RandomForest_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>No</th>\n",
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       "      <td>9.3</td>\n",
       "      <td>Shunyi</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1027.3</td>\n",
       "      <td>-23.3</td>\n",
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       "</table>\n",
       "<p>35064 rows × 18 columns</p>\n",
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      "text/plain": [
       "          No  year  month  day  hour  PM2.5  PM10   SO2   NO2      CO    O3  \\\n",
       "0          1  2013      3    1     0    3.0   6.0   3.0   8.0   300.0  44.0   \n",
       "1          2  2013      3    1     1   12.0  12.0   3.0   7.0   300.0  47.0   \n",
       "2          3  2013      3    1     2   14.0  14.0   NaN   7.0   200.0  22.0   \n",
       "3          4  2013      3    1     3   12.0  12.0   3.0   5.0     NaN   NaN   \n",
       "4          5  2013      3    1     4   12.0  12.0   3.0   NaN   200.0  11.0   \n",
       "...      ...   ...    ...  ...   ...    ...   ...   ...   ...     ...   ...   \n",
       "35059  35060  2017      2   28    19   27.0  72.0   8.0  92.0   800.0  16.0   \n",
       "35060  35061  2017      2   28    20   47.0  55.0  17.0  86.0  1100.0  19.0   \n",
       "35061  35062  2017      2   28    21   18.0  28.0   4.0  30.0   500.0  64.0   \n",
       "35062  35063  2017      2   28    22   18.0  20.0   9.0  33.0   500.0  59.0   \n",
       "35063  35064  2017      2   28    23   15.0  22.0  13.0  34.0   500.0  60.0   \n",
       "\n",
       "       TEMP    PRES  DEWP  RAIN   wd  WSPM station  \n",
       "0      -0.9  1025.8 -20.5   0.0   NW   9.3  Shunyi  \n",
       "1      -1.1  1026.1 -21.3   0.0   NW   9.4  Shunyi  \n",
       "2      -1.7  1026.2 -23.0   0.0   NW   8.6  Shunyi  \n",
       "3      -2.1  1027.3 -23.3   0.0   NW   6.6  Shunyi  \n",
       "4      -2.4  1027.7 -22.9   0.0   NW   4.5  Shunyi  \n",
       "...     ...     ...   ...   ...  ...   ...     ...  \n",
       "35059  10.3  1014.2 -12.4   0.0    W   1.8  Shunyi  \n",
       "35060   9.8  1014.5  -9.9   0.0   NW   1.5  Shunyi  \n",
       "35061   9.1  1014.6 -12.7   0.0   NE   1.7  Shunyi  \n",
       "35062   7.1  1015.2 -13.2   0.0  WNW   1.8  Shunyi  \n",
       "35063   7.4  1014.9 -11.9   0.0    N   1.4  Shunyi  \n",
       "\n",
       "[35064 rows x 18 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data_shunyi = pd.read_csv('data/PRSA_Data_Shunyi_20130301-20170228.csv')\n",
    "data_shunyi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35060</td>\n",
       "      <td>35061</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>47.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35061</td>\n",
       "      <td>35062</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>18.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>500.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35062</td>\n",
       "      <td>35063</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>500.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35063</td>\n",
       "      <td>35064</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>500.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35064 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          No  year  month  day  hour  PM2.5  PM10   SO2   NO2      CO  ...  \\\n",
       "0          1  2013      3    1     0    3.0   6.0   3.0   8.0   300.0  ...   \n",
       "1          2  2013      3    1     1   12.0  12.0   3.0   7.0   300.0  ...   \n",
       "2          3  2013      3    1     2   14.0  14.0   NaN   7.0   200.0  ...   \n",
       "3          4  2013      3    1     3   12.0  12.0   3.0   5.0     NaN  ...   \n",
       "4          5  2013      3    1     4   12.0  12.0   3.0   NaN   200.0  ...   \n",
       "...      ...   ...    ...  ...   ...    ...   ...   ...   ...     ...  ...   \n",
       "35059  35060  2017      2   28    19   27.0  72.0   8.0  92.0   800.0  ...   \n",
       "35060  35061  2017      2   28    20   47.0  55.0  17.0  86.0  1100.0  ...   \n",
       "35061  35062  2017      2   28    21   18.0  28.0   4.0  30.0   500.0  ...   \n",
       "35062  35063  2017      2   28    22   18.0  20.0   9.0  33.0   500.0  ...   \n",
       "35063  35064  2017      2   28    23   15.0  22.0  13.0  34.0   500.0  ...   \n",
       "\n",
       "       wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW wd_SW  wd_W  wd_WNW  wd_WSW  \n",
       "0           0      1     0      0       0       0     0     0       0       0  \n",
       "1           0      1     0      0       0       0     0     0       0       0  \n",
       "2           0      1     0      0       0       0     0     0       0       0  \n",
       "3           0      1     0      0       0       0     0     0       0       0  \n",
       "4           0      1     0      0       0       0     0     0       0       0  \n",
       "...       ...    ...   ...    ...     ...     ...   ...   ...     ...     ...  \n",
       "35059       0      0     0      0       0       0     0     1       0       0  \n",
       "35060       0      1     0      0       0       0     0     0       0       0  \n",
       "35061       0      0     0      0       0       0     0     0       0       0  \n",
       "35062       0      0     0      0       0       0     0     0       1       0  \n",
       "35063       0      0     0      0       0       0     0     0       0       0  \n",
       "\n",
       "[35064 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_shunyi = pd.get_dummies(data_shunyi, columns=['wd'])\n",
    "data_shunyi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>...</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2013</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>200.0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>300.0</td>\n",
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       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>400.0</td>\n",
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       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
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       "      <td>10</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>400.0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    No  year  month  day  hour  PM2.5  PM10  SO2   NO2     CO  ...  wd_NNW  \\\n",
       "0    1  2013      3    1     0    3.0   6.0  3.0   8.0  300.0  ...       0   \n",
       "1    2  2013      3    1     1   12.0  12.0  3.0   7.0  300.0  ...       0   \n",
       "5    6  2013      3    1     5   11.0  11.0  3.0   7.0  200.0  ...       1   \n",
       "6    7  2013      3    1     6   12.0  12.0  3.0   9.0  300.0  ...       0   \n",
       "7    8  2013      3    1     7   13.0  13.0  3.0  23.0  300.0  ...       0   \n",
       "8    9  2013      3    1     8    8.0   8.0  3.0  19.0  400.0  ...       1   \n",
       "9   10  2013      3    1     9    3.0   6.0  3.0  21.0  400.0  ...       0   \n",
       "10  11  2013      3    1    10    3.0   6.0  3.0  22.0  400.0  ...       1   \n",
       "11  12  2013      3    1    11    6.0   6.0  3.0  16.0  300.0  ...       1   \n",
       "12  13  2013      3    1    12    8.0   8.0  3.0  12.0  300.0  ...       0   \n",
       "\n",
       "    wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW wd_SW  wd_W  wd_WNW  wd_WSW  \n",
       "0       1     0      0       0       0     0     0       0       0  \n",
       "1       1     0      0       0       0     0     0       0       0  \n",
       "5       0     0      0       0       0     0     0       0       0  \n",
       "6       0     0      0       0       0     0     0       0       0  \n",
       "7       0     0      0       0       0     0     0       0       0  \n",
       "8       0     0      0       0       0     0     0       0       0  \n",
       "9       0     0      0       0       1     0     0       0       0  \n",
       "10      0     0      0       0       0     0     0       0       0  \n",
       "11      0     0      0       0       0     0     0       0       0  \n",
       "12      0     0      0       0       0     0     0       0       0  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalization\n",
    "data_concentration = data_shunyi.dropna()\n",
    "data_concentration[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PM2.5', 'SO2', 'NO2', 'CO', 'O3', 'TEMP', 'PRES', 'DEWP', 'RAIN',\n",
       "       'WSPM', 'wd_E', 'wd_ENE', 'wd_ESE', 'wd_N', 'wd_NE', 'wd_NNE', 'wd_NNW',\n",
       "       'wd_NW', 'wd_S', 'wd_SE', 'wd_SSE', 'wd_SSW', 'wd_SW', 'wd_W', 'wd_WNW',\n",
       "       'wd_WSW'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prenormalized = data_concentration.drop(['No','year','month','day','hour','PM10','station'],axis = 1)\n",
    "data_prenormalized.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 762.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_min = np.min(data_prenormalized['PM2.5'])\n",
    "data_max = np.max(data_prenormalized['PM2.5'])\n",
    "data_min,data_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>...</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.128863</td>\n",
       "      <td>0.284946</td>\n",
       "      <td>0.689781</td>\n",
       "      <td>0.244094</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.726562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.137692</td>\n",
       "      <td>0.281362</td>\n",
       "      <td>0.695255</td>\n",
       "      <td>0.231496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.131806</td>\n",
       "      <td>0.250896</td>\n",
       "      <td>0.733577</td>\n",
       "      <td>0.218898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.217154</td>\n",
       "      <td>0.229391</td>\n",
       "      <td>0.748175</td>\n",
       "      <td>0.233071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.014474</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.082031</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.173008</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.775547</td>\n",
       "      <td>0.231496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <td>35059</td>\n",
       "      <td>0.032895</td>\n",
       "      <td>0.033981</td>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.070707</td>\n",
       "      <td>0.046458</td>\n",
       "      <td>0.485663</td>\n",
       "      <td>0.478102</td>\n",
       "      <td>0.371654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35060</td>\n",
       "      <td>0.059211</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>0.055287</td>\n",
       "      <td>0.476703</td>\n",
       "      <td>0.483577</td>\n",
       "      <td>0.411024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35061</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.014563</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.187724</td>\n",
       "      <td>0.464158</td>\n",
       "      <td>0.485401</td>\n",
       "      <td>0.366929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35062</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.038835</td>\n",
       "      <td>0.121094</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.173008</td>\n",
       "      <td>0.428315</td>\n",
       "      <td>0.496350</td>\n",
       "      <td>0.359055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35063</td>\n",
       "      <td>0.017105</td>\n",
       "      <td>0.058252</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.175951</td>\n",
       "      <td>0.433692</td>\n",
       "      <td>0.490876</td>\n",
       "      <td>0.379528</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30587 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PM2.5       SO2       NO2        CO        O3      TEMP      PRES  \\\n",
       "0      0.001316  0.009709  0.023438  0.020202  0.128863  0.284946  0.689781   \n",
       "1      0.013158  0.009709  0.019531  0.020202  0.137692  0.281362  0.695255   \n",
       "5      0.011842  0.009709  0.019531  0.010101  0.131806  0.250896  0.733577   \n",
       "6      0.013158  0.009709  0.027344  0.020202  0.217154  0.229391  0.748175   \n",
       "7      0.014474  0.009709  0.082031  0.020202  0.173008  0.258065  0.775547   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "35059  0.032895  0.033981  0.351562  0.070707  0.046458  0.485663  0.478102   \n",
       "35060  0.059211  0.077670  0.328125  0.101010  0.055287  0.476703  0.483577   \n",
       "35061  0.021053  0.014563  0.109375  0.040404  0.187724  0.464158  0.485401   \n",
       "35062  0.021053  0.038835  0.121094  0.040404  0.173008  0.428315  0.496350   \n",
       "35063  0.017105  0.058252  0.125000  0.040404  0.175951  0.433692  0.490876   \n",
       "\n",
       "           DEWP  RAIN      WSPM  ...  wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  \\\n",
       "0      0.244094   0.0  0.726562  ...     0.0    1.0   0.0    0.0     0.0   \n",
       "1      0.231496   0.0  0.734375  ...     0.0    1.0   0.0    0.0     0.0   \n",
       "5      0.218898   0.0  0.132812  ...     1.0    0.0   0.0    0.0     0.0   \n",
       "6      0.233071   0.0  0.125000  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "7      0.231496   0.0  0.132812  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "...         ...   ...       ...  ...     ...    ...   ...    ...     ...   \n",
       "35059  0.371654   0.0  0.140625  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "35060  0.411024   0.0  0.117188  ...     0.0    1.0   0.0    0.0     0.0   \n",
       "35061  0.366929   0.0  0.132812  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "35062  0.359055   0.0  0.140625  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "35063  0.379528   0.0  0.109375  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "\n",
       "       wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  \n",
       "0         0.0    0.0   0.0     0.0     0.0  \n",
       "1         0.0    0.0   0.0     0.0     0.0  \n",
       "5         0.0    0.0   0.0     0.0     0.0  \n",
       "6         0.0    0.0   0.0     0.0     0.0  \n",
       "7         0.0    0.0   0.0     0.0     0.0  \n",
       "...       ...    ...   ...     ...     ...  \n",
       "35059     0.0    0.0   1.0     0.0     0.0  \n",
       "35060     0.0    0.0   0.0     0.0     0.0  \n",
       "35061     0.0    0.0   0.0     0.0     0.0  \n",
       "35062     0.0    0.0   0.0     1.0     0.0  \n",
       "35063     0.0    0.0   0.0     0.0     0.0  \n",
       "\n",
       "[30587 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_normalized = data_prenormalized.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))\n",
    "data_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PM2.5', 'SO2', 'NO2', 'CO', 'O3', 'TEMP', 'PRES', 'DEWP', 'RAIN',\n",
       "       'WSPM', 'wd_E', 'wd_ENE', 'wd_ESE', 'wd_N', 'wd_NE', 'wd_NNE', 'wd_NNW',\n",
       "       'wd_NW', 'wd_S', 'wd_SE', 'wd_SSE', 'wd_SSW', 'wd_SW', 'wd_W', 'wd_WNW',\n",
       "       'wd_WSW'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_normalized.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_processing(filepath):\n",
    "    data = pd.read_csv(filepath)\n",
    "    data = pd.get_dummies(data, columns=['wd'])\n",
    "    data_concentration = data.dropna()\n",
    "    data_prenormalized = data_concentration.drop(['No','year','month','day','hour','PM10','station'],axis = 1)\n",
    "    data_min = np.min(data_prenormalized['PM2.5'])\n",
    "    data_max = np.max(data_prenormalized['PM2.5'])\n",
    "    data_normalized = data_prenormalized.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))\n",
    "    return data_min,data_max,data_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data preprocessing for all districts\n",
    "shunyi_min,shunyi_max,shunyi_data = data_processing('data/PRSA_Data_Shunyi_20130301-20170228.csv')\n",
    "changping_min,changping_max,changping_data = data_processing('data/PRSA_Data_Changping_20130301-20170228.csv')\n",
    "huairou_min,huairou_max,huairou_data = data_processing('data/PRSA_Data_Huairou_20130301-20170228.csv')\n",
    "dongcheng_min,dongcheng_max,dongcheng_data = data_processing('data/PRSA_Data_Tiantan_20130301-20170228.csv')\n",
    "chaoyang_min,chaoyang_max,chaoyang_data = data_processing('data/PRSA_Data_Aotizhongxin_20130301-20170228.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>PM2.5</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>...</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.128863</td>\n",
       "      <td>0.284946</td>\n",
       "      <td>0.689781</td>\n",
       "      <td>0.244094</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.726562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.137692</td>\n",
       "      <td>0.281362</td>\n",
       "      <td>0.695255</td>\n",
       "      <td>0.231496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.019531</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.131806</td>\n",
       "      <td>0.250896</td>\n",
       "      <td>0.733577</td>\n",
       "      <td>0.218898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.027344</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.217154</td>\n",
       "      <td>0.229391</td>\n",
       "      <td>0.748175</td>\n",
       "      <td>0.233071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.014474</td>\n",
       "      <td>0.009709</td>\n",
       "      <td>0.082031</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.173008</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.775547</td>\n",
       "      <td>0.231496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <td>35059</td>\n",
       "      <td>0.032895</td>\n",
       "      <td>0.033981</td>\n",
       "      <td>0.351562</td>\n",
       "      <td>0.070707</td>\n",
       "      <td>0.046458</td>\n",
       "      <td>0.485663</td>\n",
       "      <td>0.478102</td>\n",
       "      <td>0.371654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35060</td>\n",
       "      <td>0.059211</td>\n",
       "      <td>0.077670</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>0.055287</td>\n",
       "      <td>0.476703</td>\n",
       "      <td>0.483577</td>\n",
       "      <td>0.411024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35061</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.014563</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.187724</td>\n",
       "      <td>0.464158</td>\n",
       "      <td>0.485401</td>\n",
       "      <td>0.366929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35062</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.038835</td>\n",
       "      <td>0.121094</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.173008</td>\n",
       "      <td>0.428315</td>\n",
       "      <td>0.496350</td>\n",
       "      <td>0.359055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35063</td>\n",
       "      <td>0.017105</td>\n",
       "      <td>0.058252</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.175951</td>\n",
       "      <td>0.433692</td>\n",
       "      <td>0.490876</td>\n",
       "      <td>0.379528</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30587 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PM2.5       SO2       NO2        CO        O3      TEMP      PRES  \\\n",
       "0      0.001316  0.009709  0.023438  0.020202  0.128863  0.284946  0.689781   \n",
       "1      0.013158  0.009709  0.019531  0.020202  0.137692  0.281362  0.695255   \n",
       "5      0.011842  0.009709  0.019531  0.010101  0.131806  0.250896  0.733577   \n",
       "6      0.013158  0.009709  0.027344  0.020202  0.217154  0.229391  0.748175   \n",
       "7      0.014474  0.009709  0.082031  0.020202  0.173008  0.258065  0.775547   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "35059  0.032895  0.033981  0.351562  0.070707  0.046458  0.485663  0.478102   \n",
       "35060  0.059211  0.077670  0.328125  0.101010  0.055287  0.476703  0.483577   \n",
       "35061  0.021053  0.014563  0.109375  0.040404  0.187724  0.464158  0.485401   \n",
       "35062  0.021053  0.038835  0.121094  0.040404  0.173008  0.428315  0.496350   \n",
       "35063  0.017105  0.058252  0.125000  0.040404  0.175951  0.433692  0.490876   \n",
       "\n",
       "           DEWP  RAIN      WSPM  ...  wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  \\\n",
       "0      0.244094   0.0  0.726562  ...     0.0    1.0   0.0    0.0     0.0   \n",
       "1      0.231496   0.0  0.734375  ...     0.0    1.0   0.0    0.0     0.0   \n",
       "5      0.218898   0.0  0.132812  ...     1.0    0.0   0.0    0.0     0.0   \n",
       "6      0.233071   0.0  0.125000  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "7      0.231496   0.0  0.132812  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "...         ...   ...       ...  ...     ...    ...   ...    ...     ...   \n",
       "35059  0.371654   0.0  0.140625  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "35060  0.411024   0.0  0.117188  ...     0.0    1.0   0.0    0.0     0.0   \n",
       "35061  0.366929   0.0  0.132812  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "35062  0.359055   0.0  0.140625  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "35063  0.379528   0.0  0.109375  ...     0.0    0.0   0.0    0.0     0.0   \n",
       "\n",
       "       wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  \n",
       "0         0.0    0.0   0.0     0.0     0.0  \n",
       "1         0.0    0.0   0.0     0.0     0.0  \n",
       "5         0.0    0.0   0.0     0.0     0.0  \n",
       "6         0.0    0.0   0.0     0.0     0.0  \n",
       "7         0.0    0.0   0.0     0.0     0.0  \n",
       "...       ...    ...   ...     ...     ...  \n",
       "35059     0.0    0.0   1.0     0.0     0.0  \n",
       "35060     0.0    0.0   0.0     0.0     0.0  \n",
       "35061     0.0    0.0   0.0     0.0     0.0  \n",
       "35062     0.0    0.0   0.0     1.0     0.0  \n",
       "35063     0.0    0.0   0.0     0.0     0.0  \n",
       "\n",
       "[30587 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shunyi_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.RandomForest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from warnings import simplefilter\n",
    "# ignore all future warnings\n",
    "simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def valid(col,drop_cols):\n",
    "    return col not in drop_cols\n",
    "\n",
    "def gen_train_test_data(data,n,dependent_col,drop_cols):\n",
    "    X = data[[x for x in data.columns if valid(x,drop_cols)]]\n",
    "    y = data[dependent_col]\n",
    "    if n>0:\n",
    "        X = X.iloc[:-n]\n",
    "        y = y.shift(-n).dropna()\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['No','year','day','hour','PM2.5','PM10','station']\n",
    "dependent_col = 'PM2.5'\n",
    "X_train, X_test, y_train, y_test = gen_train_test_data(shunyi_data,1,dependent_col,drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_tunning():\n",
    "    criterion=['mse','mae']\n",
    "    n_estimators = [200,300,400,500,600]\n",
    "    max_features = [8,11,15,20]\n",
    "    max_depth = [10,20,30,40,50]\n",
    "    max_depth.append(None)\n",
    "    min_samples_split = [1.0,2,3,4,5]\n",
    "    min_samples_leaf = [1, 2, 4]\n",
    "    bootstrap = [True, False]\n",
    "    random_grid = {'criterion':criterion,\n",
    "                  'n_estimators': n_estimators,\n",
    "                  'max_features': max_features,\n",
    "                  'max_depth': max_depth,\n",
    "                  'min_samples_split': min_samples_split,\n",
    "                  'min_samples_leaf': min_samples_leaf,\n",
    "                  'bootstrap': bootstrap}\n",
    "\n",
    "    clf= RandomForestRegressor()\n",
    "    clf_random = RandomizedSearchCV(estimator=clf, param_distributions=random_grid,\n",
    "                              n_iter = 10,  \n",
    "                              cv = 3, verbose=2, random_state=42, n_jobs=1)\n",
    "    \n",
    "    clf_random.fit(X_train, y_train)\n",
    "    print (clf_random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] n_estimators=200, min_samples_split=3, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mse, bootstrap=True \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=200, min_samples_split=3, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mse, bootstrap=True, total=  13.1s\n",
      "[CV] n_estimators=200, min_samples_split=3, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mse, bootstrap=True \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   13.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=200, min_samples_split=3, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mse, bootstrap=True, total=  12.8s\n",
      "[CV] n_estimators=200, min_samples_split=3, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mse, bootstrap=True \n",
      "[CV]  n_estimators=200, min_samples_split=3, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mse, bootstrap=True, total=  12.5s\n",
      "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=20, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=20, max_depth=None, criterion=mse, bootstrap=False, total=  16.7s\n",
      "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=20, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=20, max_depth=None, criterion=mse, bootstrap=False, total=  16.8s\n",
      "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=20, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=4, max_features=20, max_depth=None, criterion=mse, bootstrap=False, total=  16.9s\n",
      "[CV] n_estimators=300, min_samples_split=1.0, min_samples_leaf=4, max_features=11, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=300, min_samples_split=1.0, min_samples_leaf=4, max_features=11, max_depth=None, criterion=mse, bootstrap=False, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=1.0, min_samples_leaf=4, max_features=11, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=300, min_samples_split=1.0, min_samples_leaf=4, max_features=11, max_depth=None, criterion=mse, bootstrap=False, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=1.0, min_samples_leaf=4, max_features=11, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=300, min_samples_split=1.0, min_samples_leaf=4, max_features=11, max_depth=None, criterion=mse, bootstrap=False, total=   1.1s\n",
      "[CV] n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion=mse, bootstrap=False, total=  18.8s\n",
      "[CV] n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion=mse, bootstrap=False, total=  18.9s\n",
      "[CV] n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion=mse, bootstrap=False, total=  18.9s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=1, max_features=15, max_depth=10, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=1, max_features=15, max_depth=10, criterion=mse, bootstrap=False, total=  16.7s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=1, max_features=15, max_depth=10, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=1, max_features=15, max_depth=10, criterion=mse, bootstrap=False, total=  16.5s\n",
      "[CV] n_estimators=400, min_samples_split=5, min_samples_leaf=1, max_features=15, max_depth=10, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=400, min_samples_split=5, min_samples_leaf=1, max_features=15, max_depth=10, criterion=mse, bootstrap=False, total=  16.6s\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=50, criterion=mae, bootstrap=True \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=50, criterion=mae, bootstrap=True, total=19.7min\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=50, criterion=mae, bootstrap=True \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=50, criterion=mae, bootstrap=True, total=19.7min\n",
      "[CV] n_estimators=400, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=50, criterion=mae, bootstrap=True \n",
      "[CV]  n_estimators=400, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=50, criterion=mae, bootstrap=True, total=19.5min\n",
      "[CV] n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features=8, max_depth=20, criterion=mae, bootstrap=False \n",
      "[CV]  n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features=8, max_depth=20, criterion=mae, bootstrap=False, total=53.8min\n",
      "[CV] n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features=8, max_depth=20, criterion=mae, bootstrap=False \n",
      "[CV]  n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features=8, max_depth=20, criterion=mae, bootstrap=False, total=50.9min\n",
      "[CV] n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features=8, max_depth=20, criterion=mae, bootstrap=False \n",
      "[CV]  n_estimators=600, min_samples_split=2, min_samples_leaf=2, max_features=8, max_depth=20, criterion=mae, bootstrap=False, total=50.1min\n",
      "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mae, bootstrap=False \n",
      "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mae, bootstrap=False, total=35.3min\n",
      "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mae, bootstrap=False \n",
      "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mae, bootstrap=False, total=35.5min\n",
      "[CV] n_estimators=200, min_samples_split=4, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mae, bootstrap=False \n",
      "[CV]  n_estimators=200, min_samples_split=4, min_samples_leaf=2, max_features=20, max_depth=30, criterion=mae, bootstrap=False, total=35.4min\n",
      "[CV] n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=15, max_depth=20, criterion=mse, bootstrap=True \n",
      "[CV]  n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=15, max_depth=20, criterion=mse, bootstrap=True, total=  13.4s\n",
      "[CV] n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=15, max_depth=20, criterion=mse, bootstrap=True \n",
      "[CV]  n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=15, max_depth=20, criterion=mse, bootstrap=True, total=  13.5s\n",
      "[CV] n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=15, max_depth=20, criterion=mse, bootstrap=True \n",
      "[CV]  n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=15, max_depth=20, criterion=mse, bootstrap=True, total=  13.5s\n",
      "[CV] n_estimators=600, min_samples_split=2, min_samples_leaf=1, max_features=20, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=600, min_samples_split=2, min_samples_leaf=1, max_features=20, max_depth=None, criterion=mse, bootstrap=False, total= 1.1min\n",
      "[CV] n_estimators=600, min_samples_split=2, min_samples_leaf=1, max_features=20, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=600, min_samples_split=2, min_samples_leaf=1, max_features=20, max_depth=None, criterion=mse, bootstrap=False, total= 1.0min\n",
      "[CV] n_estimators=600, min_samples_split=2, min_samples_leaf=1, max_features=20, max_depth=None, criterion=mse, bootstrap=False \n",
      "[CV]  n_estimators=600, min_samples_split=2, min_samples_leaf=1, max_features=20, max_depth=None, criterion=mse, bootstrap=False, total= 1.0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 327.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 300, 'min_samples_split': 4, 'min_samples_leaf': 1, 'max_features': 11, 'max_depth': None, 'criterion': 'mse', 'bootstrap': False}\n"
     ]
    }
   ],
   "source": [
    "model_tunning()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "without tuning:\n",
      "0.8145766542718328\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor()\n",
    "# Train the model\n",
    "rf_model.fit(X_train, y_train)\n",
    "print(\"without tuning:\")\n",
    "print(rf_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8397282358546755\n"
     ]
    }
   ],
   "source": [
    "# Create a model \n",
    "rf_model1 = RandomForestRegressor(oob_score=True,n_estimators=300, min_samples_split=4, min_samples_leaf=1, max_features=11, max_depth=None, criterion='mse', bootstrap=True)\n",
    "# Train the model\n",
    "rf_model1.fit(X_train, y_train)\n",
    "print(rf_model1.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted data and real data:\n",
      "             0     PM2.5\n",
      "0     0.134269  0.178947\n",
      "1     0.130561  0.177632\n",
      "2     0.130999  0.181579\n",
      "3     0.232988  0.069737\n",
      "4     0.100087  0.118421\n",
      "...        ...       ...\n",
      "7642  0.182287  0.217105\n",
      "7643  0.053002  0.050000\n",
      "7644  0.031140  0.006579\n",
      "7645  0.092499  0.055263\n",
      "7646  0.108817  0.064474\n",
      "\n",
      "[7647 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"predicted data and real data:\")\n",
    "print(pd.concat([pd.Series(rf_model1.predict(X_test)), y_test.reset_index(\n",
    "drop=True)], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get test data\n",
    "def get_test_data(data,n,dependent_col,drop_cols):\n",
    "    X = data[[x for x in data.columns if valid(x,drop_cols)]]\n",
    "    y = data[dependent_col]\n",
    "    if n>0:\n",
    "        X = X.iloc[:-n]\n",
    "        y = y.shift(-n).dropna()\n",
    "    return X.iloc[-24:],y.iloc[-24:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(            SO2       NO2        CO        O3      TEMP      PRES      DEWP  \\\n",
       " 35039  0.082524  0.394531  0.141414  0.028800  0.446237  0.516423  0.464567   \n",
       " 35040  0.067961  0.320312  0.111111  0.064116  0.435484  0.520073  0.462992   \n",
       " 35041  0.048544  0.175781  0.070707  0.152407  0.439068  0.523723  0.447244   \n",
       " 35042  0.053398  0.152344  0.060606  0.152407  0.430108  0.521898  0.434646   \n",
       " 35043  0.029126  0.195312  0.070707  0.125920  0.387097  0.521898  0.455118   \n",
       " 35044  0.019417  0.164062  0.050505  0.128863  0.405018  0.523723  0.426772   \n",
       " 35045  0.014563  0.117188  0.040404  0.149464  0.365591  0.521898  0.439370   \n",
       " 35046  0.019417  0.152344  0.040404  0.131806  0.344086  0.523723  0.453543   \n",
       " 35047  0.038835  0.273438  0.080808  0.046458  0.358423  0.529197  0.447244   \n",
       " 35048  0.043689  0.269531  0.080808  0.052344  0.365591  0.536496  0.470866   \n",
       " 35049  0.033981  0.148438  0.050505  0.137692  0.451613  0.540146  0.415748   \n",
       " 35050  0.029126  0.101562  0.040404  0.178894  0.494624  0.536496  0.407874   \n",
       " 35051  0.029126  0.121094  0.050505  0.199496  0.532258  0.523723  0.384252   \n",
       " 35052  0.009709  0.035156  0.020202  0.264242  0.550179  0.509124  0.359055   \n",
       " 35053  0.004854  0.015625  0.010101  0.284844  0.559140  0.490876  0.351181   \n",
       " 35054  0.004854  0.019531  0.010101  0.281901  0.560932  0.472628  0.366929   \n",
       " 35055  0.004854  0.023438  0.010101  0.284844  0.575269  0.459854  0.362205   \n",
       " 35056  0.004854  0.019531  0.010101  0.290730  0.571685  0.454380  0.359055   \n",
       " 35057  0.009709  0.046875  0.020202  0.270128  0.562724  0.456204  0.352756   \n",
       " 35058  0.024272  0.144531  0.040404  0.196553  0.526882  0.465328  0.379528   \n",
       " 35059  0.033981  0.351562  0.070707  0.046458  0.485663  0.478102  0.371654   \n",
       " 35060  0.077670  0.328125  0.101010  0.055287  0.476703  0.483577  0.411024   \n",
       " 35061  0.014563  0.109375  0.040404  0.187724  0.464158  0.485401  0.366929   \n",
       " 35062  0.038835  0.121094  0.040404  0.173008  0.428315  0.496350  0.359055   \n",
       " \n",
       "        RAIN      WSPM  wd_E  ...  wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW  \\\n",
       " 35039   0.0  0.078125   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35040   0.0  0.078125   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35041   0.0  0.093750   0.0  ...     1.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35042   0.0  0.078125   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35043   0.0  0.085938   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35044   0.0  0.093750   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35045   0.0  0.023437   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35046   0.0  0.062500   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35047   0.0  0.117188   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35048   0.0  0.031250   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35049   0.0  0.132812   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35050   0.0  0.062500   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35051   0.0  0.203125   0.0  ...     0.0    1.0   0.0    0.0     0.0     0.0   \n",
       " 35052   0.0  0.304688   0.0  ...     0.0    1.0   0.0    0.0     0.0     0.0   \n",
       " 35053   0.0  0.453125   0.0  ...     0.0    1.0   0.0    0.0     0.0     0.0   \n",
       " 35054   0.0  0.398437   0.0  ...     0.0    1.0   0.0    0.0     0.0     0.0   \n",
       " 35055   0.0  0.328125   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35056   0.0  0.257812   0.0  ...     0.0    1.0   0.0    0.0     0.0     0.0   \n",
       " 35057   0.0  0.250000   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35058   0.0  0.101562   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35059   0.0  0.140625   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35060   0.0  0.117188   0.0  ...     0.0    1.0   0.0    0.0     0.0     0.0   \n",
       " 35061   0.0  0.132812   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " 35062   0.0  0.140625   0.0  ...     0.0    0.0   0.0    0.0     0.0     0.0   \n",
       " \n",
       "        wd_SW  wd_W  wd_WNW  wd_WSW  \n",
       " 35039    0.0   0.0     0.0     0.0  \n",
       " 35040    0.0   0.0     0.0     0.0  \n",
       " 35041    0.0   0.0     0.0     0.0  \n",
       " 35042    0.0   0.0     0.0     0.0  \n",
       " 35043    0.0   0.0     0.0     0.0  \n",
       " 35044    0.0   0.0     0.0     0.0  \n",
       " 35045    0.0   0.0     0.0     0.0  \n",
       " 35046    0.0   0.0     0.0     0.0  \n",
       " 35047    0.0   0.0     0.0     0.0  \n",
       " 35048    0.0   0.0     0.0     0.0  \n",
       " 35049    0.0   0.0     0.0     0.0  \n",
       " 35050    0.0   0.0     0.0     0.0  \n",
       " 35051    0.0   0.0     0.0     0.0  \n",
       " 35052    0.0   0.0     0.0     0.0  \n",
       " 35053    0.0   0.0     0.0     0.0  \n",
       " 35054    0.0   0.0     0.0     0.0  \n",
       " 35055    0.0   0.0     1.0     0.0  \n",
       " 35056    0.0   0.0     0.0     0.0  \n",
       " 35057    0.0   0.0     1.0     0.0  \n",
       " 35058    0.0   0.0     0.0     1.0  \n",
       " 35059    0.0   1.0     0.0     0.0  \n",
       " 35060    0.0   0.0     0.0     0.0  \n",
       " 35061    0.0   0.0     0.0     0.0  \n",
       " 35062    0.0   0.0     1.0     0.0  \n",
       " \n",
       " [24 rows x 25 columns], 35039    0.088158\n",
       " 35040    0.056579\n",
       " 35041    0.035526\n",
       " 35042    0.035526\n",
       " 35043    0.025000\n",
       " 35044    0.014474\n",
       " 35045    0.014474\n",
       " 35046    0.019737\n",
       " 35047    0.021053\n",
       " 35048    0.015789\n",
       " 35049    0.018421\n",
       " 35050    0.027632\n",
       " 35051    0.009211\n",
       " 35052    0.007895\n",
       " 35053    0.005263\n",
       " 35054    0.005263\n",
       " 35055    0.010526\n",
       " 35056    0.011842\n",
       " 35057    0.017105\n",
       " 35058    0.032895\n",
       " 35059    0.059211\n",
       " 35060    0.021053\n",
       " 35061    0.021053\n",
       " 35062    0.017105\n",
       " Name: PM2.5, dtype: float64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_shunyi,y_test_shunyi = get_test_data(shunyi_data,1,dependent_col,drop_cols)\n",
    "X_test_shunyi,y_test_shunyi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted data and real data:\n",
      "           0     PM2.5\n",
      "0   0.111548  0.088158\n",
      "1   0.072852  0.056579\n",
      "2   0.045120  0.035526\n",
      "3   0.043434  0.035526\n",
      "4   0.061714  0.025000\n",
      "5   0.024269  0.014474\n",
      "6   0.021271  0.014474\n",
      "7   0.033187  0.019737\n",
      "8   0.042193  0.021053\n",
      "9   0.040073  0.015789\n",
      "10  0.026212  0.018421\n",
      "11  0.029270  0.027632\n",
      "12  0.022104  0.009211\n",
      "13  0.013269  0.007895\n",
      "14  0.007615  0.005263\n",
      "15  0.008022  0.005263\n",
      "16  0.012885  0.010526\n",
      "17  0.011732  0.011842\n",
      "18  0.018224  0.017105\n",
      "19  0.037785  0.032895\n",
      "20  0.059409  0.059211\n",
      "21  0.049226  0.021053\n",
      "22  0.023605  0.021053\n",
      "23  0.023918  0.017105\n"
     ]
    }
   ],
   "source": [
    "print(\"predicted data and real data:\")\n",
    "print(pd.concat([pd.Series(rf_model1.predict(X_test_shunyi)), y_test_shunyi.reset_index(\n",
    "drop=True)], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get test for all districts\n",
    "X_test_shunyi,y_test_shunyi = get_test_data(shunyi_data,1,dependent_col,drop_cols)\n",
    "X_test_changping,y_test_changping = get_test_data(changping_data,1,dependent_col,drop_cols)\n",
    "X_test_huairou,y_test_huairou = get_test_data(huairou_data,1,dependent_col,drop_cols)\n",
    "X_test_dongcheng,y_test_dongcheng = get_test_data(dongcheng_data,1,dependent_col,drop_cols)\n",
    "X_test_chaoyang,y_test_chaoyang = get_test_data(chaoyang_data,1,dependent_col,drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict for all districts\n",
    "prediction_shunyi = rf_model1.predict(X_test_shunyi)\n",
    "prediction_changping = rf_model1.predict(X_test_changping)\n",
    "prediction_huairou = rf_model1.predict(X_test_huairou)\n",
    "prediction_dongcheng = rf_model1.predict(X_test_dongcheng)\n",
    "prediction_chaoyang = rf_model1.predict(X_test_chaoyang)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11154782, 0.07285199, 0.0451198 , 0.04343406, 0.06171366,\n",
       "       0.02426909, 0.02127123, 0.03318693, 0.04219286, 0.04007333,\n",
       "       0.02621229, 0.02927047, 0.02210392, 0.01326913, 0.00761488,\n",
       "       0.00802213, 0.01288545, 0.01173188, 0.0182239 , 0.03778466,\n",
       "       0.05940921, 0.04922624, 0.02360533, 0.02391836])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_shunyi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([86.77634259, 57.36751587, 36.29104714, 35.00988889, 48.90238095,\n",
       "       20.44450794, 18.16613432, 27.22206674, 34.0665754 , 32.45573016,\n",
       "       21.92134277, 24.24555952, 18.79898016, 12.08453836,  7.78730556,\n",
       "        8.09681878, 11.79294048, 10.91623148, 15.8501627 , 30.71634524,\n",
       "       47.15099868, 39.4119418 , 19.94005291, 20.1779537 ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#de-normalization\n",
    "prediction_shunyi_final = prediction_shunyi*(shunyi_max - shunyi_min) + shunyi_min\n",
    "prediction_changping_final = prediction_changping*(changping_max - changping_min) + changping_min\n",
    "prediction_huairou_final = prediction_huairou*(huairou_max - huairou_min) + huairou_min\n",
    "prediction_dongcheng_final = prediction_dongcheng*(dongcheng_max - dongcheng_min) + dongcheng_min\n",
    "prediction_chaoyang_final = prediction_chaoyang*(chaoyang_max - chaoyang_min) + chaoyang_min\n",
    "prediction_shunyi_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_test_shunyi_denormal = y_test_shunyi*(shunyi_max - shunyi_min) + shunyi_min\n",
    "#y_test_shunyi_denormal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts.charts import Bar3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_bardata(dist_id, dist_data):\n",
    "    bar_data = []\n",
    "    for i in range(24):\n",
    "        hours_data = []\n",
    "        hours_data.append(dist_id)\n",
    "        hours_data.append(i)\n",
    "        hours_data.append(dist_data[i])\n",
    "        bar_data.append(hours_data)\n",
    "    return bar_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([86.77634259, 57.36751587, 36.29104714, 35.00988889, 48.90238095,\n",
       "       20.44450794, 18.16613432, 27.22206674, 34.0665754 , 32.45573016,\n",
       "       21.92134277, 24.24555952, 18.79898016, 12.08453836,  7.78730556,\n",
       "        8.09681878, 11.79294048, 10.91623148, 15.8501627 , 30.71634524,\n",
       "       47.15099868, 39.4119418 , 19.94005291, 20.1779537 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_shunyi_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "expectation_shunyi = y_test_shunyi*(shunyi_max - shunyi_min) + shunyi_min\n",
    "expectation_shunyi_final = expectation_shunyi.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm_bar_expectation = generate_bardata(0,expectation_shunyi_final)\n",
    "pm_bar_prediction = generate_bardata(1,prediction_shunyi_final)\n",
    "data_bar = pm_bar_expectation + pm_bar_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'echarts-gl':'https://assets.pyecharts.org/assets/echarts-gl.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"6a877928f139454c97da2d66c1648397\" style=\"width:800px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'echarts-gl'], function(echarts) {\n",
       "                var chart_6a877928f139454c97da2d66c1648397 = echarts.init(\n",
       "                    document.getElementById('6a877928f139454c97da2d66c1648397'), 'white', {renderer: 'canvas'});\n",
       "                var option_6a877928f139454c97da2d66c1648397 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
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       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
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       "                [\n",
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       "                    39.41194179894172\n",
       "                ],\n",
       "                [\n",
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       "                    19.940052910052916\n",
       "                ],\n",
       "                [\n",
       "                    23,\n",
       "                    1,\n",
       "                    20.177953703703707\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 100,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#313695\",\n",
       "                \"#4575b4\",\n",
       "                \"#74add1\",\n",
       "                \"#abd9e9\",\n",
       "                \"#e0f3f8\",\n",
       "                \"#ffffbf\",\n",
       "                \"#fee090\",\n",
       "                \"#fdae61\",\n",
       "                \"#f46d43\",\n",
       "                \"#d73027\",\n",
       "                \"#a50026\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"xAxis3D\": {\n",
       "        \"data\": [\n",
       "            \"12a\",\n",
       "            \"1a\",\n",
       "            \"2a\",\n",
       "            \"3a\",\n",
       "            \"4a\",\n",
       "            \"5a\",\n",
       "            \"6a\",\n",
       "            \"7a\",\n",
       "            \"8a\",\n",
       "            \"9a\",\n",
       "            \"10a\",\n",
       "            \"11a\",\n",
       "            \"12p\",\n",
       "            \"1p\",\n",
       "            \"2p\",\n",
       "            \"3p\",\n",
       "            \"4p\",\n",
       "            \"5p\",\n",
       "            \"6p\",\n",
       "            \"7p\",\n",
       "            \"8p\",\n",
       "            \"9p\",\n",
       "            \"10p\",\n",
       "            \"11p\"\n",
       "        ],\n",
       "        \"nameGap\": 20,\n",
       "        \"type\": \"category\",\n",
       "        \"axisLabel\": {\n",
       "            \"margin\": 8\n",
       "        }\n",
       "    },\n",
       "    \"yAxis3D\": {\n",
       "        \"data\": [\n",
       "            \"expectation\",\n",
       "            \"prediction\"\n",
       "        ],\n",
       "        \"nameGap\": 20,\n",
       "        \"type\": \"category\",\n",
       "        \"axisLabel\": {\n",
       "            \"margin\": 8\n",
       "        }\n",
       "    },\n",
       "    \"zAxis3D\": {\n",
       "        \"nameGap\": 20,\n",
       "        \"type\": \"value\",\n",
       "        \"axisLabel\": {\n",
       "            \"margin\": 8\n",
       "        }\n",
       "    },\n",
       "    \"grid3D\": {\n",
       "        \"boxWidth\": 200,\n",
       "        \"boxHeight\": 100,\n",
       "        \"boxDepth\": 80,\n",
       "        \"viewControl\": {\n",
       "            \"autoRotate\": false,\n",
       "            \"autoRotateSpeed\": 10,\n",
       "            \"rotateSensitivity\": 1\n",
       "        }\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_6a877928f139454c97da2d66c1648397.setOption(option_6a877928f139454c97da2d66c1648397);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x13e84bf90>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = ['expectation','prediction']\n",
    "hours = [\n",
    "    \"12a\",\n",
    "    \"1a\",\n",
    "    \"2a\",\n",
    "    \"3a\",\n",
    "    \"4a\",\n",
    "    \"5a\",\n",
    "    \"6a\",\n",
    "    \"7a\",\n",
    "    \"8a\",\n",
    "    \"9a\",\n",
    "    \"10a\",\n",
    "    \"11a\",\n",
    "    \"12p\",\n",
    "    \"1p\",\n",
    "    \"2p\",\n",
    "    \"3p\",\n",
    "    \"4p\",\n",
    "    \"5p\",\n",
    "    \"6p\",\n",
    "    \"7p\",\n",
    "    \"8p\",\n",
    "    \"9p\",\n",
    "    \"10p\",\n",
    "    \"11p\",\n",
    "]\n",
    "\n",
    "data = [[d[1], d[0], d[2]] for d in data_bar]\n",
    "\n",
    "\n",
    "(\n",
    "    Bar3D(init_opts=opts.InitOpts(width=\"800px\", height=\"500px\"))\n",
    "    .add(\n",
    "        series_name=\"\",\n",
    "        data=data,\n",
    "        xaxis3d_opts=opts.Axis3DOpts(type_=\"category\", data=hours),\n",
    "        yaxis3d_opts=opts.Axis3DOpts(type_=\"category\", data=items),\n",
    "        zaxis3d_opts=opts.Axis3DOpts(type_=\"value\"),\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            max_=100,\n",
    "            range_color=[\n",
    "                \"#313695\",\n",
    "                \"#4575b4\",\n",
    "                \"#74add1\",\n",
    "                \"#abd9e9\",\n",
    "                \"#e0f3f8\",\n",
    "                \"#ffffbf\",\n",
    "                \"#fee090\",\n",
    "                \"#fdae61\",\n",
    "                \"#f46d43\",\n",
    "                \"#d73027\",\n",
    "                \"#a50026\",\n",
    "            ],\n",
    "        )\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts.faker import Faker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic information about the beijing map\n",
    "dic = {'顺义区':'shunyi','怀柔区':'huairou','昌平区':'changping','东城区':'dongcheng','朝阳区':'chaoyang','延庆区':' ','密云区':' ',\n",
    "      '平谷区':' ','海淀区':' ','门头沟区':' ','丰台区':' ','西城区':' ','石景山区':' ','房山区':' ','大兴区':' ','通州区':' '}\n",
    "districts = ['shunyi','huairou','changping','dongcheng','chaoyang']\n",
    "\n",
    "def generate_map(districts,values):\n",
    "    c = (\n",
    "    Map()\n",
    "    .add(\"Beijing PM2.5\", [list(z) for z in zip(districts,values)], \"北京\",zoom=1,name_map=dic)\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Map-Beijing\"), visualmap_opts=opts.VisualMapOpts()\n",
    "    )\n",
    "    )\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', '北京':'https://assets.pyecharts.org/assets/maps/beijing'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"d6ea098eb66249709c807dc9408fa027\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', '北京'], function(echarts) {\n",
       "                var chart_d6ea098eb66249709c807dc9408fa027 = echarts.init(\n",
       "                    document.getElementById('d6ea098eb66249709c807dc9408fa027'), 'white', {renderer: 'canvas'});\n",
       "                var option_d6ea098eb66249709c807dc9408fa027 = {\n",
       "    \"baseOption\": {\n",
       "        \"series\": [\n",
       "            {\n",
       "                \"type\": \"map\",\n",
       "                \"name\": \"Beijing PM2.5\",\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"top\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"mapType\": \"\\u5317\\u4eac\",\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"shunyi\",\n",
       "                        \"value\": 21.013242063492047\n",
       "                    },\n",
       "                    {\n",
       "                        \"name\": \"huairou\",\n",
       "                        \"value\": 32.68095588972433\n",
       "                    },\n",
       "                    {\n",
       "                        \"name\": \"changping\",\n",
       "                        \"value\": 14.073147545948206\n",
       "                    },\n",
       "                    {\n",
       "                        \"name\": \"dongcheng\",\n",
       "                        \"value\": 39.127517554302415\n",
       "                    },\n",
       "                    {\n",
       "                        \"name\": \"chaoyang\",\n",
       "                        \"value\": 48.98311984823167\n",
       "                    }\n",
       "                ],\n",
       "                \"roam\": true,\n",
       "                \"zoom\": 1,\n",
       "                \"nameMap\": {\n",
       "                    \"\\u987a\\u4e49\\u533a\": \"shunyi\",\n",
       "                    \"\\u6000\\u67d4\\u533a\": \"huairou\",\n",
       "                    \"\\u660c\\u5e73\\u533a\": \"changping\",\n",
       "                    \"\\u4e1c\\u57ce\\u533a\": \"dongcheng\",\n",
       "                    \"\\u671d\\u9633\\u533a\": \"chaoyang\",\n",
       "                    \"\\u5ef6\\u5e86\\u533a\": \" \",\n",
       "                    \"\\u5bc6\\u4e91\\u533a\": \" \",\n",
       "                    \"\\u5e73\\u8c37\\u533a\": \" \",\n",
       "                    \"\\u6d77\\u6dc0\\u533a\": \" \",\n",
       "                    \"\\u95e8\\u5934\\u6c9f\\u533a\": \" \",\n",
       "                    \"\\u4e30\\u53f0\\u533a\": \" \",\n",
       "                    \"\\u897f\\u57ce\\u533a\": \" \",\n",
       "                    \"\\u77f3\\u666f\\u5c71\\u533a\": \" \",\n",
       "                    \"\\u623f\\u5c71\\u533a\": \" \",\n",
       "                    \"\\u5927\\u5174\\u533a\": \" \",\n",
       "                    \"\\u901a\\u5dde\\u533a\": \" \"\n",
       "                },\n",
       "                \"showLegendSymbol\": true,\n",
       "                \"emphasis\": {}\n",
       "            }\n",
       "        ],\n",
       "        \"timeline\": {\n",
       "            \"axisType\": \"category\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"autoPlay\": false,\n",
       "            \"controlPosition\": \"left\",\n",
       "            \"loop\": true,\n",
       "            \"rewind\": false,\n",
       "            \"show\": true,\n",
       "            \"inverse\": false,\n",
       "            \"bottom\": \"-5px\",\n",
       "            \"data\": [\n",
       "                \"1\",\n",
       "                \"2\",\n",
       "                \"3\",\n",
       "                \"4\",\n",
       "                \"5\",\n",
       "                \"6\",\n",
       "                \"7\",\n",
       "                \"8\",\n",
       "                \"9\",\n",
       "                \"10\",\n",
       "                \"11\",\n",
       "                \"12\",\n",
       "                \"13\",\n",
       "                \"14\",\n",
       "                \"15\",\n",
       "                \"16\",\n",
       "                \"17\",\n",
       "                \"18\",\n",
       "                \"19\",\n",
       "                \"20\",\n",
       "                \"21\",\n",
       "                \"22\",\n",
       "                \"23\",\n",
       "                \"24\"\n",
       "            ]\n",
       "        },\n",
       "        \"visualMap\": {\n",
       "            \"show\": true,\n",
       "            \"type\": \"continuous\",\n",
       "            \"min\": 0,\n",
       "            \"max\": 100,\n",
       "            \"inRange\": {\n",
       "                \"color\": [\n",
       "                    \"#50a3ba\",\n",
       "                    \"#eac763\",\n",
       "                    \"#d94e5d\"\n",
       "                ]\n",
       "            },\n",
       "            \"calculable\": true,\n",
       "            \"inverse\": false,\n",
       "            \"splitNumber\": 5,\n",
       "            \"orient\": \"vertical\",\n",
       "            \"showLabel\": true,\n",
       "            \"itemWidth\": 20,\n",
       "            \"itemHeight\": 140,\n",
       "            \"borderWidth\": 0\n",
       "        }\n",
       "    },\n",
       "    \"options\": [\n",
       "        {\n",
       "            \"legend\": [\n",
       "                {\n",
       "                    \"data\": [\n",
       "                        \"Beijing PM2.5\"\n",
       "                    ],\n",
       "                    \"selected\": {\n",
       "                        \"Beijing PM2.5\": true\n",
       "                    },\n",
       "                    \"show\": true,\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10,\n",
       "                    \"itemWidth\": 25,\n",
       "                    \"itemHeight\": 14\n",
       "                }\n",
       "            ],\n",
       "            \"series\": [\n",
       "                {\n",
       "                    \"type\": \"map\",\n",
       "                    \"name\": \"Beijing PM2.5\",\n",
       "                    \"label\": {\n",
       "                        \"show\": true,\n",
       "                        \"position\": \"top\",\n",
       "                        \"margin\": 8\n",
       "                    },\n",
       "                    \"mapType\": \"\\u5317\\u4eac\",\n",
       "                    \"data\": [\n",
       "                        {\n",
       "                            \"name\": \"shunyi\",\n",
       "                            \"value\": 85.37434126984121\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"huairou\",\n",
       "                            \"value\": 66.10930515350883\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"changping\",\n",
       "                            \"value\": 57.1341179302423\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"dongcheng\",\n",
       "                            \"value\": 57.365381088137056\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"chaoyang\",\n",
       "                            \"value\": 124.25565136800329\n",
       "                        }\n",
       "                    ],\n",
       "                    \"roam\": true,\n",
       "                    \"zoom\": 1,\n",
       "                    \"nameMap\": {\n",
       "                        \"\\u987a\\u4e49\\u533a\": \"shunyi\",\n",
       "                        \"\\u6000\\u67d4\\u533a\": \"huairou\",\n",
       "                        \"\\u660c\\u5e73\\u533a\": \"changping\",\n",
       "                        \"\\u4e1c\\u57ce\\u533a\": \"dongcheng\",\n",
       "                        \"\\u671d\\u9633\\u533a\": \"chaoyang\",\n",
       "                        \"\\u5ef6\\u5e86\\u533a\": \" \",\n",
       "                        \"\\u5bc6\\u4e91\\u533a\": \" \",\n",
       "                        \"\\u5e73\\u8c37\\u533a\": \" \",\n",
       "                        \"\\u6d77\\u6dc0\\u533a\": \" \",\n",
       "                        \"\\u95e8\\u5934\\u6c9f\\u533a\": \" \",\n",
       "                        \"\\u4e30\\u53f0\\u533a\": \" \",\n",
       "                        \"\\u897f\\u57ce\\u533a\": \" \",\n",
       "                        \"\\u77f3\\u666f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u623f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u5927\\u5174\\u533a\": \" \",\n",
       "                        \"\\u901a\\u5dde\\u533a\": \" \"\n",
       "                    },\n",
       "                    \"showLegendSymbol\": true,\n",
       "                    \"emphasis\": {}\n",
       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
       "                {\n",
       "                    \"text\": \"Map-Beijing\",\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10\n",
       "                }\n",
       "            ],\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"visualMap\": {\n",
       "                \"show\": true,\n",
       "                \"type\": \"continuous\",\n",
       "                \"min\": 0,\n",
       "                \"max\": 100,\n",
       "                \"inRange\": {\n",
       "                    \"color\": [\n",
       "                        \"#50a3ba\",\n",
       "                        \"#eac763\",\n",
       "                        \"#d94e5d\"\n",
       "                    ]\n",
       "                },\n",
       "                \"calculable\": true,\n",
       "                \"inverse\": false,\n",
       "                \"splitNumber\": 5,\n",
       "                \"orient\": \"vertical\",\n",
       "                \"showLabel\": true,\n",
       "                \"itemWidth\": 20,\n",
       "                \"itemHeight\": 140,\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"color\": [\n",
       "                \"#c23531\",\n",
       "                \"#2f4554\",\n",
       "                \"#61a0a8\",\n",
       "                \"#d48265\",\n",
       "                \"#749f83\",\n",
       "                \"#ca8622\",\n",
       "                \"#bda29a\",\n",
       "                \"#6e7074\",\n",
       "                \"#546570\",\n",
       "                \"#c4ccd3\",\n",
       "                \"#f05b72\",\n",
       "                \"#ef5b9c\",\n",
       "                \"#f47920\",\n",
       "                \"#905a3d\",\n",
       "                \"#fab27b\",\n",
       "                \"#2a5caa\",\n",
       "                \"#444693\",\n",
       "                \"#726930\",\n",
       "                \"#b2d235\",\n",
       "                \"#6d8346\",\n",
       "                \"#ac6767\",\n",
       "                \"#1d953f\",\n",
       "                \"#6950a1\",\n",
       "                \"#918597\"\n",
       "            ]\n",
       "        },\n",
       "        {\n",
       "            \"legend\": [\n",
       "                {\n",
       "                    \"data\": [\n",
       "                        \"Beijing PM2.5\"\n",
       "                    ],\n",
       "                    \"selected\": {\n",
       "                        \"Beijing PM2.5\": true\n",
       "                    },\n",
       "                    \"show\": true,\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10,\n",
       "                    \"itemWidth\": 25,\n",
       "                    \"itemHeight\": 14\n",
       "                }\n",
       "            ],\n",
       "            \"series\": [\n",
       "                {\n",
       "                    \"type\": \"map\",\n",
       "                    \"name\": \"Beijing PM2.5\",\n",
       "                    \"label\": {\n",
       "                        \"show\": true,\n",
       "                        \"position\": \"top\",\n",
       "                        \"margin\": 8\n",
       "                    },\n",
       "                    \"mapType\": \"\\u5317\\u4eac\",\n",
       "                    \"data\": [\n",
       "                        {\n",
       "                            \"name\": \"shunyi\",\n",
       "                            \"value\": 59.278800745550704\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"huairou\",\n",
       "                            \"value\": 95.3531810776942\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"changping\",\n",
       "                            \"value\": 24.178110129490417\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"dongcheng\",\n",
       "                            \"value\": 81.22971246170982\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"chaoyang\",\n",
       "                            \"value\": 86.0257868803954\n",
       "                        }\n",
       "                    ],\n",
       "                    \"roam\": true,\n",
       "                    \"zoom\": 1,\n",
       "                    \"nameMap\": {\n",
       "                        \"\\u987a\\u4e49\\u533a\": \"shunyi\",\n",
       "                        \"\\u6000\\u67d4\\u533a\": \"huairou\",\n",
       "                        \"\\u660c\\u5e73\\u533a\": \"changping\",\n",
       "                        \"\\u4e1c\\u57ce\\u533a\": \"dongcheng\",\n",
       "                        \"\\u671d\\u9633\\u533a\": \"chaoyang\",\n",
       "                        \"\\u5ef6\\u5e86\\u533a\": \" \",\n",
       "                        \"\\u5bc6\\u4e91\\u533a\": \" \",\n",
       "                        \"\\u5e73\\u8c37\\u533a\": \" \",\n",
       "                        \"\\u6d77\\u6dc0\\u533a\": \" \",\n",
       "                        \"\\u95e8\\u5934\\u6c9f\\u533a\": \" \",\n",
       "                        \"\\u4e30\\u53f0\\u533a\": \" \",\n",
       "                        \"\\u897f\\u57ce\\u533a\": \" \",\n",
       "                        \"\\u77f3\\u666f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u623f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u5927\\u5174\\u533a\": \" \",\n",
       "                        \"\\u901a\\u5dde\\u533a\": \" \"\n",
       "                    },\n",
       "                    \"showLegendSymbol\": true,\n",
       "                    \"emphasis\": {}\n",
       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
       "                {\n",
       "                    \"text\": \"Map-Beijing\",\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10\n",
       "                }\n",
       "            ],\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"visualMap\": {\n",
       "                \"show\": true,\n",
       "                \"type\": \"continuous\",\n",
       "                \"min\": 0,\n",
       "                \"max\": 100,\n",
       "                \"inRange\": {\n",
       "                    \"color\": [\n",
       "                        \"#50a3ba\",\n",
       "                        \"#eac763\",\n",
       "                        \"#d94e5d\"\n",
       "                    ]\n",
       "                },\n",
       "                \"calculable\": true,\n",
       "                \"inverse\": false,\n",
       "                \"splitNumber\": 5,\n",
       "                \"orient\": \"vertical\",\n",
       "                \"showLabel\": true,\n",
       "                \"itemWidth\": 20,\n",
       "                \"itemHeight\": 140,\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"color\": [\n",
       "                \"#c23531\",\n",
       "                \"#2f4554\",\n",
       "                \"#61a0a8\",\n",
       "                \"#d48265\",\n",
       "                \"#749f83\",\n",
       "                \"#ca8622\",\n",
       "                \"#bda29a\",\n",
       "                \"#6e7074\",\n",
       "                \"#546570\",\n",
       "                \"#c4ccd3\",\n",
       "                \"#f05b72\",\n",
       "                \"#ef5b9c\",\n",
       "                \"#f47920\",\n",
       "                \"#905a3d\",\n",
       "                \"#fab27b\",\n",
       "                \"#2a5caa\",\n",
       "                \"#444693\",\n",
       "                \"#726930\",\n",
       "                \"#b2d235\",\n",
       "                \"#6d8346\",\n",
       "                \"#ac6767\",\n",
       "                \"#1d953f\",\n",
       "                \"#6950a1\",\n",
       "                \"#918597\"\n",
       "            ]\n",
       "        },\n",
       "        {\n",
       "            \"legend\": [\n",
       "                {\n",
       "                    \"data\": [\n",
       "                        \"Beijing PM2.5\"\n",
       "                    ],\n",
       "                    \"selected\": {\n",
       "                        \"Beijing PM2.5\": true\n",
       "                    },\n",
       "                    \"show\": true,\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10,\n",
       "                    \"itemWidth\": 25,\n",
       "                    \"itemHeight\": 14\n",
       "                }\n",
       "            ],\n",
       "            \"series\": [\n",
       "                {\n",
       "                    \"type\": \"map\",\n",
       "                    \"name\": \"Beijing PM2.5\",\n",
       "                    \"label\": {\n",
       "                        \"show\": true,\n",
       "                        \"position\": \"top\",\n",
       "                        \"margin\": 8\n",
       "                    },\n",
       "                    \"mapType\": \"\\u5317\\u4eac\",\n",
       "                    \"data\": [\n",
       "                        {\n",
       "                            \"name\": \"shunyi\",\n",
       "                            \"value\": 37.73605423280423\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"huairou\",\n",
       "                            \"value\": 99.57461303258147\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"changping\",\n",
       "                            \"value\": 14.801061769005845\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"dongcheng\",\n",
       "                            \"value\": 94.77043850250625\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"chaoyang\",\n",
       "                            \"value\": 29.0751812691451\n",
       "                        }\n",
       "                    ],\n",
       "                    \"roam\": true,\n",
       "                    \"zoom\": 1,\n",
       "                    \"nameMap\": {\n",
       "                        \"\\u987a\\u4e49\\u533a\": \"shunyi\",\n",
       "                        \"\\u6000\\u67d4\\u533a\": \"huairou\",\n",
       "                        \"\\u660c\\u5e73\\u533a\": \"changping\",\n",
       "                        \"\\u4e1c\\u57ce\\u533a\": \"dongcheng\",\n",
       "                        \"\\u671d\\u9633\\u533a\": \"chaoyang\",\n",
       "                        \"\\u5ef6\\u5e86\\u533a\": \" \",\n",
       "                        \"\\u5bc6\\u4e91\\u533a\": \" \",\n",
       "                        \"\\u5e73\\u8c37\\u533a\": \" \",\n",
       "                        \"\\u6d77\\u6dc0\\u533a\": \" \",\n",
       "                        \"\\u95e8\\u5934\\u6c9f\\u533a\": \" \",\n",
       "                        \"\\u4e30\\u53f0\\u533a\": \" \",\n",
       "                        \"\\u897f\\u57ce\\u533a\": \" \",\n",
       "                        \"\\u77f3\\u666f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u623f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u5927\\u5174\\u533a\": \" \",\n",
       "                        \"\\u901a\\u5dde\\u533a\": \" \"\n",
       "                    },\n",
       "                    \"showLegendSymbol\": true,\n",
       "                    \"emphasis\": {}\n",
       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
       "                {\n",
       "                    \"text\": \"Map-Beijing\",\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10\n",
       "                }\n",
       "            ],\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"visualMap\": {\n",
       "                \"show\": true,\n",
       "                \"type\": \"continuous\",\n",
       "                \"min\": 0,\n",
       "                \"max\": 100,\n",
       "                \"inRange\": {\n",
       "                    \"color\": [\n",
       "                        \"#50a3ba\",\n",
       "                        \"#eac763\",\n",
       "                        \"#d94e5d\"\n",
       "                    ]\n",
       "                },\n",
       "                \"calculable\": true,\n",
       "                \"inverse\": false,\n",
       "                \"splitNumber\": 5,\n",
       "                \"orient\": \"vertical\",\n",
       "                \"showLabel\": true,\n",
       "                \"itemWidth\": 20,\n",
       "                \"itemHeight\": 140,\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"color\": [\n",
       "                \"#c23531\",\n",
       "                \"#2f4554\",\n",
       "                \"#61a0a8\",\n",
       "                \"#d48265\",\n",
       "                \"#749f83\",\n",
       "                \"#ca8622\",\n",
       "                \"#bda29a\",\n",
       "                \"#6e7074\",\n",
       "                \"#546570\",\n",
       "                \"#c4ccd3\",\n",
       "                \"#f05b72\",\n",
       "                \"#ef5b9c\",\n",
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       "                        \"\\u897f\\u57ce\\u533a\": \" \",\n",
       "                        \"\\u77f3\\u666f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u623f\\u5c71\\u533a\": \" \",\n",
       "                        \"\\u5927\\u5174\\u533a\": \" \",\n",
       "                        \"\\u901a\\u5dde\\u533a\": \" \"\n",
       "                    },\n",
       "                    \"showLegendSymbol\": true,\n",
       "                    \"emphasis\": {}\n",
       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
       "                {\n",
       "                    \"text\": \"Map-Beijing\",\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10\n",
       "                }\n",
       "            ],\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"visualMap\": {\n",
       "                \"show\": true,\n",
       "                \"type\": \"continuous\",\n",
       "                \"min\": 0,\n",
       "                \"max\": 100,\n",
       "                \"inRange\": {\n",
       "                    \"color\": [\n",
       "                        \"#50a3ba\",\n",
       "                        \"#eac763\",\n",
       "                        \"#d94e5d\"\n",
       "                    ]\n",
       "                },\n",
       "                \"calculable\": true,\n",
       "                \"inverse\": false,\n",
       "                \"splitNumber\": 5,\n",
       "                \"orient\": \"vertical\",\n",
       "                \"showLabel\": true,\n",
       "                \"itemWidth\": 20,\n",
       "                \"itemHeight\": 140,\n",
       "                \"borderWidth\": 0\n",
       "            },\n",
       "            \"color\": [\n",
       "                \"#c23531\",\n",
       "                \"#2f4554\",\n",
       "                \"#61a0a8\",\n",
       "                \"#d48265\",\n",
       "                \"#749f83\",\n",
       "                \"#ca8622\",\n",
       "                \"#bda29a\",\n",
       "                \"#6e7074\",\n",
       "                \"#546570\",\n",
       "                \"#c4ccd3\",\n",
       "                \"#f05b72\",\n",
       "                \"#ef5b9c\",\n",
       "                \"#f47920\",\n",
       "                \"#905a3d\",\n",
       "                \"#fab27b\",\n",
       "                \"#2a5caa\",\n",
       "                \"#444693\",\n",
       "                \"#726930\",\n",
       "                \"#b2d235\",\n",
       "                \"#6d8346\",\n",
       "                \"#ac6767\",\n",
       "                \"#1d953f\",\n",
       "                \"#6950a1\",\n",
       "                \"#918597\"\n",
       "            ]\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_d6ea098eb66249709c807dc9408fa027.setOption(option_d6ea098eb66249709c807dc9408fa027);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x12f95cd10>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add time line and display\n",
    "from pyecharts.charts import Timeline\n",
    "\n",
    "timeline = Timeline()\n",
    "\n",
    "i=1\n",
    "for values in zip(prediction_shunyi_final,prediction_huairou_final,prediction_changping_final,prediction_dongcheng_final,prediction_chaoyang_final):\n",
    "    timeline.add(generate_map(districts,values),str(i))\n",
    "    i=i+1\n",
    "\n",
    "timeline.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}