Resources/Bonus_ReadFromDBExamples/read_form_database_sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "from matplotlib import cm"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "from sqlalchemy import create_engine\r\n",
    "from config2 import db_user, db_pass #credentials to connect to postgresql database"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "# Creating an engine to connect to postgresql database 'employee_info'\r\n",
    "my_engine = create_engine(f'postgresql://{db_user}:{db_pass}@localhost:5432/ETL')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "# creating connection\r\n",
    "connection = my_engine.connect()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "# fetch salaries of all employees from the salaries table\r\n",
    "state_wide_cases_query = '''select  v.sabbr as \"State\", sum(p.population_2020)\r\n",
    "    as \"Total Population\", sum(v.no_of_cases) as \"Total Reported Cases\",\r\n",
    "    sum(no_of_deaths) as \"Total Reported Deaths\" ,\r\n",
    "    round(((CAST(sum(no_of_deaths)  AS DECIMAL) / sum(p.population_2020)) * 100000),0) as \"Deaths Per 100000\"\r\n",
    "    from us_covid_data_by_county_state v inner join\r\n",
    "    us_census_data p on v.county_fips = p.county_fips\r\n",
    "    where  cdate = (select max(cdate) from us_covid_data_by_county_state) \r\n",
    "    group by  v.sabbr\r\n",
    "    order by \"Total Reported Deaths\" desc'''\r\n",
    "\r\n",
    "state_wide_cases = pd.read_sql(state_wide_cases_query, connection)\r\n",
    "state_wide_cases\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   State  Total Population  Total Reported Cases  Total Reported Deaths  \\\n",
       "0     CA          39368078               4134375                  64777   \n",
       "1     TX          29360759               3229852                  53816   \n",
       "2     FL          21733312               2725448                  36973   \n",
       "3     PA          12783254               1240072                  27902   \n",
       "4     NJ           8882371               1048397                  26650   \n",
       "5     IL          12587530               1440658                  25527   \n",
       "6     MI           9966555               1016277                  21192   \n",
       "7     GA          10710017               1181153                  21090   \n",
       "8     OH          11693217               1142330                  20552   \n",
       "9     NY          11083563               1185684                  19758   \n",
       "10    AZ           7421401                946054                  18388   \n",
       "11    MA           6893574                677578                  18083   \n",
       "12    IN           6754953                784685                  14054   \n",
       "13    NC          10600823               1072440                  13752   \n",
       "14    TN           6886834                891628                  12636   \n",
       "15    AL           4929860                608701                  11628   \n",
       "16    VA           8590563                704664                  11558   \n",
       "17    LA           4645318                573481                  11210   \n",
       "18    SC           5218040                634310                   9950   \n",
       "19    MD           6055802                474187                   9830   \n",
       "20    MO           6151548                658058                   9731   \n",
       "21    WI           5832655                695436                   8300   \n",
       "22    CT           3557006                356869                   8295   \n",
       "23    MN           5657342                617304                   7694   \n",
       "24    MS           2966786                358149                   7621   \n",
       "25    OK           3980783                486129                   7531   \n",
       "26    KY           4477251                496451                   7432   \n",
       "27    CO           5807719                585462                   7140   \n",
       "28    AR           3030522                400501                   6301   \n",
       "29    WA           7693612                489960                   6209   \n",
       "30    IA           3163561                382393                   6193   \n",
       "31    NV           3138259                363574                   6005   \n",
       "32    KS           2913805                340741                   5335   \n",
       "33    NM           2106319                213793                   4427   \n",
       "34    WV           1784787                169162                   2965   \n",
       "35    OR           4241507                226899                   2915   \n",
       "36    RI           1057125                142814                   2735   \n",
       "37    NE           1937552                229409                   2553   \n",
       "38    UT           3249879                437289                   2470   \n",
       "39    ID           1826913                204170                   2218   \n",
       "40    SD            892717                125592                   2050   \n",
       "41    DE            986809                112542                   1835   \n",
       "42    MT           1080577                117995                   1722   \n",
       "43    ND            765309                112336                   1542   \n",
       "44    NH           1366275                101303                   1388   \n",
       "45    DC            712816                 51103                   1149   \n",
       "46    ME           1350141                 71304                    901   \n",
       "47    WY            582328                 66453                    786   \n",
       "48    HI           1407006                 44609                    538   \n",
       "49    AK            708425                 73872                    373   \n",
       "50    VT            623347                 25301                    260   \n",
       "\n",
       "    Deaths Per 100000  \n",
       "0               165.0  \n",
       "1               183.0  \n",
       "2               170.0  \n",
       "3               218.0  \n",
       "4               300.0  \n",
       "5               203.0  \n",
       "6               213.0  \n",
       "7               197.0  \n",
       "8               176.0  \n",
       "9               178.0  \n",
       "10              248.0  \n",
       "11              262.0  \n",
       "12              208.0  \n",
       "13              130.0  \n",
       "14              183.0  \n",
       "15              236.0  \n",
       "16              135.0  \n",
       "17              241.0  \n",
       "18              191.0  \n",
       "19              162.0  \n",
       "20              158.0  \n",
       "21              142.0  \n",
       "22              233.0  \n",
       "23              136.0  \n",
       "24              257.0  \n",
       "25              189.0  \n",
       "26              166.0  \n",
       "27              123.0  \n",
       "28              208.0  \n",
       "29               81.0  \n",
       "30              196.0  \n",
       "31              191.0  \n",
       "32              183.0  \n",
       "33              210.0  \n",
       "34              166.0  \n",
       "35               69.0  \n",
       "36              259.0  \n",
       "37              132.0  \n",
       "38               76.0  \n",
       "39              121.0  \n",
       "40              230.0  \n",
       "41              186.0  \n",
       "42              159.0  \n",
       "43              201.0  \n",
       "44              102.0  \n",
       "45              161.0  \n",
       "46               67.0  \n",
       "47              135.0  \n",
       "48               38.0  \n",
       "49               53.0  \n",
       "50               42.0  "
      ],
      "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>State</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Total Reported Cases</th>\n",
       "      <th>Total Reported Deaths</th>\n",
       "      <th>Deaths Per 100000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>39368078</td>\n",
       "      <td>4134375</td>\n",
       "      <td>64777</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TX</td>\n",
       "      <td>29360759</td>\n",
       "      <td>3229852</td>\n",
       "      <td>53816</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>21733312</td>\n",
       "      <td>2725448</td>\n",
       "      <td>36973</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PA</td>\n",
       "      <td>12783254</td>\n",
       "      <td>1240072</td>\n",
       "      <td>27902</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NJ</td>\n",
       "      <td>8882371</td>\n",
       "      <td>1048397</td>\n",
       "      <td>26650</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IL</td>\n",
       "      <td>12587530</td>\n",
       "      <td>1440658</td>\n",
       "      <td>25527</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MI</td>\n",
       "      <td>9966555</td>\n",
       "      <td>1016277</td>\n",
       "      <td>21192</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GA</td>\n",
       "      <td>10710017</td>\n",
       "      <td>1181153</td>\n",
       "      <td>21090</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OH</td>\n",
       "      <td>11693217</td>\n",
       "      <td>1142330</td>\n",
       "      <td>20552</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NY</td>\n",
       "      <td>11083563</td>\n",
       "      <td>1185684</td>\n",
       "      <td>19758</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AZ</td>\n",
       "      <td>7421401</td>\n",
       "      <td>946054</td>\n",
       "      <td>18388</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MA</td>\n",
       "      <td>6893574</td>\n",
       "      <td>677578</td>\n",
       "      <td>18083</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IN</td>\n",
       "      <td>6754953</td>\n",
       "      <td>784685</td>\n",
       "      <td>14054</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NC</td>\n",
       "      <td>10600823</td>\n",
       "      <td>1072440</td>\n",
       "      <td>13752</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TN</td>\n",
       "      <td>6886834</td>\n",
       "      <td>891628</td>\n",
       "      <td>12636</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AL</td>\n",
       "      <td>4929860</td>\n",
       "      <td>608701</td>\n",
       "      <td>11628</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>VA</td>\n",
       "      <td>8590563</td>\n",
       "      <td>704664</td>\n",
       "      <td>11558</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LA</td>\n",
       "      <td>4645318</td>\n",
       "      <td>573481</td>\n",
       "      <td>11210</td>\n",
       "      <td>241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SC</td>\n",
       "      <td>5218040</td>\n",
       "      <td>634310</td>\n",
       "      <td>9950</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MD</td>\n",
       "      <td>6055802</td>\n",
       "      <td>474187</td>\n",
       "      <td>9830</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MO</td>\n",
       "      <td>6151548</td>\n",
       "      <td>658058</td>\n",
       "      <td>9731</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>WI</td>\n",
       "      <td>5832655</td>\n",
       "      <td>695436</td>\n",
       "      <td>8300</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CT</td>\n",
       "      <td>3557006</td>\n",
       "      <td>356869</td>\n",
       "      <td>8295</td>\n",
       "      <td>233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>5657342</td>\n",
       "      <td>617304</td>\n",
       "      <td>7694</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MS</td>\n",
       "      <td>2966786</td>\n",
       "      <td>358149</td>\n",
       "      <td>7621</td>\n",
       "      <td>257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>OK</td>\n",
       "      <td>3980783</td>\n",
       "      <td>486129</td>\n",
       "      <td>7531</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>KY</td>\n",
       "      <td>4477251</td>\n",
       "      <td>496451</td>\n",
       "      <td>7432</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CO</td>\n",
       "      <td>5807719</td>\n",
       "      <td>585462</td>\n",
       "      <td>7140</td>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AR</td>\n",
       "      <td>3030522</td>\n",
       "      <td>400501</td>\n",
       "      <td>6301</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WA</td>\n",
       "      <td>7693612</td>\n",
       "      <td>489960</td>\n",
       "      <td>6209</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>IA</td>\n",
       "      <td>3163561</td>\n",
       "      <td>382393</td>\n",
       "      <td>6193</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NV</td>\n",
       "      <td>3138259</td>\n",
       "      <td>363574</td>\n",
       "      <td>6005</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>KS</td>\n",
       "      <td>2913805</td>\n",
       "      <td>340741</td>\n",
       "      <td>5335</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NM</td>\n",
       "      <td>2106319</td>\n",
       "      <td>213793</td>\n",
       "      <td>4427</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>WV</td>\n",
       "      <td>1784787</td>\n",
       "      <td>169162</td>\n",
       "      <td>2965</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>OR</td>\n",
       "      <td>4241507</td>\n",
       "      <td>226899</td>\n",
       "      <td>2915</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RI</td>\n",
       "      <td>1057125</td>\n",
       "      <td>142814</td>\n",
       "      <td>2735</td>\n",
       "      <td>259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NE</td>\n",
       "      <td>1937552</td>\n",
       "      <td>229409</td>\n",
       "      <td>2553</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>UT</td>\n",
       "      <td>3249879</td>\n",
       "      <td>437289</td>\n",
       "      <td>2470</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ID</td>\n",
       "      <td>1826913</td>\n",
       "      <td>204170</td>\n",
       "      <td>2218</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SD</td>\n",
       "      <td>892717</td>\n",
       "      <td>125592</td>\n",
       "      <td>2050</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>DE</td>\n",
       "      <td>986809</td>\n",
       "      <td>112542</td>\n",
       "      <td>1835</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>MT</td>\n",
       "      <td>1080577</td>\n",
       "      <td>117995</td>\n",
       "      <td>1722</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ND</td>\n",
       "      <td>765309</td>\n",
       "      <td>112336</td>\n",
       "      <td>1542</td>\n",
       "      <td>201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NH</td>\n",
       "      <td>1366275</td>\n",
       "      <td>101303</td>\n",
       "      <td>1388</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>DC</td>\n",
       "      <td>712816</td>\n",
       "      <td>51103</td>\n",
       "      <td>1149</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ME</td>\n",
       "      <td>1350141</td>\n",
       "      <td>71304</td>\n",
       "      <td>901</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WY</td>\n",
       "      <td>582328</td>\n",
       "      <td>66453</td>\n",
       "      <td>786</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>HI</td>\n",
       "      <td>1407006</td>\n",
       "      <td>44609</td>\n",
       "      <td>538</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>AK</td>\n",
       "      <td>708425</td>\n",
       "      <td>73872</td>\n",
       "      <td>373</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>VT</td>\n",
       "      <td>623347</td>\n",
       "      <td>25301</td>\n",
       "      <td>260</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "\r\n",
    "\r\n",
    "# Bar chart of average salary by title using pandas dataframe.plot.bar\r\n",
    "\r\n",
    "x_label      = \"States\"\r\n",
    "y_label      = \"Total Deaths / 100000\"\r\n",
    "graph_title  = \"Country Wide Deaths per 100000\"\r\n",
    "\r\n",
    "tick_angle = 90\r\n",
    "fsize=(14,6)\r\n",
    "# using matplotlib colormap\r\n",
    "bar_color = cm.cool(np.linspace(.4, .8, len(state_wide_cases)))\r\n",
    "bar_alpha = 0.9\r\n",
    "bar_width = 0.8\r\n",
    "\r\n",
    "state_wide_cases.plot.bar(x='State', y='deaths per 100000', color=bar_color, figsize=fsize, title=graph_title, xlabel=x_label, ylabel=y_label, legend=\"\", alpha=bar_alpha, width=bar_width,  rot=tick_angle)\r\n",
    "plt.tight_layout()\r\n",
    "plt.show()\r\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
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xlink:href=\"#DejaVuSans-67\"/>\r\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-84\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_24\">\r\n     <g id=\"line2d_24\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"483.211312\" xlink:href=\"#mf73f9187a5\" y=\"378.968125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_24\">\r\n      <!-- MN -->\r\n      <g transform=\"translate(485.970687 402.0775)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-77\"/>\r\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-78\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_25\">\r\n     <g id=\"line2d_25\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"501.657219\" xlink:href=\"#mf73f9187a5\" y=\"378.968125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_25\">\r\n      <!-- MS -->\r\n      <g transform=\"translate(504.416594 400.944687)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-77\"/>\r\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-83\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_26\">\r\n     <g id=\"line2d_26\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"520.103125\" xlink:href=\"#mf73f9187a5\" y=\"378.968125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_26\">\r\n      <!-- OK -->\r\n      <g transform=\"translate(522.8625 400.397812)rotate(-90)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 9.8125 72.90625 \r\nL 19.671875 72.90625 \r\nL 19.671875 42.09375 \r\nL 52.390625 72.90625 \r\nL 65.09375 72.90625 \r\nL 28.90625 38.921875 \r\nL 67.671875 0 \r\nL 54.6875 0 \r\nL 19.671875 35.109375 \r\nL 19.671875 0 \r\nL 9.8125 0 \r\nz\r\n\" id=\"DejaVuSans-75\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-79\"/>\r\n       <use x=\"78.710938\" xlink:href=\"#DejaVuSans-75\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_27\">\r\n     <g 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xlink:href=\"#mf73f9187a5\" y=\"378.968125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_33\">\r\n      <!-- KS -->\r\n      <g transform=\"translate(651.983845 398.874375)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-75\"/>\r\n       <use x=\"65.576172\" xlink:href=\"#DejaVuSans-83\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_34\">\r\n     <g id=\"line2d_34\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"667.670376\" xlink:href=\"#mf73f9187a5\" y=\"378.968125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_34\">\r\n      <!-- NM -->\r\n      <g transform=\"translate(670.429751 402.0775)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-78\"/>\r\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-77\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_35\">\r\n     <g id=\"line2d_35\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"686.116283\" 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# fetching from tables another example. This can be extended to use with covid data and do a comparision analysys\r\n",
    "query_flu = \"select s.sname, f.year, f.week,\tf.total_specimens,f.total_a,\tf.total_b,f.percent_positive,f.percent_a,f.percent_b from who_flu_data f inner join us_states s on f.state_fips = s.state_fips\"\r\n",
    "\r\n",
    "wflu_df = pd.read_sql(query_flu, connection)\r\n",
    "wflu_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               sname  year  week  total_specimens  total_a  total_b  \\\n",
       "0            Alabama  2019    40              512        2       13   \n",
       "1             Alaska  2019    40               -1       -1       -1   \n",
       "2            Arizona  2019    40              278        0        2   \n",
       "3           Arkansas  2019    40               89        0        0   \n",
       "4         California  2019    40             1776       18       10   \n",
       "...              ...   ...   ...              ...      ...      ...   \n",
       "5125       Wisconsin  2021    29               -1       -1       -1   \n",
       "5126         Wyoming  2021    29               -1       -1       -1   \n",
       "5127     Puerto Rico  2021    29                0        0        0   \n",
       "5128  Virgin Islands  2021    29               -1       -1       -1   \n",
       "5129        New York  2021    29               -1       -1       -1   \n",
       "\n",
       "      percent_positive  percent_a  percent_b  \n",
       "0                 2.93       0.39       2.54  \n",
       "1                -1.00      -1.00      -1.00  \n",
       "2                 0.72       0.00       0.72  \n",
       "3                 0.00       0.00       0.00  \n",
       "4                 1.58       1.01       0.56  \n",
       "...                ...        ...        ...  \n",
       "5125             -1.00      -1.00      -1.00  \n",
       "5126             -1.00      -1.00      -1.00  \n",
       "5127              0.00       0.00       0.00  \n",
       "5128             -1.00      -1.00      -1.00  \n",
       "5129             -1.00      -1.00      -1.00  \n",
       "\n",
       "[5130 rows x 9 columns]"
      ],
      "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>sname</th>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th>total_specimens</th>\n",
       "      <th>total_a</th>\n",
       "      <th>total_b</th>\n",
       "      <th>percent_positive</th>\n",
       "      <th>percent_a</th>\n",
       "      <th>percent_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>1776</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5125</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5126</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5127</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5128</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5129</th>\n",
       "      <td>New York</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5130 rows × 9 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "AX_values = wflu_df[wflu_df['total_a'] == -1].index\r\n",
    "wflu_df.drop(AX_values, inplace= True)\r\n",
    "wflu_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              sname  year  week  total_specimens  total_a  total_b  \\\n",
       "0           Alabama  2019    40              512        2       13   \n",
       "2           Arizona  2019    40              278        0        2   \n",
       "3          Arkansas  2019    40               89        0        0   \n",
       "4        California  2019    40             1776       18       10   \n",
       "5          Colorado  2019    40              458        2        0   \n",
       "...             ...   ...   ...              ...      ...      ...   \n",
       "5121        Vermont  2021    29               38        0        0   \n",
       "5122       Virginia  2021    29              232        0        0   \n",
       "5123     Washington  2021    29              326        0        0   \n",
       "5124  West Virginia  2021    29               90        0        0   \n",
       "5127    Puerto Rico  2021    29                0        0        0   \n",
       "\n",
       "      percent_positive  percent_a  percent_b  \n",
       "0                 2.93       0.39       2.54  \n",
       "2                 0.72       0.00       0.72  \n",
       "3                 0.00       0.00       0.00  \n",
       "4                 1.58       1.01       0.56  \n",
       "5                 0.44       0.44       0.00  \n",
       "...                ...        ...        ...  \n",
       "5121              0.00       0.00       0.00  \n",
       "5122              0.00       0.00       0.00  \n",
       "5123              0.00       0.00       0.00  \n",
       "5124              0.00       0.00       0.00  \n",
       "5127              0.00       0.00       0.00  \n",
       "\n",
       "[4003 rows x 9 columns]"
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       "      <th>total_specimens</th>\n",
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       "      <td>Alabama</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.54</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.72</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>1776</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>458</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.44</td>\n",
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       "    <tr>\n",
       "      <th>5121</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5122</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5123</th>\n",
       "      <td>Washington</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>326</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5124</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5127</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4003 rows × 9 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "combined_wflu = pd.to_numeric(wflu_df[\"total_a\"]) + pd.to_numeric(wflu_df[\"total_a\"])\r\n",
    "wflu_df[\"TOTAL_FLU\"] = combined_wflu\r\n",
    "combined_wflu"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0        4\n",
       "2        0\n",
       "3        0\n",
       "4       36\n",
       "5        4\n",
       "        ..\n",
       "5121     0\n",
       "5122     0\n",
       "5123     0\n",
       "5124     0\n",
       "5127     0\n",
       "Name: total_a, Length: 4003, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "wflu_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              sname  year  week  total_specimens  total_a  total_b  \\\n",
       "0           Alabama  2019    40              512        2       13   \n",
       "2           Arizona  2019    40              278        0        2   \n",
       "3          Arkansas  2019    40               89        0        0   \n",
       "4        California  2019    40             1776       18       10   \n",
       "5          Colorado  2019    40              458        2        0   \n",
       "...             ...   ...   ...              ...      ...      ...   \n",
       "5121        Vermont  2021    29               38        0        0   \n",
       "5122       Virginia  2021    29              232        0        0   \n",
       "5123     Washington  2021    29              326        0        0   \n",
       "5124  West Virginia  2021    29               90        0        0   \n",
       "5127    Puerto Rico  2021    29                0        0        0   \n",
       "\n",
       "      percent_positive  percent_a  percent_b  TOTAL_FLU  \n",
       "0                 2.93       0.39       2.54          4  \n",
       "2                 0.72       0.00       0.72          0  \n",
       "3                 0.00       0.00       0.00          0  \n",
       "4                 1.58       1.01       0.56         36  \n",
       "5                 0.44       0.44       0.00          4  \n",
       "...                ...        ...        ...        ...  \n",
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       "5122              0.00       0.00       0.00          0  \n",
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       "\n",
       "[4003 rows x 10 columns]"
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       "      <td>Alabama</td>\n",
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       "      <td>10</td>\n",
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       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>458</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.44</td>\n",
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       "      <th>5121</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5122</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5123</th>\n",
       "      <td>Washington</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>326</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5124</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>5127</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.00</td>\n",
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       "</table>\n",
       "<p>4003 rows × 10 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "clean_flu_df = wflu_df[['sname', 'year', 'week', 'total_specimens', 'TOTAL_FLU']]\r\n",
    "clean_flu_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              sname  year  week  total_specimens  TOTAL_FLU\n",
       "0           Alabama  2019    40              512          4\n",
       "2           Arizona  2019    40              278          0\n",
       "3          Arkansas  2019    40               89          0\n",
       "4        California  2019    40             1776         36\n",
       "5          Colorado  2019    40              458          4\n",
       "...             ...   ...   ...              ...        ...\n",
       "5121        Vermont  2021    29               38          0\n",
       "5122       Virginia  2021    29              232          0\n",
       "5123     Washington  2021    29              326          0\n",
       "5124  West Virginia  2021    29               90          0\n",
       "5127    Puerto Rico  2021    29                0          0\n",
       "\n",
       "[4003 rows x 5 columns]"
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       "      <th>total_specimens</th>\n",
       "      <th>TOTAL_FLU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>1776</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>458</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5121</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5122</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5123</th>\n",
       "      <td>Washington</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>326</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5124</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5127</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>2021</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4003 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 12
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  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
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