82  \n",
       "21  41.090909  0.000000  0.963636  1.035455     41.29  \n",
       "31  33.904594  0.023569  1.191166  1.422756     42.94  \n",
       "8   32.613457  0.040315  1.006447  0.923361     45.75  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# acesending the df by latitude\n",
    "df_city = df_city.sort_values('latitude')\n",
    "df_city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate thermal satisfaction rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>city</th>\n",
       "      <th>records</th>\n",
       "      <th>thermal_sensation</th>\n",
       "      <th>ta</th>\n",
       "      <th>top</th>\n",
       "      <th>rh</th>\n",
       "      <th>vel</th>\n",
       "      <th>met</th>\n",
       "      <th>clo</th>\n",
       "      <th>latitude</th>\n",
       "      <th>tsr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HainanSanya</td>\n",
       "      <td>5</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>24.880000</td>\n",
       "      <td>24.880000</td>\n",
       "      <td>70.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.332000</td>\n",
       "      <td>18.25</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GuangdongGuangzhou</td>\n",
       "      <td>997</td>\n",
       "      <td>-0.013039</td>\n",
       "      <td>23.850150</td>\n",
       "      <td>23.876128</td>\n",
       "      <td>52.936810</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.859910</td>\n",
       "      <td>23.13</td>\n",
       "      <td>0.900702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YunnanKunming</td>\n",
       "      <td>415</td>\n",
       "      <td>-1.154217</td>\n",
       "      <td>13.617108</td>\n",
       "      <td>13.617108</td>\n",
       "      <td>16.946988</td>\n",
       "      <td>0.275325</td>\n",
       "      <td>1.084337</td>\n",
       "      <td>0.647133</td>\n",
       "      <td>24.87</td>\n",
       "      <td>0.660241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FujianFuzhou</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>67.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.351429</td>\n",
       "      <td>26.08</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FujianZhenjiang</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>26.10</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city  records  thermal_sensation         ta        top  \\\n",
       "0         HainanSanya        5           0.600000  24.880000  24.880000   \n",
       "1  GuangdongGuangzhou      997          -0.013039  23.850150  23.876128   \n",
       "2       YunnanKunming      415          -1.154217  13.617108  13.617108   \n",
       "3        FujianFuzhou        7          -0.714286  18.100000  18.100000   \n",
       "4     FujianZhenjiang        2          -0.500000  22.900000  22.900000   \n",
       "\n",
       "          rh       vel       met       clo  latitude       tsr  \n",
       "0  70.200000  0.000000  0.980000  0.332000     18.25  0.800000  \n",
       "1  52.936810  0.100000  1.000000  0.859910     23.13  0.900702  \n",
       "2  16.946988  0.275325  1.084337  0.647133     24.87  0.660241  \n",
       "3  67.428571  0.000000  0.885714  0.351429     26.08  0.857143  \n",
       "4  74.500000  0.000000  0.900000  0.305000     26.10  0.500000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tsv between +- 2 (without ==) in df, then merge it with df_city, assuming integer sacle unit\n",
    "df['satisfied'] = df['thermal_sensation'].apply(lambda x: 1 if x > -2 and x < 2 else 0)\n",
    "df_satisfied = df.groupby('city').agg(\n",
    "    tsr=('satisfied', 'mean')\n",
    ").reset_index()\n",
    "df_city = pd.merge(df_city, df_satisfied, on='city')\n",
    "df_city.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calcualte the likelihood of central Heating of cities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Radiator heating' 'Convection heating' 'Radiant floor heating'\n",
      " 'Naturally Ventilated' 'Ceiling capillary heating'\n",
      " 'Air conditioning heating' 'Others' 'self-heating' 'Furnace heating'\n",
      " 'Floor radiation + fan coil' 'Small electric heater heating']\n"
     ]
    }
   ],
   "source": [
    "print(df['heating_type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calcualte the percentage of Radiator heating for each city and assign the value to df_city\n",
    "df_city['p_dh'] = df_city['city'].apply(lambda x: df[df['city'] == x]['heating_type'].value_counts().get('Radiator heating', 0) / df[df['city'] == x]['heating_type'].count())\n",
    "# calcualte the percentage of Natural ventilation for each city and assign the value to df_city\n",
    "df_city['p_nv'] = df_city['city'].apply(lambda x: df[df['city'] == x]['heating_type'].value_counts().get('Naturally Ventilated', 0) / df[df['city'] == x]['heating_type'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resort the df_city by latitude\n",
    "df_city = df_city.sort_values(by='latitude')\n",
    "# reset the index\n",
    "df_city = df_city.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'probability of district heating')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot the probability of district heating across latitude\n",
    "plt.scatter(df_city['latitude'], df_city['p_dh'])\n",
    "plt.xlabel('latitude')\n",
    "plt.ylabel('probability of district heating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HubeiWuhan\n",
      "0.0\n",
      "Naturally Ventilated    526\n",
      "Name: heating_type, dtype: int64\n",
      "Dormitory       348\n",
      "Residential      95\n",
      "Educationnal     83\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "Shanghai\n",
      "0.0\n",
      "Convection heating               784\n",
      "Radiant floor heating            726\n",
      "Naturally Ventilated             390\n",
      "Air conditioning heating         200\n",
      "Small electric heater heating     23\n",
      "Others                             8\n",
      "Name: heating_type, dtype: int64\n",
      "Office         906\n",
      "Residential    858\n",
      "Educational    315\n",
      "Dormitory       52\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "JiangsuChangzhou\n",
      "0.0\n",
      "Ceiling capillary heating    144\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    144\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HenanNanyang\n",
      "0.0\n",
      "Naturally Ventilated        155\n",
      "Air conditioning heating     96\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    251\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HenanPingdingshan\n",
      "0.2\n",
      "Naturally Ventilated    4\n",
      "Radiator heating        1\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    5\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "ShaanxiXi'an\n",
      "0.3469750889679715\n",
      "Radiant floor heating    594\n",
      "Radiator heating         585\n",
      "Convection heating       507\n",
      "Name: heating_type, dtype: int64\n",
      "Office    1686\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HenanLuoyang\n",
      "0.0\n",
      "Convection heating      43\n",
      "Naturally Ventilated     2\n",
      "Name: heating_type, dtype: int64\n",
      "Office         38\n",
      "Residential     7\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HenanZhengzhou\n",
      "0.0\n",
      "Convection heating       92\n",
      "Radiant floor heating     4\n",
      "Name: heating_type, dtype: int64\n",
      "Office         92\n",
      "Residential     4\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HenanKaifeng\n",
      "0.0\n",
      "Naturally Ventilated    1\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    1\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HenanJiaozuo\n",
      "0.9988738738738738\n",
      "Radiator heating        887\n",
      "Naturally Ventilated      1\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    888\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "ShandongXintai\n",
      "1.0\n",
      "Radiator heating    3\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    3\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "ShandongQingdao\n",
      "1.0\n",
      "Radiator heating    1\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    1\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HebeiHandan\n",
      "1.0\n",
      "Radiator heating    26\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    26\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "ShanxiLvliangfenyang\n",
      "1.0\n",
      "Radiator heating    9\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    9\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "ShandongYantai\n",
      "0.8571428571428571\n",
      "Radiator heating        6\n",
      "Naturally Ventilated    1\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    6\n",
      "Office         1\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "LiaoningDalian\n",
      "0.7586611861421022\n",
      "Radiator heating         1292\n",
      "Convection heating        278\n",
      "Radiant floor heating      67\n",
      "Furnace heating            58\n",
      "Naturally Ventilated        8\n",
      "Name: heating_type, dtype: int64\n",
      "Office             1065\n",
      "Dormitory           468\n",
      "Residential         112\n",
      "Rural residence      58\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "Tianjin\n",
      "0.04246575342465753\n",
      "Floor radiation + fan coil    684\n",
      "Radiator heating               31\n",
      "Convection heating             15\n",
      "Name: heating_type, dtype: int64\n",
      "Office    730\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "Beijing\n",
      "0.7588325652841782\n",
      "Radiator heating      494\n",
      "Convection heating    157\n",
      "Name: heating_type, dtype: int64\n",
      "Educational    590\n",
      "Office          61\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "Baotou\n",
      "0.0\n",
      "Naturally Ventilated    303\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    303\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "Hohhot\n",
      "1.0\n",
      "Radiator heating    12\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    12\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "LiaoningBenxi\n",
      "0.045454545454545456\n",
      "Radiant floor heating    21\n",
      "Radiator heating          1\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    22\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "Turpan\n",
      "0.0\n",
      "Furnace heating         212\n",
      "Naturally Ventilated     53\n",
      "self-heating             18\n",
      "Name: heating_type, dtype: int64\n",
      "Residential    283\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n",
      "HeilongjiangHarbin\n",
      "0.9324324324324325\n",
      "Radiator heating         3312\n",
      "Radiant floor heating     240\n",
      "Name: heating_type, dtype: int64\n",
      "Educational    1073\n",
      "Residential    1037\n",
      "Office          973\n",
      "Dormitory       469\n",
      "Name: building_type, dtype: int64\n",
      "***************************************\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print each city name to check details\n",
    "for i in range(len(df_city)):\n",
    "    if i >= 10:\n",
    "        cityname = df_city['city'][i]\n",
    "        print(cityname)\n",
    "        print(df_city['p_dh'][i])\n",
    "        print(df[df['city'] == cityname]['heating_type'].value_counts())\n",
    "        print(df[df['city'] == cityname]['building_type'].value_counts())\n",
    "        print(\"***************************************\\n\")\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, the \"Radiator heating\" should indicate central heating systems.</br>\n",
    "However, for Northern cities, the \"Raidiant floor heating\", \"Floor radiation + fan coil\" should also indicate central heating systems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include Radiant floor heating as central heating\n",
    "df_city.loc[df_city['city'] == \"ShaanxiXi'an\", 'p_dh'] = (594+585) / 1686\n",
    "# Include Radiant floor heating as central heating\n",
    "df_city.loc[df_city['city'] == \"HenanZhengzhou\", 'p_dh'] = 4 / 96\n",
    "# Include Radiant floor heating as central heating\n",
    "df_city.loc[df_city['city'] == 'LiaoningDalian', 'p_dh'] = (1292+67) / (1292+278+67+58+8)\n",
    "# Include (floor heating + fan coil) as central heating\n",
    "df_city.loc[df_city['city'] == 'Tianjin', 'p_dh'] = (684+31)/(684+31+15)\n",
    "# Wrong data, http://dx.doi.org/10.1016/j.enbuild.2015.12.053\n",
    "df_city.loc[df_city['city'] == 'Baotou', 'p_dh'] = 1\n",
    "# Include Radiant floor heating as central heating\n",
    "df_city.loc[df_city['city'] == 'LiaoningBenxi', 'p_dh'] = 1\n",
    "# Include Radiant floor heating as central heating\n",
    "df_city.loc[df_city['city'] == 'HeilongjiangHarbin', 'p_dh'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'probability of district heating')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot the probability of district heating across latitude\n",
    "plt.scatter(df_city['latitude'], df_city['p_dh'])\n",
    "plt.xlabel('latitude')\n",
    "plt.ylabel('probability of district heating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>city</th>\n",
       "      <th>records</th>\n",
       "      <th>latitude</th>\n",
       "      <th>p_dh</th>\n",
       "      <th>p_nv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HainanSanya</td>\n",
       "      <td>5</td>\n",
       "      <td>18.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GuangdongGuangzhou</td>\n",
       "      <td>997</td>\n",
       "      <td>23.13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YunnanKunming</td>\n",
       "      <td>415</td>\n",
       "      <td>24.87</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FujianFuzhou</td>\n",
       "      <td>7</td>\n",
       "      <td>26.08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FujianZhenjiang</td>\n",
       "      <td>2</td>\n",
       "      <td>26.10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FujianNanping</td>\n",
       "      <td>4</td>\n",
       "      <td>26.64</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HunanHengyang</td>\n",
       "      <td>1</td>\n",
       "      <td>26.89</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HunanChangsha</td>\n",
       "      <td>1108</td>\n",
       "      <td>28.23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.575812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SichuanZigong</td>\n",
       "      <td>23</td>\n",
       "      <td>29.34</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lhasa</td>\n",
       "      <td>355</td>\n",
       "      <td>29.65</td>\n",
       "      <td>0.005634</td>\n",
       "      <td>0.684507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HubeiWuhan</td>\n",
       "      <td>526</td>\n",
       "      <td>30.58</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>2131</td>\n",
       "      <td>31.23</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.183013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>JiangsuChangzhou</td>\n",
       "      <td>144</td>\n",
       "      <td>31.78</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HenanNanyang</td>\n",
       "      <td>251</td>\n",
       "      <td>32.99</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.617530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HenanPingdingshan</td>\n",
       "      <td>5</td>\n",
       "      <td>33.74</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ShaanxiXi'an</td>\n",
       "      <td>1686</td>\n",
       "      <td>34.26</td>\n",
       "      <td>0.699288</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HenanLuoyang</td>\n",
       "      <td>45</td>\n",
       "      <td>34.62</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HenanZhengzhou</td>\n",
       "      <td>96</td>\n",
       "      <td>34.75</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HenanKaifeng</td>\n",
       "      <td>1</td>\n",
       "      <td>34.79</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HenanJiaozuo</td>\n",
       "      <td>888</td>\n",
       "      <td>35.24</td>\n",
       "      <td>0.998874</td>\n",
       "      <td>0.001126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ShandongXintai</td>\n",
       "      <td>3</td>\n",
       "      <td>35.90</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ShandongQingdao</td>\n",
       "      <td>1</td>\n",
       "      <td>36.07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HebeiHandan</td>\n",
       "      <td>26</td>\n",
       "      <td>36.62</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ShanxiLvliangfenyang</td>\n",
       "      <td>9</td>\n",
       "      <td>37.51</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ShandongYantai</td>\n",
       "      <td>7</td>\n",
       "      <td>37.53</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LiaoningDalian</td>\n",
       "      <td>1703</td>\n",
       "      <td>38.92</td>\n",
       "      <td>0.798004</td>\n",
       "      <td>0.004698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>730</td>\n",
       "      <td>39.12</td>\n",
       "      <td>0.979452</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>651</td>\n",
       "      <td>39.90</td>\n",
       "      <td>0.758833</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Baotou</td>\n",
       "      <td>303</td>\n",
       "      <td>40.65</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Hohhot</td>\n",
       "      <td>12</td>\n",
       "      <td>40.82</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>LiaoningBenxi</td>\n",
       "      <td>22</td>\n",
       "      <td>41.29</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Turpan</td>\n",
       "      <td>283</td>\n",
       "      <td>42.94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.187279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>HeilongjiangHarbin</td>\n",
       "      <td>3552</td>\n",
       "      <td>45.75</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    city  records  latitude      p_dh      p_nv\n",
       "0            HainanSanya        5     18.25  0.000000  1.000000\n",
       "1     GuangdongGuangzhou      997     23.13  0.000000  0.000000\n",
       "2          YunnanKunming      415     24.87  0.000000  0.997590\n",
       "3           FujianFuzhou        7     26.08  0.000000  1.000000\n",
       "4        FujianZhenjiang        2     26.10  0.000000  1.000000\n",
       "5          FujianNanping        4     26.64  0.000000  1.000000\n",
       "6          HunanHengyang        1     26.89  0.000000  1.000000\n",
       "7          HunanChangsha     1108     28.23  0.000000  0.575812\n",
       "8          SichuanZigong       23     29.34  0.000000  1.000000\n",
       "9                  Lhasa      355     29.65  0.005634  0.684507\n",
       "10            HubeiWuhan      526     30.58  0.000000  1.000000\n",
       "11              Shanghai     2131     31.23  0.000000  0.183013\n",
       "12      JiangsuChangzhou      144     31.78  0.000000  0.000000\n",
       "13          HenanNanyang      251     32.99  0.000000  0.617530\n",
       "14     HenanPingdingshan        5     33.74  0.200000  0.800000\n",
       "15          ShaanxiXi'an     1686     34.26  0.699288  0.000000\n",
       "16          HenanLuoyang       45     34.62  0.000000  0.044444\n",
       "17        HenanZhengzhou       96     34.75  0.041667  0.000000\n",
       "18          HenanKaifeng        1     34.79  0.000000  1.000000\n",
       "19          HenanJiaozuo      888     35.24  0.998874  0.001126\n",
       "20        ShandongXintai        3     35.90  1.000000  0.000000\n",
       "21       ShandongQingdao        1     36.07  1.000000  0.000000\n",
       "22           HebeiHandan       26     36.62  1.000000  0.000000\n",
       "23  ShanxiLvliangfenyang        9     37.51  1.000000  0.000000\n",
       "24        ShandongYantai        7     37.53  0.857143  0.142857\n",
       "25        LiaoningDalian     1703     38.92  0.798004  0.004698\n",
       "26               Tianjin      730     39.12  0.979452  0.000000\n",
       "27               Beijing      651     39.90  0.758833  0.000000\n",
       "28                Baotou      303     40.65  1.000000  1.000000\n",
       "29                Hohhot       12     40.82  1.000000  0.000000\n",
       "30         LiaoningBenxi       22     41.29  1.000000  0.000000\n",
       "31                Turpan      283     42.94  0.000000  0.187279\n",
       "32    HeilongjiangHarbin     3552     45.75  1.000000  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city[[\"city\", 'records', 'latitude', 'p_dh', 'p_nv']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_city.loc[df_city['city'] == 'Baotou', 'p_nv'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'probability of naturally ventilated')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAds0lEQVR4nO3de5wcZZ3v8c+XcZRRLgOCl0wSElmMq9wiLUHxuOKuJ4hgAsrKRVfwgqzg5eVujsS7q7tkjbqgqNksiyKyoCvZEJVjXiiIF0QzSdAIGMiCQCYsRDyDQUZIht/5o6pDZ+hLzUxX9XT39/169Wumnqqu+j1dPfWbqnrqeRQRmJlZ99qt1QGYmVlrORGYmXU5JwIzsy7nRGBm1uWcCMzMutxTWh3AeO23334xa9asVodhZtZW1q5d+7uI2L/avLZLBLNmzWJwcLDVYZiZtRVJd9ea50tDZmZdzonAzKzLORGYmXU5JwIzsy7nRGBm1uVyazUk6RLgeOCBiDi4ynwBFwLHAY8AZ0TEujxiWbl+iKWrN7JleIRp/X0smj+HhXMH8thU1/nwyg1c8fN7GY2gR+LUeTP41MJDdvnM9+7rRYLhR7bn/vlPdF+363ekXeO2qSXP5qNfBS4CvlZj/muAg9LXPODL6c+mWrl+iMUrNjCyfRSAoeERFq/YAOA/mDHGe1D58MoNfP2me3ZOj0bw9Zvu4a6tD7Punod2fubDI9t3LpPn5z/Rfd2u35F2jdumntwuDUXEj4Df11lkAfC1SNwE9Et6brPjWLp6484/lLKR7aMsXb2x2Ztqa+WDytDwCMETB5WV64dqvueKn99btfyn//37J33mlfL6/Ce6r9v1O9KucdvU08p7BANA5ZFkc1r2JJLOkjQoaXDr1q3j2siW4ZFxlXeriRxURicxlkUen/9E93W7fkfaNW6belqZCFSlrOqRJSKWR0QpIkr771/1CemapvX3jau8W03koNKjarswmzw+/4nu63b9jrRr3Db1tDIRbAZmVExPB7Y0eyOL5s+hr7dnl7K+3h4WzZ/T7E21tYkcVE6dN6Nq+dEH7vukz7xSXp//RPd1u35H2jVum3pamQhWAX+jxFHAQxFxX7M3snDuAOefdAgD/X0IGOjv4/yTDvHNtDEmclD51MJDeNNRM3eeGfRIvOmomVz+jpfu8pn39/Wyz9N7c//8J7qv2/U70q5x29SjvMYslnQF8EpgP+B+4GNAL0BELEubj14EHEvSfPTMiGjYm1ypVAp3OpcPN0U061yS1kZEqeq8dhu83onAzGz86iUCP1lsZtblnAjMzLqcE4GZWZdzIjAz63JOBGZmXc6JwMysyzkRmJl1OScCM7Mu50RgZtblnAjMzLqcE4GZWZdzIjAz63I1xyyWdFK9N0bEiuaHY2ZmRas3eP0J6c9nAS8DrkunjwF+CDgRmJl1gJqJICLOBJD0HeCF5UFj0gHmv1hMeGZmlrcs9whmjRk57H7g+TnFY2ZmBat3aajsh5JWA1eQDC5/CnB9rlGZmVlhGiaCiDhX0onAK9Ki5RHxX/mGZWZmRclyRgCwDtgWEd+X9HRJe0bEtjwDMzOzYjS8RyDpHcC3gH9NiwaAlTnGZGZmBcpys/gc4GjgDwARcQdJk1IzM+sAWRLBoxHxWHlC0lNIbhqbmVkHyJIIbpD0QaBP0quB/wS+nW9YZmZWlCyJ4DxgK7ABeCdwTUR8KNeozMysMFlaDb07Ii4E/q1cIOm9aZmZmbW5LGcEb6lSdkaT4zAzsxap1/voqcBpwGxJqypm7Qk8mHdgZmZWjHqXhm4E7gP2Az5bUb4N+FWeQZmZWXHq9T56N3A38NLiwjEzs6JlebL4KElrJD0s6TFJo5L+UERwZmaWvyw3iy8CTgXuAPqAtwNfyDMoMzMrTqYxiyNiE9ATEaMR8RWSUcoaknSspI2SNkk6r8r8vSV9W9IvJd0i6czxhW/WeVauH+LoJdcx+7zvcvSS61i5fqjVIVmHy/IcwSOSngrcLOnTJDeQn9HoTZJ6SEYyezWwGVgjaVVE3Fqx2DnArRFxgqT9gY2SLq/s0sKsm6xcP8TiFRsY2T4KwNDwCItXbABg4dyBVoZmHSzLGcGbgR7gXOCPwAzg9RnedySwKSLuTA/sVwILxiwTwJ6SBOwB/B7YkTF2s46zdPXGnUmgbGT7KEtXb2xRRNYNsgxMc3f66wjwiXGsewC4t2J6MzBvzDIXAauALSTPJ7wxIh4fuyJJZwFnAcycOXMcIZi1ly3DI+MqN2uGeg+UbaBOL6MRcWiDdava28ZMzwduBl4FHAhcK+nHEbFLq6SIWA4sByiVSu751DrWtP4+hqoc9Kf197UgGusW9c4Ijp/kujeTXEYqm07yn3+lM4ElERHAJkl3AS8AfjHJbZu1pUXz5+xyjwCgr7eHRfPntDAq63SNHiibjDXAQZJmA0Mkg96fNmaZe4C/BH4s6dnAHODOSW7XrG2VbwgvXb2RLcMjTOvvY9H8Ob5RbLmqd2noJxHxcknb2PWSjoCIiL3qrTgidkg6F1hNcrP5koi4RdLZ6fxlwCeBr6aXoQR8ICJ+N7kqmbW3hXMHfOC3Qim5KtM+SqVSDA4OtjoMM7O2ImltRJSqzcvSxcRlWcrMzKw9ZXmO4EWVE+mYxUfkE46ZmRWtZiKQtDi9P3CopD+kr23A/cDVhUVoZma5qpkIIuL8iNgTWBoRe6WvPSPimRGxuMAYzcwsR1meLF4saQA4oHL5iPhRnoGZmVkxGiYCSUtIngG4FSg/5RKAE4GZWQfI0vvoicCciHg072DMzKx4WVoN3Qn05h2ImZm1RqbxCEjGIvgBsPOsICLek1tUZmZWmCyJYFX6MjOzDpSl1dClkvqAmRHh0THMzDpMli4mTiAZM+B76fThknyGYGbWIbLcLP44ybCTwwARcTMwO7eIzMysUFkSwY6IeGhMWXt1WWpmZjVluVn8a0mnAT2SDgLeA9yYb1hmzbFy/ZAHeTFrIMsZwbtJeiB9FPgP4CHgfTnGZNYUK9cPsXjFBoaGRwhgaHiExSs2sHL9UKtDM5tSsiSCORHxoYh4Sfr6cET8KffIzCZp6eqNu4z9CzCyfZSlq934zaxSlkTwOUm/kfRJSS9qvLjZ1LBleGRc5WbdqmEiiIhjgFcCW4HlkjZI+nDegZlN1rT+vnGVm3WrLGcERMT/RMTngbNJnin4aJ5BmTXDovlz6Ovt2aWsr7eHRfPntCgis6kpSzfUfw68EXgD8CBwJfB3OcdlNmnl1kFuNWRWX5bmo18BrgD+d0RsyTkes6ZaOHfAB36zBrL0NXRUEYGYmVlrZLpHYGZmncuJwMysy2XpffTgIgIxM7PWyHJGsEzSLyS9S1J/3gGZmVmxstwsfnna2dxbgUFJvwC+EhHX5h6dWQdyR3g21WRpPkpE3JE+TTwIfB6YK0nAByNiRZ4BmnWSckd45T6Qyh3hAU4G1jJZ7hEcKulfgNuAVwEnRMSfp7//S87xmXUUd4RnU1GWewQXAeuBwyLinIhYB5A+XFa3zyFJx0raKGmTpPNqLPNKSTdLukXSDeOtgFk7cUd4NhVluUfwijrzLqs1T1IP8EXg1cBmYI2kVRFxa8Uy/cCXgGMj4h5JzxpH7GZtZ1p/H0NVDvruCM9aqWYikLSB6kNSCoiIOLTBuo8ENkXEnen6rgQWALdWLHMasCIi7iFZ6QPjiN2s7SyaP2eXewTgjvCs9eqdERw/yXUPAPdWTG8G5o1Z5vlAr6QfAnsCF0bE18auSNJZwFkAM2fOnGRYZq3jjvBsKqqZCCLi7kmuW9VWW2X7RwB/CfQBP5N0U0TcPiaW5cBygFKpVO0sxaxtuCM8m2rqXRraRv1LQ3s1WPdmYEbF9HRgbO+lm4HfRcQfgT9K+hFwGHA7ZrjNvVkR6p0R7DnJda8BDpI0GxgCTiG5J1DpauAiSU8Bnkpy6chNUg1wm3uzomTudE7SsyTNLL8aLR8RO4BzgdUkzyB8MyJukXS2pLPTZW4Dvgf8CvgFcHFE/HoiFbHO4zb3ZsXIMkLZ64DPAtOAB4ADSA7sDQeyj4hrgGvGlC0bM70UWJo9ZOsWbnNvVowsZwSfBI4Cbo+I2SQ3dn+aa1RmePB5s6JkSQTbI+JBYDdJu0XE9cDh+YZl5sHnzYqSpdO5YUl7AD8CLpf0ALAj37DM3OberCiKqN8sX9IzgBGSs4fTgb2By9OzhMKVSqUYHBxsxabNzNqWpLURUao2r+4ZQdpf0NUR8VfA48ClOcRnZmYtVPceQUSMAo9I2rugeMzMrGBZ7hH8Cdgg6Vrgj+XCiHhPblGZmVlhsiSC76YvMzPrQFnGI/B9ATOzDpblyeK7qNL5XEQ8L5eIzMysUFkuDVU2N9odOBnYN59wzMysaA2fLI6IByteQxFxAcnA9WZm1gGyXBp6ccXkbiRnCJPtotrMzKaILJeGPlvx+w7gLuCv8wnHzMyKliURvK08AH1ZOtiMmZl1gCy9j34rY5mZmbWhemMWv4Bk8Jm9JZ1UMWsvktZDZmbWAepdGpoDHA/0AydUlG8D3pFjTGZmVqB6g9dfDVwt6aUR8bMCYzIzswJluVm8XtI5JJeJdl4Sioi35haVmZkVJsvN4suA5wDzgRuA6SSXh8zMrANkSQR/FhEfAf6YdkD3WuCQfMMyM7OiZBq8Pv05LOlgkqEqZ+UWkZmZFSrLPYLlkvYBPgKsAvYAPpprVGZmVpgs4xFcnP56A+Cup83MOkyWTueeBrye5HLQzuUj4h/yC8vMzIqS5dLQ1cBDwFrg0XzDMTOzomVJBNMj4tjcIzEzs5bI0mroRkluLmpm1qGynBG8HDgjHbv4UUBARMShuUZmZmaFyJIIXpN7FGZm1jJZxiy+u9ory8olHStpo6RNks6rs9xLJI1KesN4gjczs8nLco9gQiT1AF8kOaN4IXCqpBfWWO6fgdV5xWJmZrXVTATp8wOTcSSwKSLujIjHgCuBBVWWezdwFfDAJLdnZmYTUO+M4GcAki6b4LoHgHsrpjenZTtJGgBOBJbVW5GksyQNShrcunXrBMMxM7Nq6t0sfqqktwAvGzNUJQARsaLBulWlLMZMXwB8ICJGpWqL79zWcmA5QKlUGrsOMzObhHqJ4GzgdJ48VCUkB/RGiWAzMKNiejqwZcwyJeDKNAnsBxwnaUdErGywbjMza5J6Q1X+BPiJpMGI+PcJrHsNcJCk2cAQcApw2phtzC7/LumrwHecBMzMipXlOYLLJL0HeEU6fQOwLCK213kPEbFD0rkkrYF6gEsi4hZJZ6fz694XMDOzYiii/iV3SRcDvcCladGbgdGIeHvOsVVVKpVicHCwFZs2M2tbktZGRKnavCxnBC+JiMMqpq+T9MvmhGZmZq2W5YGyUUkHlickPQ8YzS8kMzMrUpYzgkXA9ZLuJGkSegBwZq5RmZlZYbIMVfkDSQcBc0gSwW8iwgPUmJl1iCxnBKQH/l/lHIuZmbVAbp3OmZlZe3AiMDPrcg0TgaSrJL1WkpOGmVkHynJw/zJJ1xB3SFoi6QU5x2RmZgXKMkLZ9yPidODFwG+BayXdKOlMSb15B2hmZvnKdLlH0jOBM4C3A+uBC0kSw7W5RWZmZoVo2HxU0grgBcBlwAkRcV866xuS3OmPmVmby/IcwcURcU1lgaSnRcSjtTowMjOz9pHl0tCnqpT9rNmBmJlZa9Q8I5D0HJIxhvskzeWJoSf3Ap5eQGxmZlaAepeG5pPcIJ4OfK6ifBvwwRxjMjOzAtUbqvJS4FJJr4+IqwqMyczMClTv0tCbIuLrwCxJ7x87PyI+V+VtZmbWZupdGnpG+nOPIgIxM7PWqHdp6F/Tn58oLhwzMytavUtDn6/3xoh4T/PDMTOzotW7NLS2sCjMzKxlGrUaMjOzDlfv0tAFEfE+Sd8GYuz8iHhdrpGZmVkh6l0auiz9+ZkiAjEzs9aod2lobfrzBklPJemBNICNEfFYQfGZmVnOsnRD/VpgGfDfJP0NzZb0zoj4v3kHZ2Zm+cvSDfVngWMiYhOApAOB7wJOBGZmHSBLN9QPlJNA6k7ggZziMTOzgtVrNXRS+ustkq4Bvklyj+BkYE0BsZmZWQHqnRGckL52B+4H/gJ4JbAV2CfLyiUdK2mjpE2Szqsy/3RJv0pfN0o6bNw1MDOzSanXaujMyaxYUg/wReDVwGZgjaRVEXFrxWJ3AX8REf9P0muA5cC8yWzXzMzGJ0urod2BtwEvIjk7ACAi3trgrUcCmyLiznQ9VwILgJ2JICJurFj+JpJBcMzMrEBZbhZfBjyHZMSyG0gO1tsyvG8AuLdienNaVsvbqNESSdJZkgYlDW7dujXDps3Mmmvl+iGOXnIds8/7LkcvuY6V64daHVLTZGk++mcRcbKkBRFxqaT/AFZneJ+qlD2pqwoASceQJIKXV5sfEctJLhtRKpWqrsMsLyvXD7F09Ua2DI8wrb+PRfPnsHBuvf9prNOsXD/E4hUbGNk+CsDQ8AiLV2wA6IjvQpYzgu3pz2FJBwN7A7MyvG8zMKNiejqwZexCkg4FLgYWRMSDGdZrVpjyAWBoeITgiQNAJ/03aI0tXb1xZxIoG9k+ytLVG1sUUXNlSQTLJe0DfARYRXKN/58zvG8NcJCk2WkXFaek799J0kxgBfDmiLh9XJGbFaDTDwCWzZbhkXGVt5uGl4Yi4uL01xuA52VdcUTskHQuyWWkHuCSiLhF0tnp/GXAR4FnAl+SBLAjIkrjq4JZfjr9AGDZTOvvY6jKPp/W39eCaJqv4RmBpGdK+oKkdZLWSrpA0jOzrDwiromI50fEgRHxj2nZsjQJEBFvj4h9IuLw9OUkYFNKrT/0TjkAWDaL5s+hr7dnl7K+3h4WzZ/TooiaK8uloStJupR4PfAG4HfAN/IMymyq6PQDgGWzcO4A5590CAP9fQgY6O/j/JMO6YgbxQCKqN8IR9LaiDhiTNlgq/57L5VKMTg42IpNW5dyqyHrBOmxvOpxO0vz0eslnULS1xAkZwXfbVZwZlPdwrkDPvBbR6vX6dw2knb/At4PfD2dtRvwMPCx3KMzsynLZ0qdo15fQ3sWGYiZtY9Of8Cq22S5WYyk10n6TPo6Pu+gzGxq8/MVnSVL89ElwHtJHiS7FXhvWmZmXcrPV3SWLDeLjwMOj4jHASRdCqwHnjS+gJl1h05/wKrbZLo0BPRX/L53DnGYWRvx8xWdJcsZwT8B6yVdT9KC6BXA4lyjMrMprXxD2K2GOkPdRCBpN+Bx4CjgJSSJ4AMR8T8FxGZmU5ifr+gcdRNBRDwu6dyI+CZjeg41M7POkOUewbWS/l7SDEn7ll+5R2ZmZoXIco+gPDbxORVlwTi6pDYzs6kry3gEs4sIxMzMWqNhIpC0O/AukvGEA/gxsCwi/pRzbGZmVoAsl4a+BmwDvpBOnwpcBpycV1BmZlacLIlgTkQcVjF9vaRf5hWQmZkVK0urofWSjipPSJoH/DS/kMzMrEhZzgjmAX8j6Z50eiZwm6QNQETEoblFZ2ZmucuSCI7NPQozM2uZLM1H7y4iEDMza40sZwRmNgV5qEhrFicCszbkoSKtmbKOR2BmU4iHirRmciIwa0MeKtKayYnArA3VGhLSQ0XaRDgRmLUhDxVpzeSbxWZtyENFWjM5EZi1KQ8Vac3iRGBmhfMzEFOLIiK/lUvHAhcCPcDFEbFkzHyl848DHgHOiIh19dZZKpVicHAwp4jbi/+YWqvoz7/a9uDJl4eqlVXGVV7P0PAIPRKjEQxULJe1XhOt/9hnIMr2eXovHzvhRU9aR6Pt1KtPo8+iXTTjuyZpbUSUqs7LKxFI6gFuB14NbAbWAKdGxK0VyxwHvJskEcwDLoyIefXW60SQqPbH1Nfbw/knHdKWX/R2U/TnX217vbsJBNtHo25ZZVy1DsLl5V5/xABXrR1qWK/J1P/oJdcxVKOZ69h1NNpOvfo0+izaRbO+a/USQZ6tho4ENkXEnRHxGHAlsGDMMguAr0XiJqBf0nNzjKlj+IGi1ir686+2ve2Pxy4HuVpllXFVW0/lclf8/N5M9ZpM/es96zB2HY22U68+jT6LdlHEdy3PRDAA3FsxvTktG+8ySDpL0qCkwa1btzY90HbkB4paq+jPf7LrLb+/0XpGa1whGPu+ydS/0bMOletotJ2JfC7t9jdSxHctz0SgKmVjv2VZliEilkdEKSJK+++/f1OCa3d+oKi1iv78J7ve8vsbradH1f4kn/y+ydS/2jMQtdbRaDsT+Vza7W+kiO9anolgMzCjYno6sGUCy1gVfqCotYr+/Kttr3c30dujhmWVcdU7CPf19nDqvBmZ6jWZ+i+cO8D5Jx1Cf19v1Rgq19FoO/Xq0+izaBdFfNfybD66BjhI0mxgCDgFOG3MMquAcyVdSXKz+KGIuC/HmDqGHyhqraI//1rby1pWfn/lemq1GiodsG/Dek22/uVnIBq1hmm0nUb1mUyMU0UR37W8m48eB1xA0nz0koj4R0lnA0TEsrT56EUko6A9ApwZEXWbBLnVkJnZ+NVrNZTrA2URcQ1wzZiyZRW/B3BOnjGYmVl97nTOzKzLORGYmXU5JwIzsy7nRGBm1uVybTWUB0lbgbtbHUdqP+B3rQ4iJ65b++rk+rluE3dARFR9IrftEsFUImmwVnOsdue6ta9Orp/rlg9fGjIz63JOBGZmXc6JYHKWtzqAHLlu7auT6+e65cD3CMzMupzPCMzMupwTgZlZl3MiyEDSDEnXS7pN0i2S3puW7yvpWkl3pD/3aXWsE1Gnfh+XNCTp5vR1XKtjHS9Ju0v6haRfpnX7RFre9vuuTt3afr+VSeqRtF7Sd9Lptt9vlarUryX7zvcIMkjHUX5uRKyTtCewFlgInAH8PiKWSDoP2CciPtC6SCemTv3+Gng4Ij7TyvgmI+3q/BkR8bCkXuAnwHuBk2jzfVenbsfS5vutTNL7gRKwV0QcL+nTtPl+q1Slfh+nBfvOZwQZRMR9EbEu/X0bcBvJ2MoLgEvTxS4lOXi2nTr1a3uReDid7E1fQQfsuzp16wiSpgOvBS6uKG77/VZWo34t4UQwTpJmAXOBnwPPLo+olv58VgtDa4ox9YNkBLlfSbqkXU/D09Pvm4EHgGsjomP2XY26QQfsN5JBrf4P8HhFWUfst9QFPLl+0IJ950QwDpL2AK4C3hcRf2h1PM1WpX5fBg4EDgfuAz7buugmLiJGI+JwkjGxj5R0cItDapoadWv7/SbpeOCBiFjb6ljyUKd+Ldl3TgQZpddgrwIuj4gVafH96fX18nX2B1oV32RVq19E3J8eaB4H/g04spUxTlZEDAM/JLmG3jH7DnatW4fst6OB10n6LXAl8CpJX6dz9lvV+rVq3zkRZJDelPt34LaI+FzFrFXAW9Lf3wJcXXRszVCrfuU/uNSJwK+Ljm2yJO0vqT/9vQ/4K+A3dMC+q1W3TthvEbE4IqZHxCzgFOC6iHgTHbDfoHb9WrXvch2zuIMcDbwZ2JBejwX4ILAE+KaktwH3ACe3JrxJq1W/UyUdTnID8rfAO1sR3CQ9F7hUUg/JPz7fjIjvSPoZ7b/vatXtsg7Yb7V0yt9cLZ9uxb5z81Ezsy7nS0NmZl3OicDMrMs5EZiZdTknAjOzLudEYGbW5ZwIrOtJerjB/H5J76qYnibpW+nvh0+kh8i0l8m/H3+0Zs3nRGDWWD+wMxFExJaIeEM6eTjQtt08m4ETgdlOkvaQ9ANJ6yRtkLQgnbUEODDtH36ppFmSfi3pqcA/AG9M571x7H/66XKz0t8/JGmjpO8DcyqWOVDS9yStlfRjSS8ortZmfrLYrNKfgBMj4g+S9gNukrQKOA84OO3crdxDKxHxmKSPAqWIODed9/FqK5Z0BElXAnNJ/u7WkYz7AMmg5WdHxB2S5gFfAl6VSw3NqnAiMHuCgH+S9AqSroEHgGc3ad3/C/iviHgEIE0w5R5fXwb8Z9LlEwBPa9I2zTJxIjB7wunA/sAREbE97Rly93GuYwe7XnKtfH+1/lx2A4bLZxtmreB7BGZP2Jukj/jtko4BDkjLtwF71njP2Hm/BV4MIOnFwOy0/EfAiZL60uFATwBIx324S9LJ6Xsk6bDmVcmsMScCsydcDpQkDZKcHfwGICIeBH6a3vhdOuY91wMvLN8sJhnTYd+0F9e/BW5P17EO+AZwc7rMjyvWcTrwNkm/BG4hGY7RrDDufdTMrMv5jMDMrMs5EZiZdTknAjOzLudEYGbW5ZwIzMy6nBOBmVmXcyIwM+ty/x9EqKtHhiYjhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot the probability of district heating across latitude\n",
    "plt.scatter(df_city['latitude'], df_city['p_nv'])\n",
    "plt.xlabel('latitude')\n",
    "plt.ylabel('probability of naturally ventilated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for all values in df_city, only keep two decimal places\n",
    "df_city = df_city.round(2)\n",
    "# save the df_city to a csv file\n",
    "df_city.to_csv('CN_DB_CITY.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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