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Master-DataScience-Notes/1year/2trimester/Coding for Data Science - Python language/Python/Examples/.ipynb_checkpoints/COVID-19 Analysis-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# COVID-19 Analysis\n",
"<br>"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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",
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" <th>2/27/20</th>\n",
" <th>2/28/20</th>\n",
" <th>2/29/20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
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" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
" <td>119.000000</td>\n",
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" <tr>\n",
" <th>mean</th>\n",
" <td>32.195406</td>\n",
" <td>40.126887</td>\n",
" <td>4.663866</td>\n",
" <td>5.487395</td>\n",
" <td>7.907563</td>\n",
" <td>12.050420</td>\n",
" <td>17.798319</td>\n",
" <td>24.596639</td>\n",
" <td>46.873950</td>\n",
" <td>51.815126</td>\n",
" <td>...</td>\n",
" <td>640.327731</td>\n",
" <td>645.739496</td>\n",
" <td>660.495798</td>\n",
" <td>663.739496</td>\n",
" <td>668.655462</td>\n",
" <td>675.756303</td>\n",
" <td>684.008403</td>\n",
" <td>695.428571</td>\n",
" <td>706.907563</td>\n",
" <td>722.798319</td>\n",
" </tr>\n",
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" <th>std</th>\n",
" <td>20.305522</td>\n",
" <td>85.839690</td>\n",
" <td>40.731714</td>\n",
" <td>40.823279</td>\n",
" <td>50.733934</td>\n",
" <td>70.513714</td>\n",
" <td>98.446628</td>\n",
" <td>132.578235</td>\n",
" <td>326.602349</td>\n",
" <td>328.203946</td>\n",
" <td>...</td>\n",
" <td>5719.809648</td>\n",
" <td>5739.840077</td>\n",
" <td>5869.872272</td>\n",
" <td>5869.724978</td>\n",
" <td>5888.220111</td>\n",
" <td>5933.815757</td>\n",
" <td>5970.524570</td>\n",
" <td>6008.333866</td>\n",
" <td>6038.385982</td>\n",
" <td>6079.237047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-40.900600</td>\n",
" <td>-123.869500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>26.447150</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <tr>\n",
" <th>50%</th>\n",
" <td>35.443700</td>\n",
" <td>53.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>7.000000</td>\n",
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" <th>75%</th>\n",
" <td>43.659650</td>\n",
" <td>113.487200</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
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" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" <td>8.000000</td>\n",
" <td>9.000000</td>\n",
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" <td>80.000000</td>\n",
" <td>80.500000</td>\n",
" <td>80.500000</td>\n",
" <td>90.000000</td>\n",
" <td>90.000000</td>\n",
" <td>92.000000</td>\n",
" <td>93.000000</td>\n",
" <td>93.000000</td>\n",
" <td>93.500000</td>\n",
" <td>101.000000</td>\n",
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" <td>64.963100</td>\n",
" <td>174.886000</td>\n",
" <td>444.000000</td>\n",
" <td>444.000000</td>\n",
" <td>549.000000</td>\n",
" <td>761.000000</td>\n",
" <td>1058.000000</td>\n",
" <td>1423.000000</td>\n",
" <td>3554.000000</td>\n",
" <td>3554.000000</td>\n",
" <td>...</td>\n",
" <td>62442.000000</td>\n",
" <td>62662.000000</td>\n",
" <td>64084.000000</td>\n",
" <td>64084.000000</td>\n",
" <td>64287.000000</td>\n",
" <td>64786.000000</td>\n",
" <td>65187.000000</td>\n",
" <td>65596.000000</td>\n",
" <td>65914.000000</td>\n",
" <td>66337.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 \\\n",
"count 119.000000 119.000000 119.000000 119.000000 119.000000 119.000000 \n",
"mean 32.195406 40.126887 4.663866 5.487395 7.907563 12.050420 \n",
"std 20.305522 85.839690 40.731714 40.823279 50.733934 70.513714 \n",
"min -40.900600 -123.869500 0.000000 0.000000 0.000000 0.000000 \n",
"25% 26.447150 3.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 35.443700 53.000000 0.000000 0.000000 0.000000 0.000000 \n",
"75% 43.659650 113.487200 0.000000 1.000000 2.000000 3.000000 \n",
"max 64.963100 174.886000 444.000000 444.000000 549.000000 761.000000 \n",
"\n",
" 1/26/20 1/27/20 1/28/20 1/29/20 ... 2/20/20 \\\n",
"count 119.000000 119.000000 119.000000 119.000000 ... 119.000000 \n",
"mean 17.798319 24.596639 46.873950 51.815126 ... 640.327731 \n",
"std 98.446628 132.578235 326.602349 328.203946 ... 5719.809648 \n",
"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",
"75% 4.000000 6.000000 8.000000 9.000000 ... 80.000000 \n",
"max 1058.000000 1423.000000 3554.000000 3554.000000 ... 62442.000000 \n",
"\n",
" 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 \\\n",
"count 119.000000 119.000000 119.000000 119.000000 119.000000 \n",
"mean 645.739496 660.495798 663.739496 668.655462 675.756303 \n",
"std 5739.840077 5869.872272 5869.724978 5888.220111 5933.815757 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 1.000000 \n",
"50% 2.000000 2.000000 2.000000 2.000000 2.000000 \n",
"75% 80.500000 80.500000 90.000000 90.000000 92.000000 \n",
"max 62662.000000 64084.000000 64084.000000 64287.000000 64786.000000 \n",
"\n",
" 2/26/20 2/27/20 2/28/20 2/29/20 \n",
"count 119.000000 119.000000 119.000000 119.000000 \n",
"mean 684.008403 695.428571 706.907563 722.798319 \n",
"std 5970.524570 6008.333866 6038.385982 6079.237047 \n",
"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 1.000000 1.000000 1.000000 1.000000 \n",
"50% 3.000000 4.000000 4.000000 7.000000 \n",
"75% 93.000000 93.000000 93.500000 101.000000 \n",
"max 65187.000000 65596.000000 65914.000000 66337.000000 \n",
"\n",
"[8 rows x 41 columns]"
]
},
"execution_count": 98,
"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",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Province/State</th>\n",
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" <th>Lat</th>\n",
" <th>Long</th>\n",
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" <th>2/29/20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Anhui</td>\n",
" <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>988</td>\n",
" <td>989</td>\n",
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" <td>989</td>\n",
" <td>989</td>\n",
" <td>989</td>\n",
" <td>989</td>\n",
" <td>990</td>\n",
" <td>990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <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>395</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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <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>567</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",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <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>293</td>\n",
" <td>294</td>\n",
" <td>294</td>\n",
" <td>296</td>\n",
" <td>296</td>\n",
" <td>296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <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",
" <th>5</th>\n",
" <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>1332</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",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <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>245</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",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <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",
" <th>8</th>\n",
" <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",
" <th>9</th>\n",
" <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>307</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",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <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>476</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",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <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>1265</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",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <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>62442</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",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <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>1010</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",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <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",
" <th>15</th>\n",
" <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",
" <th>16</th>\n",
" <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",
" <td>934</td>\n",
" <td>935</td>\n",
" <td>935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <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",
" <td>91</td>\n",
" <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",
" <th>18</th>\n",
" <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",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <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>71</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <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",
" <th>21</th>\n",
" <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",
" <th>22</th>\n",
" <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>546</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",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <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>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",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <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",
" <td>132</td>\n",
" <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",
" <th>25</th>\n",
" <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>520</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",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <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>131</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",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <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",
" <th>28</th>\n",
" <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",
" <th>29</th>\n",
" <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",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>NaN</td>\n",
" <td>Algeria</td>\n",
" <td>28.03390</td>\n",
" <td>1.6596</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>NaN</td>\n",
" <td>Croatia</td>\n",
" <td>45.10000</td>\n",
" <td>15.2000</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>NaN</td>\n",
" <td>Switzerland</td>\n",
" <td>46.81820</td>\n",
" <td>8.2275</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>NaN</td>\n",
" <td>Austria</td>\n",
" <td>47.51620</td>\n",
" <td>14.5501</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>NaN</td>\n",
" <td>Israel</td>\n",
" <td>31.00000</td>\n",
" <td>35.0000</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>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>NaN</td>\n",
" <td>Pakistan</td>\n",
" <td>30.37530</td>\n",
" <td>69.3451</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>NaN</td>\n",
" <td>Brazil</td>\n",
" <td>-14.23500</td>\n",
" <td>-51.9253</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>NaN</td>\n",
" <td>Georgia</td>\n",
" <td>42.31540</td>\n",
" <td>43.3569</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>NaN</td>\n",
" <td>Greece</td>\n",
" <td>39.07420</td>\n",
" <td>21.8243</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>NaN</td>\n",
" <td>North Macedonia</td>\n",
" <td>41.60860</td>\n",
" <td>21.7453</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>NaN</td>\n",
" <td>Norway</td>\n",
" <td>60.47200</td>\n",
" <td>8.4689</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>NaN</td>\n",
" <td>Romania</td>\n",
" <td>45.94320</td>\n",
" <td>24.9668</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>NaN</td>\n",
" <td>Denmark</td>\n",
" <td>56.26390</td>\n",
" <td>9.5018</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>NaN</td>\n",
" <td>Estonia</td>\n",
" <td>58.59530</td>\n",
" <td>25.0136</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>NaN</td>\n",
" <td>Netherlands</td>\n",
" <td>52.13260</td>\n",
" <td>5.2913</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>NaN</td>\n",
" <td>San Marino</td>\n",
" <td>43.94240</td>\n",
" <td>12.4578</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>NaN</td>\n",
" <td>Belarus</td>\n",
" <td>53.70980</td>\n",
" <td>27.9534</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>Montreal, QC</td>\n",
" <td>Canada</td>\n",
" <td>45.50170</td>\n",
" <td>-73.5673</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>NaN</td>\n",
" <td>Iceland</td>\n",
" <td>64.96310</td>\n",
" <td>-19.0208</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>NaN</td>\n",
" <td>Lithuania</td>\n",
" <td>55.16940</td>\n",
" <td>23.8813</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>NaN</td>\n",
" <td>Mexico</td>\n",
" <td>23.63450</td>\n",
" <td>-102.5528</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>NaN</td>\n",
" <td>New Zealand</td>\n",
" <td>-40.90060</td>\n",
" <td>174.8860</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>NaN</td>\n",
" <td>Nigeria</td>\n",
" <td>9.08200</td>\n",
" <td>8.6753</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>Western Australia</td>\n",
" <td>Australia</td>\n",
" <td>-31.95050</td>\n",
" <td>115.8605</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>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>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>NaN</td>\n",
" <td>Ireland</td>\n",
" <td>53.14240</td>\n",
" <td>-7.6921</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>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",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>NaN</td>\n",
" <td>Luxembourg</td>\n",
" <td>49.81530</td>\n",
" <td>6.1296</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>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",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>NaN</td>\n",
" <td>Monaco</td>\n",
" <td>43.73330</td>\n",
" <td>7.4167</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>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",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>NaN</td>\n",
" <td>Qatar</td>\n",
" <td>25.35480</td>\n",
" <td>51.1839</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>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",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>Portland, OR</td>\n",
" <td>US</td>\n",
" <td>45.50510</td>\n",
" <td>-122.6750</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>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",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>Snohomish County, WA</td>\n",
" <td>US</td>\n",
" <td>48.03300</td>\n",
" <td>-121.8339</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>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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>119 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Province/State Country/Region Lat Long 1/22/20 \\\n",
"0 Anhui Mainland China 31.82570 117.2264 1 \n",
"1 Beijing Mainland China 40.18240 116.4142 14 \n",
"2 Chongqing Mainland China 30.05720 107.8740 6 \n",
"3 Fujian Mainland China 26.07890 117.9874 1 \n",
"4 Gansu Mainland China 36.06110 103.8343 0 \n",
"5 Guangdong Mainland China 23.34170 113.4244 26 \n",
"6 Guangxi Mainland China 23.82980 108.7881 2 \n",
"7 Guizhou Mainland China 26.81540 106.8748 1 \n",
"8 Hainan Mainland China 19.19590 109.7453 4 \n",
"9 Hebei Mainland China 38.04280 114.5149 1 \n",
"10 Heilongjiang Mainland China 47.86200 127.7615 0 \n",
"11 Henan Mainland China 33.88202 113.6140 5 \n",
"12 Hubei Mainland China 30.97560 112.2707 444 \n",
"13 Hunan Mainland China 27.61040 111.7088 4 \n",
"14 Inner Mongolia Mainland China 44.09350 113.9448 0 \n",
"15 Jiangsu Mainland China 32.97110 119.4550 1 \n",
"16 Jiangxi Mainland China 27.61400 115.7221 2 \n",
"17 Jilin Mainland China 43.66610 126.1923 0 \n",
"18 Liaoning Mainland China 41.29560 122.6085 2 \n",
"19 Ningxia Mainland China 37.26920 106.1655 1 \n",
"20 Qinghai Mainland China 35.74520 95.9956 0 \n",
"21 Shaanxi Mainland China 35.19170 108.8701 0 \n",
"22 Shandong Mainland China 36.34270 118.1498 2 \n",
"23 Shanghai Mainland China 31.20200 121.4491 9 \n",
"24 Shanxi Mainland China 37.57770 112.2922 1 \n",
"25 Sichuan Mainland China 30.61710 102.7103 5 \n",
"26 Tianjin Mainland China 39.30540 117.3230 4 \n",
"27 Tibet Mainland China 31.69270 88.0924 0 \n",
"28 Xinjiang Mainland China 41.11290 85.2401 0 \n",
"29 Yunnan Mainland China 24.97400 101.4870 1 \n",
".. ... ... ... ... ... \n",
"89 NaN Algeria 28.03390 1.6596 0 \n",
"90 NaN Croatia 45.10000 15.2000 0 \n",
"91 NaN Switzerland 46.81820 8.2275 0 \n",
"92 NaN Austria 47.51620 14.5501 0 \n",
"93 NaN Israel 31.00000 35.0000 0 \n",
"94 NaN Pakistan 30.37530 69.3451 0 \n",
"95 NaN Brazil -14.23500 -51.9253 0 \n",
"96 NaN Georgia 42.31540 43.3569 0 \n",
"97 NaN Greece 39.07420 21.8243 0 \n",
"98 NaN North Macedonia 41.60860 21.7453 0 \n",
"99 NaN Norway 60.47200 8.4689 0 \n",
"100 NaN Romania 45.94320 24.9668 0 \n",
"101 NaN Denmark 56.26390 9.5018 0 \n",
"102 NaN Estonia 58.59530 25.0136 0 \n",
"103 NaN Netherlands 52.13260 5.2913 0 \n",
"104 NaN San Marino 43.94240 12.4578 0 \n",
"105 NaN Belarus 53.70980 27.9534 0 \n",
"106 Montreal, QC Canada 45.50170 -73.5673 0 \n",
"107 NaN Iceland 64.96310 -19.0208 0 \n",
"108 NaN Lithuania 55.16940 23.8813 0 \n",
"109 NaN Mexico 23.63450 -102.5528 0 \n",
"110 NaN New Zealand -40.90060 174.8860 0 \n",
"111 NaN Nigeria 9.08200 8.6753 0 \n",
"112 Western Australia Australia -31.95050 115.8605 0 \n",
"113 NaN Ireland 53.14240 -7.6921 0 \n",
"114 NaN Luxembourg 49.81530 6.1296 0 \n",
"115 NaN Monaco 43.73330 7.4167 0 \n",
"116 NaN Qatar 25.35480 51.1839 0 \n",
"117 Portland, OR US 45.50510 -122.6750 0 \n",
"118 Snohomish County, WA US 48.03300 -121.8339 0 \n",
"\n",
" 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/20/20 2/21/20 \\\n",
"0 9 15 39 60 70 ... 987 988 \n",
"1 22 36 41 68 80 ... 395 396 \n",
"2 9 27 57 75 110 ... 567 572 \n",
"3 5 10 18 35 59 ... 293 293 \n",
"4 2 2 4 7 14 ... 91 91 \n",
"5 32 53 78 111 151 ... 1332 1333 \n",
"6 5 23 23 36 46 ... 245 246 \n",
"7 3 3 4 5 7 ... 146 146 \n",
"8 5 8 19 22 33 ... 168 168 \n",
"9 1 2 8 13 18 ... 307 308 \n",
"10 2 4 9 15 21 ... 476 479 \n",
"11 5 9 32 83 128 ... 1265 1267 \n",
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[0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[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\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"dates\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"values\"}}},\n",
" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('2f1f1fc3-f192-4c36-8e22-da24b66eeea0');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" });\n",
" </script>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"fig = px.line(df,x = \"dates\", y=\"values\")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LINEAR REGRESSION with Sklearn"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.6462621687070127\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt # To visualize\n",
"import pandas as pd # To read data\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"\n",
"#rescaling the values to get a better accuracy\n",
"df[\"el\"]\n",
"\n",
"j = 1\n",
"for i in range(len(df[\"el\"])):\n",
" if(i >= 20):\n",
" df[\"el\"][i]=j\n",
" j+=1\n",
" \n",
"X = df.iloc[20:, 2].values.reshape(-1, 1) # values converts it into a numpy array\n",
"Y = df.iloc[20:, 1].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",
"plt.scatter(X, Y)\n",
"plt.plot(X, Y_pred, color='red')\n",
"plt.show()\n",
"print(\"Accuracy:\" ,acc)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EVALUATION IN CHINA"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>2/20/20</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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Anhui</td>\n",
" <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",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>987</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",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <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>395</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",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <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>567</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",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <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>293</td>\n",
" <td>294</td>\n",
" <td>294</td>\n",
" <td>296</td>\n",
" <td>296</td>\n",
" <td>296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <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",
" <th>5</th>\n",
" <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>1332</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",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <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>245</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",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <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",
" <th>8</th>\n",
" <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",
" <th>9</th>\n",
" <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>307</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",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <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>476</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",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <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>1265</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",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <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>62442</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",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <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>1010</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",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <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",
" <th>15</th>\n",
" <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",
" <th>16</th>\n",
" <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",
" <td>934</td>\n",
" <td>935</td>\n",
" <td>935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <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",
" <td>91</td>\n",
" <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",
" <th>18</th>\n",
" <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",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <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>71</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <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",
" <th>21</th>\n",
" <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",
" <th>22</th>\n",
" <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>546</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",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <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>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",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <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",
" <td>132</td>\n",
" <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",
" <th>25</th>\n",
" <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>520</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",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <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>131</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",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <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",
" <th>28</th>\n",
" <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",
" <th>29</th>\n",
" <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",
" <th>30</th>\n",
" <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>1175</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>31 rows × 43 columns</p>\n",
"</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",
" 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/20/20 2/21/20 2/22/20 \\\n",
"0 15 39 60 70 ... 987 988 989 \n",
"1 36 41 68 80 ... 395 396 399 \n",
"2 27 57 75 110 ... 567 572 573 \n",
"3 10 18 35 59 ... 293 293 293 \n",
"4 2 4 7 14 ... 91 91 91 \n",
"5 53 78 111 151 ... 1332 1333 1339 \n",
"6 23 23 36 46 ... 245 246 249 \n",
"7 3 4 5 7 ... 146 146 146 \n",
"8 8 19 22 33 ... 168 168 168 \n",
"9 2 8 13 18 ... 307 308 309 \n",
"10 4 9 15 21 ... 476 479 479 \n",
"11 9 32 83 128 ... 1265 1267 1270 \n",
"12 549 761 1058 1423 ... 62442 62662 64084 \n",
"13 24 43 69 100 ... 1010 1011 1013 \n",
"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",
"22 15 27 46 75 ... 546 749 750 \n",
"23 20 33 40 53 ... 334 334 335 \n",
"24 1 6 9 13 ... 132 132 132 \n",
"25 15 28 44 69 ... 520 525 526 \n",
"26 8 10 14 23 ... 131 132 135 \n",
"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",
"30 43 62 104 128 ... 1175 1203 1205 \n",
"\n",
" 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 \n",
"0 989 989 989 989 989 990 990 \n",
"1 399 399 400 400 410 410 411 \n",
"2 575 576 576 576 576 576 576 \n",
"3 293 293 294 294 296 296 296 \n",
"4 91 91 91 91 91 91 91 \n",
"5 1342 1345 1347 1347 1347 1348 1349 \n",
"6 249 251 252 252 252 252 252 \n",
"7 146 146 146 146 146 146 146 \n",
"8 168 168 168 168 168 168 168 \n",
"9 311 311 311 312 317 318 318 \n",
"10 480 480 480 480 480 480 480 \n",
"11 1271 1271 1271 1271 1272 1272 1272 \n",
"12 64084 64287 64786 65187 65596 65914 66337 \n",
"13 1016 1016 1016 1016 1017 1017 1018 \n",
"14 75 75 75 75 75 75 75 \n",
"15 631 631 631 631 631 631 631 \n",
"16 934 934 934 934 934 935 935 \n",
"17 91 93 93 93 93 93 93 \n",
"18 121 121 121 121 121 121 121 \n",
"19 71 71 71 71 72 72 73 \n",
"20 18 18 18 18 18 18 18 \n",
"21 245 245 245 245 245 245 245 \n",
"22 754 755 756 756 756 756 756 \n",
"23 335 335 336 337 337 337 337 \n",
"24 132 133 133 133 133 133 133 \n",
"25 526 527 529 531 534 538 538 \n",
"26 135 135 135 135 136 136 136 \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",
"\n",
"[31 rows x 43 columns]"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_china = data[data[\"Country/Region\"] == \"Mainland China\"]\n",
"data_china"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1205\n",
"1 [Zhejiang, Mainland China, 29.1832, 120.0934, ...\n",
"Name: values, dtype: object"
]
},
"execution_count": 107,
"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",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"40.1824"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_china.values[1][2]"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"china_cities = []\n",
"count = 1\n",
"ind = []\n",
"for i in range(len(data_china.values)):\n",
" count = 0\n",
" for j in range(len(data_china.values[i][4:])):\n",
" china_cities+= [data_china.values[i][0]]\n",
" ind += [count]\n",
" count += 1"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"china_values=[]\n",
"for i in range(len(data_china.values)):\n",
" for j in range(4,len(data_china.values[i])): \n",
" china_values+= [data_china.values[i][j]]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"df_china = pd.DataFrame(list(zip(china_values, china_cities,ind)), \n",
" columns =['Values', 'Cities','Lags']) "
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Values</th>\n",
" <th>Cities</th>\n",
" <th>Lags</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Anhui</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9</td>\n",
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" <th>2</th>\n",
" <td>15</td>\n",
" <td>Anhui</td>\n",
" <td>2</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>39</td>\n",
" <td>Anhui</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>60</td>\n",
" <td>Anhui</td>\n",
" <td>4</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>70</td>\n",
" <td>Anhui</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>106</td>\n",
" <td>Anhui</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>152</td>\n",
" <td>Anhui</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>200</td>\n",
" <td>Anhui</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>237</td>\n",
" <td>Anhui</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>297</td>\n",
" <td>Anhui</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>340</td>\n",
" <td>Anhui</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>408</td>\n",
" <td>Anhui</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>480</td>\n",
" <td>Anhui</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>530</td>\n",
" <td>Anhui</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>591</td>\n",
" <td>Anhui</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>665</td>\n",
" <td>Anhui</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>733</td>\n",
" <td>Anhui</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>779</td>\n",
" <td>Anhui</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>830</td>\n",
" <td>Anhui</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>860</td>\n",
" <td>Anhui</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>889</td>\n",
" <td>Anhui</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>910</td>\n",
" <td>Anhui</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>934</td>\n",
" <td>Anhui</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>950</td>\n",
" <td>Anhui</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>962</td>\n",
" <td>Anhui</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>973</td>\n",
" <td>Anhui</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>982</td>\n",
" <td>Anhui</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>986</td>\n",
" <td>Anhui</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>987</td>\n",
" <td>Anhui</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1179</th>\n",
" <td>538</td>\n",
" <td>Zhejiang</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1180</th>\n",
" <td>599</td>\n",
" <td>Zhejiang</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1181</th>\n",
" <td>661</td>\n",
" <td>Zhejiang</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1182</th>\n",
" <td>724</td>\n",
" <td>Zhejiang</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1183</th>\n",
" <td>829</td>\n",
" <td>Zhejiang</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1184</th>\n",
" <td>895</td>\n",
" <td>Zhejiang</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1185</th>\n",
" <td>954</td>\n",
" <td>Zhejiang</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1186</th>\n",
" <td>1006</td>\n",
" <td>Zhejiang</td>\n",
" <td>16</td>\n",
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" <tr>\n",
" <th>1187</th>\n",
" <td>1048</td>\n",
" <td>Zhejiang</td>\n",
" <td>17</td>\n",
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" <tr>\n",
" <th>1188</th>\n",
" <td>1075</td>\n",
" <td>Zhejiang</td>\n",
" <td>18</td>\n",
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" <tr>\n",
" <th>1189</th>\n",
" <td>1092</td>\n",
" <td>Zhejiang</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1190</th>\n",
" <td>1117</td>\n",
" <td>Zhejiang</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1191</th>\n",
" <td>1131</td>\n",
" <td>Zhejiang</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1192</th>\n",
" <td>1145</td>\n",
" <td>Zhejiang</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1193</th>\n",
" <td>1155</td>\n",
" <td>Zhejiang</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1194</th>\n",
" <td>1162</td>\n",
" <td>Zhejiang</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1195</th>\n",
" <td>1167</td>\n",
" <td>Zhejiang</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1196</th>\n",
" <td>1171</td>\n",
" <td>Zhejiang</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1197</th>\n",
" <td>1172</td>\n",
" <td>Zhejiang</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1198</th>\n",
" <td>1174</td>\n",
" <td>Zhejiang</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1199</th>\n",
" <td>1175</td>\n",
" <td>Zhejiang</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1200</th>\n",
" <td>1203</td>\n",
" <td>Zhejiang</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1201</th>\n",
" <td>1205</td>\n",
" <td>Zhejiang</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1202</th>\n",
" <td>1205</td>\n",
" <td>Zhejiang</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1203</th>\n",
" <td>1205</td>\n",
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" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1204</th>\n",
" <td>1205</td>\n",
" <td>Zhejiang</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1205</th>\n",
" <td>1205</td>\n",
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" <td>35</td>\n",
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" <tr>\n",
" <th>1206</th>\n",
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" <td>Zhejiang</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1207</th>\n",
" <td>1205</td>\n",
" <td>Zhejiang</td>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1208</th>\n",
" <td>1205</td>\n",
" <td>Zhejiang</td>\n",
" <td>38</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1209 rows × 3 columns</p>\n",
"</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",
"5 70 Anhui 5\n",
"6 106 Anhui 6\n",
"7 152 Anhui 7\n",
"8 200 Anhui 8\n",
"9 237 Anhui 9\n",
"10 297 Anhui 10\n",
"11 340 Anhui 11\n",
"12 408 Anhui 12\n",
"13 480 Anhui 13\n",
"14 530 Anhui 14\n",
"15 591 Anhui 15\n",
"16 665 Anhui 16\n",
"17 733 Anhui 17\n",
"18 779 Anhui 18\n",
"19 830 Anhui 19\n",
"20 860 Anhui 20\n",
"21 889 Anhui 21\n",
"22 910 Anhui 22\n",
"23 934 Anhui 23\n",
"24 950 Anhui 24\n",
"25 962 Anhui 25\n",
"26 973 Anhui 26\n",
"27 982 Anhui 27\n",
"28 986 Anhui 28\n",
"29 987 Anhui 29\n",
"... ... ... ...\n",
"1179 538 Zhejiang 9\n",
"1180 599 Zhejiang 10\n",
"1181 661 Zhejiang 11\n",
"1182 724 Zhejiang 12\n",
"1183 829 Zhejiang 13\n",
"1184 895 Zhejiang 14\n",
"1185 954 Zhejiang 15\n",
"1186 1006 Zhejiang 16\n",
"1187 1048 Zhejiang 17\n",
"1188 1075 Zhejiang 18\n",
"1189 1092 Zhejiang 19\n",
"1190 1117 Zhejiang 20\n",
"1191 1131 Zhejiang 21\n",
"1192 1145 Zhejiang 22\n",
"1193 1155 Zhejiang 23\n",
"1194 1162 Zhejiang 24\n",
"1195 1167 Zhejiang 25\n",
"1196 1171 Zhejiang 26\n",
"1197 1172 Zhejiang 27\n",
"1198 1174 Zhejiang 28\n",
"1199 1175 Zhejiang 29\n",
"1200 1203 Zhejiang 30\n",
"1201 1205 Zhejiang 31\n",
"1202 1205 Zhejiang 32\n",
"1203 1205 Zhejiang 33\n",
"1204 1205 Zhejiang 34\n",
"1205 1205 Zhejiang 35\n",
"1206 1205 Zhejiang 36\n",
"1207 1205 Zhejiang 37\n",
"1208 1205 Zhejiang 38\n",
"\n",
"[1209 rows x 3 columns]"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_china"
]
},
{
"cell_type": "code",
"execution_count": 113,
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},
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Lags\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Values\"}}},\n",
" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('bc24bc7a-f85d-48e8-8f3f-a29a40239aad');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
" var display = window.getComputedStyle(gd).display;\n",
" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
"// Listen for the clearing of the current output cell\n",
"var outputEl = gd.closest('.output');\n",
"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" });\n",
" </script>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import plotly.express as px\n",
"fig = px.line(df_china, x=\"Lags\", y = \"Values\", color=\"Cities\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'plotnine'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-114-3bbf302614c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtypes\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCategoricalDtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mplotnine\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mplotnine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmpg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m (ggplot(df_china)\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotnine'"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from pandas.api.types import CategoricalDtype\n",
"from plotnine import *\n",
"from plotnine.data import mpg\n",
"(ggplot(df_china)\n",
" + aes(x='Lags', y='Values', color='Cities')\n",
" + geom_line()\n",
" + labs(title='China Covid-19 Virus cases', x='Days', y='Number of case')\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 115,
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],
"source": [
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},
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"metadata": {},
"source": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"df_hubei = df_china[df_china[\"Cities\"] == \"Hubei\"]\n",
"df_hubei = df_hubei.drop([\"Cities\"],axis=1) \n",
"\n",
"fig = px.scatter(df_hubei, x=\"Lags\", y = \"Values\", trendline=\"ols\")\n",
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]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
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10L07nHIKbLddyDY6YIACgSSkYCCSS/7977B4bPBg6NkT3n0Xfv7zdNdKsoCCgUguWLkSunYNU0X33BPeeQfuuEMZRiVlCgYi2cw97D186KHwwgtw881QXBxWE4tUggaQRbLVp5+GxHJjxiixnFSbWgYi2Wbz5rDT2GGHhRlDd9+txHJSbWoZiGSTRYvgwgvh9ddDgrmBA5VPSGpEyi0DM6tnZrPN7KXoeXMzm2Zmi8zsWTPbISrfMXpeEh1vFnON3lH5QjNrF1PePiorMbNeNffrieSIjRuhb184/HCYMwcGDYIJExQIpMZUppuoB7Ag5vntwN3ufhDwNXB+VH4+8LW7HwjcHZ2HmbUAugCHAe2BB6MAUw94AOgAtAC6RueKCMB774UVxH/7G7RrB/Pnh0RzZumumeSQlIKBmTUBfgUMip4bcAowPDrlCaBT9Lhj9Jzo+KnR+R2BYe7+vbt/BJQAx0b/lbj7YndfDwyLzhXJb99/D//4R0gs9/HH8Oyz8OKLsO++6a6Z5KBUWwb3AH8DNkfP9wRWu/vG6PlSoCz1YSHwKUB0fE10/pbycq+pqHwbZtbdzIrNrHjlypUpVl0kC73zDhx1FNxyC3TpAgsWwG9+o9aA1JqkwcDMzgS+cPeZscVxTvUkxypbvm2h+0B3L3L3okaNGiWotUiW+u9/4aqr4IQT4JtvwrTRJ58MC8lEalEqs4naAr82s18COwG7E1oKDcxs++jbfxNgeXT+UqApsNTMtgd+BKyKKS8T+5qKykXyx8SJYabQ4sVh/UCfPsonJHUmacvA3Xu7exN3b0YYAJ7o7r8HJgFnR6d1A0ZFj0dHz4mOT3R3j8q7RLONmgMHAdOBGcBB0eykHaL3GF0jv51INli9OgSBU08New+//nrYfUyBQOpQddYZXAMMM7N/ArOBwVH5YOBJMyshtAi6ALj7PDN7DpgPbAQudfdNAGZ2GTAeqAc86u7zqlEvkewxalRoBXz+eZgtdOONUFCQ7lpJHrLwpT37FBUVeXFxcbqrIVI1X3wBl18eZggdfnhIJVFUlO5aSY4zs5nuHvcPTekoROqSOzz1VEgs9+KLYbZQcbECgaSd0lGI1JVPPw07jo0dC23ahNZAC62vlMygloFIbdu8Oew0dthhYdexu++Gt95SIJCMopaBSG1atCjsQ/zGG3DaaSGxXPPm6a6VyDbUMhCpDRs3hp3GDj88bD05eDC88ooCgWQstQxEatq774ZEcjNnQqdO8MADyickGU/BQKSmfP89/POfYeXwHnvA88/D//7vlnxCI2cvo+/4hSxfXcq+DQro2e4QOrX+IQ1XsuMitUnBQKQmTJ0aWgMLFsAf/xgGiWPyCY2cvYzeI+ZSumETAMtWl9J7xFwAOrUuTHpcpLZpzECkOv77X7jiCmjbNjx++WUYMmSbxHJ9xy/ccqMvU7phE33HL0zpuEhtU8tApKpefTXkFFqyBC69FG67DXbbLe6py1eXJixPdlyktqllIFJZq1eHLqHTT4f69cO00fvvZ2TJN7TtM5HmvcbQts9ERs5etuUl+zaIn2+orDzZcZHapmAgUhmjRoXFYk88Ab16hZlDJ520pc9/2epSnB/6/MsCQs92h1BQv95WlyqoX4+e7Q5J6bhIbVMwEEnF55/Db38bpoo2bgzTpoVuoSjDaLI+/06tC7mtcysKGxRgQGGDAm7r3GrL4HCy4yK1TWMGIom4w9NPQ48e8O23cOut0LNn6B6KkUqff6fWhQlv7smOi9QmtQxEKvLJJ/CrX4WpooccAnPmwLXXbhMIQH3+kv0UDETKi00s98Yb0L8/vPkmI7/bvcIBYvX5S7ZTN5FIrA8/DInl3nwzzBYaOBCaNUu6KKyse0criCVbKRiIQEgsd9ddcMMNsNNO8Nhj0K3bllQSiQaIYweBdfOXbKVgIPLuu3DeeTBrFpx1Vkgst88+W52iRWGS6zRmIPnru+/g738PW04uWwbDh8OIEdsEAtAAseQ+BQPJT2+/Da1bh6miv/89zJ8fMoxWQAPEkuvUTST55dtv4brr4L77oGlTGDcO2rUDEqeQ1gCx5DoFA8kfEyZA9+4hsdxll8G//rUlsVwqKaQ1QCy5TN1Ekvu+/joMEJ9xBuy4Y5g2et99W2UYVQppyXcKBpLbXnwxJJYbMiQklpszB048cZvTNFtI8p2CgeSmzz6Dc86Bzp3hxz+G6dNDYrmddop7umYLSb5TMJDc4h5aAS1awL//HcYFpk+Ho45i5OxlSichUgENIEvu+OQTuOiiMEPohBNg8GD46U+B5APEmi0k+U7BQLJfWWK5Xr1Cy+C+++CSS2C7Hxq+SichkpiCgWS3hQtDYrm33gqzhR5+GJo12+Y0DRCLJKYxA8lOGzdCnz5wxBHw/vshsdy4cXEDAWiAWCQZBQPJPnPmwHHHQe/eYfOZBQvgT3/akmE0Hg0QiySmYCDZoyyx3DHH/JBY7oUXwtTRJLTHsEhiGjOQ7PD223D++fDBB2Gfgbvugj322OqURLmFQAPEIomoZSCZ7dtv4fLLw6rhdevCuMDjj8cNBL1HzGXZ6lKcH6aOxq4lEJGKKRhI5nrlFWjZEu6/Hy69NAwURxlGy1NuIZHqUTCQzPP11/DnP4cb/047hU3pyyWWK09TR0WqR8FAMsuIESGVxJNPhtlCFSSWK09TR0WqJ2kwMLOmZjbJzBaY2Twz6xGV72FmE8xsUfSzYVRuZtbfzErM7D0zOyrmWt2i8xeZWbeY8qPNbG70mv5mCeYISm767DM4++yw29iPfwwzZoS8QjGJ5ZRbSKT2pNIy2Ahc7e6HAm2AS82sBdALeM3dDwJei54DdAAOiv7rDgyAEDyAG4DjgGOBG8oCSHRO95jXta/+ryZZwR2eeCK0Bl566YfEcq1bb3VasgFiTR0VqZ6kU0vdfQWwInq81swWAIVAR+Dk6LQngMnANVH5EHd34B0za2Bm+0TnTnD3VQBmNgFob2aTgd3dfWpUPgToBLxcM7+iZKyPPw6J5caPh7ZtYdCgLYnlylNuIZHaVakxAzNrBrQGpgF7R4GiLGA0jk4rBD6NednSqCxR+dI45fHev7uZFZtZ8cqVKytTdckkmzeHGUKHHQZTpoTHb7xRYSAADRCL1LaUg4GZ7Qq8AFzh7t8kOjVOmVehfNtC94HuXuTuRY0aNUpWZclECxfCz34Gf/1rGBh+//0wbXS7xH+KGiAWqV0pBQMzq08IBE+7+4io+POo+4fo5xdR+VKgaczLmwDLk5Q3iVMuuWTDhrDT2BFHwPz5YZzg5Zdh//1TerkGiEVqVyqziQwYDCxw97tiDo0GymYEdQNGxZSfG80qagOsibqRxgNnmFnDaOD4DGB8dGytmbWJ3uvcmGtJLpg9OySWu/Za+J//CcHg3HMTJpYrTwPEIrUrldxEbYE/AnPNbE5Udi3QB3jOzM4HPgHOiY6NBX4JlADrgD8DuPsqM7sFmBGdd3PZYDLwF+BxoIAwcKzB41zw3Xdw881wxx3QqFFIKte5c4WnK7eQSPpYmPSTfYqKiry4uDjd1ZCKTJkSEsstXBhWE995JzRsWOHp5belhNANpG//IjXHzGa6e1G8Y1qBLDVr7dowOHzSSfD99yG/0KOPJgwEoNxCIummYCA1Z/x4aNUKHnggBIS5c+H001N6qaaOiqSXgoFU36pVYaex9u2hoCDsR3zvvbDrrilfQlNHRdJLwUCq54UXQiqJp56C664LM4dOOKHSl9HUUZH00k5nUjUrVsBll4Uso61bh01njjyyypcrGyRONJtIRGqPgoFUTlliuSuvhNJS6NMHrr4atq/+n5Kmjoqkj4KBpG7JkpBY7pVXQiqJQYPgkNS7cZKtIxCR9NGYgSS3eXPYaaxly7Ax/f33w+uvVzoQaI9ikcylYCCJffBBSCx3+eVh7UCKieXK0zoCkcymYCDxbdgQNpo54ghYsACGDIGxY1NOLFee1hGIZDaNGci2Zs0KqSTmzIFzzgldRHvvXa1L7tuggGVxbvxaRyCSGdQykB+UloZN6I89NuxJPGIEPPdcyoFAexSLZC+1DCR4663QGvjwQzjvPOjXL2k+oVjlE82VDRDD1lNGNZtIJDMpGOS7tWtDa+CBB6BZM5gwAU47rdKX0R7FItlN3UT5bNy4MF30wQfhiivCTKEqBALQALFItlMwyEdffQXdukGHDrDLLmHvgbvvDo+rSInmRLKbgkE+cYfhw0NiuaFD4frrQ2K544+v9qU1QCyS3TRmkC9WrAiLxV58EY4+OqSUOOKIGru8BohFspuCQa5zh8cfh6uuCnsS3357eFwDieXK0wCxSPZSMMhlH30E3bvDq6+GVBKDBsHBB6e7ViKSgTRmkIs2bYL+/cNMoXfeCbOFJk9WIBCRCqllkGsWLAiLx6ZODbOFHnoI9tuvRi6tFNQiuUstg1yxYQPcemvYbWzhQnjySRgzpkYDgVJQi+QuBYNcMHMmFBXB3/8OnTrB/Pnwhz+AWY29hVJQi+Q2dRNls9JSuOmmkEeoceMwbbRTpypfLlE3kFYYi+Q2BYNs9cYbcMEFsGhRGCPo1w8aNKjy5ZIlmlMKapHcpm6ibPPNN2Hx2M9/Dhs3hmmjgwalFAgSpZhO1g2kFcYiuU0tg2zy8sthQ/qlS+HKK+GWW1LOJ5Tsm3+ybiCtMBbJbQoG2eCrr8LN/8knQ16ht9+GNm0qdYlkKaZT6QbSCmOR3KVuokzmHnYaO/RQeOYZPrjwCk7ueifNR361TTcPJO4GSvbNX91AIvlNwSBTLV8OnTvDb38L++/PxKfGctbe7Vjy7aa48/yTrQNIlmK6U+tCbuvcisIGBRhQ2KCA2zq3UktAJE8oGGQadxg8OHQHjRsHffvC1Klc/1G9hAO8NTEA3Kl1IVN6ncJHfX7FlF6nKBCI5BGNGWSSxYtDYrnXXguzhQYNggMPBJJ382gAWESqQ8EgE2zaBPfdB9ddB/XqwYABIShs90PDLdkArwaARaQ61E2UbvPnw4knwpVX8tlRbTjrkoE0X9KUtndM3moAOFk3jwaARaQ61DKoQ7HpHpruVp+Hl03g0MH9YbfdKP5nf/5YeiClGzcD264DSNbNo24gEakOc/d016FKioqKvLi4uFKvSZaCuTaPxy76avlZCX3H3sOhK5ewtF1HmgwZSNtH34/bzVPYoIApvU6p7McjIrINM5vp7kXxjmVMy8DM2gP3AvWAQe7epyavn2wFbm0f7zt+IZvXraPXlKFcOP1FVu7SgAs6X8+CY05mSuPGSgQnImmVEWMGZlYPeADoALQAuppZi5p8j2RTL2v7eJO5M3j5sb9y8bQXeK7VaZxx/oO8etBxW272ydYBiIjUpowIBsCxQIm7L3b39cAwoGNNvkF1p2ZW9fg3n38Fl1zCs0N7Uc8307XLrfTucDnf7LQr8MPNXgPAIpJOmdJNVAh8GvN8KXBc+ZPMrDvQHWC/Su7gVd2pmVU5fvJ/ZtBnwgBY+yUlf+jOb/btwCqrv+V47M1eA8Aikk6Z0jKItyXXNiPb7j7Q3YvcvahRo0aVeoPqTs2szPGG69Zw10t38vjwm9h5zwbw9tsc+OTD/KPLMQnTPWgFsIikS6a0DJYCTWOeNwGW1+QbVHdqZkrH3ZnZbyA9RvWnwXff8kH3K/lp/9tgxx23nKMbvIhkooyYWmpm2wMfAqcCy4AZwO/cfV5Fr6nK1NJatXw5XHIJjBoV9iN+9FFo1SrdtRIR2SLR1NKM6CZy943AZcB4YAHwXKJAkFFiE8uNHx+2n5w6VYFARLJKpnQT4e5jgbHprkelLF4MF14IEyduk1hORCSbZETLIOts2gT33BO+/c+YAQ8/HAKCAoGIZKmMaRlkjXnz4PzzYdo0+NWv4KGHoEmTdNdKRKRa1DJI1fr1YQP61q3hP/+BoUPh3/9WIBCRnKCWQSpmzAitgblzoWtXuPdeqOQ6BxGRTKaWQSLr1kHPntCmDaxaBaNHhxaBAoGI5Bi1DCoyeXKYKVRSEnYdu+MO+NGP0l0rEZFaoZZBeWvWwMUXwy9+EdYQTJwYZgspEIhIDlMwiDVmDLRsCY88AlddBe+9F4KCiEiOU0o9oOUAAATtSURBVDAAWLkSfv97OPPM0AJ4+224807Yeed010xEpE7kdzBwh2HDQiqJ55+HG26AWbPguG2yZ4uI5LT8HUBetgz+8pewVuCYY0J+IeUTEpE8lX8tA/cwJtCiBbz6augOUmI5Eclz+dUyKC0NKSQmTQoDw488Aj/5SbprJSKSdvkVDAoK4OCDwyriCy4Ai7fBmohI/smvYAAhsZyIiGwl/8YMRERkGwoGIiKiYCAiIgoGIiKCgoGIiKBgICIiKBiIiAgKBiIiApi7p7sOVWJmK4GPq/jyvYAva7A6NU31qx7Vr3pUv+rJ5Prt7+5x9+3N2mBQHWZW7O5F6a5HRVS/6lH9qkf1q55Mr19F1E0kIiIKBiIikr/BYGC6K5CE6lc9ql/1qH7Vk+n1iysvxwxERGRr+doyEBGRGAoGIiKSX8HAzNqb2UIzKzGzXumuTzxmtsTM5prZHDMrzoD6PGpmX5jZ+zFle5jZBDNbFP1smGH1u9HMlkWf4Rwz+2Wa6tbUzCaZ2QIzm2dmPaLyjPj8EtQvIz6/qC47mdl0M3s3quNNUXlzM5sWfYbPmtkOGVS3x83so5jP78i6rltV5M2YgZnVAz4ETgeWAjOAru4+P60VK8fMlgBF7p4Ri1bM7GfAt8AQd28Zld0BrHL3PlFQbeju12RQ/W4EvnX3fumoU0zd9gH2cfdZZrYbMBPoBPyJDPj8EtTvN2TA5wdgZgbs4u7fmll94C2gB3AVMMLdh5nZQ8C77j4gQ+p2MfCSuw+vy/pUVz61DI4FStx9sbuvB4YBHdNcp4zn7m8Aq8oVdwSeiB4/QbiBpEUF9csI7r7C3WdFj9cCC4BCMuTzS1C/jOHBt9HT+tF/DpwClN1s0/IZJqhbVsqnYFAIfBrzfCkZ9ocfceAVM5tpZt3TXZkK7O3uKyDcUIDGaa5PPJeZ2XtRN1LaurHKmFkzoDUwjQz8/MrVDzLo8zOzemY2B/gCmAD8B1jt7hujU9L2b7l83dy97PO7Nfr87jazHdNRt8rKp2BgccoyMYq3dfejgA7ApVE3iFTOAOAnwJHACuDOdFbGzHYFXgCucPdv0lmXeOLUL6M+P3ff5O5HAk0ILfxD451Wt7WK3rRc3cysJdAb+ClwDLAHkJYu1MrKp2CwFGga87wJsDxNdamQuy+Pfn4BvEj44880n0f9zWX9zl+kuT5bcffPo3+km4FHSONnGPUlvwA87e4jouKM+fzi1S+TPr9Y7r4amAy0ARqY2fbRobT/W46pW/uo+83d/XvgMTLk80smn4LBDOCgaBbCDkAXYHSa67QVM9slGsjDzHYBzgDeT/yqtBgNdIsedwNGpbEu2yi70UbOIk2fYTTAOBhY4O53xRzKiM+vovplyucX1aWRmTWIHhcApxHGNiYBZ0enpeUzrKBuH8QEeiOMZWTiv+Ft5M1sIoBoitw9QD3gUXe/Nc1V2oqZHUBoDQBsDwxNdx3N7BngZEJa3s+BG4CRwHPAfsAnwDnunpZB3ArqdzKhi8OBJcBFZX30dVy3E4E3gbnA5qj4WkK/fNo/vwT160oGfH5RHQ8nDBDXI3x5fc7db47+rQwjdMPMBv4QfRPPhLpNBBoRuqbnABfHDDRnrLwKBiIiEl8+dROJiEgFFAxERETBQEREFAxERAQFAxERQcFARERQMBAREeD/ARQ2x//GLRvHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9458346650136403\n"
]
}
],
"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",
"plt.scatter(X, Y)\n",
"plt.plot(X, Y_pred, color='red')\n",
"plt.show()\n",
"print(\"Accuracy:\" ,acc)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"\n",
"predictions = linear_regressor.predict(X)\n"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction: [-7927.82307692] - Day: [0] - Actual: [444]\n",
"Prediction: [-5703.45587045] - Day: [1] - Actual: [444]\n",
"Prediction: [-3479.08866397] - Day: [2] - Actual: [549]\n",
"Prediction: [-1254.72145749] - Day: [3] - Actual: [761]\n",
"Prediction: [969.64574899] - Day: [4] - Actual: [1058]\n",
"Prediction: [3194.01295547] - Day: [5] - Actual: [1423]\n",
"Prediction: [5418.38016194] - Day: [6] - Actual: [3554]\n",
"Prediction: [7642.74736842] - Day: [7] - Actual: [3554]\n",
"Prediction: [9867.1145749] - Day: [8] - Actual: [4903]\n",
"Prediction: [12091.48178138] - Day: [9] - Actual: [5806]\n",
"Prediction: [14315.84898785] - Day: [10] - Actual: [7153]\n",
"Prediction: [16540.21619433] - Day: [11] - Actual: [11177]\n",
"Prediction: [18764.58340081] - Day: [12] - Actual: [13522]\n",
"Prediction: [20988.95060729] - Day: [13] - Actual: [16678]\n",
"Prediction: [23213.31781377] - Day: [14] - Actual: [19665]\n",
"Prediction: [25437.68502024] - Day: [15] - Actual: [22112]\n",
"Prediction: [27662.05222672] - Day: [16] - Actual: [24953]\n",
"Prediction: [29886.4194332] - Day: [17] - Actual: [27100]\n",
"Prediction: [32110.78663968] - Day: [18] - Actual: [29631]\n",
"Prediction: [34335.15384615] - Day: [19] - Actual: [31728]\n",
"Prediction: [36559.52105263] - Day: [20] - Actual: [33366]\n",
"Prediction: [38783.88825911] - Day: [21] - Actual: [33366]\n",
"Prediction: [41008.25546559] - Day: [22] - Actual: [48206]\n",
"Prediction: [43232.62267206] - Day: [23] - Actual: [54406]\n",
"Prediction: [45456.98987854] - Day: [24] - Actual: [56249]\n",
"Prediction: [47681.35708502] - Day: [25] - Actual: [58182]\n",
"Prediction: [49905.7242915] - Day: [26] - Actual: [59989]\n",
"Prediction: [52130.09149798] - Day: [27] - Actual: [61682]\n",
"Prediction: [54354.45870445] - Day: [28] - Actual: [62031]\n",
"Prediction: [56578.82591093] - Day: [29] - Actual: [62442]\n",
"Prediction: [58803.19311741] - Day: [30] - Actual: [62662]\n",
"Prediction: [61027.56032389] - Day: [31] - Actual: [64084]\n",
"Prediction: [63251.92753036] - Day: [32] - Actual: [64084]\n",
"Prediction: [65476.29473684] - Day: [33] - Actual: [64287]\n",
"Prediction: [67700.66194332] - Day: [34] - Actual: [64786]\n",
"Prediction: [69925.0291498] - Day: [35] - Actual: [65187]\n",
"Prediction: [72149.39635628] - Day: [36] - Actual: [65596]\n",
"Prediction: [74373.76356275] - Day: [37] - Actual: [65914]\n",
"Prediction: [76598.13076923] - Day: [38] - Actual: [66337]\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": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Giorno 39 : 78822.49797570852\n",
"Giorno 40 : 81046.86518218626\n",
"Giorno 41 : 83271.23238866398\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": 138,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\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",
"C:\\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",
"C:\\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": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.02564102564102564\n"
]
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"\n",
"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",
"logistic = LogisticRegression(random_state=0) # create object for the class\n",
"logistic.fit(X, Y) # perform linear regression\n",
"acc = logistic.score(X, Y)\n",
"Y_pred = logistic.predict(X) # make predictions\n",
"\n",
"\n",
"plt.scatter(X, Y)\n",
"plt.plot(X, Y_pred, color='red')\n",
"plt.show()\n",
"print(\"Accuracy:\" ,acc)\n",
"\n",
"#plt.plot(X_test, ols.coef_ * X_test + ols.intercept_, linewidth=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([64084], dtype=int64)"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_pred = logistic.predict([[41]])\n",
"Y_pred "
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [],
"source": [
"prob = logistic.predict_proba(X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.2564102564102564\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\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",
"C:\\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",
"C:\\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"
]
}
],
"source": [
"from sklearn import linear_model\n",
"from scipy.special import expit\n",
"clf = linear_model.LogisticRegression(C=1e5)\n",
"clf.fit(X, Y)\n",
"acc = clf.score(X, Y)\n",
"\n",
"print(\"Accuracy:\" ,acc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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