2020-03-01 21:33:06 +01:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# COVID-19 Analysis\n",
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"<br>"
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]
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},
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{
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"cell_type": "code",
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2020-03-02 21:22:49 +01:00
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"execution_count": 2,
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2020-03-01 21:33:06 +01:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Lat</th>\n",
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" <th>Long</th>\n",
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" <th>1/22/20</th>\n",
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" <th>1/23/20</th>\n",
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" <th>1/24/20</th>\n",
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" <th>1/25/20</th>\n",
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" <th>1/26/20</th>\n",
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" <th>1/27/20</th>\n",
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" <th>1/28/20</th>\n",
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" <th>1/29/20</th>\n",
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" <th>...</th>\n",
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" <th>2/21/20</th>\n",
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" <th>2/22/20</th>\n",
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" <th>2/23/20</th>\n",
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" <th>2/24/20</th>\n",
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" <th>2/25/20</th>\n",
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" <th>2/26/20</th>\n",
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" <th>2/27/20</th>\n",
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" <th>2/28/20</th>\n",
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" <th>2/29/20</th>\n",
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2020-03-02 21:22:49 +01:00
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" <th>3/1/20</th>\n",
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2020-03-01 21:33:06 +01:00
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>count</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>...</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.00000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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" <td>125.000000</td>\n",
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2020-03-01 21:33:06 +01:00
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>mean</td>\n",
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" <td>32.160092</td>\n",
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" <td>37.307430</td>\n",
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" <td>4.440000</td>\n",
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" <td>5.224000</td>\n",
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" <td>7.528000</td>\n",
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" <td>11.472000</td>\n",
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" <td>16.944000</td>\n",
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" <td>23.416000</td>\n",
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" <td>44.624000</td>\n",
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" <td>49.328000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>...</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>614.744000</td>\n",
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" <td>628.792000</td>\n",
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" <td>631.880000</td>\n",
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" <td>636.560000</td>\n",
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" <td>643.32000</td>\n",
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" <td>651.176000</td>\n",
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" <td>662.048000</td>\n",
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" <td>672.976000</td>\n",
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" <td>688.104000</td>\n",
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" <td>706.968000</td>\n",
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2020-03-01 21:33:06 +01:00
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>std</td>\n",
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" <td>20.184693</td>\n",
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" <td>85.561399</td>\n",
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" <td>39.746657</td>\n",
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" <td>39.840785</td>\n",
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" <td>49.520373</td>\n",
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" <td>68.835191</td>\n",
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" <td>96.111268</td>\n",
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" <td>129.438626</td>\n",
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" <td>318.761506</td>\n",
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" <td>320.358161</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>...</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>5600.966184</td>\n",
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" <td>5727.852962</td>\n",
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" <td>5727.726592</td>\n",
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" <td>5745.789407</td>\n",
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" <td>5790.29246</td>\n",
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" <td>5826.135337</td>\n",
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" <td>5863.068799</td>\n",
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" <td>5892.438301</td>\n",
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" <td>5932.363864</td>\n",
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" <td>5985.776481</td>\n",
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2020-03-01 21:33:06 +01:00
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>min</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>-40.900600</td>\n",
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" <td>-123.869500</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>...</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>0.00000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>25%</td>\n",
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" <td>26.078900</td>\n",
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" <td>1.659600</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>...</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>0.00000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>50%</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>35.443700</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>47.750000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>...</td>\n",
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" <td>2.000000</td>\n",
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" <td>2.000000</td>\n",
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" <td>2.000000</td>\n",
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" <td>2.000000</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>2.00000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>2.000000</td>\n",
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" <td>3.000000</td>\n",
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" <td>4.000000</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>6.000000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>7.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>75%</td>\n",
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" <td>43.653200</td>\n",
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" <td>112.500000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>0.000000</td>\n",
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|
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" <td>1.000000</td>\n",
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|
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" <td>2.000000</td>\n",
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" <td>3.000000</td>\n",
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" <td>4.000000</td>\n",
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" <td>6.000000</td>\n",
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|
|
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" <td>8.000000</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>8.000000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>...</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>75.000000</td>\n",
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" <td>75.000000</td>\n",
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" <td>76.000000</td>\n",
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" <td>79.000000</td>\n",
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" <td>91.00000</td>\n",
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" <td>91.000000</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>92.000000</td>\n",
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" <td>93.000000</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>95.000000</td>\n",
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" <td>106.000000</td>\n",
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2020-03-01 21:33:06 +01:00
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" </tr>\n",
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" <tr>\n",
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2020-03-02 21:22:49 +01:00
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" <td>max</td>\n",
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2020-03-01 21:33:06 +01:00
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" <td>64.963100</td>\n",
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" <td>174.886000</td>\n",
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" <td>444.000000</td>\n",
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" <td>444.000000</td>\n",
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" <td>549.000000</td>\n",
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" <td>761.000000</td>\n",
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" <td>1058.000000</td>\n",
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" <td>1423.000000</td>\n",
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" <td>3554.000000</td>\n",
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" <td>3554.000000</td>\n",
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" <td>...</td>\n",
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" <td>62662.000000</td>\n",
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" <td>64084.000000</td>\n",
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" <td>64084.000000</td>\n",
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" <td>64287.000000</td>\n",
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2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>64786.00000</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>65187.000000</td>\n",
|
|
|
|
|
|
" <td>65596.000000</td>\n",
|
|
|
|
|
|
" <td>65914.000000</td>\n",
|
|
|
|
|
|
" <td>66337.000000</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>66907.000000</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"<p>8 rows × 42 columns</p>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"count 125.000000 125.000000 125.000000 125.000000 125.000000 125.000000 \n",
|
|
|
|
|
|
"mean 32.160092 37.307430 4.440000 5.224000 7.528000 11.472000 \n",
|
|
|
|
|
|
"std 20.184693 85.561399 39.746657 39.840785 49.520373 68.835191 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"min -40.900600 -123.869500 0.000000 0.000000 0.000000 0.000000 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"25% 26.078900 1.659600 0.000000 0.000000 0.000000 0.000000 \n",
|
|
|
|
|
|
"50% 35.443700 47.750000 0.000000 0.000000 0.000000 0.000000 \n",
|
|
|
|
|
|
"75% 43.653200 112.500000 0.000000 1.000000 2.000000 3.000000 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"max 64.963100 174.886000 444.000000 444.000000 549.000000 761.000000 \n",
|
|
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/21/20 \\\n",
|
|
|
|
|
|
"count 125.000000 125.000000 125.000000 125.000000 ... 125.000000 \n",
|
|
|
|
|
|
"mean 16.944000 23.416000 44.624000 49.328000 ... 614.744000 \n",
|
|
|
|
|
|
"std 96.111268 129.438626 318.761506 320.358161 ... 5600.966184 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"min 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
|
|
|
|
|
|
"25% 0.000000 0.000000 0.000000 0.000000 ... 0.000000 \n",
|
|
|
|
|
|
"50% 0.000000 0.000000 0.000000 0.000000 ... 2.000000 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"75% 4.000000 6.000000 8.000000 8.000000 ... 75.000000 \n",
|
|
|
|
|
|
"max 1058.000000 1423.000000 3554.000000 3554.000000 ... 62662.000000 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 \\\n",
|
|
|
|
|
|
"count 125.000000 125.000000 125.000000 125.00000 125.000000 \n",
|
|
|
|
|
|
"mean 628.792000 631.880000 636.560000 643.32000 651.176000 \n",
|
|
|
|
|
|
"std 5727.852962 5727.726592 5745.789407 5790.29246 5826.135337 \n",
|
|
|
|
|
|
"min 0.000000 0.000000 0.000000 0.00000 0.000000 \n",
|
|
|
|
|
|
"25% 0.000000 0.000000 0.000000 0.00000 1.000000 \n",
|
|
|
|
|
|
"50% 2.000000 2.000000 2.000000 2.00000 2.000000 \n",
|
|
|
|
|
|
"75% 75.000000 76.000000 79.000000 91.00000 91.000000 \n",
|
|
|
|
|
|
"max 64084.000000 64084.000000 64287.000000 64786.00000 65187.000000 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" 2/27/20 2/28/20 2/29/20 3/1/20 \n",
|
|
|
|
|
|
"count 125.000000 125.000000 125.000000 125.000000 \n",
|
|
|
|
|
|
"mean 662.048000 672.976000 688.104000 706.968000 \n",
|
|
|
|
|
|
"std 5863.068799 5892.438301 5932.363864 5985.776481 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
|
|
|
|
|
"25% 1.000000 1.000000 1.000000 1.000000 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"50% 3.000000 4.000000 6.000000 7.000000 \n",
|
|
|
|
|
|
"75% 92.000000 93.000000 95.000000 106.000000 \n",
|
|
|
|
|
|
"max 65596.000000 65914.000000 66337.000000 66907.000000 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"[8 rows x 42 columns]"
|
2020-03-01 21:33:06 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 2,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import numpy as np\n",
|
|
|
|
|
|
"import pandas as pd\n",
|
|
|
|
|
|
"data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv\")\n",
|
|
|
|
|
|
"data.describe()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 3,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
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|
|
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|
|
"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
|
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|
|
|
" }\n",
|
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"\n",
|
|
|
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" .dataframe thead th {\n",
|
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|
" 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>Province/State</th>\n",
|
|
|
|
|
|
" <th>Country/Region</th>\n",
|
|
|
|
|
|
" <th>Lat</th>\n",
|
|
|
|
|
|
" <th>Long</th>\n",
|
|
|
|
|
|
" <th>1/22/20</th>\n",
|
|
|
|
|
|
" <th>1/23/20</th>\n",
|
|
|
|
|
|
" <th>1/24/20</th>\n",
|
|
|
|
|
|
" <th>1/25/20</th>\n",
|
|
|
|
|
|
" <th>1/26/20</th>\n",
|
|
|
|
|
|
" <th>1/27/20</th>\n",
|
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
|
" <th>2/21/20</th>\n",
|
|
|
|
|
|
" <th>2/22/20</th>\n",
|
|
|
|
|
|
" <th>2/23/20</th>\n",
|
|
|
|
|
|
" <th>2/24/20</th>\n",
|
|
|
|
|
|
" <th>2/25/20</th>\n",
|
|
|
|
|
|
" <th>2/26/20</th>\n",
|
|
|
|
|
|
" <th>2/27/20</th>\n",
|
|
|
|
|
|
" <th>2/28/20</th>\n",
|
|
|
|
|
|
" <th>2/29/20</th>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <th>3/1/20</th>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>0</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Anhui</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>31.8257</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>117.2264</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>39</td>\n",
|
|
|
|
|
|
" <td>60</td>\n",
|
|
|
|
|
|
" <td>70</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>988</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>990</td>\n",
|
|
|
|
|
|
" <td>990</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>990</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Beijing</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>40.1824</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>116.4142</td>\n",
|
|
|
|
|
|
" <td>14</td>\n",
|
|
|
|
|
|
" <td>22</td>\n",
|
|
|
|
|
|
" <td>36</td>\n",
|
|
|
|
|
|
" <td>41</td>\n",
|
|
|
|
|
|
" <td>68</td>\n",
|
|
|
|
|
|
" <td>80</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>396</td>\n",
|
|
|
|
|
|
" <td>399</td>\n",
|
|
|
|
|
|
" <td>399</td>\n",
|
|
|
|
|
|
" <td>399</td>\n",
|
|
|
|
|
|
" <td>400</td>\n",
|
|
|
|
|
|
" <td>400</td>\n",
|
|
|
|
|
|
" <td>410</td>\n",
|
|
|
|
|
|
" <td>410</td>\n",
|
|
|
|
|
|
" <td>411</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>413</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>2</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Chongqing</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>30.0572</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>107.8740</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>27</td>\n",
|
|
|
|
|
|
" <td>57</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>110</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>572</td>\n",
|
|
|
|
|
|
" <td>573</td>\n",
|
|
|
|
|
|
" <td>575</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>576</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>3</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Fujian</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>26.0789</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>117.9874</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>10</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>35</td>\n",
|
|
|
|
|
|
" <td>59</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>294</td>\n",
|
|
|
|
|
|
" <td>294</td>\n",
|
|
|
|
|
|
" <td>296</td>\n",
|
|
|
|
|
|
" <td>296</td>\n",
|
|
|
|
|
|
" <td>296</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>296</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>4</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>Gansu</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>36.0611</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>103.8343</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>14</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <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",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>120</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>Azerbaijan</td>\n",
|
|
|
|
|
|
" <td>40.1431</td>\n",
|
|
|
|
|
|
" <td>47.5769</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",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>...</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <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>3</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>Czech Republic</td>\n",
|
|
|
|
|
|
" <td>49.8175</td>\n",
|
|
|
|
|
|
" <td>15.4730</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",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>...</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <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>3</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>122</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>Armenia</td>\n",
|
|
|
|
|
|
" <td>40.0691</td>\n",
|
|
|
|
|
|
" <td>45.0382</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <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>1</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>123</td>\n",
|
|
|
|
|
|
" <td>NaN</td>\n",
|
|
|
|
|
|
" <td>Dominican Republic</td>\n",
|
|
|
|
|
|
" <td>18.7357</td>\n",
|
|
|
|
|
|
" <td>-70.1627</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",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>...</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <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>1</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>124</td>\n",
|
|
|
|
|
|
" <td>Providence, RI</td>\n",
|
|
|
|
|
|
" <td>US</td>\n",
|
|
|
|
|
|
" <td>41.8240</td>\n",
|
|
|
|
|
|
" <td>-71.4128</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",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" <td>...</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <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>1</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
|
|
|
|
|
"<p>125 rows × 44 columns</p>\n",
|
|
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n",
|
|
|
|
|
|
"0 Anhui Mainland China 31.8257 117.2264 1 9 \n",
|
|
|
|
|
|
"1 Beijing Mainland China 40.1824 116.4142 14 22 \n",
|
|
|
|
|
|
"2 Chongqing Mainland China 30.0572 107.8740 6 9 \n",
|
|
|
|
|
|
"3 Fujian Mainland China 26.0789 117.9874 1 5 \n",
|
|
|
|
|
|
"4 Gansu Mainland China 36.0611 103.8343 0 2 \n",
|
|
|
|
|
|
".. ... ... ... ... ... ... \n",
|
|
|
|
|
|
"120 NaN Azerbaijan 40.1431 47.5769 0 0 \n",
|
|
|
|
|
|
"121 NaN Czech Republic 49.8175 15.4730 0 0 \n",
|
|
|
|
|
|
"122 NaN Armenia 40.0691 45.0382 0 0 \n",
|
|
|
|
|
|
"123 NaN Dominican Republic 18.7357 -70.1627 0 0 \n",
|
|
|
|
|
|
"124 Providence, RI US 41.8240 -71.4128 0 0 \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/21/20 2/22/20 2/23/20 \\\n",
|
|
|
|
|
|
"0 15 39 60 70 ... 988 989 989 \n",
|
|
|
|
|
|
"1 36 41 68 80 ... 396 399 399 \n",
|
|
|
|
|
|
"2 27 57 75 110 ... 572 573 575 \n",
|
|
|
|
|
|
"3 10 18 35 59 ... 293 293 293 \n",
|
|
|
|
|
|
"4 2 4 7 14 ... 91 91 91 \n",
|
|
|
|
|
|
".. ... ... ... ... ... ... ... ... \n",
|
|
|
|
|
|
"120 0 0 0 0 ... 0 0 0 \n",
|
|
|
|
|
|
"121 0 0 0 0 ... 0 0 0 \n",
|
|
|
|
|
|
"122 0 0 0 0 ... 0 0 0 \n",
|
|
|
|
|
|
"123 0 0 0 0 ... 0 0 0 \n",
|
|
|
|
|
|
"124 0 0 0 0 ... 0 0 0 \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 \n",
|
|
|
|
|
|
"0 989 989 989 989 990 990 990 \n",
|
|
|
|
|
|
"1 399 400 400 410 410 411 413 \n",
|
|
|
|
|
|
"2 576 576 576 576 576 576 576 \n",
|
|
|
|
|
|
"3 293 294 294 296 296 296 296 \n",
|
|
|
|
|
|
"4 91 91 91 91 91 91 91 \n",
|
|
|
|
|
|
".. ... ... ... ... ... ... ... \n",
|
|
|
|
|
|
"120 0 0 0 0 0 0 3 \n",
|
|
|
|
|
|
"121 0 0 0 0 0 0 3 \n",
|
|
|
|
|
|
"122 0 0 0 0 0 0 1 \n",
|
|
|
|
|
|
"123 0 0 0 0 0 0 1 \n",
|
|
|
|
|
|
"124 0 0 0 0 0 0 1 \n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"[125 rows x 44 columns]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 3,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 4,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"[0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 0,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 2,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 3,\n",
|
|
|
|
|
|
" 20,\n",
|
|
|
|
|
|
" 62,\n",
|
|
|
|
|
|
" 155,\n",
|
|
|
|
|
|
" 229,\n",
|
|
|
|
|
|
" 322,\n",
|
|
|
|
|
|
" 453,\n",
|
|
|
|
|
|
" 655,\n",
|
|
|
|
|
|
" 888,\n",
|
|
|
|
|
|
" 1128,\n",
|
|
|
|
|
|
" 1694]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 4,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data_italy = data[data[\"Country/Region\"] == \"Italy\"]\n",
|
|
|
|
|
|
"#data_italy.values[0,[3]]\n",
|
|
|
|
|
|
"#type(data_italy.values)\n",
|
|
|
|
|
|
"values =[]\n",
|
|
|
|
|
|
"for i in range(4,len(data_italy.values[0])):\n",
|
|
|
|
|
|
" values += [data_italy.values[0][i]]\n",
|
|
|
|
|
|
" \n",
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"values"
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"dates = []\n",
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" dates+= [x]\n",
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"dates = dates[4:]\n",
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"dates\n",
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"\n",
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"df = pd.DataFrame( {\n",
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" 'dates': dates,\n",
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" 'values' : values, \n",
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2020-03-01 22:50:58 +01:00
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2020-03-02 21:22:49 +01:00
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2020-03-01 22:50:58 +01:00
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" <td>0</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>1/22/20</td>\n",
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2020-03-01 22:50:58 +01:00
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2020-03-02 21:22:49 +01:00
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" <td>1/23/20</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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2020-03-01 22:50:58 +01:00
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" <tr>\n",
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" <td>2</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>1/24/20</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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2020-03-01 22:50:58 +01:00
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" <tr>\n",
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" <td>3</td>\n",
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2020-03-02 21:22:49 +01:00
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" <td>1/25/20</td>\n",
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" <td>0</td>\n",
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2020-03-01 22:50:58 +01:00
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" <td>4</td>\n",
|
2020-03-02 21:22:49 +01:00
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" </tr>\n",
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" <tr>\n",
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2020-03-01 22:50:58 +01:00
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" <td>4</td>\n",
|
2020-03-02 21:22:49 +01:00
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" <td>1/26/20</td>\n",
|
2020-03-01 21:33:06 +01:00
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" <td>0</td>\n",
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"3 1/25/20 0 4\n",
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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{
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-02 21:22:49 +01:00
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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20,
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155,
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229,
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322,
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453,
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655,
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888,
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2020-03-02 21:22:49 +01:00
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1128,
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1694
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2020-03-01 21:33:06 +01:00
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2,
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3,
|
|
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|
|
4,
|
|
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|
|
5,
|
|
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|
6,
|
|
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|
|
7,
|
|
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8,
|
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9,
|
|
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|
|
10,
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|
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|
11,
|
|
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|
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12,
|
|
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13,
|
|
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14,
|
|
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15,
|
|
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16,
|
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17,
|
|
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|
|
18,
|
|
|
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|
|
19,
|
|
|
|
|
|
20,
|
|
|
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|
|
21,
|
|
|
|
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|
22,
|
|
|
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|
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23,
|
|
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|
24,
|
|
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|
25,
|
|
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|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
|
|
|
|
|
38,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
39,
|
|
|
|
|
|
40
|
2020-03-01 21:33:06 +01:00
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|
|
|
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0,
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-01 21:33:06 +01:00
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" {\"responsive\": true}\n",
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" ).then(function(){\n",
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" \n",
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2020-03-02 21:22:49 +01:00
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"var gd = document.getElementById('f4f8d322-c2d4-464a-b049-00e1855058ea');\n",
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2020-03-01 21:33:06 +01:00
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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"// Listen for the removal of the full notebook cells\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"\n",
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" </div>"
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"import plotly.express as px\n",
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"fig = px.scatter(df,x = \"el\",y=\"values\", trendline=\"ols\", color=\"values\")\n",
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"fig.show()"
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-02 21:22:49 +01:00
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"ticks": "",
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"title": {
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"standoff": 15
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"xaxis": {
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"anchor": "y",
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0,
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1
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],
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"title": {
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"text": "dates"
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}
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},
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"yaxis": {
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"anchor": "x",
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"title": {
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"text": "values"
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}
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}
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"text/html": [
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"<div>\n",
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" \n",
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" \n",
|
2020-03-02 21:22:49 +01:00
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" <div id=\"ab148723-e8f7-4015-bb17-180480f46307\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
|
2020-03-01 21:33:06 +01:00
|
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|
" <script type=\"text/javascript\">\n",
|
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|
" require([\"plotly\"], function(Plotly) {\n",
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" window.PLOTLYENV=window.PLOTLYENV || {};\n",
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" \n",
|
2020-03-02 21:22:49 +01:00
|
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" if (document.getElementById(\"ab148723-e8f7-4015-bb17-180480f46307\")) {\n",
|
2020-03-01 21:33:06 +01:00
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" Plotly.newPlot(\n",
|
2020-03-02 21:22:49 +01:00
|
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" 'ab148723-e8f7-4015-bb17-180480f46307',\n",
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" [{\"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"dates=%{x}<br>values=%{y}\", \"legendgroup\": \"\", \"line\": {\"color\": \"#636efa\", \"dash\": \"solid\"}, \"mode\": \"lines\", \"name\": \"\", \"showlegend\": false, \"type\": \"scatter\", \"x\": [\"1/22/20\", \"1/23/20\", \"1/24/20\", \"1/25/20\", \"1/26/20\", \"1/27/20\", \"1/28/20\", \"1/29/20\", \"1/30/20\", \"1/31/20\", \"2/1/20\", \"2/2/20\", \"2/3/20\", \"2/4/20\", \"2/5/20\", \"2/6/20\", \"2/7/20\", \"2/8/20\", \"2/9/20\", \"2/10/20\", \"2/11/20\", \"2/12/20\", \"2/13/20\", \"2/14/20\", \"2/15/20\", \"2/16/20\", \"2/17/20\", \"2/18/20\", \"2/19/20\", \"2/20/20\", \"2/21/20\", \"2/22/20\", \"2/23/20\", \"2/24/20\", \"2/25/20\", \"2/26/20\", \"2/27/20\", \"2/28/20\", \"2/29/20\", \"3/1/20\"], \"xaxis\": \"x\", \"y\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 20, 62, 155, 229, 322, 453, 655, 888, 1128, 1694], \"yaxis\": \"y\"}],\n",
|
2020-03-01 21:33:06 +01:00
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" {\"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {
|
|
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" {\"responsive\": true}\n",
|
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" ).then(function(){\n",
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" \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"var gd = document.getElementById('ab148723-e8f7-4015-bb17-180480f46307');\n",
|
2020-03-01 21:33:06 +01:00
|
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|
"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
|
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|
|
" console.log([gd, 'removed!']);\n",
|
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|
" Plotly.purge(gd);\n",
|
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|
" observer.disconnect();\n",
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" }}\n",
|
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|
"}});\n",
|
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|
"\n",
|
|
|
|
|
|
"// Listen for the removal of the full notebook cells\n",
|
|
|
|
|
|
"var notebookContainer = gd.closest('#notebook-container');\n",
|
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|
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|
|
"if (notebookContainer) {{\n",
|
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|
" x.observe(notebookContainer, {childList: true});\n",
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|
"}}\n",
|
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|
"\n",
|
|
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|
|
"// Listen for the clearing of the current output cell\n",
|
|
|
|
|
|
"var outputEl = gd.closest('.output');\n",
|
|
|
|
|
|
"if (outputEl) {{\n",
|
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|
|
" x.observe(outputEl, {childList: true});\n",
|
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|
"}}\n",
|
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|
"\n",
|
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|
|
" })\n",
|
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|
" };\n",
|
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" });\n",
|
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|
|
" </script>\n",
|
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" </div>"
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]
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},
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|
"metadata": {},
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|
"output_type": "display_data"
|
|
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|
}
|
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|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"import plotly.express as px\n",
|
|
|
|
|
|
"fig = px.line(df,x = \"dates\", y=\"values\")\n",
|
|
|
|
|
|
"fig.show()"
|
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|
|
]
|
|
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|
},
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|
{
|
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|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
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|
"source": [
|
|
|
|
|
|
"## LINEAR REGRESSION with Sklearn"
|
|
|
|
|
|
]
|
|
|
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|
|
},
|
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|
{
|
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|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 9,
|
2020-03-01 21:33:06 +01:00
|
|
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|
"metadata": {},
|
|
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|
"outputs": [
|
|
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|
{
|
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|
"name": "stderr",
|
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|
"output_type": "stream",
|
|
|
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|
"text": [
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning:\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
|
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n"
|
|
|
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|
|
]
|
|
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|
},
|
|
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|
{
|
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|
"data": {
|
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|
"text/plain": [
|
2020-03-02 21:22:49 +01:00
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|
"<Figure size 640x480 with 1 Axes>"
|
2020-03-01 21:33:06 +01:00
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]
|
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},
|
2020-03-02 21:22:49 +01:00
|
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|
"metadata": {},
|
2020-03-01 21:33:06 +01:00
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2020-03-02 21:22:49 +01:00
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"Accuracy: 0.6278428150820454\n"
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2020-03-01 21:33:06 +01:00
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt # To visualize\n",
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"import pandas as pd # To read data\n",
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"from sklearn.linear_model import LinearRegression\n",
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"\n",
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"\n",
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"#rescaling the values to get a better accuracy\n",
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"df[\"el\"]\n",
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"\n",
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"j = 1\n",
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"for i in range(len(df[\"el\"])):\n",
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" if(i >= 20):\n",
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" df[\"el\"][i]=j\n",
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" j+=1\n",
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" \n",
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"X = df.iloc[20:, 2].values.reshape(-1, 1) # values converts it into a numpy array\n",
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"Y = df.iloc[20:, 1].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column\n",
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"linear_regressor = LinearRegression() # create object for the class\n",
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"linear_regressor.fit(X, Y) # perform linear regression\n",
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"acc = linear_regressor.score(X, Y)\n",
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"Y_pred = linear_regressor.predict(X) # make predictions\n",
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"plt.scatter(X, Y)\n",
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"plt.plot(X, Y_pred, color='red')\n",
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"plt.show()\n",
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"print(\"Accuracy:\" ,acc)\n"
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]
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},
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{
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"cell_type": "code",
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2020-03-01 22:50:58 +01:00
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"execution_count": null,
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"metadata": {},
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2020-03-01 22:50:58 +01:00
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"outputs": [],
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2020-03-01 21:33:06 +01:00
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# EVALUATION IN CHINA"
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]
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},
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{
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"cell_type": "code",
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2020-03-02 21:22:49 +01:00
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"execution_count": 10,
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2020-03-01 21:33:06 +01:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Province/State</th>\n",
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" <th>Country/Region</th>\n",
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" <th>Lat</th>\n",
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" <th>Long</th>\n",
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" <th>1/22/20</th>\n",
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" <th>1/23/20</th>\n",
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" <th>1/24/20</th>\n",
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" <th>1/25/20</th>\n",
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" <th>1/26/20</th>\n",
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" <th>1/27/20</th>\n",
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" <th>...</th>\n",
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" <th>2/21/20</th>\n",
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" <th>2/22/20</th>\n",
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" <th>2/23/20</th>\n",
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" <th>2/24/20</th>\n",
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" <th>2/25/20</th>\n",
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" <th>2/26/20</th>\n",
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" <th>2/27/20</th>\n",
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" <th>2/28/20</th>\n",
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" <th>2/29/20</th>\n",
|
2020-03-02 21:22:49 +01:00
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" <th>3/1/20</th>\n",
|
2020-03-01 21:33:06 +01:00
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
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" <td>0</td>\n",
|
2020-03-01 21:33:06 +01:00
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|
" <td>Anhui</td>\n",
|
|
|
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|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>31.82570</td>\n",
|
|
|
|
|
|
" <td>117.2264</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>39</td>\n",
|
|
|
|
|
|
" <td>60</td>\n",
|
|
|
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|
|
" <td>70</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>988</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>989</td>\n",
|
|
|
|
|
|
" <td>990</td>\n",
|
|
|
|
|
|
" <td>990</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>990</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Beijing</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>40.18240</td>\n",
|
|
|
|
|
|
" <td>116.4142</td>\n",
|
|
|
|
|
|
" <td>14</td>\n",
|
|
|
|
|
|
" <td>22</td>\n",
|
|
|
|
|
|
" <td>36</td>\n",
|
|
|
|
|
|
" <td>41</td>\n",
|
|
|
|
|
|
" <td>68</td>\n",
|
|
|
|
|
|
" <td>80</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>396</td>\n",
|
|
|
|
|
|
" <td>399</td>\n",
|
|
|
|
|
|
" <td>399</td>\n",
|
|
|
|
|
|
" <td>399</td>\n",
|
|
|
|
|
|
" <td>400</td>\n",
|
|
|
|
|
|
" <td>400</td>\n",
|
|
|
|
|
|
" <td>410</td>\n",
|
|
|
|
|
|
" <td>410</td>\n",
|
|
|
|
|
|
" <td>411</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>413</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>2</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Chongqing</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>30.05720</td>\n",
|
|
|
|
|
|
" <td>107.8740</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>27</td>\n",
|
|
|
|
|
|
" <td>57</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>110</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>572</td>\n",
|
|
|
|
|
|
" <td>573</td>\n",
|
|
|
|
|
|
" <td>575</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
|
|
|
|
|
" <td>576</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>576</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>3</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Fujian</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>26.07890</td>\n",
|
|
|
|
|
|
" <td>117.9874</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>10</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>35</td>\n",
|
|
|
|
|
|
" <td>59</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>293</td>\n",
|
|
|
|
|
|
" <td>294</td>\n",
|
|
|
|
|
|
" <td>294</td>\n",
|
|
|
|
|
|
" <td>296</td>\n",
|
|
|
|
|
|
" <td>296</td>\n",
|
|
|
|
|
|
" <td>296</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>296</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>4</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Gansu</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>36.06110</td>\n",
|
|
|
|
|
|
" <td>103.8343</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>14</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>5</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Guangdong</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>23.34170</td>\n",
|
|
|
|
|
|
" <td>113.4244</td>\n",
|
|
|
|
|
|
" <td>26</td>\n",
|
|
|
|
|
|
" <td>32</td>\n",
|
|
|
|
|
|
" <td>53</td>\n",
|
|
|
|
|
|
" <td>78</td>\n",
|
|
|
|
|
|
" <td>111</td>\n",
|
|
|
|
|
|
" <td>151</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>1333</td>\n",
|
|
|
|
|
|
" <td>1339</td>\n",
|
|
|
|
|
|
" <td>1342</td>\n",
|
|
|
|
|
|
" <td>1345</td>\n",
|
|
|
|
|
|
" <td>1347</td>\n",
|
|
|
|
|
|
" <td>1347</td>\n",
|
|
|
|
|
|
" <td>1347</td>\n",
|
|
|
|
|
|
" <td>1348</td>\n",
|
|
|
|
|
|
" <td>1349</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1349</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>6</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Guangxi</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>23.82980</td>\n",
|
|
|
|
|
|
" <td>108.7881</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>23</td>\n",
|
|
|
|
|
|
" <td>23</td>\n",
|
|
|
|
|
|
" <td>36</td>\n",
|
|
|
|
|
|
" <td>46</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>246</td>\n",
|
|
|
|
|
|
" <td>249</td>\n",
|
|
|
|
|
|
" <td>249</td>\n",
|
|
|
|
|
|
" <td>251</td>\n",
|
|
|
|
|
|
" <td>252</td>\n",
|
|
|
|
|
|
" <td>252</td>\n",
|
|
|
|
|
|
" <td>252</td>\n",
|
|
|
|
|
|
" <td>252</td>\n",
|
|
|
|
|
|
" <td>252</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>252</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>7</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Guizhou</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>26.81540</td>\n",
|
|
|
|
|
|
" <td>106.8748</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" <td>146</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>8</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Hainan</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>19.19590</td>\n",
|
|
|
|
|
|
" <td>109.7453</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>8</td>\n",
|
|
|
|
|
|
" <td>19</td>\n",
|
|
|
|
|
|
" <td>22</td>\n",
|
|
|
|
|
|
" <td>33</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" <td>168</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>9</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Hebei</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>38.04280</td>\n",
|
|
|
|
|
|
" <td>114.5149</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>8</td>\n",
|
|
|
|
|
|
" <td>13</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>308</td>\n",
|
|
|
|
|
|
" <td>309</td>\n",
|
|
|
|
|
|
" <td>311</td>\n",
|
|
|
|
|
|
" <td>311</td>\n",
|
|
|
|
|
|
" <td>311</td>\n",
|
|
|
|
|
|
" <td>312</td>\n",
|
|
|
|
|
|
" <td>317</td>\n",
|
|
|
|
|
|
" <td>318</td>\n",
|
|
|
|
|
|
" <td>318</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>318</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>10</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Heilongjiang</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>47.86200</td>\n",
|
|
|
|
|
|
" <td>127.7615</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>21</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>479</td>\n",
|
|
|
|
|
|
" <td>479</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
|
|
|
|
|
" <td>480</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>480</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>11</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Henan</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>33.88202</td>\n",
|
|
|
|
|
|
" <td>113.6140</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>32</td>\n",
|
|
|
|
|
|
" <td>83</td>\n",
|
|
|
|
|
|
" <td>128</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>1267</td>\n",
|
|
|
|
|
|
" <td>1270</td>\n",
|
|
|
|
|
|
" <td>1271</td>\n",
|
|
|
|
|
|
" <td>1271</td>\n",
|
|
|
|
|
|
" <td>1271</td>\n",
|
|
|
|
|
|
" <td>1271</td>\n",
|
|
|
|
|
|
" <td>1272</td>\n",
|
|
|
|
|
|
" <td>1272</td>\n",
|
|
|
|
|
|
" <td>1272</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1272</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>12</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Hubei</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>30.97560</td>\n",
|
|
|
|
|
|
" <td>112.2707</td>\n",
|
|
|
|
|
|
" <td>444</td>\n",
|
|
|
|
|
|
" <td>444</td>\n",
|
|
|
|
|
|
" <td>549</td>\n",
|
|
|
|
|
|
" <td>761</td>\n",
|
|
|
|
|
|
" <td>1058</td>\n",
|
|
|
|
|
|
" <td>1423</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>62662</td>\n",
|
|
|
|
|
|
" <td>64084</td>\n",
|
|
|
|
|
|
" <td>64084</td>\n",
|
|
|
|
|
|
" <td>64287</td>\n",
|
|
|
|
|
|
" <td>64786</td>\n",
|
|
|
|
|
|
" <td>65187</td>\n",
|
|
|
|
|
|
" <td>65596</td>\n",
|
|
|
|
|
|
" <td>65914</td>\n",
|
|
|
|
|
|
" <td>66337</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>66907</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>13</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Hunan</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>27.61040</td>\n",
|
|
|
|
|
|
" <td>111.7088</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>24</td>\n",
|
|
|
|
|
|
" <td>43</td>\n",
|
|
|
|
|
|
" <td>69</td>\n",
|
|
|
|
|
|
" <td>100</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>1011</td>\n",
|
|
|
|
|
|
" <td>1013</td>\n",
|
|
|
|
|
|
" <td>1016</td>\n",
|
|
|
|
|
|
" <td>1016</td>\n",
|
|
|
|
|
|
" <td>1016</td>\n",
|
|
|
|
|
|
" <td>1016</td>\n",
|
|
|
|
|
|
" <td>1017</td>\n",
|
|
|
|
|
|
" <td>1017</td>\n",
|
|
|
|
|
|
" <td>1018</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1018</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>14</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Inner Mongolia</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>44.09350</td>\n",
|
|
|
|
|
|
" <td>113.9448</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>11</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>15</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Jiangsu</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>32.97110</td>\n",
|
|
|
|
|
|
" <td>119.4550</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>33</td>\n",
|
|
|
|
|
|
" <td>47</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" <td>631</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>16</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Jiangxi</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>27.61400</td>\n",
|
|
|
|
|
|
" <td>115.7221</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>36</td>\n",
|
|
|
|
|
|
" <td>72</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
|
|
|
|
|
" <td>934</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>935</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>935</td>\n",
|
|
|
|
|
|
" <td>935</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>17</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Jilin</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>43.66610</td>\n",
|
|
|
|
|
|
" <td>126.1923</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
|
|
|
|
|
" <td>91</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>93</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>93</td>\n",
|
|
|
|
|
|
" <td>93</td>\n",
|
|
|
|
|
|
" <td>93</td>\n",
|
|
|
|
|
|
" <td>93</td>\n",
|
|
|
|
|
|
" <td>93</td>\n",
|
|
|
|
|
|
" <td>93</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>18</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Liaoning</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>41.29560</td>\n",
|
|
|
|
|
|
" <td>122.6085</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>17</td>\n",
|
|
|
|
|
|
" <td>21</td>\n",
|
|
|
|
|
|
" <td>27</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
|
|
|
|
|
" <td>121</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>122</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>19</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Ningxia</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>37.26920</td>\n",
|
|
|
|
|
|
" <td>106.1655</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>7</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>71</td>\n",
|
|
|
|
|
|
" <td>71</td>\n",
|
|
|
|
|
|
" <td>71</td>\n",
|
|
|
|
|
|
" <td>71</td>\n",
|
|
|
|
|
|
" <td>71</td>\n",
|
|
|
|
|
|
" <td>71</td>\n",
|
|
|
|
|
|
" <td>72</td>\n",
|
|
|
|
|
|
" <td>72</td>\n",
|
|
|
|
|
|
" <td>73</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>73</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>20</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Qinghai</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>35.74520</td>\n",
|
|
|
|
|
|
" <td>95.9956</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>21</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Shaanxi</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>35.19170</td>\n",
|
|
|
|
|
|
" <td>108.8701</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>22</td>\n",
|
|
|
|
|
|
" <td>35</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" <td>245</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>22</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Shandong</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>36.34270</td>\n",
|
|
|
|
|
|
" <td>118.1498</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>27</td>\n",
|
|
|
|
|
|
" <td>46</td>\n",
|
|
|
|
|
|
" <td>75</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>749</td>\n",
|
|
|
|
|
|
" <td>750</td>\n",
|
|
|
|
|
|
" <td>754</td>\n",
|
|
|
|
|
|
" <td>755</td>\n",
|
|
|
|
|
|
" <td>756</td>\n",
|
|
|
|
|
|
" <td>756</td>\n",
|
|
|
|
|
|
" <td>756</td>\n",
|
|
|
|
|
|
" <td>756</td>\n",
|
|
|
|
|
|
" <td>756</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>758</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>23</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Shanghai</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>31.20200</td>\n",
|
|
|
|
|
|
" <td>121.4491</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>16</td>\n",
|
|
|
|
|
|
" <td>20</td>\n",
|
|
|
|
|
|
" <td>33</td>\n",
|
|
|
|
|
|
" <td>40</td>\n",
|
|
|
|
|
|
" <td>53</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>334</td>\n",
|
|
|
|
|
|
" <td>335</td>\n",
|
|
|
|
|
|
" <td>335</td>\n",
|
|
|
|
|
|
" <td>335</td>\n",
|
|
|
|
|
|
" <td>336</td>\n",
|
|
|
|
|
|
" <td>337</td>\n",
|
|
|
|
|
|
" <td>337</td>\n",
|
|
|
|
|
|
" <td>337</td>\n",
|
|
|
|
|
|
" <td>337</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>337</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>24</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Shanxi</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>37.57770</td>\n",
|
|
|
|
|
|
" <td>112.2922</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>6</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>13</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>132</td>\n",
|
|
|
|
|
|
" <td>132</td>\n",
|
|
|
|
|
|
" <td>132</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>133</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>133</td>\n",
|
|
|
|
|
|
" <td>133</td>\n",
|
|
|
|
|
|
" <td>133</td>\n",
|
|
|
|
|
|
" <td>133</td>\n",
|
|
|
|
|
|
" <td>133</td>\n",
|
|
|
|
|
|
" <td>133</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>25</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Sichuan</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>30.61710</td>\n",
|
|
|
|
|
|
" <td>102.7103</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>8</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>28</td>\n",
|
|
|
|
|
|
" <td>44</td>\n",
|
|
|
|
|
|
" <td>69</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>525</td>\n",
|
|
|
|
|
|
" <td>526</td>\n",
|
|
|
|
|
|
" <td>526</td>\n",
|
|
|
|
|
|
" <td>527</td>\n",
|
|
|
|
|
|
" <td>529</td>\n",
|
|
|
|
|
|
" <td>531</td>\n",
|
|
|
|
|
|
" <td>534</td>\n",
|
|
|
|
|
|
" <td>538</td>\n",
|
|
|
|
|
|
" <td>538</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>538</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>26</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Tianjin</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>39.30540</td>\n",
|
|
|
|
|
|
" <td>117.3230</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>8</td>\n",
|
|
|
|
|
|
" <td>10</td>\n",
|
|
|
|
|
|
" <td>14</td>\n",
|
|
|
|
|
|
" <td>23</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>132</td>\n",
|
|
|
|
|
|
" <td>135</td>\n",
|
|
|
|
|
|
" <td>135</td>\n",
|
|
|
|
|
|
" <td>135</td>\n",
|
|
|
|
|
|
" <td>135</td>\n",
|
|
|
|
|
|
" <td>135</td>\n",
|
|
|
|
|
|
" <td>136</td>\n",
|
|
|
|
|
|
" <td>136</td>\n",
|
|
|
|
|
|
" <td>136</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>136</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>27</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Tibet</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>31.69270</td>\n",
|
|
|
|
|
|
" <td>88.0924</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>...</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>28</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Xinjiang</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>41.11290</td>\n",
|
|
|
|
|
|
" <td>85.2401</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" <td>76</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>29</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Yunnan</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>24.97400</td>\n",
|
|
|
|
|
|
" <td>101.4870</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
|
|
|
|
|
" <td>5</td>\n",
|
|
|
|
|
|
" <td>11</td>\n",
|
|
|
|
|
|
" <td>16</td>\n",
|
|
|
|
|
|
" <td>26</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" <td>174</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>30</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>Zhejiang</td>\n",
|
|
|
|
|
|
" <td>Mainland China</td>\n",
|
|
|
|
|
|
" <td>29.18320</td>\n",
|
|
|
|
|
|
" <td>120.0934</td>\n",
|
|
|
|
|
|
" <td>10</td>\n",
|
|
|
|
|
|
" <td>27</td>\n",
|
|
|
|
|
|
" <td>43</td>\n",
|
|
|
|
|
|
" <td>62</td>\n",
|
|
|
|
|
|
" <td>104</td>\n",
|
|
|
|
|
|
" <td>128</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>1203</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1205</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"<p>31 rows × 44 columns</p>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n",
|
|
|
|
|
|
"0 Anhui Mainland China 31.82570 117.2264 1 9 \n",
|
|
|
|
|
|
"1 Beijing Mainland China 40.18240 116.4142 14 22 \n",
|
|
|
|
|
|
"2 Chongqing Mainland China 30.05720 107.8740 6 9 \n",
|
|
|
|
|
|
"3 Fujian Mainland China 26.07890 117.9874 1 5 \n",
|
|
|
|
|
|
"4 Gansu Mainland China 36.06110 103.8343 0 2 \n",
|
|
|
|
|
|
"5 Guangdong Mainland China 23.34170 113.4244 26 32 \n",
|
|
|
|
|
|
"6 Guangxi Mainland China 23.82980 108.7881 2 5 \n",
|
|
|
|
|
|
"7 Guizhou Mainland China 26.81540 106.8748 1 3 \n",
|
|
|
|
|
|
"8 Hainan Mainland China 19.19590 109.7453 4 5 \n",
|
|
|
|
|
|
"9 Hebei Mainland China 38.04280 114.5149 1 1 \n",
|
|
|
|
|
|
"10 Heilongjiang Mainland China 47.86200 127.7615 0 2 \n",
|
|
|
|
|
|
"11 Henan Mainland China 33.88202 113.6140 5 5 \n",
|
|
|
|
|
|
"12 Hubei Mainland China 30.97560 112.2707 444 444 \n",
|
|
|
|
|
|
"13 Hunan Mainland China 27.61040 111.7088 4 9 \n",
|
|
|
|
|
|
"14 Inner Mongolia Mainland China 44.09350 113.9448 0 0 \n",
|
|
|
|
|
|
"15 Jiangsu Mainland China 32.97110 119.4550 1 5 \n",
|
|
|
|
|
|
"16 Jiangxi Mainland China 27.61400 115.7221 2 7 \n",
|
|
|
|
|
|
"17 Jilin Mainland China 43.66610 126.1923 0 1 \n",
|
|
|
|
|
|
"18 Liaoning Mainland China 41.29560 122.6085 2 3 \n",
|
|
|
|
|
|
"19 Ningxia Mainland China 37.26920 106.1655 1 1 \n",
|
|
|
|
|
|
"20 Qinghai Mainland China 35.74520 95.9956 0 0 \n",
|
|
|
|
|
|
"21 Shaanxi Mainland China 35.19170 108.8701 0 3 \n",
|
|
|
|
|
|
"22 Shandong Mainland China 36.34270 118.1498 2 6 \n",
|
|
|
|
|
|
"23 Shanghai Mainland China 31.20200 121.4491 9 16 \n",
|
|
|
|
|
|
"24 Shanxi Mainland China 37.57770 112.2922 1 1 \n",
|
|
|
|
|
|
"25 Sichuan Mainland China 30.61710 102.7103 5 8 \n",
|
|
|
|
|
|
"26 Tianjin Mainland China 39.30540 117.3230 4 4 \n",
|
|
|
|
|
|
"27 Tibet Mainland China 31.69270 88.0924 0 0 \n",
|
|
|
|
|
|
"28 Xinjiang Mainland China 41.11290 85.2401 0 2 \n",
|
|
|
|
|
|
"29 Yunnan Mainland China 24.97400 101.4870 1 2 \n",
|
|
|
|
|
|
"30 Zhejiang Mainland China 29.18320 120.0934 10 27 \n",
|
|
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/21/20 2/22/20 2/23/20 \\\n",
|
|
|
|
|
|
"0 15 39 60 70 ... 988 989 989 \n",
|
|
|
|
|
|
"1 36 41 68 80 ... 396 399 399 \n",
|
|
|
|
|
|
"2 27 57 75 110 ... 572 573 575 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"3 10 18 35 59 ... 293 293 293 \n",
|
|
|
|
|
|
"4 2 4 7 14 ... 91 91 91 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"5 53 78 111 151 ... 1333 1339 1342 \n",
|
|
|
|
|
|
"6 23 23 36 46 ... 246 249 249 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"7 3 4 5 7 ... 146 146 146 \n",
|
|
|
|
|
|
"8 8 19 22 33 ... 168 168 168 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"9 2 8 13 18 ... 308 309 311 \n",
|
|
|
|
|
|
"10 4 9 15 21 ... 479 479 480 \n",
|
|
|
|
|
|
"11 9 32 83 128 ... 1267 1270 1271 \n",
|
|
|
|
|
|
"12 549 761 1058 1423 ... 62662 64084 64084 \n",
|
|
|
|
|
|
"13 24 43 69 100 ... 1011 1013 1016 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"14 1 7 7 11 ... 75 75 75 \n",
|
|
|
|
|
|
"15 9 18 33 47 ... 631 631 631 \n",
|
|
|
|
|
|
"16 18 18 36 72 ... 934 934 934 \n",
|
|
|
|
|
|
"17 3 4 4 6 ... 91 91 91 \n",
|
|
|
|
|
|
"18 4 17 21 27 ... 121 121 121 \n",
|
|
|
|
|
|
"19 2 3 4 7 ... 71 71 71 \n",
|
|
|
|
|
|
"20 0 1 1 6 ... 18 18 18 \n",
|
|
|
|
|
|
"21 5 15 22 35 ... 245 245 245 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"22 15 27 46 75 ... 749 750 754 \n",
|
|
|
|
|
|
"23 20 33 40 53 ... 334 335 335 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"24 1 6 9 13 ... 132 132 132 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"25 15 28 44 69 ... 525 526 526 \n",
|
|
|
|
|
|
"26 8 10 14 23 ... 132 135 135 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"27 0 0 0 0 ... 1 1 1 \n",
|
|
|
|
|
|
"28 2 3 4 5 ... 76 76 76 \n",
|
|
|
|
|
|
"29 5 11 16 26 ... 174 174 174 \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"30 43 62 104 128 ... 1203 1205 1205 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 \n",
|
|
|
|
|
|
"0 989 989 989 989 990 990 990 \n",
|
|
|
|
|
|
"1 399 400 400 410 410 411 413 \n",
|
|
|
|
|
|
"2 576 576 576 576 576 576 576 \n",
|
|
|
|
|
|
"3 293 294 294 296 296 296 296 \n",
|
|
|
|
|
|
"4 91 91 91 91 91 91 91 \n",
|
|
|
|
|
|
"5 1345 1347 1347 1347 1348 1349 1349 \n",
|
|
|
|
|
|
"6 251 252 252 252 252 252 252 \n",
|
|
|
|
|
|
"7 146 146 146 146 146 146 146 \n",
|
|
|
|
|
|
"8 168 168 168 168 168 168 168 \n",
|
|
|
|
|
|
"9 311 311 312 317 318 318 318 \n",
|
|
|
|
|
|
"10 480 480 480 480 480 480 480 \n",
|
|
|
|
|
|
"11 1271 1271 1271 1272 1272 1272 1272 \n",
|
|
|
|
|
|
"12 64287 64786 65187 65596 65914 66337 66907 \n",
|
|
|
|
|
|
"13 1016 1016 1016 1017 1017 1018 1018 \n",
|
|
|
|
|
|
"14 75 75 75 75 75 75 75 \n",
|
|
|
|
|
|
"15 631 631 631 631 631 631 631 \n",
|
|
|
|
|
|
"16 934 934 934 934 935 935 935 \n",
|
|
|
|
|
|
"17 93 93 93 93 93 93 93 \n",
|
|
|
|
|
|
"18 121 121 121 121 121 121 122 \n",
|
|
|
|
|
|
"19 71 71 71 72 72 73 73 \n",
|
|
|
|
|
|
"20 18 18 18 18 18 18 18 \n",
|
|
|
|
|
|
"21 245 245 245 245 245 245 245 \n",
|
|
|
|
|
|
"22 755 756 756 756 756 756 758 \n",
|
|
|
|
|
|
"23 335 336 337 337 337 337 337 \n",
|
|
|
|
|
|
"24 133 133 133 133 133 133 133 \n",
|
|
|
|
|
|
"25 527 529 531 534 538 538 538 \n",
|
|
|
|
|
|
"26 135 135 135 136 136 136 136 \n",
|
|
|
|
|
|
"27 1 1 1 1 1 1 1 \n",
|
|
|
|
|
|
"28 76 76 76 76 76 76 76 \n",
|
|
|
|
|
|
"29 174 174 174 174 174 174 174 \n",
|
|
|
|
|
|
"30 1205 1205 1205 1205 1205 1205 1205 \n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"[31 rows x 44 columns]"
|
2020-03-01 21:33:06 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 10,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data_china = data[data[\"Country/Region\"] == \"Mainland China\"]\n",
|
|
|
|
|
|
"data_china"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 11,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"0 1205\n",
|
|
|
|
|
|
"1 [Zhejiang, Mainland China, 29.1832, 120.0934, ...\n",
|
|
|
|
|
|
"Name: values, dtype: object"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 11,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"#data_italy.values[0,[3]]\n",
|
|
|
|
|
|
"#type(data_italy.values)\n",
|
|
|
|
|
|
"df = {}\n",
|
|
|
|
|
|
"for i in range(len(data_china.values)):\n",
|
|
|
|
|
|
" for j in range(4, len(data_china.values[i])):\n",
|
|
|
|
|
|
" df[\"values\"] = [data_china.values[i][j],data_china.values[i]]\n",
|
|
|
|
|
|
"pd.DataFrame(df)[\"values\"]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 12,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"40.1824"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 12,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data_china.values[1][2]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"china_cities = []\n",
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"count = 1\n",
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"ind = []\n",
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"for i in range(len(data_china.values)):\n",
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" count = 0\n",
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" for j in range(len(data_china.values[i][4:])):\n",
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" china_cities+= [data_china.values[i][0]]\n",
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" ind += [count]\n",
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" count += 1"
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{
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"cell_type": "code",
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2020-03-02 21:22:49 +01:00
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"china_values=[]\n",
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"for i in range(len(data_china.values)):\n",
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" for j in range(4,len(data_china.values[i])): \n",
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" china_values+= [data_china.values[i][j]]\n",
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" "
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]
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},
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{
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"cell_type": "code",
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2020-03-02 21:22:49 +01:00
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"execution_count": 15,
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2020-03-01 21:33:06 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"df_china = pd.DataFrame(list(zip(china_values, china_cities,ind)), \n",
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" columns =['Values', 'Cities','Lags']) "
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]
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},
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{
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"cell_type": "code",
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2020-03-02 21:22:49 +01:00
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"execution_count": 16,
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2020-03-01 21:33:06 +01:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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2020-03-02 21:22:49 +01:00
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" <th></th>\n",
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" <th>Values</th>\n",
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" <th>Cities</th>\n",
|
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" <th>Lags</th>\n",
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2020-03-01 22:50:58 +01:00
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" </tr>\n",
|
2020-03-02 21:22:49 +01:00
|
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" </thead>\n",
|
|
|
|
|
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" <tbody>\n",
|
2020-03-01 22:50:58 +01:00
|
|
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" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
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" <td>0</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
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" <td>Anhui</td>\n",
|
|
|
|
|
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" <td>0</td>\n",
|
2020-03-01 22:50:58 +01:00
|
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" </tr>\n",
|
|
|
|
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" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
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" <td>1</td>\n",
|
|
|
|
|
|
" <td>9</td>\n",
|
|
|
|
|
|
" <td>Anhui</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
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" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
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" <td>2</td>\n",
|
|
|
|
|
|
" <td>15</td>\n",
|
|
|
|
|
|
" <td>Anhui</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
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|
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" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>39</td>\n",
|
|
|
|
|
|
" <td>Anhui</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>4</td>\n",
|
|
|
|
|
|
" <td>60</td>\n",
|
|
|
|
|
|
" <td>Anhui</td>\n",
|
|
|
|
|
|
" <td>4</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
|
" <td>...</td>\n",
|
2020-03-01 22:50:58 +01:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1235</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>Zhejiang</td>\n",
|
|
|
|
|
|
" <td>35</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1236</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>Zhejiang</td>\n",
|
|
|
|
|
|
" <td>36</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1237</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>Zhejiang</td>\n",
|
|
|
|
|
|
" <td>37</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <td>1238</td>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>Zhejiang</td>\n",
|
|
|
|
|
|
" <td>38</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
" <tr>\n",
|
|
|
|
|
|
" <td>1239</td>\n",
|
|
|
|
|
|
" <td>1205</td>\n",
|
|
|
|
|
|
" <td>Zhejiang</td>\n",
|
|
|
|
|
|
" <td>39</td>\n",
|
|
|
|
|
|
" </tr>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"<p>1240 rows × 3 columns</p>\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" Values Cities Lags\n",
|
|
|
|
|
|
"0 1 Anhui 0\n",
|
|
|
|
|
|
"1 9 Anhui 1\n",
|
|
|
|
|
|
"2 15 Anhui 2\n",
|
|
|
|
|
|
"3 39 Anhui 3\n",
|
|
|
|
|
|
"4 60 Anhui 4\n",
|
|
|
|
|
|
"... ... ... ...\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"1235 1205 Zhejiang 35\n",
|
|
|
|
|
|
"1236 1205 Zhejiang 36\n",
|
|
|
|
|
|
"1237 1205 Zhejiang 37\n",
|
|
|
|
|
|
"1238 1205 Zhejiang 38\n",
|
|
|
|
|
|
"1239 1205 Zhejiang 39\n",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"[1240 rows x 3 columns]"
|
2020-03-01 21:33:06 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 16,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
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|
],
|
|
|
|
|
|
"source": [
|
|
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|
|
"df_china"
|
|
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|
|
|
]
|
|
|
|
|
|
},
|
|
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|
|
|
{
|
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|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 17,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {
|
|
|
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|
|
"scrolled": true
|
|
|
|
|
|
},
|
|
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|
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"outputs": [
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|
{
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"data": {
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|
"application/vnd.plotly.v1+json": {
|
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|
|
"config": {
|
|
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|
|
"plotlyServerURL": "https://plot.ly"
|
|
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|
},
|
|
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|
"data": [
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|
{
|
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"hoverlabel": {
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|
"namelength": 0
|
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|
},
|
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|
"hovertemplate": "Cities=Anhui<br>Lags=%{x}<br>Values=%{y}",
|
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|
"legendgroup": "Anhui",
|
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"line": {
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|
"color": "#636efa",
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"dash": "solid"
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},
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|
"mode": "lines",
|
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|
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|
"name": "Anhui",
|
|
|
|
|
|
"showlegend": true,
|
|
|
|
|
|
"type": "scattergl",
|
|
|
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"x": [
|
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0,
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1,
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2,
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3,
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4,
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5,
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6,
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7,
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8,
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9,
|
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10,
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11,
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12,
|
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13,
|
|
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14,
|
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15,
|
|
|
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16,
|
|
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17,
|
|
|
|
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18,
|
|
|
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|
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19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
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22,
|
|
|
|
|
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23,
|
|
|
|
|
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24,
|
|
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|
|
|
25,
|
|
|
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|
|
26,
|
|
|
|
|
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27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
1,
|
|
|
|
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|
9,
|
|
|
|
|
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15,
|
|
|
|
|
|
39,
|
|
|
|
|
|
60,
|
|
|
|
|
|
70,
|
|
|
|
|
|
106,
|
|
|
|
|
|
152,
|
|
|
|
|
|
200,
|
|
|
|
|
|
237,
|
|
|
|
|
|
297,
|
|
|
|
|
|
340,
|
|
|
|
|
|
408,
|
|
|
|
|
|
480,
|
|
|
|
|
|
530,
|
|
|
|
|
|
591,
|
|
|
|
|
|
665,
|
|
|
|
|
|
733,
|
|
|
|
|
|
779,
|
|
|
|
|
|
830,
|
|
|
|
|
|
860,
|
|
|
|
|
|
889,
|
|
|
|
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910,
|
|
|
|
|
|
934,
|
|
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|
|
|
950,
|
|
|
|
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|
962,
|
|
|
|
|
|
973,
|
|
|
|
|
|
982,
|
|
|
|
|
|
986,
|
|
|
|
|
|
987,
|
|
|
|
|
|
988,
|
|
|
|
|
|
989,
|
|
|
|
|
|
989,
|
|
|
|
|
|
989,
|
|
|
|
|
|
989,
|
|
|
|
|
|
989,
|
|
|
|
|
|
989,
|
|
|
|
|
|
990,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
990,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
990
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
|
},
|
|
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|
|
|
{
|
|
|
|
|
|
"hoverlabel": {
|
|
|
|
|
|
"namelength": 0
|
|
|
|
|
|
},
|
|
|
|
|
|
"hovertemplate": "Cities=Beijing<br>Lags=%{x}<br>Values=%{y}",
|
|
|
|
|
|
"legendgroup": "Beijing",
|
|
|
|
|
|
"line": {
|
|
|
|
|
|
"color": "#EF553B",
|
|
|
|
|
|
"dash": "solid"
|
|
|
|
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18,
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20,
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21,
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22,
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|
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23,
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24,
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|
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25,
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26,
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27,
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28,
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29,
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33,
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34,
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35,
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|
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36,
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|
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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2020-03-01 21:33:06 +01:00
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22,
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68,
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91,
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111,
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139,
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168,
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253,
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274,
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297,
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315,
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326,
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337,
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342,
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352,
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372,
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375,
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380,
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381,
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387,
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393,
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395,
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396,
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399,
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399,
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399,
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400,
|
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400,
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|
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410,
|
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410,
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2020-03-02 21:22:49 +01:00
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411,
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413
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2020-03-01 21:33:06 +01:00
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21,
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34,
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35,
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36,
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|
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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2020-03-01 21:33:06 +01:00
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132,
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147,
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182,
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211,
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247,
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300,
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389,
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411,
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426,
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428,
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468,
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486,
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505,
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518,
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529,
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537,
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544,
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551,
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553,
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555,
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575,
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576,
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576,
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576,
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576,
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576,
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2020-03-02 21:22:49 +01:00
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576,
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2020-03-01 21:33:06 +01:00
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576
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19,
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20,
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21,
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22,
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23,
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24,
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25,
|
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26,
|
|
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27,
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28,
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29,
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30,
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31,
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32,
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33,
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34,
|
|
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|
|
35,
|
|
|
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|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
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|
39
|
2020-03-01 21:33:06 +01:00
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5,
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35,
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59,
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80,
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84,
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101,
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120,
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144,
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159,
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179,
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194,
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205,
|
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215,
|
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224,
|
|
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|
239,
|
|
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|
|
250,
|
|
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|
|
261,
|
|
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|
267,
|
|
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272,
|
|
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279,
|
|
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281,
|
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285,
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287,
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290,
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292,
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293,
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293,
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293,
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293,
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293,
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293,
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294,
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294,
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296,
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296,
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2020-03-02 21:22:49 +01:00
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296,
|
2020-03-01 21:33:06 +01:00
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296
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1,
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3,
|
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4,
|
|
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|
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5,
|
|
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|
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6,
|
|
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|
|
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7,
|
|
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|
|
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8,
|
|
|
|
|
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9,
|
|
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|
|
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10,
|
|
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|
|
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11,
|
|
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|
|
12,
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|
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|
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13,
|
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|
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14,
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|
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15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
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|
|
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|
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|
|
|
0,
|
|
|
|
|
|
2,
|
|
|
|
|
|
2,
|
|
|
|
|
|
4,
|
|
|
|
|
|
7,
|
|
|
|
|
|
14,
|
|
|
|
|
|
19,
|
|
|
|
|
|
24,
|
|
|
|
|
|
26,
|
|
|
|
|
|
29,
|
|
|
|
|
|
40,
|
|
|
|
|
|
51,
|
|
|
|
|
|
55,
|
|
|
|
|
|
57,
|
|
|
|
|
|
62,
|
|
|
|
|
|
62,
|
|
|
|
|
|
67,
|
|
|
|
|
|
79,
|
|
|
|
|
|
83,
|
|
|
|
|
|
83,
|
|
|
|
|
|
86,
|
|
|
|
|
|
87,
|
|
|
|
|
|
90,
|
|
|
|
|
|
90,
|
|
|
|
|
|
90,
|
|
|
|
|
|
90,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
91,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
91
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
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|
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},
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{
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"hoverlabel": {
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"namelength": 0
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},
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"hovertemplate": "Cities=Guangdong<br>Lags=%{x}<br>Values=%{y}",
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"legendgroup": "Guangdong",
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|
|
"line": {
|
|
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"color": "#19d3f3",
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|
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"dash": "solid"
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|
|
|
},
|
|
|
|
|
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"mode": "lines",
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"name": "Guangdong",
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"showlegend": true,
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"type": "scattergl",
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|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
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|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
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|
6,
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|
|
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|
7,
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|
|
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|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
26,
|
|
|
|
|
|
32,
|
|
|
|
|
|
53,
|
|
|
|
|
|
78,
|
|
|
|
|
|
111,
|
|
|
|
|
|
151,
|
|
|
|
|
|
207,
|
|
|
|
|
|
277,
|
|
|
|
|
|
354,
|
|
|
|
|
|
436,
|
|
|
|
|
|
535,
|
|
|
|
|
|
632,
|
|
|
|
|
|
725,
|
|
|
|
|
|
813,
|
|
|
|
|
|
895,
|
|
|
|
|
|
970,
|
|
|
|
|
|
1034,
|
|
|
|
|
|
1095,
|
|
|
|
|
|
1131,
|
|
|
|
|
|
1159,
|
|
|
|
|
|
1177,
|
|
|
|
|
|
1219,
|
|
|
|
|
|
1241,
|
|
|
|
|
|
1261,
|
|
|
|
|
|
1294,
|
|
|
|
|
|
1316,
|
|
|
|
|
|
1322,
|
|
|
|
|
|
1328,
|
|
|
|
|
|
1331,
|
|
|
|
|
|
1332,
|
|
|
|
|
|
1333,
|
|
|
|
|
|
1339,
|
|
|
|
|
|
1342,
|
|
|
|
|
|
1345,
|
|
|
|
|
|
1347,
|
|
|
|
|
|
1347,
|
|
|
|
|
|
1347,
|
|
|
|
|
|
1348,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
1349,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
1349
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
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},
|
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|
{
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|
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"hoverlabel": {
|
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"namelength": 0
|
|
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|
|
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},
|
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|
|
"hovertemplate": "Cities=Guangxi<br>Lags=%{x}<br>Values=%{y}",
|
|
|
|
|
|
"legendgroup": "Guangxi",
|
|
|
|
|
|
"line": {
|
|
|
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|
|
"color": "#FF6692",
|
|
|
|
|
|
"dash": "solid"
|
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|
|
|
|
},
|
|
|
|
|
|
"mode": "lines",
|
|
|
|
|
|
"name": "Guangxi",
|
|
|
|
|
|
"showlegend": true,
|
|
|
|
|
|
"type": "scattergl",
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|
|
|
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|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
2,
|
|
|
|
|
|
5,
|
|
|
|
|
|
23,
|
|
|
|
|
|
23,
|
|
|
|
|
|
36,
|
|
|
|
|
|
46,
|
|
|
|
|
|
51,
|
|
|
|
|
|
58,
|
|
|
|
|
|
78,
|
|
|
|
|
|
87,
|
|
|
|
|
|
100,
|
|
|
|
|
|
111,
|
|
|
|
|
|
127,
|
|
|
|
|
|
139,
|
|
|
|
|
|
150,
|
|
|
|
|
|
168,
|
|
|
|
|
|
172,
|
|
|
|
|
|
183,
|
|
|
|
|
|
195,
|
|
|
|
|
|
210,
|
|
|
|
|
|
215,
|
|
|
|
|
|
222,
|
|
|
|
|
|
222,
|
|
|
|
|
|
226,
|
|
|
|
|
|
235,
|
|
|
|
|
|
237,
|
|
|
|
|
|
238,
|
|
|
|
|
|
242,
|
|
|
|
|
|
244,
|
|
|
|
|
|
245,
|
|
|
|
|
|
246,
|
|
|
|
|
|
249,
|
|
|
|
|
|
249,
|
|
|
|
|
|
251,
|
|
|
|
|
|
252,
|
|
|
|
|
|
252,
|
|
|
|
|
|
252,
|
|
|
|
|
|
252,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
252,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
252
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
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},
|
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|
{
|
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|
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"hoverlabel": {
|
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"namelength": 0
|
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},
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|
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|
|
"hovertemplate": "Cities=Guizhou<br>Lags=%{x}<br>Values=%{y}",
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|
|
|
|
|
"legendgroup": "Guizhou",
|
|
|
|
|
|
"line": {
|
|
|
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|
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"color": "#B6E880",
|
|
|
|
|
|
"dash": "solid"
|
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|
|
|
|
},
|
|
|
|
|
|
"mode": "lines",
|
|
|
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|
|
"name": "Guizhou",
|
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|
|
|
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"showlegend": true,
|
|
|
|
|
|
"type": "scattergl",
|
|
|
|
|
|
"x": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
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|
|
5,
|
|
|
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|
|
6,
|
|
|
|
|
|
7,
|
|
|
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|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
1,
|
|
|
|
|
|
3,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
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|
5,
|
|
|
|
|
|
7,
|
|
|
|
|
|
9,
|
|
|
|
|
|
9,
|
|
|
|
|
|
12,
|
|
|
|
|
|
29,
|
|
|
|
|
|
29,
|
|
|
|
|
|
38,
|
|
|
|
|
|
46,
|
|
|
|
|
|
58,
|
|
|
|
|
|
64,
|
|
|
|
|
|
71,
|
|
|
|
|
|
81,
|
|
|
|
|
|
89,
|
|
|
|
|
|
99,
|
|
|
|
|
|
109,
|
|
|
|
|
|
127,
|
|
|
|
|
|
133,
|
|
|
|
|
|
135,
|
|
|
|
|
|
140,
|
|
|
|
|
|
143,
|
|
|
|
|
|
144,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
|
|
|
|
|
146,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
146,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
146
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"hoverlabel": {
|
|
|
|
|
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"namelength": 0
|
|
|
|
|
|
},
|
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|
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"mode": "lines",
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"showlegend": true,
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2,
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3,
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|
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4,
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|
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5,
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|
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6,
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7,
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|
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8,
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9,
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10,
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|
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11,
|
|
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12,
|
|
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|
|
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13,
|
|
|
|
|
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14,
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|
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|
|
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15,
|
|
|
|
|
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16,
|
|
|
|
|
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17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
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"xaxis": "x",
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4,
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|
5,
|
|
|
|
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8,
|
|
|
|
|
|
19,
|
|
|
|
|
|
22,
|
|
|
|
|
|
33,
|
|
|
|
|
|
40,
|
|
|
|
|
|
43,
|
|
|
|
|
|
46,
|
|
|
|
|
|
52,
|
|
|
|
|
|
62,
|
|
|
|
|
|
64,
|
|
|
|
|
|
72,
|
|
|
|
|
|
80,
|
|
|
|
|
|
99,
|
|
|
|
|
|
106,
|
|
|
|
|
|
117,
|
|
|
|
|
|
124,
|
|
|
|
|
|
131,
|
|
|
|
|
|
138,
|
|
|
|
|
|
144,
|
|
|
|
|
|
157,
|
|
|
|
|
|
157,
|
|
|
|
|
|
159,
|
|
|
|
|
|
162,
|
|
|
|
|
|
162,
|
|
|
|
|
|
163,
|
|
|
|
|
|
163,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
|
|
|
|
|
168,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
168,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
168
|
|
|
|
|
|
],
|
|
|
|
|
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},
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{
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"hoverlabel": {
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"namelength": 0
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},
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"hovertemplate": "Cities=Hebei<br>Lags=%{x}<br>Values=%{y}",
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|
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"legendgroup": "Hebei",
|
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|
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"line": {
|
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"color": "#FECB52",
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"dash": "solid"
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|
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},
|
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|
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"mode": "lines",
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"name": "Hebei",
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"showlegend": true,
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|
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"type": "scattergl",
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0,
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1,
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2,
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|
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3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
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|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
8,
|
|
|
|
|
|
13,
|
|
|
|
|
|
18,
|
|
|
|
|
|
33,
|
|
|
|
|
|
48,
|
|
|
|
|
|
65,
|
|
|
|
|
|
82,
|
|
|
|
|
|
96,
|
|
|
|
|
|
104,
|
|
|
|
|
|
113,
|
|
|
|
|
|
126,
|
|
|
|
|
|
135,
|
|
|
|
|
|
157,
|
|
|
|
|
|
172,
|
|
|
|
|
|
195,
|
|
|
|
|
|
206,
|
|
|
|
|
|
218,
|
|
|
|
|
|
239,
|
|
|
|
|
|
251,
|
|
|
|
|
|
265,
|
|
|
|
|
|
283,
|
|
|
|
|
|
291,
|
|
|
|
|
|
300,
|
|
|
|
|
|
301,
|
|
|
|
|
|
306,
|
|
|
|
|
|
306,
|
|
|
|
|
|
307,
|
|
|
|
|
|
308,
|
|
|
|
|
|
309,
|
|
|
|
|
|
311,
|
|
|
|
|
|
311,
|
|
|
|
|
|
311,
|
|
|
|
|
|
312,
|
|
|
|
|
|
317,
|
|
|
|
|
|
318,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
318,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
318
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
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},
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{
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|
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"hoverlabel": {
|
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"namelength": 0
|
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|
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},
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|
"hovertemplate": "Cities=Heilongjiang<br>Lags=%{x}<br>Values=%{y}",
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|
|
|
|
|
"legendgroup": "Heilongjiang",
|
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|
|
|
|
"line": {
|
|
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|
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"color": "#636efa",
|
|
|
|
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"dash": "solid"
|
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|
|
|
|
},
|
|
|
|
|
|
"mode": "lines",
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"name": "Heilongjiang",
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"showlegend": true,
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|
|
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"type": "scattergl",
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|
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0,
|
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1,
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2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
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"y": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
2,
|
|
|
|
|
|
4,
|
|
|
|
|
|
9,
|
|
|
|
|
|
15,
|
|
|
|
|
|
21,
|
|
|
|
|
|
33,
|
|
|
|
|
|
38,
|
|
|
|
|
|
44,
|
|
|
|
|
|
59,
|
|
|
|
|
|
80,
|
|
|
|
|
|
95,
|
|
|
|
|
|
121,
|
|
|
|
|
|
155,
|
|
|
|
|
|
190,
|
|
|
|
|
|
227,
|
|
|
|
|
|
277,
|
|
|
|
|
|
295,
|
|
|
|
|
|
307,
|
|
|
|
|
|
331,
|
|
|
|
|
|
360,
|
|
|
|
|
|
378,
|
|
|
|
|
|
395,
|
|
|
|
|
|
419,
|
|
|
|
|
|
425,
|
|
|
|
|
|
445,
|
|
|
|
|
|
457,
|
|
|
|
|
|
464,
|
|
|
|
|
|
470,
|
|
|
|
|
|
476,
|
|
|
|
|
|
479,
|
|
|
|
|
|
479,
|
|
|
|
|
|
480,
|
|
|
|
|
|
480,
|
|
|
|
|
|
480,
|
|
|
|
|
|
480,
|
|
|
|
|
|
480,
|
|
|
|
|
|
480,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
480,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
480
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
|
},
|
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{
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|
|
|
|
"hoverlabel": {
|
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"namelength": 0
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|
|
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},
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|
|
"hovertemplate": "Cities=Henan<br>Lags=%{x}<br>Values=%{y}",
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|
|
|
|
|
"legendgroup": "Henan",
|
|
|
|
|
|
"line": {
|
|
|
|
|
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"color": "#EF553B",
|
|
|
|
|
|
"dash": "solid"
|
|
|
|
|
|
},
|
|
|
|
|
|
"mode": "lines",
|
|
|
|
|
|
"name": "Henan",
|
|
|
|
|
|
"showlegend": true,
|
|
|
|
|
|
"type": "scattergl",
|
|
|
|
|
|
"x": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
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|
|
5,
|
|
|
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|
|
6,
|
|
|
|
|
|
7,
|
|
|
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|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
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66337,
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|
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37,
|
2020-03-02 21:22:49 +01:00
|
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|
38,
|
|
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39
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2020-03-01 21:33:06 +01:00
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|
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|
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|
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|
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|
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772,
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968,
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988,
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|
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1004,
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1006,
|
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1007,
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1008,
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1010,
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1011,
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1013,
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1016,
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1016,
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1016,
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1016,
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1017,
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1017,
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2020-03-02 21:22:49 +01:00
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1018,
|
2020-03-01 21:33:06 +01:00
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1018
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|
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|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
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|
|
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|
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0,
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75,
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|
75,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
75,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
75
|
|
|
|
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"hoverlabel": {
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"hovertemplate": "Cities=Jiangsu<br>Lags=%{x}<br>Values=%{y}",
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"legendgroup": "Jiangsu",
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"line": {
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"color": "#19d3f3",
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"dash": "solid"
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},
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"mode": "lines",
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"name": "Jiangsu",
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"showlegend": true,
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"type": "scattergl",
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0,
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1,
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2,
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3,
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|
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|
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4,
|
|
|
|
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5,
|
|
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|
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6,
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|
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7,
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8,
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9,
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10,
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|
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11,
|
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12,
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|
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13,
|
|
|
|
|
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14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
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26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
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32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
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|
|
|
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1,
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|
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|
5,
|
|
|
|
|
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9,
|
|
|
|
|
|
18,
|
|
|
|
|
|
33,
|
|
|
|
|
|
47,
|
|
|
|
|
|
70,
|
|
|
|
|
|
99,
|
|
|
|
|
|
129,
|
|
|
|
|
|
168,
|
|
|
|
|
|
202,
|
|
|
|
|
|
236,
|
|
|
|
|
|
271,
|
|
|
|
|
|
308,
|
|
|
|
|
|
341,
|
|
|
|
|
|
373,
|
|
|
|
|
|
408,
|
|
|
|
|
|
439,
|
|
|
|
|
|
468,
|
|
|
|
|
|
492,
|
|
|
|
|
|
515,
|
|
|
|
|
|
543,
|
|
|
|
|
|
570,
|
|
|
|
|
|
593,
|
|
|
|
|
|
604,
|
|
|
|
|
|
617,
|
|
|
|
|
|
626,
|
|
|
|
|
|
629,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
|
|
|
|
|
631,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
631,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
631
|
|
|
|
|
|
],
|
|
|
|
|
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"yaxis": "y"
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},
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{
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"hoverlabel": {
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"namelength": 0
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},
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"hovertemplate": "Cities=Jiangxi<br>Lags=%{x}<br>Values=%{y}",
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"legendgroup": "Jiangxi",
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"line": {
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"color": "#FF6692",
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"dash": "solid"
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|
},
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|
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"mode": "lines",
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"name": "Jiangxi",
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"showlegend": true,
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|
|
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"type": "scattergl",
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|
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0,
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1,
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2,
|
|
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|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
2,
|
|
|
|
|
|
7,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
36,
|
|
|
|
|
|
72,
|
|
|
|
|
|
109,
|
|
|
|
|
|
109,
|
|
|
|
|
|
162,
|
|
|
|
|
|
240,
|
|
|
|
|
|
286,
|
|
|
|
|
|
333,
|
|
|
|
|
|
391,
|
|
|
|
|
|
476,
|
|
|
|
|
|
548,
|
|
|
|
|
|
600,
|
|
|
|
|
|
661,
|
|
|
|
|
|
698,
|
|
|
|
|
|
740,
|
|
|
|
|
|
771,
|
|
|
|
|
|
804,
|
|
|
|
|
|
844,
|
|
|
|
|
|
872,
|
|
|
|
|
|
900,
|
|
|
|
|
|
913,
|
|
|
|
|
|
925,
|
|
|
|
|
|
930,
|
|
|
|
|
|
933,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
934,
|
|
|
|
|
|
935,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
935,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
935
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
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|
|
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{
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"hoverlabel": {
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"namelength": 0
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},
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|
"hovertemplate": "Cities=Jilin<br>Lags=%{x}<br>Values=%{y}",
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|
|
|
|
|
"legendgroup": "Jilin",
|
|
|
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|
"line": {
|
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"color": "#B6E880",
|
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|
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"dash": "solid"
|
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|
|
|
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},
|
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|
|
"mode": "lines",
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"name": "Jilin",
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|
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"showlegend": true,
|
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|
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|
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"type": "scattergl",
|
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|
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0,
|
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1,
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2,
|
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3,
|
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4,
|
|
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|
|
5,
|
|
|
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|
6,
|
|
|
|
|
|
7,
|
|
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|
|
8,
|
|
|
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|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
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|
|
4,
|
|
|
|
|
|
6,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
14,
|
|
|
|
|
|
14,
|
|
|
|
|
|
17,
|
|
|
|
|
|
23,
|
|
|
|
|
|
31,
|
|
|
|
|
|
42,
|
|
|
|
|
|
54,
|
|
|
|
|
|
59,
|
|
|
|
|
|
65,
|
|
|
|
|
|
69,
|
|
|
|
|
|
78,
|
|
|
|
|
|
80,
|
|
|
|
|
|
81,
|
|
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|
|
83,
|
|
|
|
|
|
84,
|
|
|
|
|
|
86,
|
|
|
|
|
|
88,
|
|
|
|
|
|
89,
|
|
|
|
|
|
89,
|
|
|
|
|
|
89,
|
|
|
|
|
|
90,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
91,
|
|
|
|
|
|
93,
|
|
|
|
|
|
93,
|
|
|
|
|
|
93,
|
|
|
|
|
|
93,
|
|
|
|
|
|
93,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
93,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
93
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"hoverlabel": {
|
|
|
|
|
|
"namelength": 0
|
|
|
|
|
|
},
|
|
|
|
|
|
"hovertemplate": "Cities=Liaoning<br>Lags=%{x}<br>Values=%{y}",
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|
|
|
|
|
"legendgroup": "Liaoning",
|
|
|
|
|
|
"line": {
|
|
|
|
|
|
"color": "#FF97FF",
|
|
|
|
|
|
"dash": "solid"
|
|
|
|
|
|
},
|
|
|
|
|
|
"mode": "lines",
|
|
|
|
|
|
"name": "Liaoning",
|
|
|
|
|
|
"showlegend": true,
|
|
|
|
|
|
"type": "scattergl",
|
|
|
|
|
|
"x": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
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|
|
3,
|
|
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9,
|
|
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|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
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|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
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2,
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3,
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4,
|
|
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17,
|
|
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21,
|
|
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27,
|
|
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|
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34,
|
|
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|
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39,
|
|
|
|
|
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41,
|
|
|
|
|
|
48,
|
|
|
|
|
|
64,
|
|
|
|
|
|
70,
|
|
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74,
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|
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81,
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89,
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94,
|
|
|
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99,
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|
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105,
|
|
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107,
|
|
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|
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|
108,
|
|
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|
|
111,
|
|
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|
|
|
116,
|
|
|
|
|
|
117,
|
|
|
|
|
|
119,
|
|
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119,
|
|
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121,
|
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121,
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121,
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121,
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121,
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121,
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121,
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121,
|
|
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121,
|
|
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|
|
|
121,
|
|
|
|
|
|
121,
|
|
|
|
|
|
121,
|
|
|
|
|
|
121,
|
2020-03-02 21:22:49 +01:00
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|
|
|
121,
|
|
|
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|
122
|
2020-03-01 21:33:06 +01:00
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"hoverlabel": {
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"hovertemplate": "Cities=Ningxia<br>Lags=%{x}<br>Values=%{y}",
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"legendgroup": "Ningxia",
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3,
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4,
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5,
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6,
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7,
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8,
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9,
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10,
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11,
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12,
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13,
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14,
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15,
|
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16,
|
|
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17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
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"xaxis": "x",
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1,
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|
1,
|
|
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|
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2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
7,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
17,
|
|
|
|
|
|
21,
|
|
|
|
|
|
26,
|
|
|
|
|
|
28,
|
|
|
|
|
|
31,
|
|
|
|
|
|
34,
|
|
|
|
|
|
34,
|
|
|
|
|
|
40,
|
|
|
|
|
|
43,
|
|
|
|
|
|
45,
|
|
|
|
|
|
45,
|
|
|
|
|
|
49,
|
|
|
|
|
|
53,
|
|
|
|
|
|
58,
|
|
|
|
|
|
64,
|
|
|
|
|
|
67,
|
|
|
|
|
|
70,
|
|
|
|
|
|
70,
|
|
|
|
|
|
70,
|
|
|
|
|
|
70,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
71,
|
|
|
|
|
|
72,
|
|
|
|
|
|
72,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
73,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
73
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
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},
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{
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"hoverlabel": {
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"namelength": 0
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},
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|
"hovertemplate": "Cities=Qinghai<br>Lags=%{x}<br>Values=%{y}",
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|
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|
"legendgroup": "Qinghai",
|
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|
|
|
"line": {
|
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"color": "#636efa",
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"dash": "solid"
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|
|
|
},
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|
|
"mode": "lines",
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"name": "Qinghai",
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"showlegend": true,
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|
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"type": "scattergl",
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0,
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1,
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2,
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3,
|
|
|
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|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
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|
|
|
|
|
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0,
|
|
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|
|
|
0,
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
6,
|
|
|
|
|
|
6,
|
|
|
|
|
|
6,
|
|
|
|
|
|
8,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
11,
|
|
|
|
|
|
13,
|
|
|
|
|
|
15,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
|
|
|
|
|
18,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
18,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
18
|
|
|
|
|
|
],
|
|
|
|
|
|
"yaxis": "y"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"hoverlabel": {
|
|
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|
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"namelength": 0
|
|
|
|
|
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},
|
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|
|
"hovertemplate": "Cities=Shaanxi<br>Lags=%{x}<br>Values=%{y}",
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|
|
|
|
|
"legendgroup": "Shaanxi",
|
|
|
|
|
|
"line": {
|
|
|
|
|
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"color": "#EF553B",
|
|
|
|
|
|
"dash": "solid"
|
|
|
|
|
|
},
|
|
|
|
|
|
"mode": "lines",
|
|
|
|
|
|
"name": "Shaanxi",
|
|
|
|
|
|
"showlegend": true,
|
|
|
|
|
|
"type": "scattergl",
|
|
|
|
|
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|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
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|
|
4,
|
|
|
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|
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|
|
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|
|
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|
|
|
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|
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|
|
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|
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|
|
|
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|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
3,
|
|
|
|
|
|
5,
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|
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|
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|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
56,
|
|
|
|
|
|
63,
|
|
|
|
|
|
87,
|
|
|
|
|
|
101,
|
|
|
|
|
|
116,
|
|
|
|
|
|
128,
|
|
|
|
|
|
142,
|
|
|
|
|
|
165,
|
|
|
|
|
|
173,
|
|
|
|
|
|
184,
|
|
|
|
|
|
195,
|
|
|
|
|
|
208,
|
|
|
|
|
|
213,
|
|
|
|
|
|
219,
|
|
|
|
|
|
225,
|
|
|
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|
|
229,
|
|
|
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|
|
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|
|
|
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|
232,
|
|
|
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|
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|
|
|
|
|
|
240,
|
|
|
|
|
|
240,
|
|
|
|
|
|
242,
|
|
|
|
|
|
245,
|
|
|
|
|
|
245,
|
|
|
|
|
|
245,
|
|
|
|
|
|
245,
|
|
|
|
|
|
245,
|
|
|
|
|
|
245,
|
|
|
|
|
|
245,
|
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245,
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245,
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2020-03-02 21:22:49 +01:00
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38,
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487,
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497,
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509,
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523,
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532,
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537,
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541,
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543,
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544,
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546,
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749,
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754,
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756,
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756,
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756,
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2020-03-02 21:22:49 +01:00
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756,
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758
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2020-03-01 21:33:06 +01:00
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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2020-03-01 21:33:06 +01:00
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169,
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203,
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243,
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257,
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299,
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318,
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326,
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328,
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333,
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337,
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337,
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337,
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2020-03-01 21:33:06 +01:00
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337
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20,
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21,
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22,
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23,
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25,
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26,
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27,
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28,
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29,
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30,
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31,
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32,
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33,
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34,
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35,
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36,
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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2020-03-01 21:33:06 +01:00
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27,
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27,
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35,
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39,
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47,
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66,
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74,
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81,
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81,
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96,
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104,
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115,
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119,
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119,
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124,
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126,
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126,
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127,
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128,
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129,
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130,
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131,
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131,
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132,
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132,
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132,
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132,
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133,
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133,
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|
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133,
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|
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133,
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133,
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2020-03-02 21:22:49 +01:00
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|
133,
|
2020-03-01 21:33:06 +01:00
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133
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1,
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2,
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3,
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4,
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5,
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6,
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7,
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8,
|
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9,
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10,
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11,
|
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12,
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13,
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14,
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15,
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|
16,
|
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17,
|
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18,
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19,
|
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20,
|
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|
21,
|
|
|
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22,
|
|
|
|
|
|
23,
|
|
|
|
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|
24,
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25,
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26,
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27,
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28,
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29,
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30,
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31,
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32,
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33,
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34,
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35,
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|
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|
36,
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|
|
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37,
|
2020-03-02 21:22:49 +01:00
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|
|
|
38,
|
|
|
|
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39
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2020-03-01 21:33:06 +01:00
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8,
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15,
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28,
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44,
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69,
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90,
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108,
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142,
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177,
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207,
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231,
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254,
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282,
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301,
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321,
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344,
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364,
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386,
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405,
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417,
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436,
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451,
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463,
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470,
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481,
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495,
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508,
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514,
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520,
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525,
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526,
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526,
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527,
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529,
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531,
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534,
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538,
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2020-03-02 21:22:49 +01:00
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538,
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2020-03-01 21:33:06 +01:00
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538
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18,
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19,
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20,
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21,
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22,
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23,
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24,
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25,
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26,
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27,
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28,
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29,
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30,
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33,
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34,
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35,
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36,
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37,
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2020-03-02 21:22:49 +01:00
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|
38,
|
|
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39
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2020-03-01 21:33:06 +01:00
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4,
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14,
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23,
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24,
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27,
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31,
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32,
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41,
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48,
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60,
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67,
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69,
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79,
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81,
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88,
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91,
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95,
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106,
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112,
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119,
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120,
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122,
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124,
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125,
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128,
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130,
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131,
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132,
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135,
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135,
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135,
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135,
|
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135,
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|
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136,
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136,
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2020-03-02 21:22:49 +01:00
|
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|
136,
|
2020-03-01 21:33:06 +01:00
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|
136
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"hoverlabel": {
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"hovertemplate": "Cities=Tibet<br>Lags=%{x}<br>Values=%{y}",
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20,
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21,
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22,
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23,
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24,
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25,
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26,
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27,
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28,
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29,
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30,
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31,
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32,
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33,
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34,
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35,
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36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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|
|
|
1,
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|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
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1,
|
|
|
|
|
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1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
|
|
|
|
|
1,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
1,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
1
|
|
|
|
|
|
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|
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"hoverlabel": {
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"dash": "solid"
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},
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0,
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|
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1,
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2,
|
|
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|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"xaxis": "x",
|
|
|
|
|
|
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|
|
|
|
|
|
0,
|
|
|
|
|
|
2,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
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|
|
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|
|
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|
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|
|
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|
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|
|
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|
|
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|
|
|
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|
18,
|
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
|
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|
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|
|
|
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|
|
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|
|
|
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|
|
42,
|
|
|
|
|
|
45,
|
|
|
|
|
|
49,
|
|
|
|
|
|
55,
|
|
|
|
|
|
59,
|
|
|
|
|
|
63,
|
|
|
|
|
|
65,
|
|
|
|
|
|
70,
|
|
|
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|
|
71,
|
|
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|
|
75,
|
|
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|
|
76,
|
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|
76,
|
|
|
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|
|
76,
|
|
|
|
|
|
76,
|
|
|
|
|
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76,
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76,
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76,
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76,
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76,
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|
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76,
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|
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76,
|
2020-03-02 21:22:49 +01:00
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|
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76,
|
2020-03-01 21:33:06 +01:00
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76
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18,
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19,
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20,
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21,
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|
|
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22,
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|
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23,
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|
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24,
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25,
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26,
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27,
|
|
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28,
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29,
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30,
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31,
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32,
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33,
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34,
|
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|
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35,
|
|
|
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|
|
36,
|
|
|
|
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|
37,
|
2020-03-02 21:22:49 +01:00
|
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38,
|
|
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39
|
2020-03-01 21:33:06 +01:00
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11,
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16,
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26,
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44,
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55,
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70,
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83,
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93,
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105,
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117,
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122,
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128,
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133,
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138,
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138,
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141,
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149,
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153,
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154,
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156,
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162,
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168,
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171,
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171,
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172,
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172,
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174,
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174,
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174,
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174,
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174,
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174,
|
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174,
|
|
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174,
|
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|
|
174,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
174,
|
2020-03-01 21:33:06 +01:00
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|
174
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6,
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18,
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22,
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23,
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24,
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25,
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26,
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27,
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28,
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29,
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30,
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33,
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34,
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35,
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|
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36,
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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2020-03-01 21:33:06 +01:00
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954,
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1117,
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1131,
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1155,
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1162,
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1171,
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1172,
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1205,
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1205,
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1205,
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2020-03-02 21:22:49 +01:00
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1205,
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2020-03-01 21:33:06 +01:00
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"title": {
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"text": "Lags"
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}
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},
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"title": {
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"text": "Values"
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}
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}
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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" {\"responsive\": true}\n",
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" ).then(function(){\n",
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" \n",
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2020-03-02 21:22:49 +01:00
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"var gd = document.getElementById('ad9bb659-7254-4e05-b1cd-3ecb4337e720');\n",
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2020-03-01 21:33:06 +01:00
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"\n",
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"}}\n",
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"\n",
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" })\n",
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" });\n",
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" </script>\n",
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" </div>"
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]
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import plotly.express as px\n",
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"fig = px.line(df_china, x=\"Lags\", y = \"Values\", color=\"Cities\")\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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2020-03-02 21:22:49 +01:00
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"execution_count": 18,
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2020-03-01 21:33:06 +01:00
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"metadata": {},
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"outputs": [
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{
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2020-03-02 21:22:49 +01:00
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"data": {
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<ggplot: (-9223371897234348628)>"
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]
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},
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"execution_count": 18,
|
|
|
|
|
|
"metadata": {},
|
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|
"output_type": "execute_result"
|
2020-03-01 21:33:06 +01:00
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}
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],
|
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|
|
"source": [
|
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|
|
|
"import pandas as pd\n",
|
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|
"import numpy as np\n",
|
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|
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"from pandas.api.types import CategoricalDtype\n",
|
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|
|
|
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"from plotnine import *\n",
|
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|
|
"from plotnine.data import mpg\n",
|
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|
|
|
|
"(ggplot(df_china)\n",
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" + aes(x='Lags', y='Values', color='Cities')\n",
|
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|
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" + geom_line()\n",
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" + labs(title='China Covid-19 Virus cases', x='Days', y='Number of case')\n",
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")"
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]
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},
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{
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"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
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|
"execution_count": 19,
|
2020-03-01 21:33:06 +01:00
|
|
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|
"metadata": {},
|
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|
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|
|
"outputs": [
|
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|
|
|
|
{
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"data": {
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"application/vnd.plotly.v1+json": {
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"config": {
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"plotlyServerURL": "https://plot.ly"
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},
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"data": [
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{
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"hoverlabel": {
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"namelength": 0
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},
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"hovertemplate": "Cities=Anhui<br>Lags=%{x}<br>Values=%{y}",
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"legendgroup": "Anhui",
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"marker": {
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"color": "#636efa",
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"symbol": "circle"
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},
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"mode": "markers",
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"name": "Anhui",
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"showlegend": true,
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"type": "scattergl",
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0,
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3,
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5,
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6,
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7,
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8,
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9,
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10,
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11,
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12,
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13,
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14,
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15,
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16,
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17,
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18,
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19,
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20,
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21,
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22,
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23,
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|
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24,
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|
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25,
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26,
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27,
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28,
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29,
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|
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30,
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31,
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32,
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33,
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34,
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35,
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36,
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37,
|
2020-03-02 21:22:49 +01:00
|
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38,
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39
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2020-03-01 21:33:06 +01:00
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],
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"xaxis": "x",
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"y": [
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9,
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15,
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39,
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60,
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70,
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106,
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152,
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200,
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237,
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297,
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340,
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408,
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480,
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530,
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591,
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665,
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733,
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779,
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830,
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860,
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889,
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910,
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934,
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950,
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962,
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973,
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982,
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986,
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987,
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988,
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989,
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989,
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989,
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989,
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989,
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989,
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990,
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2020-03-02 21:22:49 +01:00
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990,
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2020-03-01 21:33:06 +01:00
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990
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],
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"yaxis": "y"
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},
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{
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"hoverlabel": {
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"namelength": 0
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},
|
2020-03-02 21:22:49 +01:00
|
|
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|
"hovertemplate": "<b>OLS trendline</b><br>Values = 30.4596 * Lags + 52.7134<br>R<sup>2</sup>=0.879861<br><br>Cities=Anhui<br>Lags=%{x}<br>Values=%{y} <b>(trend)</b>",
|
2020-03-01 21:33:06 +01:00
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"legendgroup": "Anhui",
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"marker": {
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"color": "#636efa",
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"symbol": "circle"
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},
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"mode": "lines",
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"name": "Anhui",
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"showlegend": false,
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"type": "scattergl",
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"x": [
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0,
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6,
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12,
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13,
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14,
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15,
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16,
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17,
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18,
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20,
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21,
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22,
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23,
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25,
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26,
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29,
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30,
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31,
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32,
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33,
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34,
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35,
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36,
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37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
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|
|
|
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|
39
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2020-03-01 21:33:06 +01:00
|
|
|
|
],
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"xaxis": "x",
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"y": [
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2020-03-02 21:22:49 +01:00
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52.7134146341468,
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83.17298311444696,
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113.63255159474713,
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144.09212007504726,
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174.55168855534743,
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205.0112570356476,
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235.47082551594775,
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265.9303939962479,
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18,
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19,
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20,
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21,
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22,
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23,
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24,
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25,
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26,
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27,
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28,
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29,
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30,
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31,
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32,
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33,
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34,
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35,
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36,
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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2020-03-01 21:33:06 +01:00
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90,
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91,
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91,
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91,
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91,
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91,
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91,
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91,
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2020-03-02 21:22:49 +01:00
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91,
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2020-03-01 21:33:06 +01:00
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91
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13,
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14,
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15,
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16,
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17,
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18,
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19,
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20,
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21,
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22,
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23,
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24,
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25,
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26,
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27,
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28,
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29,
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30,
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31,
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32,
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33,
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34,
|
|
|
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|
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35,
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|
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|
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36,
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|
|
|
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|
37,
|
2020-03-02 21:22:49 +01:00
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|
|
|
38,
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|
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39
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2020-03-01 21:33:06 +01:00
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34,
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35,
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36,
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37,
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2020-03-02 21:22:49 +01:00
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38,
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39
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277,
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436,
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535,
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632,
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1347,
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1349,
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2020-03-01 21:33:06 +01:00
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1349
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16,
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17,
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18,
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19,
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20,
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2020-03-02 21:22:49 +01:00
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21,
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34,
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35,
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36,
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37,
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2020-03-02 21:22:49 +01:00
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38,
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2020-03-01 21:33:06 +01:00
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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2020-03-02 21:22:49 +01:00
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18,
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2020-03-01 21:33:06 +01:00
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18
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37,
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2020-03-02 21:22:49 +01:00
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38,
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34,
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35,
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36,
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2020-03-02 21:22:49 +01:00
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38,
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2020-03-01 21:33:06 +01:00
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2020-03-02 21:22:49 +01:00
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538,
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2020-03-01 21:33:06 +01:00
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538
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37,
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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136
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],
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"title": {
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"text": "Lags"
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}
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},
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"text": "Values"
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" {\"responsive\": true}\n",
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" \n",
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"var gd = document.getElementById('296680a4-18db-4aa6-ba33-13658ca394fc');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"if (notebookContainer) {{\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"var outputEl = gd.closest('.output');\n",
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"if (outputEl) {{\n",
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" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" })\n",
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" };\n",
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" });\n",
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" </script>\n",
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" </div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import plotly.express as px\n",
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"fig = px.scatter(df_china, x=\"Lags\", y = \"Values\", color=\"Cities\", trendline=\"ols\")\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Hubei Linear regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.plotly.v1+json": {
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"config": {
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"plotlyServerURL": "https://plot.ly"
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},
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"data": [
|
2020-03-01 21:33:06 +01:00
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{
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"hoverlabel": {
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"namelength": 0
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},
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2020-03-02 21:22:49 +01:00
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"hovertemplate": "Lags=%{x}<br>Values=%{y}",
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"legendgroup": "",
|
2020-03-01 21:33:06 +01:00
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"marker": {
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"color": "#636efa",
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"symbol": "circle"
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},
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"mode": "markers",
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2020-03-02 21:22:49 +01:00
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"name": "",
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"showlegend": false,
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"type": "scatter",
|
2020-03-01 21:33:06 +01:00
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"x": [
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0,
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1,
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" \n",
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2020-03-02 21:22:49 +01:00
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" <div id=\"fda1ecb9-17f3-46a9-9148-3e5de56ddcce\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
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2020-03-01 21:33:06 +01:00
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" <script type=\"text/javascript\">\n",
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" require([\"plotly\"], function(Plotly) {\n",
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" window.PLOTLYENV=window.PLOTLYENV || {};\n",
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" \n",
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2020-03-02 21:22:49 +01:00
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" if (document.getElementById(\"fda1ecb9-17f3-46a9-9148-3e5de56ddcce\")) {\n",
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2020-03-01 21:33:06 +01:00
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" Plotly.newPlot(\n",
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2020-03-02 21:22:49 +01:00
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2020-03-01 21:33:06 +01:00
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" {\"responsive\": true}\n",
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" ).then(function(){\n",
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" \n",
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2020-03-02 21:22:49 +01:00
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"var gd = document.getElementById('fda1ecb9-17f3-46a9-9148-3e5de56ddcce');\n",
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2020-03-01 21:33:06 +01:00
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"if (notebookContainer) {{\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"var outputEl = gd.closest('.output');\n",
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"if (outputEl) {{\n",
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" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" })\n",
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" };\n",
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" });\n",
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" </script>\n",
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" </div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|
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|
|
|
"df_hubei = df_china[df_china[\"Cities\"] == \"Hubei\"]\n",
|
|
|
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|
|
"df_hubei = df_hubei.drop([\"Cities\"],axis=1) \n",
|
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"\n",
|
|
|
|
|
|
"fig = px.scatter(df_hubei, x=\"Lags\", y = \"Values\", trendline=\"ols\")\n",
|
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|
|
"fig.show()"
|
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]
|
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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|
"<Figure size 432x288 with 1 Axes>"
|
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|
]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.9431456103160005\n"
|
|
|
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|
]
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}
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],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"X = df_hubei.iloc[:, 1].values.reshape(-1, 1) # values converts it into a numpy array\n",
|
|
|
|
|
|
"Y = df_hubei.iloc[:, 0].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column\n",
|
|
|
|
|
|
"linear_regressor = LinearRegression() # create object for the class\n",
|
|
|
|
|
|
"linear_regressor.fit(X, Y) # perform linear regression\n",
|
|
|
|
|
|
"acc = linear_regressor.score(X, Y)\n",
|
|
|
|
|
|
"Y_pred = linear_regressor.predict(X) # make predictions\n",
|
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|
|
"plt.scatter(X, Y)\n",
|
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|
|
"plt.plot(X, Y_pred, color='red')\n",
|
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|
|
"plt.show()\n",
|
|
|
|
|
|
"print(\"Accuracy:\" ,acc)"
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|
]
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},
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{
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"cell_type": "code",
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"execution_count": 111,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"array([[-7375.64146341],\n",
|
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" [-5194.86754221],\n",
|
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|
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|
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" [-3014.09362101],\n",
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|
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" [ -833.31969981],\n",
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" [ 1347.45422139],\n",
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" [ 3528.22814259],\n",
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" [ 5709.00206379],\n",
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" [ 7889.77598499],\n",
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" [10070.54990619],\n",
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" [12251.32382739],\n",
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" [14432.09774859],\n",
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" [16612.87166979],\n",
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" [18793.64559099],\n",
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" [20974.4195122 ],\n",
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" [23155.1934334 ],\n",
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" [25335.9673546 ],\n",
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" [27516.7412758 ],\n",
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" [29697.515197 ],\n",
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" [31878.2891182 ],\n",
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" [34059.0630394 ],\n",
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" [36239.8369606 ],\n",
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" [38420.6108818 ],\n",
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" [40601.384803 ],\n",
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" [42782.1587242 ],\n",
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" [44962.9326454 ],\n",
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" [47143.7065666 ],\n",
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" [49324.4804878 ],\n",
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" [51505.25440901],\n",
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" [53686.02833021],\n",
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" [55866.80225141],\n",
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" [58047.57617261],\n",
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" [60228.35009381],\n",
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" [62409.12401501],\n",
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" [64589.89793621],\n",
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" [66770.67185741],\n",
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" [68951.44577861],\n",
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" [71132.21969981],\n",
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" [73312.99362101],\n",
|
|
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" [75493.76754221],\n",
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" [77674.54146341]])"
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]
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},
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"execution_count": 111,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
|
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|
"predictions = linear_regressor.predict(X)"
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]
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},
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{
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|
"cell_type": "code",
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|
|
"execution_count": 23,
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|
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|
|
"metadata": {},
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"outputs": [
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{
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|
"name": "stdout",
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|
|
|
"output_type": "stream",
|
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|
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|
|
"text": [
|
|
|
|
|
|
"Prediction: [-7375.64146341] - Day: [0] - Actual: [444]\n",
|
|
|
|
|
|
"Prediction: [-5194.86754221] - Day: [1] - Actual: [444]\n",
|
|
|
|
|
|
"Prediction: [-3014.09362101] - Day: [2] - Actual: [549]\n",
|
|
|
|
|
|
"Prediction: [-833.31969981] - Day: [3] - Actual: [761]\n",
|
|
|
|
|
|
"Prediction: [1347.45422139] - Day: [4] - Actual: [1058]\n",
|
|
|
|
|
|
"Prediction: [3528.22814259] - Day: [5] - Actual: [1423]\n",
|
|
|
|
|
|
"Prediction: [5709.00206379] - Day: [6] - Actual: [3554]\n",
|
|
|
|
|
|
"Prediction: [7889.77598499] - Day: [7] - Actual: [3554]\n",
|
|
|
|
|
|
"Prediction: [10070.54990619] - Day: [8] - Actual: [4903]\n",
|
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|
|
|
|
"Prediction: [12251.32382739] - Day: [9] - Actual: [5806]\n",
|
|
|
|
|
|
"Prediction: [14432.09774859] - Day: [10] - Actual: [7153]\n",
|
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|
|
|
|
"Prediction: [16612.87166979] - Day: [11] - Actual: [11177]\n",
|
|
|
|
|
|
"Prediction: [18793.64559099] - Day: [12] - Actual: [13522]\n",
|
|
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|
|
|
"Prediction: [20974.4195122] - Day: [13] - Actual: [16678]\n",
|
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|
|
"Prediction: [23155.1934334] - Day: [14] - Actual: [19665]\n",
|
|
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|
|
|
"Prediction: [25335.9673546] - Day: [15] - Actual: [22112]\n",
|
|
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|
|
|
"Prediction: [27516.7412758] - Day: [16] - Actual: [24953]\n",
|
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|
|
|
|
"Prediction: [29697.515197] - Day: [17] - Actual: [27100]\n",
|
|
|
|
|
|
"Prediction: [31878.2891182] - Day: [18] - Actual: [29631]\n",
|
|
|
|
|
|
"Prediction: [34059.0630394] - Day: [19] - Actual: [31728]\n",
|
|
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|
|
|
"Prediction: [36239.8369606] - Day: [20] - Actual: [33366]\n",
|
|
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|
|
"Prediction: [38420.6108818] - Day: [21] - Actual: [33366]\n",
|
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|
|
|
|
"Prediction: [40601.384803] - Day: [22] - Actual: [48206]\n",
|
|
|
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|
|
"Prediction: [42782.1587242] - Day: [23] - Actual: [54406]\n",
|
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|
|
"Prediction: [44962.9326454] - Day: [24] - Actual: [56249]\n",
|
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|
|
"Prediction: [47143.7065666] - Day: [25] - Actual: [58182]\n",
|
|
|
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|
|
"Prediction: [49324.4804878] - Day: [26] - Actual: [59989]\n",
|
|
|
|
|
|
"Prediction: [51505.25440901] - Day: [27] - Actual: [61682]\n",
|
|
|
|
|
|
"Prediction: [53686.02833021] - Day: [28] - Actual: [62031]\n",
|
|
|
|
|
|
"Prediction: [55866.80225141] - Day: [29] - Actual: [62442]\n",
|
|
|
|
|
|
"Prediction: [58047.57617261] - Day: [30] - Actual: [62662]\n",
|
|
|
|
|
|
"Prediction: [60228.35009381] - Day: [31] - Actual: [64084]\n",
|
|
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|
|
"Prediction: [62409.12401501] - Day: [32] - Actual: [64084]\n",
|
|
|
|
|
|
"Prediction: [64589.89793621] - Day: [33] - Actual: [64287]\n",
|
|
|
|
|
|
"Prediction: [66770.67185741] - Day: [34] - Actual: [64786]\n",
|
|
|
|
|
|
"Prediction: [68951.44577861] - Day: [35] - Actual: [65187]\n",
|
|
|
|
|
|
"Prediction: [71132.21969981] - Day: [36] - Actual: [65596]\n",
|
|
|
|
|
|
"Prediction: [73312.99362101] - Day: [37] - Actual: [65914]\n",
|
|
|
|
|
|
"Prediction: [75493.76754221] - Day: [38] - Actual: [66337]\n",
|
|
|
|
|
|
"Prediction: [77674.54146341] - Day: [39] - Actual: [66907]\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"for x in range(len(predictions)):\n",
|
|
|
|
|
|
" print(\"Prediction: \",predictions[x],\"- Day:\", X[x],\"- Actual: \", Y[x])"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"### Prediction for 01/03/2020, 02/03/2020, 03/03/2020"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 24,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"Giorno 39 : 77674.54146341463\n",
|
|
|
|
|
|
"Giorno 40 : 79855.31538461539\n",
|
|
|
|
|
|
"Giorno 41 : 82036.08930581613\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"prediction = linear_regressor.predict([[39],[40],[41]])\n",
|
|
|
|
|
|
"l = 39\n",
|
|
|
|
|
|
"for x in prediction:\n",
|
|
|
|
|
|
" print(\"Giorno \",l,\": \",x[0])\n",
|
|
|
|
|
|
" l+=1"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Logistic regression"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 25,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stderr",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning:\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning:\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning:\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
|
|
|
|
|
|
"\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.125\n"
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]
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}
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],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"\n",
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"X = df_hubei.iloc[:, 1].values.reshape(-1, 1) # values converts it into a numpy array\n",
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"Y = df_hubei.iloc[:, 0].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column\n",
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"logistic = LogisticRegression(random_state=0) # create object for the class\n",
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"logistic.fit(X, Y) # perform linear regression\n",
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"acc = logistic.score(X, Y)\n",
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"Y_pred = logistic.predict(X) # make predictions\n",
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"\n",
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"\n",
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"plt.scatter(X, Y)\n",
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"plt.plot(X, Y_pred, color='red')\n",
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"plt.show()\n",
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"print(\"Accuracy:\" ,acc)\n",
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"\n",
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"#plt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([64084], dtype=int64)"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Y_pred = logistic.predict([[41]])\n",
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"Y_pred "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.25\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning:\n",
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"\n",
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"Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
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"\n",
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"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning:\n",
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"\n",
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"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
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"\n",
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"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning:\n",
|
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"\n",
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"Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
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"\n"
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]
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}
|
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],
|
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"source": [
|
|
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|
|
|
"prob = logistic.predict_proba(X)\n",
|
|
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|
|
|
"from sklearn import linear_model\n",
|
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|
"from scipy.special import expit\n",
|
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"clf = linear_model.LogisticRegression(C=1e5)\n",
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"clf.fit(X, Y)\n",
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"acc = clf.score(X, Y)\n",
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"\n",
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"print(\"Accuracy:\" ,acc)"
|
2020-03-01 21:33:06 +01:00
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
2020-03-02 21:22:49 +01:00
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"\n",
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"\n",
|
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"# Random Forest regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 217,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = df_hubei.iloc[:, 1].values.reshape(-1, 1) # values converts it into a numpy array\n",
|
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|
|
|
"Y = df_hubei.iloc[:, 0].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 237,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning:\n",
|
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"\n",
|
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"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
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"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
|
|
|
|
|
|
" max_features='auto', max_leaf_nodes=None,\n",
|
|
|
|
|
|
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
|
|
|
|
|
|
" min_samples_leaf=1, min_samples_split=2,\n",
|
|
|
|
|
|
" min_weight_fraction_leaf=0.0, n_estimators=10,\n",
|
|
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|
|
|
" n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
|
|
|
|
|
|
" warm_start=False)"
|
|
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]
|
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},
|
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"execution_count": 237,
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
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|
],
|
|
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|
"source": [
|
|
|
|
|
|
"from sklearn.ensemble import RandomForestRegressor\n",
|
|
|
|
|
|
"regressor = RandomForestRegressor(n_estimators=10,random_state=42)\n",
|
|
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"regressor.fit(X,Y)"
|
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]
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 238,
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"metadata": {
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"scrolled": true
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},
|
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
|
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"Prediction: 475.7 - Day: [0] - Actual: [444]\n",
|
|
|
|
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|
"Prediction: 475.7 - Day: [1] - Actual: [444]\n",
|
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"Prediction: 549.2 - Day: [2] - Actual: [549]\n",
|
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|
"Prediction: 769.5 - Day: [3] - Actual: [761]\n",
|
|
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|
"Prediction: 939.2 - Day: [4] - Actual: [1058]\n",
|
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|
"Prediction: 1247.3 - Day: [5] - Actual: [1423]\n",
|
|
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|
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|
"Prediction: 3091.3 - Day: [6] - Actual: [3554]\n",
|
|
|
|
|
|
"Prediction: 3304.4 - Day: [7] - Actual: [3554]\n",
|
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|
|
"Prediction: 4948.5 - Day: [8] - Actual: [4903]\n",
|
|
|
|
|
|
"Prediction: 5894.8 - Day: [9] - Actual: [5806]\n",
|
|
|
|
|
|
"Prediction: 6883.6 - Day: [10] - Actual: [7153]\n",
|
|
|
|
|
|
"Prediction: 9969.8 - Day: [11] - Actual: [11177]\n",
|
|
|
|
|
|
"Prediction: 13287.5 - Day: [12] - Actual: [13522]\n",
|
|
|
|
|
|
"Prediction: 14865.5 - Day: [13] - Actual: [16678]\n",
|
|
|
|
|
|
"Prediction: 19841.1 - Day: [14] - Actual: [19665]\n",
|
|
|
|
|
|
"Prediction: 21946.1 - Day: [15] - Actual: [22112]\n",
|
|
|
|
|
|
"Prediction: 24315.4 - Day: [16] - Actual: [24953]\n",
|
|
|
|
|
|
"Prediction: 26709.0 - Day: [17] - Actual: [27100]\n",
|
|
|
|
|
|
"Prediction: 28910.1 - Day: [18] - Actual: [29631]\n",
|
|
|
|
|
|
"Prediction: 31262.7 - Day: [19] - Actual: [31728]\n",
|
|
|
|
|
|
"Prediction: 32664.9 - Day: [20] - Actual: [33366]\n",
|
|
|
|
|
|
"Prediction: 35306.2 - Day: [21] - Actual: [33366]\n",
|
|
|
|
|
|
"Prediction: 47282.3 - Day: [22] - Actual: [48206]\n",
|
|
|
|
|
|
"Prediction: 53970.3 - Day: [23] - Actual: [54406]\n",
|
|
|
|
|
|
"Prediction: 55880.4 - Day: [24] - Actual: [56249]\n",
|
|
|
|
|
|
"Prediction: 57782.8 - Day: [25] - Actual: [58182]\n",
|
|
|
|
|
|
"Prediction: 59977.6 - Day: [26] - Actual: [59989]\n",
|
|
|
|
|
|
"Prediction: 61004.8 - Day: [27] - Actual: [61682]\n",
|
|
|
|
|
|
"Prediction: 61897.6 - Day: [28] - Actual: [62031]\n",
|
|
|
|
|
|
"Prediction: 62270.9 - Day: [29] - Actual: [62442]\n",
|
|
|
|
|
|
"Prediction: 62902.4 - Day: [30] - Actual: [62662]\n",
|
|
|
|
|
|
"Prediction: 63799.6 - Day: [31] - Actual: [64084]\n",
|
|
|
|
|
|
"Prediction: 64084.0 - Day: [32] - Actual: [64084]\n",
|
|
|
|
|
|
"Prediction: 64205.8 - Day: [33] - Actual: [64287]\n",
|
|
|
|
|
|
"Prediction: 64656.1 - Day: [34] - Actual: [64786]\n",
|
|
|
|
|
|
"Prediction: 65228.7 - Day: [35] - Actual: [65187]\n",
|
|
|
|
|
|
"Prediction: 65351.4 - Day: [36] - Actual: [65596]\n",
|
|
|
|
|
|
"Prediction: 65917.9 - Day: [37] - Actual: [65914]\n",
|
|
|
|
|
|
"Prediction: 66366.4 - Day: [38] - Actual: [66337]\n",
|
|
|
|
|
|
"Prediction: 66636.7 - Day: [39] - Actual: [66907]\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"Accuracy of : 0.9995179670300028\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"predictions = regressor.predict(X)\n",
|
|
|
|
|
|
"for x in range(len(predictions)):\n",
|
|
|
|
|
|
" print(\"Prediction: \",predictions[x],\"- Day:\", X[x],\"- Actual: \", Y[x])\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
"acc = regressor.score(X,Y)\n",
|
|
|
|
|
|
"print(\"\\nAccuracy of : \",acc)"
|
2020-03-01 21:33:06 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 239,
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"application/vnd.plotly.v1+json": {
|
|
|
|
|
|
"config": {
|
|
|
|
|
|
"plotlyServerURL": "https://plot.ly"
|
|
|
|
|
|
},
|
|
|
|
|
|
"data": [
|
|
|
|
|
|
{
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"name": "Actual Cases",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"type": "scatter",
|
|
|
|
|
|
"x": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"y": [
|
|
|
|
|
|
444,
|
|
|
|
|
|
444,
|
|
|
|
|
|
549,
|
|
|
|
|
|
761,
|
|
|
|
|
|
1058,
|
|
|
|
|
|
1423,
|
|
|
|
|
|
3554,
|
|
|
|
|
|
3554,
|
|
|
|
|
|
4903,
|
|
|
|
|
|
5806,
|
|
|
|
|
|
7153,
|
|
|
|
|
|
11177,
|
|
|
|
|
|
13522,
|
|
|
|
|
|
16678,
|
|
|
|
|
|
19665,
|
|
|
|
|
|
22112,
|
|
|
|
|
|
24953,
|
|
|
|
|
|
27100,
|
|
|
|
|
|
29631,
|
|
|
|
|
|
31728,
|
|
|
|
|
|
33366,
|
|
|
|
|
|
33366,
|
|
|
|
|
|
48206,
|
|
|
|
|
|
54406,
|
|
|
|
|
|
56249,
|
|
|
|
|
|
58182,
|
|
|
|
|
|
59989,
|
|
|
|
|
|
61682,
|
|
|
|
|
|
62031,
|
|
|
|
|
|
62442,
|
|
|
|
|
|
62662,
|
|
|
|
|
|
64084,
|
|
|
|
|
|
64084,
|
|
|
|
|
|
64287,
|
|
|
|
|
|
64786,
|
|
|
|
|
|
65187,
|
|
|
|
|
|
65596,
|
|
|
|
|
|
65914,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
66337,
|
|
|
|
|
|
66907
|
|
|
|
|
|
]
|
2020-03-01 21:33:06 +01:00
|
|
|
|
},
|
|
|
|
|
|
{
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"name": "Predicted",
|
2020-03-01 21:33:06 +01:00
|
|
|
|
"type": "scatter",
|
|
|
|
|
|
"x": [
|
|
|
|
|
|
0,
|
|
|
|
|
|
1,
|
|
|
|
|
|
2,
|
|
|
|
|
|
3,
|
|
|
|
|
|
4,
|
|
|
|
|
|
5,
|
|
|
|
|
|
6,
|
|
|
|
|
|
7,
|
|
|
|
|
|
8,
|
|
|
|
|
|
9,
|
|
|
|
|
|
10,
|
|
|
|
|
|
11,
|
|
|
|
|
|
12,
|
|
|
|
|
|
13,
|
|
|
|
|
|
14,
|
|
|
|
|
|
15,
|
|
|
|
|
|
16,
|
|
|
|
|
|
17,
|
|
|
|
|
|
18,
|
|
|
|
|
|
19,
|
|
|
|
|
|
20,
|
|
|
|
|
|
21,
|
|
|
|
|
|
22,
|
|
|
|
|
|
23,
|
|
|
|
|
|
24,
|
|
|
|
|
|
25,
|
|
|
|
|
|
26,
|
|
|
|
|
|
27,
|
|
|
|
|
|
28,
|
|
|
|
|
|
29,
|
|
|
|
|
|
30,
|
|
|
|
|
|
31,
|
|
|
|
|
|
32,
|
|
|
|
|
|
33,
|
|
|
|
|
|
34,
|
|
|
|
|
|
35,
|
|
|
|
|
|
36,
|
|
|
|
|
|
37,
|
2020-03-02 21:22:49 +01:00
|
|
|
|
38,
|
|
|
|
|
|
39
|
2020-03-01 21:33:06 +01:00
|
|
|
|
],
|
|
|
|
|
|
"y": [
|
2020-03-02 21:22:49 +01:00
|
|
|
|
475.7,
|
|
|
|
|
|
475.7,
|
|
|
|
|
|
549.2,
|
|
|
|
|
|
769.5,
|
|
|
|
|
|
939.2,
|
|
|
|
|
|
1247.3,
|
|
|
|
|
|
3091.3,
|
|
|
|
|
|
3304.4,
|
|
|
|
|
|
4948.5,
|
|
|
|
|
|
5894.8,
|
|
|
|
|
|
6883.6,
|
|
|
|
|
|
9969.8,
|
|
|
|
|
|
13287.5,
|
|
|
|
|
|
14865.5,
|
|
|
|
|
|
19841.1,
|
|
|
|
|
|
21946.1,
|
|
|
|
|
|
24315.4,
|
|
|
|
|
|
26709,
|
|
|
|
|
|
28910.1,
|
|
|
|
|
|
31262.7,
|
|
|
|
|
|
32664.9,
|
|
|
|
|
|
35306.2,
|
|
|
|
|
|
47282.3,
|
|
|
|
|
|
53970.3,
|
|
|
|
|
|
55880.4,
|
|
|
|
|
|
57782.8,
|
|
|
|
|
|
59977.6,
|
|
|
|
|
|
61004.8,
|
|
|
|
|
|
61897.6,
|
|
|
|
|
|
62270.9,
|
|
|
|
|
|
62902.4,
|
|
|
|
|
|
63799.6,
|
|
|
|
|
|
64084,
|
|
|
|
|
|
64205.8,
|
|
|
|
|
|
64656.1,
|
|
|
|
|
|
65228.7,
|
|
|
|
|
|
65351.4,
|
|
|
|
|
|
65917.9,
|
|
|
|
|
|
66366.4,
|
|
|
|
|
|
66636.7
|
|
|
|
|
|
]
|
2020-03-01 21:33:06 +01:00
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"layout": {
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"font": {
|
|
|
|
|
|
"color": "#7f7f7f",
|
|
|
|
|
|
"family": "Courier New, monospace",
|
|
|
|
|
|
"size": 18
|
2020-03-01 21:33:06 +01:00
|
|
|
|
},
|
|
|
|
|
|
"template": {
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"bar": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"error_x": {
|
|
|
|
|
|
"color": "#2a3f5f"
|
|
|
|
|
|
},
|
|
|
|
|
|
"error_y": {
|
|
|
|
|
|
"color": "#2a3f5f"
|
|
|
|
|
|
},
|
|
|
|
|
|
"marker": {
|
|
|
|
|
|
"line": {
|
|
|
|
|
|
"color": "#E5ECF6",
|
|
|
|
|
|
"width": 0.5
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"type": "bar"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"barpolar": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"marker": {
|
|
|
|
|
|
"line": {
|
|
|
|
|
|
"color": "#E5ECF6",
|
|
|
|
|
|
"width": 0.5
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"type": "barpolar"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"carpet": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"aaxis": {
|
|
|
|
|
|
"endlinecolor": "#2a3f5f",
|
|
|
|
|
|
"gridcolor": "white",
|
|
|
|
|
|
"linecolor": "white",
|
|
|
|
|
|
"minorgridcolor": "white",
|
|
|
|
|
|
"startlinecolor": "#2a3f5f"
|
|
|
|
|
|
},
|
|
|
|
|
|
"baxis": {
|
|
|
|
|
|
"endlinecolor": "#2a3f5f",
|
|
|
|
|
|
"gridcolor": "white",
|
|
|
|
|
|
"linecolor": "white",
|
|
|
|
|
|
"minorgridcolor": "white",
|
|
|
|
|
|
"startlinecolor": "#2a3f5f"
|
|
|
|
|
|
},
|
|
|
|
|
|
"type": "carpet"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"choropleth": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"colorbar": {
|
|
|
|
|
|
"outlinewidth": 0,
|
|
|
|
|
|
"ticks": ""
|
|
|
|
|
|
},
|
|
|
|
|
|
"type": "choropleth"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"contour": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"colorbar": {
|
|
|
|
|
|
"outlinewidth": 0,
|
|
|
|
|
|
"ticks": ""
|
|
|
|
|
|
},
|
|
|
|
|
|
"colorscale": [
|
|
|
|
|
|
[
|
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"bgcolor": "#E5ECF6",
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"caxis": {
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"x": 0.05
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"automargin": true,
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"title": {
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"standoff": 15
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"zerolinecolor": "white",
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"zerolinewidth": 2
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"yaxis": {
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"automargin": true,
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
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"standoff": 15
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},
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"zerolinecolor": "white",
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"zerolinewidth": 2
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}
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}
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},
|
2020-03-02 21:22:49 +01:00
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"title": {
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"text": "Forecast of cases using RF"
|
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},
|
2020-03-01 21:33:06 +01:00
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"xaxis": {
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"title": {
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2020-03-02 21:22:49 +01:00
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"text": "Day"
|
2020-03-01 21:33:06 +01:00
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}
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},
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"yaxis": {
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"title": {
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"text": "Values"
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}
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}
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}
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},
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"text/html": [
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"<div>\n",
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" \n",
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" \n",
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2020-03-02 21:22:49 +01:00
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" <div id=\"cb5c3113-39a6-469f-80e3-14aff8c4f144\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
|
2020-03-01 21:33:06 +01:00
|
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" <script type=\"text/javascript\">\n",
|
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|
" require([\"plotly\"], function(Plotly) {\n",
|
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" window.PLOTLYENV=window.PLOTLYENV || {};\n",
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" \n",
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2020-03-02 21:22:49 +01:00
|
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" if (document.getElementById(\"cb5c3113-39a6-469f-80e3-14aff8c4f144\")) {\n",
|
2020-03-01 21:33:06 +01:00
|
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" Plotly.newPlot(\n",
|
2020-03-02 21:22:49 +01:00
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" 'cb5c3113-39a6-469f-80e3-14aff8c4f144',\n",
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|
" [{\"name\": \"Actual Cases\", \"type\": \"scatter\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"y\": [444, 444, 549, 761, 1058, 1423, 3554, 3554, 4903, 5806, 7153, 11177, 13522, 16678, 19665, 22112, 24953, 27100, 29631, 31728, 33366, 33366, 48206, 54406, 56249, 58182, 59989, 61682, 62031, 62442, 62662, 64084, 64084, 64287, 64786, 65187, 65596, 65914, 66337, 66907]}, {\"name\": \"Predicted\", \"type\": \"scatter\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"y\": [475.7, 475.7, 549.2, 769.5, 939.2, 1247.3, 3091.3, 3304.4, 4948.5, 5894.8, 6883.6, 9969.8, 13287.5, 14865.5, 19841.1, 21946.1, 24315.4, 26709.0, 28910.1, 31262.7, 32664.9, 35306.2, 47282.3, 53970.3, 55880.4, 57782.8, 59977.6, 61004.8, 61897.6, 62270.9, 62902.4, 63799.6, 64084.0, 64205.8, 64656.1, 65228.7, 65351.4, 65917.9, 66366.4, 66636.7]}],\n",
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" {\"font\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [
|
2020-03-01 21:33:06 +01:00
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" {\"responsive\": true}\n",
|
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" ).then(function(){\n",
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" \n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"var gd = document.getElementById('cb5c3113-39a6-469f-80e3-14aff8c4f144');\n",
|
2020-03-01 21:33:06 +01:00
|
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|
"var x = new MutationObserver(function (mutations, observer) {{\n",
|
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|
" var display = window.getComputedStyle(gd).display;\n",
|
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|
" if (!display || display === 'none') {{\n",
|
|
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|
|
|
" console.log([gd, 'removed!']);\n",
|
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" Plotly.purge(gd);\n",
|
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|
" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
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|
"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
|
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|
"if (notebookContainer) {{\n",
|
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
|
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|
|
"// Listen for the clearing of the current output cell\n",
|
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"var outputEl = gd.closest('.output');\n",
|
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|
"if (outputEl) {{\n",
|
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|
|
" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" })\n",
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" };\n",
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" });\n",
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" </script>\n",
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" </div>"
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]
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},
|
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"metadata": {},
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"output_type": "display_data"
|
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},
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{
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
2020-03-01 22:50:58 +01:00
|
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"\n",
|
2020-03-02 21:22:49 +01:00
|
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|
"Accuracy of : 0.9995179670300028\n"
|
2020-03-01 22:50:58 +01:00
|
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|
]
|
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}
|
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],
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"source": [
|
2020-03-02 21:22:49 +01:00
|
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|
|
"#plt.scatter(X, Y)\n",
|
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|
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"#plt.plot(X, Y, color=\"red\")\n",
|
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"newY=[]\n",
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"newX=[]\n",
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2020-03-01 22:50:58 +01:00
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"\n",
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"\n",
|
2020-03-02 21:22:49 +01:00
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" \n",
|
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"for i in range(len(Y)):\n",
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" newY+=[Y[i][0]]\n",
|
2020-03-01 22:50:58 +01:00
|
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"\n",
|
2020-03-02 21:22:49 +01:00
|
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"for i in range(len(X)):\n",
|
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" newX+=[X[i][0]]\n",
|
|
|
|
|
|
"import plotly.graph_objects as go\n",
|
2020-03-01 22:50:58 +01:00
|
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|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"fig = go.Figure()\n",
|
2020-03-01 22:50:58 +01:00
|
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|
"\n",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"fig.add_trace(go.Scatter(\n",
|
|
|
|
|
|
" x=newX,\n",
|
|
|
|
|
|
" y=newY,\n",
|
|
|
|
|
|
" name=\"Actual Cases\" # this sets its legend entry\n",
|
|
|
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|
|
"))\n",
|
|
|
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|
|
"\n",
|
|
|
|
|
|
"fig.add_trace(go.Scatter(\n",
|
|
|
|
|
|
" x=newX,\n",
|
|
|
|
|
|
" y=predictions,\n",
|
|
|
|
|
|
" name=\"Predicted\" # this sets its legend entry\n",
|
|
|
|
|
|
"))\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"fig.update_layout(\n",
|
|
|
|
|
|
" title=\"Forecast of cases using RF\",\n",
|
|
|
|
|
|
" xaxis_title=\"Day\",\n",
|
|
|
|
|
|
" yaxis_title=\"Values\",\n",
|
|
|
|
|
|
" font=dict(\n",
|
|
|
|
|
|
" family=\"Courier New, monospace\",\n",
|
|
|
|
|
|
" size=18,\n",
|
|
|
|
|
|
" color=\"#7f7f7f\"\n",
|
|
|
|
|
|
" )\n",
|
|
|
|
|
|
")\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"fig.show()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"acc = regressor.score(X,Y)\n",
|
|
|
|
|
|
"print(\"\\nAccuracy of : \",acc)\n"
|
2020-03-01 22:50:58 +01:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 240,
|
2020-03-01 22:50:58 +01:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"array([ 475.7, 475.7, 549.2, 769.5, 939.2, 1247.3, 3091.3,\n",
|
|
|
|
|
|
" 3304.4, 4948.5, 5894.8, 6883.6, 9969.8, 13287.5, 14865.5,\n",
|
|
|
|
|
|
" 19841.1, 21946.1, 24315.4, 26709. , 28910.1, 31262.7, 32664.9,\n",
|
|
|
|
|
|
" 35306.2, 47282.3, 53970.3, 55880.4, 57782.8, 59977.6, 61004.8,\n",
|
|
|
|
|
|
" 61897.6, 62270.9, 62902.4, 63799.6, 64084. , 64205.8, 64656.1,\n",
|
|
|
|
|
|
" 65228.7, 65351.4, 65917.9, 66366.4, 66636.7])"
|
2020-03-01 22:50:58 +01:00
|
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|
]
|
|
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|
},
|
2020-03-02 21:22:49 +01:00
|
|
|
|
"execution_count": 240,
|
2020-03-01 22:50:58 +01:00
|
|
|
|
"metadata": {},
|
|
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|
|
|
"output_type": "execute_result"
|
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}
|
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|
],
|
|
|
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|
"source": [
|
2020-03-02 21:22:49 +01:00
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"predictions"
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2020-03-01 22:50:58 +01:00
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]
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},
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{
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"cell_type": "code",
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2020-03-04 16:54:16 +01:00
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"execution_count": 1,
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2020-03-01 22:50:58 +01:00
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"metadata": {},
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2020-03-04 16:54:16 +01:00
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"outputs": [],
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2020-03-01 22:50:58 +01:00
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"source": [
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2020-03-02 21:22:49 +01:00
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"# Import tools needed for visualization\n",
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2020-03-01 22:50:58 +01:00
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"\n",
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2020-03-04 16:54:16 +01:00
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"#feature_list = list(df_hubei.columns[0])\n",
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"#from sklearn.tree import export_graphviz\n",
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"#import pydot\n",
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"#rf_small = RandomForestRegressor(n_estimators=10, max_depth = 3)\n",
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"#rf_small.fit(train_features, train_labels)# Extract the small tree\n",
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"#tree_small = rf_small.estimators_[5]# Save the tree as a png image\n",
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"#export_graphviz(tree_small, out_file = 'small_tree.dot', feature_names = [\"Values\"], rounded = True, precision = 1)\n",
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2020-03-02 21:22:49 +01:00
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"\n",
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2020-03-04 16:54:16 +01:00
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"#(graph, ) = pydot.graph_from_dot_file('small_tree.dot')\n",
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"#graph.write_png('small_tree.png');"
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2020-03-01 22:50:58 +01:00
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]
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},
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2020-03-01 21:33:06 +01:00
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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2020-03-02 21:22:49 +01:00
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"version": "3.7.4"
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2020-03-01 21:33:06 +01:00
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}
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},
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"nbformat": 4,
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2020-03-04 16:54:16 +01:00
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"nbformat_minor": 4
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2020-03-01 21:33:06 +01:00
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}
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