Covid-19 SVI.ipynb

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   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import datetime\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import imgkit\n",
    "\n",
    "\n",
    "\n",
    "# Only for testing\n",
    "# from vega_datasets import data\n",
    "\n",
    "# Importing data\n",
    "\n",
    "svi = pd.read_csv('./SVI2018_US.csv')\n",
    "us_cases = pd.read_csv('./confirmed-covid-19-cases-in-us-by-state-and-county.csv')\n",
    "us_deaths = pd.read_csv('./confirmed-covid-19-deaths-in-us-by-state-and-county.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Write a function that provides a first look at the new DataFrames. \n",
    "\n",
    "dataframe_list = [svi, us_cases, us_deaths]\n",
    "\n",
    "def first_look (lst):\n",
    "    \"\"\" Provides useful information about a list of dataframes: Name, Shape, NaNs and describe().\"\"\"\n",
    "    for item in lst:\n",
    "        def get_df_name(df):\n",
    "            name =[x for x in globals() if globals()[x] is df][0]\n",
    "            return name\n",
    "        print('Information for DataFrame ' + get_df_name(item))\n",
    "        print(item.shape)\n",
    "        print((np.sum(item.isna() == True))/item.shape[0])\n",
    "        print(item.describe())\n",
    "        print('---')\n",
    "        \n",
    "\n",
    "#first_look(dataframe_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparation of the Social Vulnerability Indicators (SVI) dataframe and of selected indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are interested in 14 indicators, all of them are percentage values. \n",
    "# They include poverty, minorities, unemployment, etc. \n",
    "# Each category has a MOE (margin of error) that is not taken into account in this research.\n",
    "# The value -999 means no value. I am going to treat them as NaNs and drop them. \n",
    "\n",
    "# Select relevant columns of SVI, including state, county and county fips. Rename 'STCNTY' to county_fips for mergers.\n",
    "svi = svi[['STATE', 'STCNTY','COUNTY', 'EP_POV', 'EP_LIMENG', 'EP_AGE65', 'EP_MINRTY', 'EP_UNEMP', 'EP_NOHSDP', 'EP_AGE17', 'EP_DISABL', 'EP_SNGPNT', 'EP_MUNIT', 'EP_MOBILE', 'EP_CROWD', 'EP_NOVEH', 'EP_GROUPQ', 'RPL_THEMES']]\n",
    "svi = svi.rename(columns = {'STCNTY': 'county_fips'})\n",
    "\n",
    "# Drop all -999/NaN columns in the dataframe\n",
    "svi = svi[svi[svi.columns] != -999]\n",
    "svi = svi.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVI and confirmed cases of Covid-19 in the US\n",
    "\n",
    "We will now have a look at the confirmed cases of Covid-19 in the US, on a state and county level. \n",
    "1. Preparation of the confirmed cases dataset\n",
    "2. General overview\n",
    "    1. Which US states have the most confirmed cases of Covid-19?\n",
    "    2. Identify correlation between SVI and Covid-19 confirmed cases in all US counties\n",
    "3. Analysis of the ten US counties with most confirmed cases\n",
    "    1. Which are the ten US counties with most confirmed cases\n",
    "    2. Identify correlation between SVI and the number of Covid-19 in the ten most affected US counties\n",
    "4. Building a Machine Learning model for confirmed cases\n",
    "    1. Multiple Linear Regression for confirmed cases all US counties\n",
    "    2. Multiple Linear Regression for the ten US counties with most confirmed cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preparation of Covid-19 confirmed cases dataset in US states and counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>state_fips</th>\n",
       "      <th>date</th>\n",
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       "      <th>75</th>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga County</td>\n",
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       "      <td>1</td>\n",
       "      <td>2020-01-22</td>\n",
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       "      <td>POINT (-86.64408227 32.53952745)</td>\n",
       "      <td>2020-01-22</td>\n",
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       "      <th>76</th>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga County</td>\n",
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       "      <td>2020-01-23</td>\n",
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       "      <td>2020-01-25</td>\n",
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       "      <td>2020-01-26</td>\n",
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       "    county_fips     county_name state_name  state_fips        date  \\\n",
       "75         1001  Autauga County         AL           1  2020-01-22   \n",
       "76         1001  Autauga County         AL           1  2020-01-23   \n",
       "77         1001  Autauga County         AL           1  2020-01-24   \n",
       "78         1001  Autauga County         AL           1  2020-01-25   \n",
       "79         1001  Autauga County         AL           1  2020-01-26   \n",
       "\n",
       "    confirmed_cases        lat       long                          geometry  \\\n",
       "75              0.0  32.539527 -86.644082  POINT (-86.64408227 32.53952745)   \n",
       "76              0.0  32.539527 -86.644082  POINT (-86.64408227 32.53952745)   \n",
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       "79              0.0  32.539527 -86.644082  POINT (-86.64408227 32.53952745)   \n",
       "\n",
       "     datetime  \n",
       "75 2020-01-22  \n",
       "76 2020-01-23  \n",
       "77 2020-01-24  \n",
       "78 2020-01-25  \n",
       "79 2020-01-26  "
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   "source": [
    "# Apply datetime\n",
    "format_str = '%Y-%m-%d'\n",
    "us_cases['datetime'] = us_cases.date.apply(lambda date: datetime.datetime.strptime(date, format_str))\n",
    "\n",
    "# Rename column 'confirmed' in 'confirmed_cases' for better understanding:\n",
    "us_cases = us_cases.rename(columns = {'confirmed': 'confirmed_cases'})\n",
    "\n",
    "# Drop rows that do not refer to a specific county ('Statewide Unallocated'):\n",
    "us_cases = us_cases[us_cases.county_name != 'Statewide Unallocated']\n",
    "\n",
    "us_cases.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 184,
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       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>state_fips</th>\n",
       "      <th>date</th>\n",
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       "      <th>239695</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.839612</td>\n",
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       "      <td>2020-04-01</td>\n",
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       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-02</td>\n",
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       "      <th>239697</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.839612</td>\n",
       "      <td>-104.567488</td>\n",
       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-03</td>\n",
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       "      <th>239698</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.839612</td>\n",
       "      <td>-104.567488</td>\n",
       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-04</td>\n",
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       "      <th>239699</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-05</td>\n",
       "    </tr>\n",
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       "        county_fips    county_name state_name  state_fips        date  \\\n",
       "239695        56045  Weston County         WY          56  2020-04-01   \n",
       "239696        56045  Weston County         WY          56  2020-04-02   \n",
       "239697        56045  Weston County         WY          56  2020-04-03   \n",
       "239698        56045  Weston County         WY          56  2020-04-04   \n",
       "239699        56045  Weston County         WY          56  2020-04-05   \n",
       "\n",
       "        confirmed_cases        lat        long  \\\n",
       "239695              NaN  43.839612 -104.567488   \n",
       "239696              NaN  43.839612 -104.567488   \n",
       "239697              NaN  43.839612 -104.567488   \n",
       "239698              NaN  43.839612 -104.567488   \n",
       "239699              NaN  43.839612 -104.567488   \n",
       "\n",
       "                                geometry   datetime  \n",
       "239695  POINT (-104.5674881 43.83961191) 2020-04-01  \n",
       "239696  POINT (-104.5674881 43.83961191) 2020-04-02  \n",
       "239697  POINT (-104.5674881 43.83961191) 2020-04-03  \n",
       "239698  POINT (-104.5674881 43.83961191) 2020-04-04  \n",
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     "execution_count": 184,
     "metadata": {},
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   ],
   "source": [
    "# For which counties do we have missing values? \n",
    "us_cases[us_cases.confirmed_cases.isna() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Weston County appears to have no information for April.\n",
    "# However there have been no confirmed cases in Weston County: \n",
    "print(us_cases[us_cases.county_name == 'Weston County'].confirmed_cases.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. General Overview\n",
    "### A. Which US states have the most confirmed cases of Covid-19?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
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\"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}}}, \"title\": {\"text\": \"Confirmed cases of Covid-19 in the US states as of April 8th\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('7edad0e5-083b-45a9-8ff4-b859211177ef');\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": [
    "# First group by county and state, then sum up all the counties' confirmed cases to get to the total sum for a state\n",
    "us_cases_grouping_county = us_cases.groupby(['county_fips', 'state_name']).confirmed_cases.max().reset_index().sort_values('confirmed_cases', ascending = False)\n",
    "us_cases_grouping_states = us_cases_grouping_county.groupby('state_name').confirmed_cases.sum().reset_index().sort_values('confirmed_cases', ascending = False)\n",
    "\n",
    "# Plot the map using plotly\n",
    "\n",
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=us_cases_grouping_states['state_name'], \n",
    "    z = us_cases_grouping_states['confirmed_cases'].astype(float), \n",
    "    locationmode = 'USA-states', \n",
    "    colorscale = 'blues',\n",
    "    colorbar_title = \"Confirmed cases\",\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = 'Confirmed cases of Covid-19 in the US states as of April 8th',\n",
    "    geo_scope='usa', \n",
    ")\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>confirmed_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>36081</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>NY</td>\n",
       "      <td>21436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>36047</td>\n",
       "      <td>Kings County</td>\n",
       "      <td>NY</td>\n",
       "      <td>18558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau County</td>\n",
       "      <td>NY</td>\n",
       "      <td>14398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>36119</td>\n",
       "      <td>Westchester County</td>\n",
       "      <td>NY</td>\n",
       "      <td>13723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>36005</td>\n",
       "      <td>Bronx County</td>\n",
       "      <td>NY</td>\n",
       "      <td>13368.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      county_fips         county_name state_name  confirmed_cases\n",
       "1871        36081       Queens County         NY          21436.0\n",
       "1854        36047        Kings County         NY          18558.0\n",
       "1860        36059       Nassau County         NY          14398.0\n",
       "1890        36119  Westchester County         NY          13723.0\n",
       "1833        36005        Bronx County         NY          13368.0"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show confirmed cases in all US counties\n",
    "us_cases_all_counties = us_cases.groupby(['county_fips', 'county_name', 'state_name']).confirmed_cases.max().reset_index().sort_values('confirmed_cases', ascending = False)\n",
    "us_cases_all_counties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Identify correlation between SVI and Covid-19 confirmed cases in all US counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>EP_POV</th>\n",
       "      <th>EP_LIMENG</th>\n",
       "      <th>EP_AGE65</th>\n",
       "      <th>EP_MINRTY</th>\n",
       "      <th>EP_UNEMP</th>\n",
       "      <th>EP_NOHSDP</th>\n",
       "      <th>EP_AGE17</th>\n",
       "      <th>EP_DISABL</th>\n",
       "      <th>EP_SNGPNT</th>\n",
       "      <th>EP_MUNIT</th>\n",
       "      <th>EP_MOBILE</th>\n",
       "      <th>EP_CROWD</th>\n",
       "      <th>EP_NOVEH</th>\n",
       "      <th>EP_GROUPQ</th>\n",
       "      <th>RPL_THEMES</th>\n",
       "      <th>confirmed_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>36081</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>12.464752</td>\n",
       "      <td>13.553882</td>\n",
       "      <td>14.910714</td>\n",
       "      <td>74.194255</td>\n",
       "      <td>6.271273</td>\n",
       "      <td>18.169565</td>\n",
       "      <td>19.846894</td>\n",
       "      <td>9.428882</td>\n",
       "      <td>8.004348</td>\n",
       "      <td>24.628727</td>\n",
       "      <td>0.202174</td>\n",
       "      <td>9.048602</td>\n",
       "      <td>33.080124</td>\n",
       "      <td>1.169410</td>\n",
       "      <td>0.599525</td>\n",
       "      <td>21436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>36047</td>\n",
       "      <td>Kings</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>19.702270</td>\n",
       "      <td>11.425234</td>\n",
       "      <td>13.666622</td>\n",
       "      <td>62.745127</td>\n",
       "      <td>7.002670</td>\n",
       "      <td>17.972497</td>\n",
       "      <td>22.563418</td>\n",
       "      <td>10.203071</td>\n",
       "      <td>9.318692</td>\n",
       "      <td>34.845260</td>\n",
       "      <td>0.153805</td>\n",
       "      <td>10.716021</td>\n",
       "      <td>51.801068</td>\n",
       "      <td>1.593458</td>\n",
       "      <td>0.671966</td>\n",
       "      <td>18558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>5.569065</td>\n",
       "      <td>5.234532</td>\n",
       "      <td>17.138489</td>\n",
       "      <td>37.538489</td>\n",
       "      <td>4.215827</td>\n",
       "      <td>8.625540</td>\n",
       "      <td>21.671223</td>\n",
       "      <td>8.404317</td>\n",
       "      <td>5.633453</td>\n",
       "      <td>9.069784</td>\n",
       "      <td>0.181295</td>\n",
       "      <td>2.625180</td>\n",
       "      <td>6.764029</td>\n",
       "      <td>2.047842</td>\n",
       "      <td>0.278768</td>\n",
       "      <td>14398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>36119</td>\n",
       "      <td>Westchester</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>9.673059</td>\n",
       "      <td>6.023288</td>\n",
       "      <td>17.057534</td>\n",
       "      <td>46.512329</td>\n",
       "      <td>5.882648</td>\n",
       "      <td>12.072146</td>\n",
       "      <td>22.125114</td>\n",
       "      <td>9.858447</td>\n",
       "      <td>7.752968</td>\n",
       "      <td>26.946575</td>\n",
       "      <td>0.141096</td>\n",
       "      <td>4.049772</td>\n",
       "      <td>14.765297</td>\n",
       "      <td>2.385845</td>\n",
       "      <td>0.459970</td>\n",
       "      <td>13723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>36005</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>27.784337</td>\n",
       "      <td>15.004217</td>\n",
       "      <td>12.581928</td>\n",
       "      <td>88.284940</td>\n",
       "      <td>10.471084</td>\n",
       "      <td>27.526807</td>\n",
       "      <td>24.577711</td>\n",
       "      <td>14.728916</td>\n",
       "      <td>18.840361</td>\n",
       "      <td>61.236446</td>\n",
       "      <td>0.155422</td>\n",
       "      <td>11.897289</td>\n",
       "      <td>55.897892</td>\n",
       "      <td>2.735241</td>\n",
       "      <td>0.876122</td>\n",
       "      <td>13368.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      county_fips       COUNTY     STATE     EP_POV  EP_LIMENG   EP_AGE65  \\\n",
       "1867        36081       Queens  NEW YORK  12.464752  13.553882  14.910714   \n",
       "1850        36047        Kings  NEW YORK  19.702270  11.425234  13.666622   \n",
       "1856        36059       Nassau  NEW YORK   5.569065   5.234532  17.138489   \n",
       "1886        36119  Westchester  NEW YORK   9.673059   6.023288  17.057534   \n",
       "1829        36005        Bronx  NEW YORK  27.784337  15.004217  12.581928   \n",
       "\n",
       "      EP_MINRTY   EP_UNEMP  EP_NOHSDP   EP_AGE17  EP_DISABL  EP_SNGPNT  \\\n",
       "1867  74.194255   6.271273  18.169565  19.846894   9.428882   8.004348   \n",
       "1850  62.745127   7.002670  17.972497  22.563418  10.203071   9.318692   \n",
       "1856  37.538489   4.215827   8.625540  21.671223   8.404317   5.633453   \n",
       "1886  46.512329   5.882648  12.072146  22.125114   9.858447   7.752968   \n",
       "1829  88.284940  10.471084  27.526807  24.577711  14.728916  18.840361   \n",
       "\n",
       "       EP_MUNIT  EP_MOBILE   EP_CROWD   EP_NOVEH  EP_GROUPQ  RPL_THEMES  \\\n",
       "1867  24.628727   0.202174   9.048602  33.080124   1.169410    0.599525   \n",
       "1850  34.845260   0.153805  10.716021  51.801068   1.593458    0.671966   \n",
       "1856   9.069784   0.181295   2.625180   6.764029   2.047842    0.278768   \n",
       "1886  26.946575   0.141096   4.049772  14.765297   2.385845    0.459970   \n",
       "1829  61.236446   0.155422  11.897289  55.897892   2.735241    0.876122   \n",
       "\n",
       "      confirmed_cases  \n",
       "1867          21436.0  \n",
       "1850          18558.0  \n",
       "1856          14398.0  \n",
       "1886          13723.0  \n",
       "1829          13368.0  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we group by Fips, County and State and look at the mean value of the selected indicators. \n",
    "svi_all_counties = svi.groupby(['county_fips', \n",
    "                                 'COUNTY', \n",
    "                                 'STATE']).agg({'EP_POV':'mean',\n",
    "                                              'EP_LIMENG':'mean',\n",
    "                                               'EP_AGE65':'mean',\n",
    "                                               'EP_MINRTY':'mean',\n",
    "                                               'EP_UNEMP':'mean',\n",
    "                                               'EP_NOHSDP':'mean',\n",
    "                                               'EP_AGE17':'mean',\n",
    "                                               'EP_DISABL':'mean',\n",
    "                                               'EP_SNGPNT':'mean',\n",
    "                                               'EP_MUNIT':'mean',\n",
    "                                               'EP_MOBILE':'mean',\n",
    "                                               'EP_CROWD':'mean',\n",
    "                                               'EP_NOVEH':'mean',\n",
    "                                               'EP_GROUPQ':'mean',\n",
    "                                                'RPL_THEMES':'mean'\n",
    "                                              }\n",
    "                                             ).reset_index()\n",
    "\n",
    "svi_all_counties_cases = svi_all_counties.merge(us_cases_all_counties, on = 'county_fips').sort_values('confirmed_cases', ascending = False)\n",
    "svi_all_counties_cases = svi_all_counties_cases.drop(['county_name', 'state_name'], axis = 1)\n",
    "svi_all_counties_cases.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col3 {\n",
       "            background-color:  #bed8ec;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col4 {\n",
       "            background-color:  #9cc9e1;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col5 {\n",
       "            background-color:  #d3e4f3;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col6 {\n",
       "            background-color:  #bed8ec;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col7 {\n",
       "            background-color:  #d1e2f3;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col8 {\n",
       "            background-color:  #b8d5ea;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col9 {\n",
       "            background-color:  #a5cde3;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col10 {\n",
       "            background-color:  #aacfe5;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col11 {\n",
       "            background-color:  #a6cee4;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col12 {\n",
       "            background-color:  #c8dcf0;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col13 {\n",
       "            background-color:  #c2d9ee;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col14 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col15 {\n",
       "            background-color:  #b8d5ea;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col0 {\n",
       "            background-color:  #e3eef9;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col1 {\n",
       "            background-color:  #1a68ae;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col2 {\n",
       "            background-color:  #7fb9da;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col3 {\n",
       "            background-color:  #b5d4e9;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col4 {\n",
       "            background-color:  #2f7fbc;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col5 {\n",
       "            background-color:  #3585bf;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col6 {\n",
       "            background-color:  #125ea6;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col7 {\n",
       "            background-color:  #63a8d3;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col8 {\n",
       "            background-color:  #3787c0;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col9 {\n",
       "            background-color:  #3080bd;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col10 {\n",
       "            background-color:  #d7e6f5;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col11 {\n",
       "            background-color:  #3181bd;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col12 {\n",
       "            background-color:  #4f9bcb;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col13 {\n",
       "            background-color:  #71b1d7;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col14 {\n",
       "            background-color:  #aed1e7;\n",
       "            color:  #000000;\n",
       "        }    #T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col15 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3c\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >confirmed_cases</th>        <th class=\"col_heading level0 col1\" >EP_POV</th>        <th class=\"col_heading level0 col2\" >EP_LIMENG</th>        <th class=\"col_heading level0 col3\" >EP_AGE65</th>        <th class=\"col_heading level0 col4\" >EP_MINRTY</th>        <th class=\"col_heading level0 col5\" >EP_UNEMP</th>        <th class=\"col_heading level0 col6\" >EP_NOHSDP</th>        <th class=\"col_heading level0 col7\" >EP_AGE17</th>        <th class=\"col_heading level0 col8\" >EP_DISABL</th>        <th class=\"col_heading level0 col9\" >EP_SNGPNT</th>        <th class=\"col_heading level0 col10\" >EP_MUNIT</th>        <th class=\"col_heading level0 col11\" >EP_MOBILE</th>        <th class=\"col_heading level0 col12\" >EP_CROWD</th>        <th class=\"col_heading level0 col13\" >EP_NOVEH</th>        <th class=\"col_heading level0 col14\" >EP_GROUPQ</th>        <th class=\"col_heading level0 col15\" >RPL_THEMES</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row0\" class=\"row_heading level0 row0\" >confirmed_cases</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col1\" class=\"data row0 col1\" >-0.028</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col2\" class=\"data row0 col2\" >0.227</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col3\" class=\"data row0 col3\" >-0.094</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col4\" class=\"data row0 col4\" >0.157</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col5\" class=\"data row0 col5\" >0.025</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col6\" class=\"data row0 col6\" >-0.004</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col7\" class=\"data row0 col7\" >-0.015</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col8\" class=\"data row0 col8\" >-0.133</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col9\" class=\"data row0 col9\" >0.040</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col10\" class=\"data row0 col10\" >0.382</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col11\" class=\"data row0 col11\" >-0.141</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col12\" class=\"data row0 col12\" >0.115</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col13\" class=\"data row0 col13\" >0.327</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col14\" class=\"data row0 col14\" >-0.033</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow0_col15\" class=\"data row0 col15\" >-0.028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row1\" class=\"row_heading level0 row1\" >EP_POV</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col0\" class=\"data row1 col0\" >-0.028</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col2\" class=\"data row1 col2\" >0.068</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col3\" class=\"data row1 col3\" >-0.141</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col4\" class=\"data row1 col4\" >0.458</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col5\" class=\"data row1 col5\" >0.647</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col6\" class=\"data row1 col6\" >0.613</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col7\" class=\"data row1 col7\" >0.092</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col8\" class=\"data row1 col8\" >0.470</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col9\" class=\"data row1 col9\" >0.528</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col10\" class=\"data row1 col10\" >-0.087</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col11\" class=\"data row1 col11\" >0.470</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col12\" class=\"data row1 col12\" >0.290</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col13\" class=\"data row1 col13\" >0.432</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col14\" class=\"data row1 col14\" >0.159</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow1_col15\" class=\"data row1 col15\" >0.754</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row2\" class=\"row_heading level0 row2\" >EP_LIMENG</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col0\" class=\"data row2 col0\" >0.227</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col1\" class=\"data row2 col1\" >0.068</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col3\" class=\"data row2 col3\" >-0.286</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col4\" class=\"data row2 col4\" >0.546</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col5\" class=\"data row2 col5\" >0.034</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col6\" class=\"data row2 col6\" >0.452</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col7\" class=\"data row2 col7\" >0.288</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col8\" class=\"data row2 col8\" >-0.217</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col9\" class=\"data row2 col9\" >0.209</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col10\" class=\"data row2 col10\" >0.249</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col11\" class=\"data row2 col11\" >-0.044</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col12\" class=\"data row2 col12\" >0.456</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col13\" class=\"data row2 col13\" >0.084</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col14\" class=\"data row2 col14\" >0.025</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow2_col15\" class=\"data row2 col15\" >0.293</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row3\" class=\"row_heading level0 row3\" >EP_AGE65</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col0\" class=\"data row3 col0\" >-0.094</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col1\" class=\"data row3 col1\" >-0.141</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col2\" class=\"data row3 col2\" >-0.286</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col4\" class=\"data row3 col4\" >-0.381</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col5\" class=\"data row3 col5\" >-0.149</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col6\" class=\"data row3 col6\" >-0.148</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col7\" class=\"data row3 col7\" >-0.584</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col8\" class=\"data row3 col8\" >0.404</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col9\" class=\"data row3 col9\" >-0.470</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col10\" class=\"data row3 col10\" >-0.336</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col11\" class=\"data row3 col11\" >0.137</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col12\" class=\"data row3 col12\" >-0.324</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col13\" class=\"data row3 col13\" >-0.203</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col14\" class=\"data row3 col14\" >-0.146</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow3_col15\" class=\"data row3 col15\" >-0.104</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row4\" class=\"row_heading level0 row4\" >EP_MINRTY</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col0\" class=\"data row4 col0\" >0.157</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col1\" class=\"data row4 col1\" >0.458</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col2\" class=\"data row4 col2\" >0.546</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col3\" class=\"data row4 col3\" >-0.381</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col5\" class=\"data row4 col5\" >0.462</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col6\" class=\"data row4 col6\" >0.499</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col7\" class=\"data row4 col7\" >0.299</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col8\" class=\"data row4 col8\" >-0.064</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col9\" class=\"data row4 col9\" >0.574</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col10\" class=\"data row4 col10\" >0.198</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col11\" class=\"data row4 col11\" >0.152</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col12\" class=\"data row4 col12\" >0.526</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col13\" class=\"data row4 col13\" >0.397</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col14\" class=\"data row4 col14\" >0.145</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow4_col15\" class=\"data row4 col15\" >0.583</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row5\" class=\"row_heading level0 row5\" >EP_UNEMP</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col0\" class=\"data row5 col0\" >0.025</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col1\" class=\"data row5 col1\" >0.647</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col2\" class=\"data row5 col2\" >0.034</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col3\" class=\"data row5 col3\" >-0.149</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col4\" class=\"data row5 col4\" >0.462</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col6\" class=\"data row5 col6\" >0.439</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col7\" class=\"data row5 col7\" >0.080</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col8\" class=\"data row5 col8\" >0.398</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col9\" class=\"data row5 col9\" >0.494</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col10\" class=\"data row5 col10\" >-0.052</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col11\" class=\"data row5 col11\" >0.320</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col12\" class=\"data row5 col12\" >0.346</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col13\" class=\"data row5 col13\" >0.460</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col14\" class=\"data row5 col14\" >0.059</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow5_col15\" class=\"data row5 col15\" >0.624</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row6\" class=\"row_heading level0 row6\" >EP_NOHSDP</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col0\" class=\"data row6 col0\" >-0.004</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col1\" class=\"data row6 col1\" >0.613</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col2\" class=\"data row6 col2\" >0.452</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col3\" class=\"data row6 col3\" >-0.148</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col4\" class=\"data row6 col4\" >0.499</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col5\" class=\"data row6 col5\" >0.439</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col7\" class=\"data row6 col7\" >0.247</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col8\" class=\"data row6 col8\" >0.429</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col9\" class=\"data row6 col9\" >0.384</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col10\" class=\"data row6 col10\" >-0.256</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col11\" class=\"data row6 col11\" >0.551</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col12\" class=\"data row6 col12\" >0.355</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col13\" class=\"data row6 col13\" >0.266</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col14\" class=\"data row6 col14\" >0.088</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow6_col15\" class=\"data row6 col15\" >0.777</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row7\" class=\"row_heading level0 row7\" >EP_AGE17</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col0\" class=\"data row7 col0\" >-0.015</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col1\" class=\"data row7 col1\" >0.092</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col2\" class=\"data row7 col2\" >0.288</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col3\" class=\"data row7 col3\" >-0.584</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col4\" class=\"data row7 col4\" >0.299</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col5\" class=\"data row7 col5\" >0.080</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col6\" class=\"data row7 col6\" >0.247</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col7\" class=\"data row7 col7\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col8\" class=\"data row7 col8\" >-0.251</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col9\" class=\"data row7 col9\" >0.428</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col10\" class=\"data row7 col10\" >-0.085</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col11\" class=\"data row7 col11\" >-0.036</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col12\" class=\"data row7 col12\" >0.441</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col13\" class=\"data row7 col13\" >0.051</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col14\" class=\"data row7 col14\" >-0.275</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow7_col15\" class=\"data row7 col15\" >0.250</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row8\" class=\"row_heading level0 row8\" >EP_DISABL</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col0\" class=\"data row8 col0\" >-0.133</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col1\" class=\"data row8 col1\" >0.470</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col2\" class=\"data row8 col2\" >-0.217</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col3\" class=\"data row8 col3\" >0.404</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col4\" class=\"data row8 col4\" >-0.064</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col5\" class=\"data row8 col5\" >0.398</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col6\" class=\"data row8 col6\" >0.429</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col7\" class=\"data row8 col7\" >-0.251</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col8\" class=\"data row8 col8\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col9\" class=\"data row8 col9\" >0.033</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col10\" class=\"data row8 col10\" >-0.411</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col11\" class=\"data row8 col11\" >0.528</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col12\" class=\"data row8 col12\" >-0.091</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col13\" class=\"data row8 col13\" >0.122</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col14\" class=\"data row8 col14\" >0.007</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow8_col15\" class=\"data row8 col15\" >0.529</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row9\" class=\"row_heading level0 row9\" >EP_SNGPNT</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col0\" class=\"data row9 col0\" >0.040</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col1\" class=\"data row9 col1\" >0.528</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col2\" class=\"data row9 col2\" >0.209</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col3\" class=\"data row9 col3\" >-0.470</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col4\" class=\"data row9 col4\" >0.574</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col5\" class=\"data row9 col5\" >0.494</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col6\" class=\"data row9 col6\" >0.384</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col7\" class=\"data row9 col7\" >0.428</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col8\" class=\"data row9 col8\" >0.033</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col9\" class=\"data row9 col9\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col10\" class=\"data row9 col10\" >0.095</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col11\" class=\"data row9 col11\" >0.061</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col12\" class=\"data row9 col12\" >0.407</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col13\" class=\"data row9 col13\" >0.431</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col14\" class=\"data row9 col14\" >0.048</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow9_col15\" class=\"data row9 col15\" >0.548</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row10\" class=\"row_heading level0 row10\" >EP_MUNIT</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col0\" class=\"data row10 col0\" >0.382</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col1\" class=\"data row10 col1\" >-0.087</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col2\" class=\"data row10 col2\" >0.249</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col3\" class=\"data row10 col3\" >-0.336</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col4\" class=\"data row10 col4\" >0.198</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col5\" class=\"data row10 col5\" >-0.052</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col6\" class=\"data row10 col6\" >-0.256</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col7\" class=\"data row10 col7\" >-0.085</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col8\" class=\"data row10 col8\" >-0.411</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col9\" class=\"data row10 col9\" >0.095</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col10\" class=\"data row10 col10\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col11\" class=\"data row10 col11\" >-0.449</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col12\" class=\"data row10 col12\" >0.092</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col13\" class=\"data row10 col13\" >0.306</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col14\" class=\"data row10 col14\" >0.042</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow10_col15\" class=\"data row10 col15\" >-0.216</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row11\" class=\"row_heading level0 row11\" >EP_MOBILE</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col0\" class=\"data row11 col0\" >-0.141</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col1\" class=\"data row11 col1\" >0.470</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col2\" class=\"data row11 col2\" >-0.044</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col3\" class=\"data row11 col3\" >0.137</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col4\" class=\"data row11 col4\" >0.152</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col5\" class=\"data row11 col5\" >0.320</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col6\" class=\"data row11 col6\" >0.551</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col7\" class=\"data row11 col7\" >-0.036</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col8\" class=\"data row11 col8\" >0.528</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col9\" class=\"data row11 col9\" >0.061</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col10\" class=\"data row11 col10\" >-0.449</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col11\" class=\"data row11 col11\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col12\" class=\"data row11 col12\" >0.087</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col13\" class=\"data row11 col13\" >-0.017</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col14\" class=\"data row11 col14\" >0.057</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow11_col15\" class=\"data row11 col15\" >0.550</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row12\" class=\"row_heading level0 row12\" >EP_CROWD</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col0\" class=\"data row12 col0\" >0.115</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col1\" class=\"data row12 col1\" >0.290</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col2\" class=\"data row12 col2\" >0.456</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col3\" class=\"data row12 col3\" >-0.324</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col4\" class=\"data row12 col4\" >0.526</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col5\" class=\"data row12 col5\" >0.346</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col6\" class=\"data row12 col6\" >0.355</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col7\" class=\"data row12 col7\" >0.441</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col8\" class=\"data row12 col8\" >-0.091</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col9\" class=\"data row12 col9\" >0.407</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col10\" class=\"data row12 col10\" >0.092</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col11\" class=\"data row12 col11\" >0.087</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col12\" class=\"data row12 col12\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col13\" class=\"data row12 col13\" >0.439</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col14\" class=\"data row12 col14\" >-0.005</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow12_col15\" class=\"data row12 col15\" >0.448</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row13\" class=\"row_heading level0 row13\" >EP_NOVEH</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col0\" class=\"data row13 col0\" >0.327</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col1\" class=\"data row13 col1\" >0.432</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col2\" class=\"data row13 col2\" >0.084</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col3\" class=\"data row13 col3\" >-0.203</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col4\" class=\"data row13 col4\" >0.397</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col5\" class=\"data row13 col5\" >0.460</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col6\" class=\"data row13 col6\" >0.266</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col7\" class=\"data row13 col7\" >0.051</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col8\" class=\"data row13 col8\" >0.122</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col9\" class=\"data row13 col9\" >0.431</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col10\" class=\"data row13 col10\" >0.306</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col11\" class=\"data row13 col11\" >-0.017</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col12\" class=\"data row13 col12\" >0.439</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col13\" class=\"data row13 col13\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col14\" class=\"data row13 col14\" >0.115</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow13_col15\" class=\"data row13 col15\" >0.383</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row14\" class=\"row_heading level0 row14\" >EP_GROUPQ</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col0\" class=\"data row14 col0\" >-0.033</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col1\" class=\"data row14 col1\" >0.159</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col2\" class=\"data row14 col2\" >0.025</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col3\" class=\"data row14 col3\" >-0.146</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col4\" class=\"data row14 col4\" >0.145</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col5\" class=\"data row14 col5\" >0.059</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col6\" class=\"data row14 col6\" >0.088</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col7\" class=\"data row14 col7\" >-0.275</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col8\" class=\"data row14 col8\" >0.007</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col9\" class=\"data row14 col9\" >0.048</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col10\" class=\"data row14 col10\" >0.042</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col11\" class=\"data row14 col11\" >0.057</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col12\" class=\"data row14 col12\" >-0.005</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col13\" class=\"data row14 col13\" >0.115</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col14\" class=\"data row14 col14\" >1.000</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow14_col15\" class=\"data row14 col15\" >0.142</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3clevel0_row15\" class=\"row_heading level0 row15\" >RPL_THEMES</th>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col0\" class=\"data row15 col0\" >-0.028</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col1\" class=\"data row15 col1\" >0.754</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col2\" class=\"data row15 col2\" >0.293</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col3\" class=\"data row15 col3\" >-0.104</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col4\" class=\"data row15 col4\" >0.583</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col5\" class=\"data row15 col5\" >0.624</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col6\" class=\"data row15 col6\" >0.777</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col7\" class=\"data row15 col7\" >0.250</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col8\" class=\"data row15 col8\" >0.529</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col9\" class=\"data row15 col9\" >0.548</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col10\" class=\"data row15 col10\" >-0.216</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col11\" class=\"data row15 col11\" >0.550</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col12\" class=\"data row15 col12\" >0.448</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col13\" class=\"data row15 col13\" >0.383</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col14\" class=\"data row15 col14\" >0.142</td>\n",
       "                        <td id=\"T_36d37474_8e52_11ea_9216_206a8aa20d3crow15_col15\" class=\"data row15 col15\" >1.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x199c99cd808>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting a correlation matrix for all US counties\n",
    "svi_all_counties_cases_corr = svi_all_counties_cases[['confirmed_cases', 'EP_POV', 'EP_LIMENG', 'EP_AGE65', 'EP_MINRTY', 'EP_UNEMP', 'EP_NOHSDP', 'EP_AGE17', 'EP_DISABL', 'EP_SNGPNT', 'EP_MUNIT', 'EP_MOBILE', 'EP_CROWD', 'EP_NOVEH', 'EP_GROUPQ', 'RPL_THEMES']]\n",
    "corr = svi_all_counties_cases_corr.corr()\n",
    "table = corr.style.background_gradient(cmap='Blues').format('{0:,.3f}')\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\r\n",
      "[>                                                           ] 0%\r",
      "[======>                                                     ] 10%\r",
      "[==============================>                             ] 50%\r",
      "[============================================================] 100%\r",
      "Rendering (2/2)                                                    \r\n",
      "[>                                                           ] 0%\r",
      "[===============>                                            ] 25%\r",
      "[============================================================] 100%\r",
      "Done                                                               \r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export Pandas style object to PNG\n",
    "html = table.render()\n",
    "\n",
    "path_wkthmltoimage = r'D:\\Tools\\wkthmltool\\bin\\wkhtmltoimage.exe'\n",
    "config = imgkit.config(wkhtmltoimage=path_wkthmltoimage)\n",
    "\n",
    "imgkit.from_string(html, './Media for Medium post/corr_confirmed_cases_SVI_all_counties.png',config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Analysis of the ten US counties with most confirmed cases\n",
    "### B. Which are the ten US counties with most confirmed cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      county_fips         county_name state_name  confirmed_cases\n",
      "1871        36081       Queens County         NY          21436.0\n",
      "1854        36047        Kings County         NY          18558.0\n",
      "1860        36059       Nassau County         NY          14398.0\n",
      "1890        36119  Westchester County         NY          13723.0\n",
      "1833        36005        Bronx County         NY          13368.0\n",
      "1882        36103      Suffolk County         NY          12405.0\n",
      "1861        36061     New York County         NY           9898.0\n",
      "613         17031         Cook County         IL           8034.0\n",
      "1315        26163        Wayne County         MI           7518.0\n",
      "1778        34003       Bergen County         NJ           6187.0\n"
     ]
    }
   ],
   "source": [
    "# Which are the 10 most affected US counties? \n",
    "us_cases_top_10 = us_cases_all_counties.head(10)\n",
    "print(us_cases_top_10)\n",
    "us_cases_top_10.to_csv('./Media for Medium post/us_cases_top_10.csv')\n",
    "\n",
    "# Preparing a list with the 10 most affected counties: \n",
    "top_10_cases_fips_list = us_cases_top_10.county_fips.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>state_fips</th>\n",
       "      <th>date</th>\n",
       "      <th>confirmed_cases</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>geometry</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46875</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46876</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46877</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46878</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46879</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       county_fips  county_name state_name  state_fips        date  \\\n",
       "46875        17031  Cook County         IL          17  2020-01-22   \n",
       "46876        17031  Cook County         IL          17  2020-01-23   \n",
       "46877        17031  Cook County         IL          17  2020-01-24   \n",
       "46878        17031  Cook County         IL          17  2020-01-25   \n",
       "46879        17031  Cook County         IL          17  2020-01-26   \n",
       "\n",
       "       confirmed_cases        lat       long  \\\n",
       "46875              0.0  41.841448 -87.816588   \n",
       "46876              0.0  41.841448 -87.816588   \n",
       "46877              1.0  41.841448 -87.816588   \n",
       "46878              1.0  41.841448 -87.816588   \n",
       "46879              1.0  41.841448 -87.816588   \n",
       "\n",
       "                               geometry   datetime  \n",
       "46875  POINT (-87.81658794 41.84144849) 2020-01-22  \n",
       "46876  POINT (-87.81658794 41.84144849) 2020-01-23  \n",
       "46877  POINT (-87.81658794 41.84144849) 2020-01-24  \n",
       "46878  POINT (-87.81658794 41.84144849) 2020-01-25  \n",
       "46879  POINT (-87.81658794 41.84144849) 2020-01-26  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the timeseries statistics of the 10 most affected US counties\n",
    "us_cases_top_10_datetime = us_cases[us_cases.county_fips.isin(top_10_cases_fips_list) == True]\n",
    "us_cases_top_10_datetime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Timeline of cases\n",
    "\n",
    "f, ax = plt.subplots(figsize = (16, 10))\n",
    "ax = sns.lineplot(x = us_cases_top_10_datetime.datetime, y = us_cases_top_10_datetime.confirmed_cases, hue = us_cases_top_10_datetime.county_name, ci = None)\n",
    "\n",
    "plt.xlabel('Timeline',fontsize=12)\n",
    "plt.ylabel('Confirmed Cases',fontsize=12)\n",
    "plt.title('Confirmed cases in US counties as of April 8th', fontsize = 16)\n",
    "plt.tick_params(labelsize=12)\n",
    "ax.yaxis.tick_right()\n",
    "plt.legend(loc=2, fontsize=12)\n",
    "plt.grid(False)\n",
    "ax.yaxis.grid()\n",
    "sns.despine()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Identify correlation between SVI and the number of Covid-19 in the ten most affected US counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>county_fips</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>EP_POV</th>\n",
       "      <th>EP_LIMENG</th>\n",
       "      <th>EP_AGE65</th>\n",
       "      <th>EP_MINRTY</th>\n",
       "      <th>EP_UNEMP</th>\n",
       "      <th>EP_NOHSDP</th>\n",
       "      <th>EP_AGE17</th>\n",
       "      <th>EP_DISABL</th>\n",
       "      <th>EP_SNGPNT</th>\n",
       "      <th>EP_MUNIT</th>\n",
       "      <th>EP_MOBILE</th>\n",
       "      <th>EP_CROWD</th>\n",
       "      <th>EP_NOVEH</th>\n",
       "      <th>EP_GROUPQ</th>\n",
       "      <th>RPL_THEMES</th>\n",
       "      <th>confirmed_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36081</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>12.464752</td>\n",
       "      <td>13.553882</td>\n",
       "      <td>14.910714</td>\n",
       "      <td>74.194255</td>\n",
       "      <td>6.271273</td>\n",
       "      <td>18.169565</td>\n",
       "      <td>19.846894</td>\n",
       "      <td>9.428882</td>\n",
       "      <td>8.004348</td>\n",
       "      <td>24.628727</td>\n",
       "      <td>0.202174</td>\n",
       "      <td>9.048602</td>\n",
       "      <td>33.080124</td>\n",
       "      <td>1.169410</td>\n",
       "      <td>0.599525</td>\n",
       "      <td>21436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36047</td>\n",
       "      <td>Kings</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>19.702270</td>\n",
       "      <td>11.425234</td>\n",
       "      <td>13.666622</td>\n",
       "      <td>62.745127</td>\n",
       "      <td>7.002670</td>\n",
       "      <td>17.972497</td>\n",
       "      <td>22.563418</td>\n",
       "      <td>10.203071</td>\n",
       "      <td>9.318692</td>\n",
       "      <td>34.845260</td>\n",
       "      <td>0.153805</td>\n",
       "      <td>10.716021</td>\n",
       "      <td>51.801068</td>\n",
       "      <td>1.593458</td>\n",
       "      <td>0.671966</td>\n",
       "      <td>18558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>5.569065</td>\n",
       "      <td>5.234532</td>\n",
       "      <td>17.138489</td>\n",
       "      <td>37.538489</td>\n",
       "      <td>4.215827</td>\n",
       "      <td>8.625540</td>\n",
       "      <td>21.671223</td>\n",
       "      <td>8.404317</td>\n",
       "      <td>5.633453</td>\n",
       "      <td>9.069784</td>\n",
       "      <td>0.181295</td>\n",
       "      <td>2.625180</td>\n",
       "      <td>6.764029</td>\n",
       "      <td>2.047842</td>\n",
       "      <td>0.278768</td>\n",
       "      <td>14398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>36119</td>\n",
       "      <td>Westchester</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>9.673059</td>\n",
       "      <td>6.023288</td>\n",
       "      <td>17.057534</td>\n",
       "      <td>46.512329</td>\n",
       "      <td>5.882648</td>\n",
       "      <td>12.072146</td>\n",
       "      <td>22.125114</td>\n",
       "      <td>9.858447</td>\n",
       "      <td>7.752968</td>\n",
       "      <td>26.946575</td>\n",
       "      <td>0.141096</td>\n",
       "      <td>4.049772</td>\n",
       "      <td>14.765297</td>\n",
       "      <td>2.385845</td>\n",
       "      <td>0.459970</td>\n",
       "      <td>13723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36005</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>27.784337</td>\n",
       "      <td>15.004217</td>\n",
       "      <td>12.581928</td>\n",
       "      <td>88.284940</td>\n",
       "      <td>10.471084</td>\n",
       "      <td>27.526807</td>\n",
       "      <td>24.577711</td>\n",
       "      <td>14.728916</td>\n",
       "      <td>18.840361</td>\n",
       "      <td>61.236446</td>\n",
       "      <td>0.155422</td>\n",
       "      <td>11.897289</td>\n",
       "      <td>55.897892</td>\n",
       "      <td>2.735241</td>\n",
       "      <td>0.876122</td>\n",
       "      <td>13368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36103</td>\n",
       "      <td>Suffolk</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>6.783540</td>\n",
       "      <td>3.598137</td>\n",
       "      <td>16.667081</td>\n",
       "      <td>29.175155</td>\n",
       "      <td>4.645652</td>\n",
       "      <td>9.039441</td>\n",
       "      <td>21.411801</td>\n",
       "      <td>9.645963</td>\n",
       "      <td>6.135714</td>\n",
       "      <td>5.222671</td>\n",
       "      <td>0.683540</td>\n",
       "      <td>2.631366</td>\n",
       "      <td>4.969565</td>\n",
       "      <td>1.676398</td>\n",
       "      <td>0.290981</td>\n",
       "      <td>12405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36061</td>\n",
       "      <td>New York</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>16.366667</td>\n",
       "      <td>8.049821</td>\n",
       "      <td>15.774194</td>\n",
       "      <td>51.795699</td>\n",
       "      <td>5.816129</td>\n",
       "      <td>12.327957</td>\n",
       "      <td>13.768100</td>\n",
       "      <td>9.988172</td>\n",
       "      <td>5.775986</td>\n",
       "      <td>89.116846</td>\n",
       "      <td>0.057706</td>\n",
       "      <td>5.920789</td>\n",
       "      <td>77.127957</td>\n",
       "      <td>4.420072</td>\n",
       "      <td>0.517380</td>\n",
       "      <td>9898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook</td>\n",
       "      <td>ILLINOIS</td>\n",
       "      <td>17.136122</td>\n",
       "      <td>6.589886</td>\n",
       "      <td>13.754373</td>\n",
       "      <td>60.660608</td>\n",
       "      <td>9.353004</td>\n",
       "      <td>14.217947</td>\n",
       "      <td>22.228973</td>\n",
       "      <td>10.653004</td>\n",
       "      <td>10.195665</td>\n",
       "      <td>18.619468</td>\n",
       "      <td>0.650798</td>\n",
       "      <td>3.551483</td>\n",
       "      <td>19.018175</td>\n",
       "      <td>1.712700</td>\n",
       "      <td>0.544628</td>\n",
       "      <td>8034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26163</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>MICHIGAN</td>\n",
       "      <td>26.183389</td>\n",
       "      <td>2.208804</td>\n",
       "      <td>14.871761</td>\n",
       "      <td>57.680399</td>\n",
       "      <td>12.413289</td>\n",
       "      <td>15.891196</td>\n",
       "      <td>23.297674</td>\n",
       "      <td>17.354651</td>\n",
       "      <td>13.248837</td>\n",
       "      <td>10.961960</td>\n",
       "      <td>1.559801</td>\n",
       "      <td>2.341030</td>\n",
       "      <td>16.699834</td>\n",
       "      <td>1.586711</td>\n",
       "      <td>0.618729</td>\n",
       "      <td>7518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34003</td>\n",
       "      <td>Bergen</td>\n",
       "      <td>NEW JERSEY</td>\n",
       "      <td>7.161453</td>\n",
       "      <td>6.894413</td>\n",
       "      <td>16.959777</td>\n",
       "      <td>42.912291</td>\n",
       "      <td>4.481564</td>\n",
       "      <td>7.659218</td>\n",
       "      <td>21.412291</td>\n",
       "      <td>8.102793</td>\n",
       "      <td>5.830168</td>\n",
       "      <td>15.555866</td>\n",
       "      <td>0.385475</td>\n",
       "      <td>2.316760</td>\n",
       "      <td>7.956425</td>\n",
       "      <td>1.210056</td>\n",
       "      <td>0.338170</td>\n",
       "      <td>6187.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   county_fips       COUNTY       STATE     EP_POV  EP_LIMENG   EP_AGE65  \\\n",
       "7        36081       Queens    NEW YORK  12.464752  13.553882  14.910714   \n",
       "4        36047        Kings    NEW YORK  19.702270  11.425234  13.666622   \n",
       "5        36059       Nassau    NEW YORK   5.569065   5.234532  17.138489   \n",
       "9        36119  Westchester    NEW YORK   9.673059   6.023288  17.057534   \n",
       "3        36005        Bronx    NEW YORK  27.784337  15.004217  12.581928   \n",
       "8        36103      Suffolk    NEW YORK   6.783540   3.598137  16.667081   \n",
       "6        36061     New York    NEW YORK  16.366667   8.049821  15.774194   \n",
       "0        17031         Cook    ILLINOIS  17.136122   6.589886  13.754373   \n",
       "1        26163        Wayne    MICHIGAN  26.183389   2.208804  14.871761   \n",
       "2        34003       Bergen  NEW JERSEY   7.161453   6.894413  16.959777   \n",
       "\n",
       "   EP_MINRTY   EP_UNEMP  EP_NOHSDP   EP_AGE17  EP_DISABL  EP_SNGPNT  \\\n",
       "7  74.194255   6.271273  18.169565  19.846894   9.428882   8.004348   \n",
       "4  62.745127   7.002670  17.972497  22.563418  10.203071   9.318692   \n",
       "5  37.538489   4.215827   8.625540  21.671223   8.404317   5.633453   \n",
       "9  46.512329   5.882648  12.072146  22.125114   9.858447   7.752968   \n",
       "3  88.284940  10.471084  27.526807  24.577711  14.728916  18.840361   \n",
       "8  29.175155   4.645652   9.039441  21.411801   9.645963   6.135714   \n",
       "6  51.795699   5.816129  12.327957  13.768100   9.988172   5.775986   \n",
       "0  60.660608   9.353004  14.217947  22.228973  10.653004  10.195665   \n",
       "1  57.680399  12.413289  15.891196  23.297674  17.354651  13.248837   \n",
       "2  42.912291   4.481564   7.659218  21.412291   8.102793   5.830168   \n",
       "\n",
       "    EP_MUNIT  EP_MOBILE   EP_CROWD   EP_NOVEH  EP_GROUPQ  RPL_THEMES  \\\n",
       "7  24.628727   0.202174   9.048602  33.080124   1.169410    0.599525   \n",
       "4  34.845260   0.153805  10.716021  51.801068   1.593458    0.671966   \n",
       "5   9.069784   0.181295   2.625180   6.764029   2.047842    0.278768   \n",
       "9  26.946575   0.141096   4.049772  14.765297   2.385845    0.459970   \n",
       "3  61.236446   0.155422  11.897289  55.897892   2.735241    0.876122   \n",
       "8   5.222671   0.683540   2.631366   4.969565   1.676398    0.290981   \n",
       "6  89.116846   0.057706   5.920789  77.127957   4.420072    0.517380   \n",
       "0  18.619468   0.650798   3.551483  19.018175   1.712700    0.544628   \n",
       "1  10.961960   1.559801   2.341030  16.699834   1.586711    0.618729   \n",
       "2  15.555866   0.385475   2.316760   7.956425   1.210056    0.338170   \n",
       "\n",
       "   confirmed_cases  \n",
       "7          21436.0  \n",
       "4          18558.0  \n",
       "5          14398.0  \n",
       "9          13723.0  \n",
       "3          13368.0  \n",
       "8          12405.0  \n",
       "6           9898.0  \n",
       "0           8034.0  \n",
       "1           7518.0  \n",
       "2           6187.0  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows in the SVI dataframe that contain the ten counties with most confirmed cases. \n",
    "svi_top_10_counties_cases = svi[svi.county_fips.isin(top_10_cases_fips_list) == True]\n",
    "\n",
    "\n",
    "svi_top_10_counties_cases = svi_top_10_counties_cases.groupby(['county_fips', \n",
    "                                                                'COUNTY', \n",
    "                                                                'STATE']).agg({'EP_POV':'mean',\n",
    "                                                                              'EP_LIMENG':'mean',\n",
    "                                                                               'EP_AGE65':'mean',\n",
    "                                                                               'EP_MINRTY':'mean',\n",
    "                                                                               'EP_UNEMP':'mean',\n",
    "                                                                               'EP_NOHSDP':'mean',\n",
    "                                                                               'EP_AGE17':'mean',\n",
    "                                                                               'EP_DISABL':'mean',\n",
    "                                                                               'EP_SNGPNT':'mean',\n",
    "                                                                               'EP_MUNIT':'mean',\n",
    "                                                                               'EP_MOBILE':'mean',\n",
    "                                                                               'EP_CROWD':'mean',\n",
    "                                                                               'EP_NOVEH':'mean',\n",
    "                                                                               'EP_GROUPQ':'mean', \n",
    "                                                                               'RPL_THEMES': 'mean'\n",
    "                                                                              }\n",
    "                                                                             ).reset_index()\n",
    "\n",
    "\n",
    "# Merge county SVI with confirmed cases of Covid-19\n",
    "\n",
    "svi_top_10_counties_cases = svi_top_10_counties_cases.merge(us_cases_top_10, on = 'county_fips').sort_values('confirmed_cases', ascending = False)\n",
    "svi_top_10_counties_cases = svi_top_10_counties_cases.drop(['county_name', 'state_name'], axis = 1)\n",
    "svi_top_10_counties_cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #82bbdb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #95c5df;\n",
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       "            background-color:  #1460a8;\n",
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       "            background-color:  #135fa7;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col0 {\n",
       "            background-color:  #caddf0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #549fcd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #82bbdb;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col3 {\n",
       "            background-color:  #79b5d9;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col4 {\n",
       "            background-color:  #6dafd7;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col5 {\n",
       "            background-color:  #8fc2de;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col6 {\n",
       "            background-color:  #63a8d3;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col7 {\n",
       "            background-color:  #f7fbff;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col8 {\n",
       "            background-color:  #92c4de;\n",
       "            color:  #000000;\n",
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       "            background-color:  #7cb7da;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col10 {\n",
       "            background-color:  #08478d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col11 {\n",
       "            background-color:  #d9e7f5;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col12 {\n",
       "            background-color:  #65aad4;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col13 {\n",
       "            background-color:  #1663aa;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col14 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col15 {\n",
       "            background-color:  #57a0ce;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col0 {\n",
       "            background-color:  #6caed6;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col1 {\n",
       "            background-color:  #083c7d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col2 {\n",
       "            background-color:  #1562a9;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col3 {\n",
       "            background-color:  #f7fbff;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col4 {\n",
       "            background-color:  #083776;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col5 {\n",
       "            background-color:  #0b559f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col6 {\n",
       "            background-color:  #083471;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col7 {\n",
       "            background-color:  #5ba3d0;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col8 {\n",
       "            background-color:  #1a68ae;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col9 {\n",
       "            background-color:  #08458a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col10 {\n",
       "            background-color:  #3282be;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col11 {\n",
       "            background-color:  #9dcae1;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col12 {\n",
       "            background-color:  #084d96;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col13 {\n",
       "            background-color:  #1b69af;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col14 {\n",
       "            background-color:  #71b1d7;\n",
       "            color:  #000000;\n",
       "        }    #T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col15 {\n",
       "            background-color:  #08306b;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3c\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >confirmed_cases</th>        <th class=\"col_heading level0 col1\" >EP_POV</th>        <th class=\"col_heading level0 col2\" >EP_LIMENG</th>        <th class=\"col_heading level0 col3\" >EP_AGE65</th>        <th class=\"col_heading level0 col4\" >EP_MINRTY</th>        <th class=\"col_heading level0 col5\" >EP_UNEMP</th>        <th class=\"col_heading level0 col6\" >EP_NOHSDP</th>        <th class=\"col_heading level0 col7\" >EP_AGE17</th>        <th class=\"col_heading level0 col8\" >EP_DISABL</th>        <th class=\"col_heading level0 col9\" >EP_SNGPNT</th>        <th class=\"col_heading level0 col10\" >EP_MUNIT</th>        <th class=\"col_heading level0 col11\" >EP_MOBILE</th>        <th class=\"col_heading level0 col12\" >EP_CROWD</th>        <th class=\"col_heading level0 col13\" >EP_NOVEH</th>        <th class=\"col_heading level0 col14\" >EP_GROUPQ</th>        <th class=\"col_heading level0 col15\" >RPL_THEMES</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row0\" class=\"row_heading level0 row0\" >confirmed_cases</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col1\" class=\"data row0 col1\" >-0.074</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col2\" class=\"data row0 col2\" >0.603</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col3\" class=\"data row0 col3\" >-0.180</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col4\" class=\"data row0 col4\" >0.327</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col5\" class=\"data row0 col5\" >-0.233</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col6\" class=\"data row0 col6\" >0.363</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col7\" class=\"data row0 col7\" >0.031</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col8\" class=\"data row0 col8\" >-0.242</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col9\" class=\"data row0 col9\" >-0.023</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col10\" class=\"data row0 col10\" >0.044</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col11\" class=\"data row0 col11\" >-0.507</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col12\" class=\"data row0 col12\" >0.649</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col13\" class=\"data row0 col13\" >0.233</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col14\" class=\"data row0 col14\" >-0.157</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow0_col15\" class=\"data row0 col15\" >0.242</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row1\" class=\"row_heading level0 row1\" >EP_POV</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col0\" class=\"data row1 col0\" >-0.074</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col2\" class=\"data row1 col2\" >0.349</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col3\" class=\"data row1 col3\" >-0.852</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col4\" class=\"data row1 col4\" >0.770</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col5\" class=\"data row1 col5\" >0.911</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col6\" class=\"data row1 col6\" >0.827</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col7\" class=\"data row1 col7\" >0.267</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col8\" class=\"data row1 col8\" >0.867</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col9\" class=\"data row1 col9\" >0.872</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col10\" class=\"data row1 col10\" >0.428</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col11\" class=\"data row1 col11\" >0.321</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col12\" class=\"data row1 col12\" >0.535</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col13\" class=\"data row1 col13\" >0.560</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col14\" class=\"data row1 col14\" >0.204</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow1_col15\" class=\"data row1 col15\" >0.906</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row2\" class=\"row_heading level0 row2\" >EP_LIMENG</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col0\" class=\"data row2 col0\" >0.603</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col1\" class=\"data row2 col1\" >0.349</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col3\" class=\"data row2 col3\" >-0.621</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col4\" class=\"data row2 col4\" >0.805</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col5\" class=\"data row2 col5\" >0.082</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col6\" class=\"data row2 col6\" >0.747</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col7\" class=\"data row2 col7\" >0.024</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col8\" class=\"data row2 col8\" >-0.030</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col9\" class=\"data row2 col9\" >0.415</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col10\" class=\"data row2 col10\" >0.523</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col11\" class=\"data row2 col11\" >-0.627</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col12\" class=\"data row2 col12\" >0.931</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col13\" class=\"data row2 col13\" >0.634</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col14\" class=\"data row2 col14\" >0.097</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow2_col15\" class=\"data row2 col15\" >0.685</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row3\" class=\"row_heading level0 row3\" >EP_AGE65</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col0\" class=\"data row3 col0\" >-0.180</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col1\" class=\"data row3 col1\" >-0.852</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col2\" class=\"data row3 col2\" >-0.621</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col4\" class=\"data row3 col4\" >-0.868</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col5\" class=\"data row3 col5\" >-0.737</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col6\" class=\"data row3 col6\" >-0.885</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col7\" class=\"data row3 col7\" >-0.321</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col8\" class=\"data row3 col8\" >-0.565</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col9\" class=\"data row3 col9\" >-0.803</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col10\" class=\"data row3 col10\" >-0.356</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col11\" class=\"data row3 col11\" >-0.060</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col12\" class=\"data row3 col12\" >-0.729</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col13\" class=\"data row3 col13\" >-0.553</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col14\" class=\"data row3 col14\" >-0.018</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow3_col15\" class=\"data row3 col15\" >-0.907</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row4\" class=\"row_heading level0 row4\" >EP_MINRTY</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col0\" class=\"data row4 col0\" >0.327</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col1\" class=\"data row4 col1\" >0.770</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col2\" class=\"data row4 col2\" >0.805</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col3\" class=\"data row4 col3\" >-0.868</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col5\" class=\"data row4 col5\" >0.642</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col6\" class=\"data row4 col6\" >0.943</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col7\" class=\"data row4 col7\" >0.234</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col8\" class=\"data row4 col8\" >0.502</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col9\" class=\"data row4 col9\" >0.784</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col10\" class=\"data row4 col10\" >0.449</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col11\" class=\"data row4 col11\" >-0.130</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col12\" class=\"data row4 col12\" >0.813</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col13\" class=\"data row4 col13\" >0.584</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col14\" class=\"data row4 col14\" >0.057</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow4_col15\" class=\"data row4 col15\" >0.942</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row5\" class=\"row_heading level0 row5\" >EP_UNEMP</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col0\" class=\"data row5 col0\" >-0.233</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col1\" class=\"data row5 col1\" >0.911</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col2\" class=\"data row5 col2\" >0.082</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col3\" class=\"data row5 col3\" >-0.737</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col4\" class=\"data row5 col4\" >0.642</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col6\" class=\"data row5 col6\" >0.679</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col7\" class=\"data row5 col7\" >0.435</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col8\" class=\"data row5 col8\" >0.930</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col9\" class=\"data row5 col9\" >0.855</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col10\" class=\"data row5 col10\" >0.104</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col11\" class=\"data row5 col11\" >0.597</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col12\" class=\"data row5 col12\" >0.236</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col13\" class=\"data row5 col13\" >0.211</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col14\" class=\"data row5 col14\" >-0.019</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow5_col15\" class=\"data row5 col15\" >0.751</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row6\" class=\"row_heading level0 row6\" >EP_NOHSDP</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col0\" class=\"data row6 col0\" >0.363</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col1\" class=\"data row6 col1\" >0.827</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col2\" class=\"data row6 col2\" >0.747</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col3\" class=\"data row6 col3\" >-0.885</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col4\" class=\"data row6 col4\" >0.943</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col5\" class=\"data row6 col5\" >0.679</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col7\" class=\"data row6 col7\" >0.352</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col8\" class=\"data row6 col8\" >0.616</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col9\" class=\"data row6 col9\" >0.882</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col10\" class=\"data row6 col10\" >0.435</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col11\" class=\"data row6 col11\" >-0.081</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col12\" class=\"data row6 col12\" >0.845</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col13\" class=\"data row6 col13\" >0.576</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col14\" class=\"data row6 col14\" >0.105</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow6_col15\" class=\"data row6 col15\" >0.965</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row7\" class=\"row_heading level0 row7\" >EP_AGE17</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col0\" class=\"data row7 col0\" >0.031</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col1\" class=\"data row7 col1\" >0.267</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col2\" class=\"data row7 col2\" >0.024</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col3\" class=\"data row7 col3\" >-0.321</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col4\" class=\"data row7 col4\" >0.234</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col5\" class=\"data row7 col5\" >0.435</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col6\" class=\"data row7 col6\" >0.352</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col7\" class=\"data row7 col7\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col8\" class=\"data row7 col8\" >0.405</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col9\" class=\"data row7 col9\" >0.588</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col10\" class=\"data row7 col10\" >-0.564</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col11\" class=\"data row7 col11\" >0.336</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col12\" class=\"data row7 col12\" >0.084</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col13\" class=\"data row7 col13\" >-0.475</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col14\" class=\"data row7 col14\" >-0.631</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow7_col15\" class=\"data row7 col15\" >0.264</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row8\" class=\"row_heading level0 row8\" >EP_DISABL</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col0\" class=\"data row8 col0\" >-0.242</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col1\" class=\"data row8 col1\" >0.867</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col2\" class=\"data row8 col2\" >-0.030</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col3\" class=\"data row8 col3\" >-0.565</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col4\" class=\"data row8 col4\" >0.502</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col5\" class=\"data row8 col5\" >0.930</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col6\" class=\"data row8 col6\" >0.616</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col7\" class=\"data row8 col7\" >0.405</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col8\" class=\"data row8 col8\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col9\" class=\"data row8 col9\" >0.827</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col10\" class=\"data row8 col10\" >0.111</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col11\" class=\"data row8 col11\" >0.649</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col12\" class=\"data row8 col12\" >0.170</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col13\" class=\"data row8 col13\" >0.183</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col14\" class=\"data row8 col14\" >0.065</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow8_col15\" class=\"data row8 col15\" >0.660</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row9\" class=\"row_heading level0 row9\" >EP_SNGPNT</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col0\" class=\"data row9 col0\" >-0.023</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col1\" class=\"data row9 col1\" >0.872</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col2\" class=\"data row9 col2\" >0.415</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col3\" class=\"data row9 col3\" >-0.803</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col4\" class=\"data row9 col4\" >0.784</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col5\" class=\"data row9 col5\" >0.855</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col6\" class=\"data row9 col6\" >0.882</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col7\" class=\"data row9 col7\" >0.588</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col8\" class=\"data row9 col8\" >0.827</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col9\" class=\"data row9 col9\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col10\" class=\"data row9 col10\" >0.221</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col11\" class=\"data row9 col11\" >0.250</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col12\" class=\"data row9 col12\" >0.527</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col13\" class=\"data row9 col13\" >0.295</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col14\" class=\"data row9 col14\" >0.021</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow9_col15\" class=\"data row9 col15\" >0.857</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row10\" class=\"row_heading level0 row10\" >EP_MUNIT</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col0\" class=\"data row10 col0\" >0.044</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col1\" class=\"data row10 col1\" >0.428</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col2\" class=\"data row10 col2\" >0.523</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col3\" class=\"data row10 col3\" >-0.356</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col4\" class=\"data row10 col4\" >0.449</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col5\" class=\"data row10 col5\" >0.104</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col6\" class=\"data row10 col6\" >0.435</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col7\" class=\"data row10 col7\" >-0.564</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col8\" class=\"data row10 col8\" >0.111</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col9\" class=\"data row10 col9\" >0.221</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col10\" class=\"data row10 col10\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col11\" class=\"data row10 col11\" >-0.500</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col12\" class=\"data row10 col12\" >0.562</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col13\" class=\"data row10 col13\" >0.937</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col14\" class=\"data row10 col14\" >0.860</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow10_col15\" class=\"data row10 col15\" >0.510</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row11\" class=\"row_heading level0 row11\" >EP_MOBILE</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col0\" class=\"data row11 col0\" >-0.507</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col1\" class=\"data row11 col1\" >0.321</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col2\" class=\"data row11 col2\" >-0.627</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col3\" class=\"data row11 col3\" >-0.060</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col4\" class=\"data row11 col4\" >-0.130</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col5\" class=\"data row11 col5\" >0.597</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col6\" class=\"data row11 col6\" >-0.081</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col7\" class=\"data row11 col7\" >0.336</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col8\" class=\"data row11 col8\" >0.649</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col9\" class=\"data row11 col9\" >0.250</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col10\" class=\"data row11 col10\" >-0.500</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col11\" class=\"data row11 col11\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col12\" class=\"data row11 col12\" >-0.500</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col13\" class=\"data row11 col13\" >-0.433</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col14\" class=\"data row11 col14\" >-0.377</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow11_col15\" class=\"data row11 col15\" >-0.016</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row12\" class=\"row_heading level0 row12\" >EP_CROWD</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col0\" class=\"data row12 col0\" >0.649</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col1\" class=\"data row12 col1\" >0.535</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col2\" class=\"data row12 col2\" >0.931</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col3\" class=\"data row12 col3\" >-0.729</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col4\" class=\"data row12 col4\" >0.813</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col5\" class=\"data row12 col5\" >0.236</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col6\" class=\"data row12 col6\" >0.845</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col7\" class=\"data row12 col7\" >0.084</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col8\" class=\"data row12 col8\" >0.170</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col9\" class=\"data row12 col9\" >0.527</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col10\" class=\"data row12 col10\" >0.562</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col11\" class=\"data row12 col11\" >-0.500</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col12\" class=\"data row12 col12\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col13\" class=\"data row12 col13\" >0.731</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col14\" class=\"data row12 col14\" >0.164</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow12_col15\" class=\"data row12 col15\" >0.805</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row13\" class=\"row_heading level0 row13\" >EP_NOVEH</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col0\" class=\"data row13 col0\" >0.233</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col1\" class=\"data row13 col1\" >0.560</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col2\" class=\"data row13 col2\" >0.634</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col3\" class=\"data row13 col3\" >-0.553</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col4\" class=\"data row13 col4\" >0.584</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col5\" class=\"data row13 col5\" >0.211</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col6\" class=\"data row13 col6\" >0.576</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col7\" class=\"data row13 col7\" >-0.475</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col8\" class=\"data row13 col8\" >0.183</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col9\" class=\"data row13 col9\" >0.295</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col10\" class=\"data row13 col10\" >0.937</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col11\" class=\"data row13 col11\" >-0.433</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col12\" class=\"data row13 col12\" >0.731</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col13\" class=\"data row13 col13\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col14\" class=\"data row13 col14\" >0.694</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow13_col15\" class=\"data row13 col15\" >0.655</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row14\" class=\"row_heading level0 row14\" >EP_GROUPQ</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col0\" class=\"data row14 col0\" >-0.157</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col1\" class=\"data row14 col1\" >0.204</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col2\" class=\"data row14 col2\" >0.097</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col3\" class=\"data row14 col3\" >-0.018</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col4\" class=\"data row14 col4\" >0.057</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col5\" class=\"data row14 col5\" >-0.019</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col6\" class=\"data row14 col6\" >0.105</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col7\" class=\"data row14 col7\" >-0.631</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col8\" class=\"data row14 col8\" >0.065</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col9\" class=\"data row14 col9\" >0.021</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col10\" class=\"data row14 col10\" >0.860</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col11\" class=\"data row14 col11\" >-0.377</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col12\" class=\"data row14 col12\" >0.164</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col13\" class=\"data row14 col13\" >0.694</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col14\" class=\"data row14 col14\" >1.000</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow14_col15\" class=\"data row14 col15\" >0.162</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3clevel0_row15\" class=\"row_heading level0 row15\" >RPL_THEMES</th>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col0\" class=\"data row15 col0\" >0.242</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col1\" class=\"data row15 col1\" >0.906</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col2\" class=\"data row15 col2\" >0.685</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col3\" class=\"data row15 col3\" >-0.907</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col4\" class=\"data row15 col4\" >0.942</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col5\" class=\"data row15 col5\" >0.751</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col6\" class=\"data row15 col6\" >0.965</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col7\" class=\"data row15 col7\" >0.264</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col8\" class=\"data row15 col8\" >0.660</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col9\" class=\"data row15 col9\" >0.857</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col10\" class=\"data row15 col10\" >0.510</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col11\" class=\"data row15 col11\" >-0.016</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col12\" class=\"data row15 col12\" >0.805</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col13\" class=\"data row15 col13\" >0.655</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col14\" class=\"data row15 col14\" >0.162</td>\n",
       "                        <td id=\"T_3789efe6_8e52_11ea_817a_206a8aa20d3crow15_col15\" class=\"data row15 col15\" >1.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x199bdfdd688>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting a correlation matrix for the ten most affected US counties\n",
    "\n",
    "svi_top_10_counties_cases_corr = svi_top_10_counties_cases[['confirmed_cases', 'EP_POV', 'EP_LIMENG', 'EP_AGE65', 'EP_MINRTY', 'EP_UNEMP', 'EP_NOHSDP', 'EP_AGE17', 'EP_DISABL', 'EP_SNGPNT', 'EP_MUNIT', 'EP_MOBILE', 'EP_CROWD', 'EP_NOVEH', 'EP_GROUPQ', 'RPL_THEMES']]\n",
    "corr = svi_top_10_counties_cases_corr.corr()\n",
    "table = corr.style.background_gradient(cmap='Blues')\n",
    "table\n",
    "\n",
    "table = corr.style.background_gradient(cmap='Blues').format('{0:,.3f}')\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\r\n",
      "[>                                                           ] 0%\r",
      "[======>                                                     ] 10%\r",
      "[==============================>                             ] 50%\r",
      "[============================================================] 100%\r",
      "Rendering (2/2)                                                    \r\n",
      "[>                                                           ] 0%\r",
      "[===============>                                            ] 25%\r",
      "[============================================================] 100%\r",
      "Done                                                               \r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export Pandas style object to PNG\n",
    "html = table.render()\n",
    "\n",
    "path_wkthmltoimage = r'D:\\Tools\\wkthmltool\\bin\\wkhtmltoimage.exe'\n",
    "config = imgkit.config(wkhtmltoimage=path_wkthmltoimage)\n",
    "\n",
    "imgkit.from_string(html, './Media for Medium post/corr_confirmed_cases_SVI_top_10.png',config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x199be096e08>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D8xEn8qOn9pFQ7uNmFjOzE3IZH/Irhd+Pw8xsU/j78bKZXZqPOJEfqfx+hG3kZTN7w8z+nusYkT8p/H5ckPDb8Xr4f8yIfMSK3EqhbVSa2UNm9kr423FWPuJEfqTQPoab2X3h+csLZjYtH3GisDDMpwCZWVTS25K+KKlB0j8lnerubyaUqZC01d3dzD4qaYG775uXgJFTqbSPhHKPSWqW9Ft3X5jrWJF7Kf5+HCbpfHc/Oi9BIm9SbB/DJD0jabq715nZHu7+fl4CRk6l+v9LQvljJH3H3T+fuyiRDyn+dlwiqdLdLzSz3SUtljTK3VvzETNyJ8X2cbWkLe7+QzPbV9Iv3f3wvASMgkHPlMJ0kKQl7r4s/A9ovqTjEgu4+xb/MNM2WBJZt8LRY/sIfUvSvZI4CSosqbYPFKZU2sdMSb939zpJIpFSUNL9/ThV0l05iQz5lkrbcElDzMwkVUhaL6k9t2EiT1JpH1MlPS5J7r5I0kQzq8ptmCg0JFMK01hJ9QnPG8J1OzCz481skaSHJX0tR7Eh/3psH2Y2VtLxkn6Vw7jQP6T0+yHpk2FX7D+a2X65CQ39QCrtY4qk4Wb2hJm9aGZn5Cw65Fuqvx8ys0GSpitI2mPgS6Vt/ELSRyStkvSapP/r7vHchIc8S6V9vCLpK5JkZgdJmiBpXE6iQ8EimVKYLMm6nXqeuPt94dCeGZJ+lPWo0F+k0j6uk3Shu8dyEA/6l1Tax0uSJrj7/pJ+Lun+rEeF/iKV9lEk6UBJX5Z0pKQfmNmUbAeGfiGlvz9Cx0h62t3XZzEe9B+ptI0jJb0saYykAyT9wsyGZjsw9AuptI+fKEjUv6yg9/S/RM8lZFlRvgNAXjRIGp/wfJyCLH9S7v6kme1lZiPdfW3Wo0O+pdI+aiTND3raaqSkL5lZu7tz0jzw9dg+3H1zwuNHzOwGfj8KRiq/Hw2S1rr7VklbzexJSfsrGA+PgS2dvz9OEUN8CkkqbeMsST8Jh6EvMbPlkvaV9EJuQkQepfq3x1mSFA4FWx4uQNbQM6Uw/VPSZDPb08xKFPzB8mBiATPbO/whkpl9TFKJpHU5jxT50GP7cPc93X2iu0+UtFDSuSRSCkYqvx+jEn4/DlLwfw2/H4Whx/Yh6QFJnzazonAoxyckvZXjOJEfqbQPmVmlpM8qaCsoDKm0jTpJh0tSOBfGPpKW5TRK5Esqf3sMC7dJ0tmSnky8uANkAz1TCpC7t5vZf0r6s6SogjuxvGFm/x5u/5Wk/y3pDDNrk7Rd0skJE9JiAEuxfaBApdg+TpD0TTNrV/D7cQq/H4Uhlfbh7m+Z2Z8kvSopLmmuu7+ev6iRK2n8/3K8pEfD3ksoACm2jR9JutXMXlMw7ONCejwWhhTbx0ck3WZmMUlvSvp63gJGweDWyAAAAAAAAGlgmA8AAAAAAEAaSKYAAAAAAACkgWQKAAAAAABAGkimAAAAAAAApIFkCgAAAAAAQBpIpgBAhpjZr8zsBymWfcLMzs5CDJeZ2e8yXW+Kx77VzC7vZvsWM5vUuayZfdrMFucqzu5k63Ppj8zsMDNrSHi+wsy+kKNjd9lOzewSM5ubizj6wsxOM7NHUyzb7fcyl+89AADIDJIpAAqSmR1qZs+Y2SYzW29mT5vZx/tSp7v/u7v/qI9xnRqeWFmn9UVm9r6ZHd2X+vPJ3SvcfVmS9f9w9306nvflxNLMJpqZm1lRX2JF/rj7le7e7xNa7n6Hux+RzxjChFg8TFQ2mdliMzsr3NbxXdgSLivM7KJO+6f8XQuTRx11bU847hYz29JVfWY2y8ye6nTM7Yn7mtkvEsq6mV3TqY4Z4fpbu3htHcvJ4fZxZnavma0Nf+NfM7NZ6b6/AAB0h2QKgIJjZkMl/UHSzyWNkDRW0g8lteQzrtB9koZJ+myn9dMluaQ/5TwiBcmcfBwXvWOBrP0fb2bRbNW9K+hn34dV7l4haaikCyXdbGZTE7YPC7efIOkHZvbF3hwkTB5VhHUd1XHchHXpOCZxX3f/z4RtSyWd3Ok9PkPS20nqGdapnrvD9bdLqpc0QdJu4f6NacYIAEC3SKYAKERTJMnd73L3mLtvd/dH3f1VSTKziJl938zeDXuD3GZmlR07J/Rq2Whm9R1XPDsNXRluZn8wszVmtiF8PK6nwNy9WdICBX/8JzpD0h2SDk0cmhEeK+nV5YSrt2eaWV14lfZ7CdsjZnaRmS01s3VmtsDMRnTa9+tmVifpr+H6e8zsvfBq75Nmtl+nw440s8fCq+R/N7MJCcdzM9s7SZwfDDcxs9slVUt6KLzSPNvMHjazb3Xa51Uzm9HD29nxmfwyrKPJzJ43s70Stn/RzBaFr+cXkjr3CPqamb0VfoZ/7ng9ZnahmT3XccJnZt80szfMrCyFmErN7DozWxUu15lZabjtLUvofWRBj6S1Zvax8PnBCW3vFTM7LKHsE2Z2hZk9LWmbpElmdlZYZ5OZLTOzc3qKr5v38UYze8TMtkr6nJl92cz+ZWabw+/BZQnlu217neouNrO7LOhJUGIJQ2JSaMPlZjYv/HzeCttLQxfH+ZWZ/bTTugfM7Lvh447vQpOZvWlmxyeUm2VB77VrzWy9pMts5x4X14fvw2Yze9HMPt0phDIzuzus/yUz27+LOLv8XnbHA/dL2iBpapLttZLekHRAT3Xl2XuSXpN0pCSFr/0QSQ+mUcfHJd3q7lvdvd3d/+Xuf8x8qACAQkYyBUAheltSLDwJO8rMhnfaPitcPidpkqQKSR3d0Ksl/VFBr5bdFZyYvJzkGBFJtyi4MlotaXtHHSmYJ+kEMysPj1kp6RhJt6W4f2eHStpH0uGSLjWzj4Trvy1phoJeMGMUnIT9stO+n5X0EYUnNgpe+2RJe0h6SUGCJ9Fpkn4kaaSC96Xz9m65++mS6vThles5Ct6Pr3aUCU9Cx0p6JMVqT1XQ82i4pCWSrgjrGSnpXknfD+NdKulTCceZIekSSV9R8Fn/Q9Jd4earJbVK+r6ZTZZ0paSvhsmwnnxP0sEK2s7+kg4KY1BY/6kJZY+UtNbdXzKzsZIelnS5gh5V50u618x2Tyh/uqRvSBoi6V1J70s6WkGvhbMkXduRmOmFmQreuyGSnpK0VUGSb5ikL0v6ZpIEV1dtT1KQDJF0v4JeYSe5e2sXx+6qnv+SNFHB9/SLSmgnSdypoMeDhcceLukISfPD7UslfVpSpYL28jszG52w/yckLVPQ9q9IUv8/FXymI8Jj3dMpuXacpHsStt9vZsVJ6knle7mTMAlzvILP47Uk2w+WNE3Bd6C/u00fJpRPkfSA0us5+JykX5rZKeFvNgAAGUcyBUDBcffNCk7OXNLNktaY2YNmVhUWOU3SNe6+zN23SLpY0ilhL4TTJP0l7NXS5u7r3H2nZEq4/l533+buTQpOvjoP3ekqvqcVdEnvuDJ+kqS3kx0nRT8Me9+8IukVBSfwknSOpO+5e4O7t0i6TEESJ7F7/WXh1d3tYWy/dfemhPL7W0KvHUkPu/uT4fbvSfqkmY3vZdwdHpA0OUxaSEHC4O5uTrw7+727v+Du7QqSOx1X5r8k6U13X+jubZKuU3BVvMM5kn7s7m+F+14p6QAzm+DucQUne99WcMV8jrv/K8V4TpP03+7+vruvUXDifnq47U5Jx5rZoPD5zHCdFCQKHnH3R9w97u6PSaoNX0eHW939jfBqfJu7P+zuS8NeC3+X9KiChEFvPODuT4fHbnb3J9z9tfD5qwoSQZ3beFdtTwoSPH9SkMQ4y91j3Ry7q3pOknSlu29w9wZJ/9NNHf9Q8J3veP0nSHrW3VdJkrvf4+6rwtdzt6R3FCS6Oqxy95+H7+32zpW7++/C7327u/9MUqmCBFCHFxPa2jWSyhQk1TpL5XuZaIyZbZS0VkFy6XR3T5zQea2ZbZf0rKQbFCSvsun+sOfUxjCuG3oqY2b/p9P2+yQdFv62nKGuE8lrO9XTkWQ7UcHn/QNJy83sZevjnFgAAHRGMgVAQQpPkGe5+zgFV2vHKDiZVvj43YTi70oqklQlabyCk79umdkgM/u1BUOFNkt6UtIwS32uicQrs6cr6J3RW4kJgm0KetpIQa+Z+xJOet6SFFPwOjvUdzwws6iZ/SQcfrBZ0opw08hk5cNE1HoF72evhSeUCyR91YJ5QE5VMCdCqrp6/WM6xeuJzxW8P9cnvD/rFQwDGhuWXyHpbwp6RvTYcyBBsvY1JqxziYLP4ZgwoXKsPkymTJB0YqcT1UMlJfaeSIxfYc+r5yyYZHmjgsRL4ueVjs51f8LM/mbBULZNkv49Sd1dvfdSkEj4qKSfhO99d1L6DDvHmCg8xnx92PNnphJ6TpnZGeFJd8d7O01dtO1kzOy8cKjRpnD/yq72D5NxDUr+3Ujle5lolbsPc/cR7n6Au8/vtH2kgvfrfEmHSUrWGyaTZoTxDHP3YZLO7amMu9+cuDFMVj2ssNdYmGBOZmSnet4K99/g7he5+34K3reXFSRwrIt6AABIG8kUAAXP3RdJulXByZMkrVJwQtOhWlK7gt4i9ZL2Us/OU3BV+hPuPlTSZ8L1qf4xf5ukw83skwpOOjtOqLdK6ui10DER6O47756SeklHdToZKXP3lQllEk9yZyoYqvAFBSeKEzvCSCjzQS8UM6tQMKRhVZpxJTuxnqegR8fhkra5+7Np1pnMau0YryU+V/D+nNPp/Sl392fC8l+S9ElJjysY9pOqZO0r8T3qGOpznIKeMx3DMuol3d4pnsHu/pOEfT947yyYh+VeST+VVBWe2D6i1NtgZ50/lzsV9MoZ7+6Vkn6VZt2PSvqxpMcTeoWla7WkxLmIeuoFdZeCXh4TFAzbuVeSwuc3S/pPSbuF79Xr2vH1dJnwCedHuVBBT5nh4f6b1PV3IxLGney7kcr3Mi0ezA31M0nNSp7c6I9uU/A7mk7idCfuvlbBd2CMgt8jAAAygmQKgIJjZvuGV5HHhc/HKzh5fS4scpek75jZnmFC4EoFw0o6hol8wcxOsmBy0N3MLNmEjkMUzJOyMZxA8b/SidHd31UwL8Vdkh5z944r828rmMjyy+F8C99XMJygN34l6Qr7cFLV3c3suG7KD1Ewb8E6BQmdK5OU+ZIFE/SWKJg75Xl37/aKfhKNCubA+ECYPIlL+pn6eHKV4GFJ+5nZV8IhFN+WNCph+68kXWzhJLtmVmlmJ4aPR0r6jaSzJZ2poCfJB8NtLJgUeFYXx71LwVwru4f1XCrpdwnb5yuYy+Ob+jCJprDMMWZ2ZNhLqMyCyXu7mti4REHbWCOp3cyOCuvNlCGS1rt7s5kdpCDZlhYP5sS5U0FCpTc9ZhYo+IyGWzCnzH92VzgcirVG0lxJf3b3jeGmwQqSJWskyYLbC09LWklyQxQkXNdIKjKzSxUMY0p0YEJb+38KvkvPaWfpfi/T8RNJs23HuVyKw7bUsfSXOxX9XcE8OD9Pd0czu8rMpoW/0UMUfJeWuPu6TAcJAChcJFMAFKImBVeln7fgziTPKbgKfV64/bcKTtiflLRcwdXcb0mSu9cpGCpxnoJhHy9rx3kgOlwnqVzBPAbPqXe3NJ6noAfDB/MFuPsmBVeW50paqaCnStK7l6TgegU9Cx41s6Ywzk90U/42BUNSVkp6U8lPBO9UkDhaL+lABb1J0vVjBcmGjWZ2fqfj/5t2TDz0WnjF+kQFJ5jrFEys+3TC9vskXSVpfjis6XUFt4SVpJsUzCHySHiC9nVJc8PkWomC27Eme3+kYALZWkmvKpgo9KVwXcdxVyuY3+IQSXcnrK9X0FvlEgUn7fWSLlAX/5d7MFfPtxUkHDYoSHakc0eUnpwr6b/DtnNpeJy0ufuPFMzj8RdL4a41nfy3gva/XNJfJC1UzxOV3qWgd9UHiSp3f1NBou5ZBcm8f1NCW0jBnxVMzvy2gu9Is3YeFvSApJMVfBanS/pKOH9KZ+l+L9PxcHj8xDlKHlGQ+O1YLsvQsbrTcbeujuW+zgXCeX4ed/f13dSzsVM93w3XD1Iw78pGBZMGT1AwZA4AgIyxnocpAwCQf2Z2hqRvuPuh+Y6lO2Z2qKT/cPdTeyyMjDKzb0o6xd1TmuwZAACgt+iZAgDo98LJWM9V0COkX3P3p0ik5IaZjTazT1lwW+B9FPQY26mXAwAAQKaRTAEA9GtmdqSCYS2N2nEOEaBE0q8VDN37q4KhNMluxYsUmdklnYbOdCx/zHdsAAD0JwzzAQAAAAAASAM9UwAAAAAAANJAMgUAAAAAACANJFMAAAAAAADSQDIFAAAAAAAgDSRTAAAAAAAA0kAyBQAAAAAAIA3/H4QqpB8x23gNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of Social Vulnerability Indicators and confirmed Covid-19 cases in the ten most affected counties\n",
    "# For the visualization, we take RPL_THEMES, which is defined as an overall ranking variable. \n",
    "svi_top_10_counties_cases_RPL = svi_top_10_counties_cases.sort_values('confirmed_cases')\n",
    "sns.set_palette('muted')\n",
    "\n",
    "f, ax = plt.subplots(figsize = (16, 10))\n",
    "c = sns.scatterplot(x = svi_top_10_counties_cases_RPL.RPL_THEMES, y = svi_top_10_counties_cases_RPL.confirmed_cases, size = svi_top_10_counties_cases_RPL.RPL_THEMES, sizes = (100, 800), hue = svi_top_10_counties_cases_RPL['COUNTY'])\n",
    "ax.yaxis.grid()\n",
    "\n",
    "plt.xlabel('Social Vulnerability Index, overall ranking variable PRL_THEMES', fontsize = 12)\n",
    "plt.ylabel('Confirmed cases of Covid-19 as of April 8th, 2020', fontsize = 12)\n",
    "plt.title('Confirmed cases of Covid-19 and overall SVI ranking in the ten most affected counties', fontsize = 14)\n",
    "sns.despine()\n",
    "plt.legend(scatterpoints=1, frameon=True, labelspacing=1.5, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building a Machine Learning model for confirmed cases \n",
    "### A. Multiple Linear Regression for confirmed cases all US counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22942594683577178\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.233138</td>\n",
       "      <td>EP_LIMENG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>62.684828</td>\n",
       "      <td>EP_NOVEH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42.282123</td>\n",
       "      <td>EP_MUNIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.618257</td>\n",
       "      <td>EP_AGE17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.245875</td>\n",
       "      <td>EP_AGE65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.455224</td>\n",
       "      <td>EP_MOBILE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.001853</td>\n",
       "      <td>EP_UNEMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.097460</td>\n",
       "      <td>EP_MINRTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.626804</td>\n",
       "      <td>EP_DISABL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-6.639489</td>\n",
       "      <td>EP_NOHSDP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-7.614515</td>\n",
       "      <td>EP_GROUPQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-13.531833</td>\n",
       "      <td>EP_POV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-19.185217</td>\n",
       "      <td>EP_SNGPNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-47.270849</td>\n",
       "      <td>EP_CROWD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef   Features\n",
       "1   69.233138  EP_LIMENG\n",
       "12  62.684828   EP_NOVEH\n",
       "9   42.282123   EP_MUNIT\n",
       "6   14.618257   EP_AGE17\n",
       "2   11.245875   EP_AGE65\n",
       "10   7.455224  EP_MOBILE\n",
       "4    5.001853   EP_UNEMP\n",
       "3    0.097460  EP_MINRTY\n",
       "7   -1.626804  EP_DISABL\n",
       "5   -6.639489  EP_NOHSDP\n",
       "13  -7.614515  EP_GROUPQ\n",
       "0  -13.531833     EP_POV\n",
       "8  -19.185217  EP_SNGPNT\n",
       "11 -47.270849   EP_CROWD"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the Multiple Linear Regression Model\n",
    "\n",
    "# Preparing the independent variable (X) and the dependent variables (y)\n",
    "y = svi_all_counties_cases['confirmed_cases']\n",
    "X = svi_all_counties_cases.drop(['confirmed_cases', 'county_fips', 'COUNTY', 'STATE', 'RPL_THEMES'], axis = 1)\n",
    "\n",
    "# Splitting the data in train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Initiate the model\n",
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, y_train)\n",
    "\n",
    "y_predict= lm.predict(X_test)\n",
    "\n",
    "\n",
    "# Score\n",
    "print(lm.score(X_test, y_test))\n",
    "# Score is very low, which may be due to the fact, that many US counties dont have confirmed cases yet. \n",
    "\n",
    "confirmed_cases_all_counties_coef = pd.DataFrame({'Coef' : model.coef_, 'Features' : X.columns}).sort_values('Coef', ascending = False)\n",
    "confirmed_cases_all_counties_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Multiple Linear Regression for the ten US counties with most confirmed cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7982567326037315\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2222.934810</td>\n",
       "      <td>EP_GROUPQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1816.414227</td>\n",
       "      <td>EP_NOHSDP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1720.623138</td>\n",
       "      <td>EP_AGE65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>953.360930</td>\n",
       "      <td>EP_CROWD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>517.075869</td>\n",
       "      <td>EP_AGE17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>262.382119</td>\n",
       "      <td>EP_NOVEH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201.861787</td>\n",
       "      <td>EP_MINRTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-344.798132</td>\n",
       "      <td>EP_MUNIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-733.352068</td>\n",
       "      <td>EP_DISABL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-936.416521</td>\n",
       "      <td>EP_UNEMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1076.201668</td>\n",
       "      <td>EP_MOBILE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1473.948276</td>\n",
       "      <td>EP_SNGPNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1495.186635</td>\n",
       "      <td>EP_LIMENG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Coef   Features\n",
       "12  2222.934810  EP_GROUPQ\n",
       "4   1816.414227  EP_NOHSDP\n",
       "1   1720.623138   EP_AGE65\n",
       "10   953.360930   EP_CROWD\n",
       "5    517.075869   EP_AGE17\n",
       "11   262.382119   EP_NOVEH\n",
       "2    201.861787  EP_MINRTY\n",
       "8   -344.798132   EP_MUNIT\n",
       "6   -733.352068  EP_DISABL\n",
       "3   -936.416521   EP_UNEMP\n",
       "9  -1076.201668  EP_MOBILE\n",
       "7  -1473.948276  EP_SNGPNT\n",
       "0  -1495.186635  EP_LIMENG"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the Multiple Linear Regression Model\n",
    "\n",
    "# Preparing the independent variable (X) and the dependent variables (y)\n",
    "y = svi_top_10_counties_cases['confirmed_cases']\n",
    "X = svi_top_10_counties_cases.drop(['confirmed_cases', 'county_fips', 'EP_POV', 'COUNTY', 'STATE', 'RPL_THEMES'], axis = 1)\n",
    "\n",
    "# Splitting the data in train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Initiate the model\n",
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, y_train)\n",
    "\n",
    "y_predict= lm.predict(X_test)\n",
    "\n",
    "\n",
    "# Score\n",
    "print(lm.score(X_test, y_test))\n",
    "\n",
    "confirmed_cases_top_10_counties_coef = pd.DataFrame({'Coef' : model.coef_, 'Features' : X.columns}).sort_values('Coef', ascending = False)\n",
    "confirmed_cases_top_10_counties_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVI and Covid-19 deaths in the US\n",
    "\n",
    "We will now have a look at the deaths of Covid-19 in the US, on a state and county level. \n",
    "1. Preparation of the deaths dataset\n",
    "2. General overview\n",
    "    1. Which US states have suffered most deaths caused by Covid-19?\n",
    "    2. Identify correlation between SVI and Covid-19 in all US counties\n",
    "3. Analysis of the ten US counties with most deaths caused by Covid-19\n",
    "    1. Which are the ten US counties with most deaths caused by Covid-19? \n",
    "    2. Identify correlation between SVI and Covid-19 in the ten most affected US counties\n",
    "4. Building a Machine Learning model for deaths caused by Covid-19 \n",
    "    1. Multiple Linear Regression for deaths caused by Covid-19 in all US counties\n",
    "    2. Multiple Linear Regression for the ten US counties with most deaths caused by Covid-19\n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preparation of the deaths dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>state_fips</th>\n",
       "      <th>date</th>\n",
       "      <th>deaths</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
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       "      <th>datetime</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga County</td>\n",
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       "      <td>2020-01-22</td>\n",
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       "      <td>2020-01-22</td>\n",
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       "      <td>1001</td>\n",
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       "      <td>1001</td>\n",
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       "      <td>1001</td>\n",
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       "      <td>1001</td>\n",
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       "      <td>2020-01-26</td>\n",
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      ],
      "text/plain": [
       "    county_fips     county_name state_name  state_fips        date  deaths  \\\n",
       "75         1001  Autauga County         AL           1  2020-01-22     0.0   \n",
       "76         1001  Autauga County         AL           1  2020-01-23     0.0   \n",
       "77         1001  Autauga County         AL           1  2020-01-24     0.0   \n",
       "78         1001  Autauga County         AL           1  2020-01-25     0.0   \n",
       "79         1001  Autauga County         AL           1  2020-01-26     0.0   \n",
       "\n",
       "          lat       long                          geometry   datetime  \n",
       "75  32.539527 -86.644082  POINT (-86.64408227 32.53952745) 2020-01-22  \n",
       "76  32.539527 -86.644082  POINT (-86.64408227 32.53952745) 2020-01-23  \n",
       "77  32.539527 -86.644082  POINT (-86.64408227 32.53952745) 2020-01-24  \n",
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       "79  32.539527 -86.644082  POINT (-86.64408227 32.53952745) 2020-01-26  "
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   ],
   "source": [
    "# Apply datetime\n",
    "format_str = '%Y-%m-%d'\n",
    "us_deaths['datetime'] = us_deaths.date.apply(lambda date: datetime.datetime.strptime(date, format_str))\n",
    "\n",
    "# Drop rows that do not refer to a specific county ('Statewide Unallocated'):\n",
    "us_deaths = us_deaths[us_deaths.county_name != 'Statewide Unallocated']\n",
    "\n",
    "us_deaths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>county_fips</th>\n",
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       "      <th>239695</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-01</td>\n",
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       "      <td>2020-04-01</td>\n",
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       "    <tr>\n",
       "      <th>239696</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
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       "    <tr>\n",
       "      <th>239697</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-03</td>\n",
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       "      <th>239698</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.839612</td>\n",
       "      <td>-104.567488</td>\n",
       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-04</td>\n",
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       "      <th>239699</th>\n",
       "      <td>56045</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>WY</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-104.567488</td>\n",
       "      <td>POINT (-104.5674881 43.83961191)</td>\n",
       "      <td>2020-04-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        county_fips    county_name state_name  state_fips        date  deaths  \\\n",
       "239695        56045  Weston County         WY          56  2020-04-01     NaN   \n",
       "239696        56045  Weston County         WY          56  2020-04-02     NaN   \n",
       "239697        56045  Weston County         WY          56  2020-04-03     NaN   \n",
       "239698        56045  Weston County         WY          56  2020-04-04     NaN   \n",
       "239699        56045  Weston County         WY          56  2020-04-05     NaN   \n",
       "\n",
       "              lat        long                          geometry   datetime  \n",
       "239695  43.839612 -104.567488  POINT (-104.5674881 43.83961191) 2020-04-01  \n",
       "239696  43.839612 -104.567488  POINT (-104.5674881 43.83961191) 2020-04-02  \n",
       "239697  43.839612 -104.567488  POINT (-104.5674881 43.83961191) 2020-04-03  \n",
       "239698  43.839612 -104.567488  POINT (-104.5674881 43.83961191) 2020-04-04  \n",
       "239699  43.839612 -104.567488  POINT (-104.5674881 43.83961191) 2020-04-05  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For which counties do we have missing values? \n",
    "us_deaths[us_deaths.deaths.isna() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Weston County appears to have no information for April. \n",
    "# However there have been no confirmed cases in Weston County: \n",
    "print(us_deaths[us_deaths.county_name == 'Weston County'].deaths.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. General Overview\n",
    "### A. Which US states have suffered most deaths caused by Covid-19?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
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\"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}}}, \"title\": {\"text\": \"Deaths caused by Covid-19 in the US states as of April 8th\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a5a9da5b-b167-4603-b29c-b75bf49e16c7');\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": [
    "# First group by county and state, then sum up all the counties' deaths to get to the total sum for a state\n",
    "us_deaths_grouping_county = us_deaths.groupby(['county_fips', 'state_name']).deaths.max().reset_index().sort_values('deaths', ascending = False)\n",
    "us_deaths_grouping_states = us_deaths_grouping_county.groupby('state_name').deaths.sum().reset_index().sort_values('deaths', ascending = False)\n",
    "\n",
    "# Plot the map using plotly\n",
    "\n",
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=us_deaths_grouping_states['state_name'], \n",
    "    z = us_deaths_grouping_states['deaths'].astype(float), \n",
    "    locationmode = 'USA-states', \n",
    "    colorscale = 'blues',\n",
    "    colorbar_title = \"Deaths\",\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text = 'Deaths caused by Covid-19 in the US states as of April 8th',\n",
    "    geo_scope='usa', \n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>36047</td>\n",
       "      <td>Kings County</td>\n",
       "      <td>NY</td>\n",
       "      <td>896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>36081</td>\n",
       "      <td>Queens County</td>\n",
       "      <td>NY</td>\n",
       "      <td>813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>36005</td>\n",
       "      <td>Bronx County</td>\n",
       "      <td>NY</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>36061</td>\n",
       "      <td>New York County</td>\n",
       "      <td>NY</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau County</td>\n",
       "      <td>NY</td>\n",
       "      <td>381.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      county_fips      county_name state_name  deaths\n",
       "1854        36047     Kings County         NY   896.0\n",
       "1871        36081    Queens County         NY   813.0\n",
       "1833        36005     Bronx County         NY   627.0\n",
       "1861        36061  New York County         NY   390.0\n",
       "1860        36059    Nassau County         NY   381.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show deaths in all US counties\n",
    "us_deaths_all_counties = us_deaths.groupby(['county_fips', 'county_name', 'state_name']).deaths.max().reset_index().sort_values('deaths', ascending = False)\n",
    "us_deaths_all_counties.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B. Identify correlation between SVI and Covid-19 in all US counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_fips</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>EP_POV</th>\n",
       "      <th>EP_LIMENG</th>\n",
       "      <th>EP_AGE65</th>\n",
       "      <th>EP_MINRTY</th>\n",
       "      <th>EP_UNEMP</th>\n",
       "      <th>EP_NOHSDP</th>\n",
       "      <th>EP_AGE17</th>\n",
       "      <th>EP_DISABL</th>\n",
       "      <th>EP_SNGPNT</th>\n",
       "      <th>EP_MUNIT</th>\n",
       "      <th>EP_MOBILE</th>\n",
       "      <th>EP_CROWD</th>\n",
       "      <th>EP_NOVEH</th>\n",
       "      <th>EP_GROUPQ</th>\n",
       "      <th>RPL_THEMES</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>36047</td>\n",
       "      <td>Kings</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>19.702270</td>\n",
       "      <td>11.425234</td>\n",
       "      <td>13.666622</td>\n",
       "      <td>62.745127</td>\n",
       "      <td>7.002670</td>\n",
       "      <td>17.972497</td>\n",
       "      <td>22.563418</td>\n",
       "      <td>10.203071</td>\n",
       "      <td>9.318692</td>\n",
       "      <td>34.845260</td>\n",
       "      <td>0.153805</td>\n",
       "      <td>10.716021</td>\n",
       "      <td>51.801068</td>\n",
       "      <td>1.593458</td>\n",
       "      <td>0.671966</td>\n",
       "      <td>896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>36081</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>12.464752</td>\n",
       "      <td>13.553882</td>\n",
       "      <td>14.910714</td>\n",
       "      <td>74.194255</td>\n",
       "      <td>6.271273</td>\n",
       "      <td>18.169565</td>\n",
       "      <td>19.846894</td>\n",
       "      <td>9.428882</td>\n",
       "      <td>8.004348</td>\n",
       "      <td>24.628727</td>\n",
       "      <td>0.202174</td>\n",
       "      <td>9.048602</td>\n",
       "      <td>33.080124</td>\n",
       "      <td>1.169410</td>\n",
       "      <td>0.599525</td>\n",
       "      <td>813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>36005</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>27.784337</td>\n",
       "      <td>15.004217</td>\n",
       "      <td>12.581928</td>\n",
       "      <td>88.284940</td>\n",
       "      <td>10.471084</td>\n",
       "      <td>27.526807</td>\n",
       "      <td>24.577711</td>\n",
       "      <td>14.728916</td>\n",
       "      <td>18.840361</td>\n",
       "      <td>61.236446</td>\n",
       "      <td>0.155422</td>\n",
       "      <td>11.897289</td>\n",
       "      <td>55.897892</td>\n",
       "      <td>2.735241</td>\n",
       "      <td>0.876122</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>36061</td>\n",
       "      <td>New York</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>16.366667</td>\n",
       "      <td>8.049821</td>\n",
       "      <td>15.774194</td>\n",
       "      <td>51.795699</td>\n",
       "      <td>5.816129</td>\n",
       "      <td>12.327957</td>\n",
       "      <td>13.768100</td>\n",
       "      <td>9.988172</td>\n",
       "      <td>5.775986</td>\n",
       "      <td>89.116846</td>\n",
       "      <td>0.057706</td>\n",
       "      <td>5.920789</td>\n",
       "      <td>77.127957</td>\n",
       "      <td>4.420072</td>\n",
       "      <td>0.517380</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>5.569065</td>\n",
       "      <td>5.234532</td>\n",
       "      <td>17.138489</td>\n",
       "      <td>37.538489</td>\n",
       "      <td>4.215827</td>\n",
       "      <td>8.625540</td>\n",
       "      <td>21.671223</td>\n",
       "      <td>8.404317</td>\n",
       "      <td>5.633453</td>\n",
       "      <td>9.069784</td>\n",
       "      <td>0.181295</td>\n",
       "      <td>2.625180</td>\n",
       "      <td>6.764029</td>\n",
       "      <td>2.047842</td>\n",
       "      <td>0.278768</td>\n",
       "      <td>381.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      county_fips    COUNTY     STATE     EP_POV  EP_LIMENG   EP_AGE65  \\\n",
       "1850        36047     Kings  NEW YORK  19.702270  11.425234  13.666622   \n",
       "1867        36081    Queens  NEW YORK  12.464752  13.553882  14.910714   \n",
       "1829        36005     Bronx  NEW YORK  27.784337  15.004217  12.581928   \n",
       "1857        36061  New York  NEW YORK  16.366667   8.049821  15.774194   \n",
       "1856        36059    Nassau  NEW YORK   5.569065   5.234532  17.138489   \n",
       "\n",
       "      EP_MINRTY   EP_UNEMP  EP_NOHSDP   EP_AGE17  EP_DISABL  EP_SNGPNT  \\\n",
       "1850  62.745127   7.002670  17.972497  22.563418  10.203071   9.318692   \n",
       "1867  74.194255   6.271273  18.169565  19.846894   9.428882   8.004348   \n",
       "1829  88.284940  10.471084  27.526807  24.577711  14.728916  18.840361   \n",
       "1857  51.795699   5.816129  12.327957  13.768100   9.988172   5.775986   \n",
       "1856  37.538489   4.215827   8.625540  21.671223   8.404317   5.633453   \n",
       "\n",
       "       EP_MUNIT  EP_MOBILE   EP_CROWD   EP_NOVEH  EP_GROUPQ  RPL_THEMES  \\\n",
       "1850  34.845260   0.153805  10.716021  51.801068   1.593458    0.671966   \n",
       "1867  24.628727   0.202174   9.048602  33.080124   1.169410    0.599525   \n",
       "1829  61.236446   0.155422  11.897289  55.897892   2.735241    0.876122   \n",
       "1857  89.116846   0.057706   5.920789  77.127957   4.420072    0.517380   \n",
       "1856   9.069784   0.181295   2.625180   6.764029   2.047842    0.278768   \n",
       "\n",
       "      deaths  \n",
       "1850   896.0  \n",
       "1867   813.0  \n",
       "1829   627.0  \n",
       "1857   390.0  \n",
       "1856   381.0  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svi_all_counties_deaths = svi_all_counties.merge(us_deaths_all_counties, on = 'county_fips').sort_values('deaths', ascending = False)\n",
    "svi_all_counties_deaths = svi_all_counties_deaths.drop(['county_name', 'state_name'], axis = 1)\n",
    "svi_all_counties_deaths.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
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       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col5 {\n",
       "            background-color:  #fed8c7;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col6 {\n",
       "            background-color:  #fdc6b0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col7 {\n",
       "            background-color:  #fc9777;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col8 {\n",
       "            background-color:  #fcc4ad;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col9 {\n",
       "            background-color:  #fc9b7c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col10 {\n",
       "            background-color:  #f6553c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col11 {\n",
       "            background-color:  #fcc1a8;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col12 {\n",
       "            background-color:  #fca183;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col13 {\n",
       "            background-color:  #fb7757;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col14 {\n",
       "            background-color:  #fdccb8;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col15 {\n",
       "            background-color:  #fdd2bf;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col0 {\n",
       "            background-color:  #fee5d9;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col1 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col2 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col3 {\n",
       "            background-color:  #fcb296;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col4 {\n",
       "            background-color:  #f14130;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col5 {\n",
       "            background-color:  #db2824;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col6 {\n",
       "            background-color:  #db2824;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col7 {\n",
       "            background-color:  #fc8161;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col8 {\n",
       "            background-color:  #ef3c2c;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col9 {\n",
       "            background-color:  #e02c26;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col10 {\n",
       "            background-color:  #fcbba1;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col11 {\n",
       "            background-color:  #ec382b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col12 {\n",
       "            background-color:  #fb7656;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col13 {\n",
       "            background-color:  #f85f43;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col14 {\n",
       "            background-color:  #fc9e80;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col15 {\n",
       "            background-color:  #bc141a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col0 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col1 {\n",
       "            background-color:  #fdd0bc;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col2 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col3 {\n",
       "            background-color:  #fdcdb9;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col4 {\n",
       "            background-color:  #e22e27;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col5 {\n",
       "            background-color:  #fdd7c6;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col6 {\n",
       "            background-color:  #f5523a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col7 {\n",
       "            background-color:  #f6583e;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col8 {\n",
       "            background-color:  #fedccd;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col9 {\n",
       "            background-color:  #fb7656;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col10 {\n",
       "            background-color:  #fb7050;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col11 {\n",
       "            background-color:  #fcb296;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col12 {\n",
       "            background-color:  #f34935;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col13 {\n",
       "            background-color:  #fcbea5;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col14 {\n",
       "            background-color:  #fcbfa7;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col15 {\n",
       "            background-color:  #fc8464;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col0 {\n",
       "            background-color:  #fff0e8;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col1 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col2 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col3 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col4 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col5 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col6 {\n",
       "            background-color:  #fee7db;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col7 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col8 {\n",
       "            background-color:  #f44d38;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col9 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col10 {\n",
       "            background-color:  #fee8de;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col11 {\n",
       "            background-color:  #fc8969;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col12 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col13 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col14 {\n",
       "            background-color:  #fee5d8;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col15 {\n",
       "            background-color:  #fee6da;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col0 {\n",
       "            background-color:  #fcc3ab;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col1 {\n",
       "            background-color:  #f96044;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col2 {\n",
       "            background-color:  #e93529;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col3 {\n",
       "            background-color:  #fee0d2;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col4 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col5 {\n",
       "            background-color:  #f85d42;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col6 {\n",
       "            background-color:  #f14432;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col7 {\n",
       "            background-color:  #f6553c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col8 {\n",
       "            background-color:  #fcbda4;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col9 {\n",
       "            background-color:  #d72322;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col10 {\n",
       "            background-color:  #fb7b5b;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col11 {\n",
       "            background-color:  #fc8565;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col12 {\n",
       "            background-color:  #ea362a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col13 {\n",
       "            background-color:  #fb6b4b;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col14 {\n",
       "            background-color:  #fca183;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col15 {\n",
       "            background-color:  #e53228;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col0 {\n",
       "            background-color:  #fee0d2;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col1 {\n",
       "            background-color:  #dc2924;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col2 {\n",
       "            background-color:  #fcbca2;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col3 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col4 {\n",
       "            background-color:  #f0402f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col5 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col6 {\n",
       "            background-color:  #f6563d;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col7 {\n",
       "            background-color:  #fc8464;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col8 {\n",
       "            background-color:  #f44f39;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col9 {\n",
       "            background-color:  #e63328;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col10 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col11 {\n",
       "            background-color:  #f85f43;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col12 {\n",
       "            background-color:  #fa6849;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col13 {\n",
       "            background-color:  #f6563d;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col14 {\n",
       "            background-color:  #fcb79c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col15 {\n",
       "            background-color:  #dc2924;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col0 {\n",
       "            background-color:  #fee3d6;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col1 {\n",
       "            background-color:  #e43027;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col2 {\n",
       "            background-color:  #f44f39;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col3 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col4 {\n",
       "            background-color:  #eb372a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col5 {\n",
       "            background-color:  #fa6547;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col6 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col7 {\n",
       "            background-color:  #f96044;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col8 {\n",
       "            background-color:  #f24633;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col9 {\n",
       "            background-color:  #f34c37;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col10 {\n",
       "            background-color:  #fedecf;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col11 {\n",
       "            background-color:  #dc2924;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col12 {\n",
       "            background-color:  #fa6547;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col13 {\n",
       "            background-color:  #fc8e6e;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col14 {\n",
       "            background-color:  #fcb095;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col15 {\n",
       "            background-color:  #b61319;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col0 {\n",
       "            background-color:  #fee7db;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col1 {\n",
       "            background-color:  #fdc9b3;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col2 {\n",
       "            background-color:  #fb7b5b;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col3 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col4 {\n",
       "            background-color:  #fb6c4c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col5 {\n",
       "            background-color:  #fdcab5;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col6 {\n",
       "            background-color:  #fc8a6a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col7 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col8 {\n",
       "            background-color:  #fee3d6;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col9 {\n",
       "            background-color:  #f0402f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col10 {\n",
       "            background-color:  #fcbba1;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col11 {\n",
       "            background-color:  #fcb095;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col12 {\n",
       "            background-color:  #f44d38;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col13 {\n",
       "            background-color:  #fdc6b0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col14 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col15 {\n",
       "            background-color:  #fc8f6f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col0 {\n",
       "            background-color:  #fff4ee;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col1 {\n",
       "            background-color:  #f75c41;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col2 {\n",
       "            background-color:  #ffece4;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col3 {\n",
       "            background-color:  #ef3c2c;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col4 {\n",
       "            background-color:  #fcc2aa;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col5 {\n",
       "            background-color:  #fb7252;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col6 {\n",
       "            background-color:  #f7593f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col7 {\n",
       "            background-color:  #fdc7b2;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col8 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col9 {\n",
       "            background-color:  #fc9d7f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col10 {\n",
       "            background-color:  #fff1ea;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col11 {\n",
       "            background-color:  #e12d26;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col12 {\n",
       "            background-color:  #fdd2bf;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col13 {\n",
       "            background-color:  #fcb499;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col14 {\n",
       "            background-color:  #fcc4ad;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col15 {\n",
       "            background-color:  #f0402f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col0 {\n",
       "            background-color:  #fedccd;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col1 {\n",
       "            background-color:  #f34935;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col2 {\n",
       "            background-color:  #fc8f6f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col3 {\n",
       "            background-color:  #fee9df;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col4 {\n",
       "            background-color:  #db2824;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col5 {\n",
       "            background-color:  #f5533b;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col6 {\n",
       "            background-color:  #fa6648;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col7 {\n",
       "            background-color:  #eb372a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col8 {\n",
       "            background-color:  #fca689;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col9 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col10 {\n",
       "            background-color:  #fc9272;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col11 {\n",
       "            background-color:  #fc997a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col12 {\n",
       "            background-color:  #f6563d;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col13 {\n",
       "            background-color:  #f96044;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col14 {\n",
       "            background-color:  #fcbba1;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col15 {\n",
       "            background-color:  #ee3a2c;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col0 {\n",
       "            background-color:  #fc8464;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col1 {\n",
       "            background-color:  #ffede5;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col2 {\n",
       "            background-color:  #fc8565;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col3 {\n",
       "            background-color:  #fdd7c6;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col4 {\n",
       "            background-color:  #fc8464;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col5 {\n",
       "            background-color:  #fee7dc;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col6 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col7 {\n",
       "            background-color:  #fca689;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col8 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col9 {\n",
       "            background-color:  #fc8f6f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col10 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col11 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col12 {\n",
       "            background-color:  #fca689;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col13 {\n",
       "            background-color:  #fc8262;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col14 {\n",
       "            background-color:  #fcbca2;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col15 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col0 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col1 {\n",
       "            background-color:  #f75c41;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col2 {\n",
       "            background-color:  #fdcdb9;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col3 {\n",
       "            background-color:  #fb7858;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col4 {\n",
       "            background-color:  #fc8f6f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col5 {\n",
       "            background-color:  #fc8767;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col6 {\n",
       "            background-color:  #ea362a;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col7 {\n",
       "            background-color:  #fc9c7d;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col8 {\n",
       "            background-color:  #e32f27;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col9 {\n",
       "            background-color:  #fc9777;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col10 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col11 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col12 {\n",
       "            background-color:  #fca78b;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col13 {\n",
       "            background-color:  #fed8c7;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col14 {\n",
       "            background-color:  #fcb89e;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col15 {\n",
       "            background-color:  #ed392b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col0 {\n",
       "            background-color:  #fdc9b3;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col1 {\n",
       "            background-color:  #fc9272;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col2 {\n",
       "            background-color:  #f44d38;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col3 {\n",
       "            background-color:  #fdd4c2;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col4 {\n",
       "            background-color:  #e53228;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col5 {\n",
       "            background-color:  #fc8060;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col6 {\n",
       "            background-color:  #fb6e4e;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col7 {\n",
       "            background-color:  #e93529;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col8 {\n",
       "            background-color:  #fcc2aa;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col9 {\n",
       "            background-color:  #f24633;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col10 {\n",
       "            background-color:  #fc9373;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col11 {\n",
       "            background-color:  #fc9474;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col12 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col13 {\n",
       "            background-color:  #f85d42;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col14 {\n",
       "            background-color:  #fdc6b0;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col15 {\n",
       "            background-color:  #f7593f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col0 {\n",
       "            background-color:  #fc8565;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col1 {\n",
       "            background-color:  #fb694a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col2 {\n",
       "            background-color:  #fcaf93;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col3 {\n",
       "            background-color:  #fcbea5;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col4 {\n",
       "            background-color:  #f5523a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col5 {\n",
       "            background-color:  #f85f43;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col6 {\n",
       "            background-color:  #fc8565;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col7 {\n",
       "            background-color:  #fc8a6a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col8 {\n",
       "            background-color:  #fc9272;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col9 {\n",
       "            background-color:  #f0402f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col10 {\n",
       "            background-color:  #f96245;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col11 {\n",
       "            background-color:  #fcab8f;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col12 {\n",
       "            background-color:  #f44d38;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col13 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col14 {\n",
       "            background-color:  #fca98c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col15 {\n",
       "            background-color:  #fb6d4d;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col0 {\n",
       "            background-color:  #fee8de;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col1 {\n",
       "            background-color:  #fcb79c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col2 {\n",
       "            background-color:  #fcbea5;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col3 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col4 {\n",
       "            background-color:  #fc9070;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col5 {\n",
       "            background-color:  #fdd0bc;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col6 {\n",
       "            background-color:  #fcb398;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col7 {\n",
       "            background-color:  #fdccb8;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col8 {\n",
       "            background-color:  #fcad90;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col9 {\n",
       "            background-color:  #fc997a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col10 {\n",
       "            background-color:  #fc9e80;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col11 {\n",
       "            background-color:  #fc9b7c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col12 {\n",
       "            background-color:  #fcbea5;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col13 {\n",
       "            background-color:  #fcb79c;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col14 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col15 {\n",
       "            background-color:  #fcad90;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col0 {\n",
       "            background-color:  #fee5d9;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col1 {\n",
       "            background-color:  #c1161b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col2 {\n",
       "            background-color:  #fb7a5a;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col3 {\n",
       "            background-color:  #fcaa8d;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col4 {\n",
       "            background-color:  #da2723;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col5 {\n",
       "            background-color:  #e12d26;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col6 {\n",
       "            background-color:  #b51318;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col7 {\n",
       "            background-color:  #f96044;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col8 {\n",
       "            background-color:  #e32f27;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col9 {\n",
       "            background-color:  #db2824;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col10 {\n",
       "            background-color:  #fdd5c4;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col11 {\n",
       "            background-color:  #dc2924;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col12 {\n",
       "            background-color:  #f34a36;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col13 {\n",
       "            background-color:  #fb6e4e;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col14 {\n",
       "            background-color:  #fca285;\n",
       "            color:  #000000;\n",
       "        }    #T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col15 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3c\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >deaths</th>        <th class=\"col_heading level0 col1\" >EP_POV</th>        <th class=\"col_heading level0 col2\" >EP_LIMENG</th>        <th class=\"col_heading level0 col3\" >EP_AGE65</th>        <th class=\"col_heading level0 col4\" >EP_MINRTY</th>        <th class=\"col_heading level0 col5\" >EP_UNEMP</th>        <th class=\"col_heading level0 col6\" >EP_NOHSDP</th>        <th class=\"col_heading level0 col7\" >EP_AGE17</th>        <th class=\"col_heading level0 col8\" >EP_DISABL</th>        <th class=\"col_heading level0 col9\" >EP_SNGPNT</th>        <th class=\"col_heading level0 col10\" >EP_MUNIT</th>        <th class=\"col_heading level0 col11\" >EP_MOBILE</th>        <th class=\"col_heading level0 col12\" >EP_CROWD</th>        <th class=\"col_heading level0 col13\" >EP_NOVEH</th>        <th class=\"col_heading level0 col14\" >EP_GROUPQ</th>        <th class=\"col_heading level0 col15\" >RPL_THEMES</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row0\" class=\"row_heading level0 row0\" >deaths</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col1\" class=\"data row0 col1\" >-0.008</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col2\" class=\"data row0 col2\" >0.195</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col3\" class=\"data row0 col3\" >-0.077</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col4\" class=\"data row0 col4\" >0.137</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col5\" class=\"data row0 col5\" >0.029</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col6\" class=\"data row0 col6\" >0.010</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col7\" class=\"data row0 col7\" >-0.014</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col8\" class=\"data row0 col8\" >-0.100</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col9\" class=\"data row0 col9\" >0.043</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col10\" class=\"data row0 col10\" >0.356</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col11\" class=\"data row0 col11\" >-0.112</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col12\" class=\"data row0 col12\" >0.114</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col13\" class=\"data row0 col13\" >0.350</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col14\" class=\"data row0 col14\" >-0.027</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow0_col15\" class=\"data row0 col15\" >-0.006</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row1\" class=\"row_heading level0 row1\" >EP_POV</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col0\" class=\"data row1 col0\" >-0.008</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col2\" class=\"data row1 col2\" >0.068</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col3\" class=\"data row1 col3\" >-0.141</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col4\" class=\"data row1 col4\" >0.458</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col5\" class=\"data row1 col5\" >0.647</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col6\" class=\"data row1 col6\" >0.613</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col7\" class=\"data row1 col7\" >0.092</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col8\" class=\"data row1 col8\" >0.470</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col9\" class=\"data row1 col9\" >0.528</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col10\" class=\"data row1 col10\" >-0.087</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col11\" class=\"data row1 col11\" >0.470</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col12\" class=\"data row1 col12\" >0.290</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col13\" class=\"data row1 col13\" >0.432</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col14\" class=\"data row1 col14\" >0.159</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow1_col15\" class=\"data row1 col15\" >0.754</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row2\" class=\"row_heading level0 row2\" >EP_LIMENG</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col0\" class=\"data row2 col0\" >0.195</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col1\" class=\"data row2 col1\" >0.068</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col3\" class=\"data row2 col3\" >-0.286</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col4\" class=\"data row2 col4\" >0.546</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col5\" class=\"data row2 col5\" >0.034</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col6\" class=\"data row2 col6\" >0.452</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col7\" class=\"data row2 col7\" >0.288</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col8\" class=\"data row2 col8\" >-0.217</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col9\" class=\"data row2 col9\" >0.209</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col10\" class=\"data row2 col10\" >0.249</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col11\" class=\"data row2 col11\" >-0.044</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col12\" class=\"data row2 col12\" >0.456</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col13\" class=\"data row2 col13\" >0.084</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col14\" class=\"data row2 col14\" >0.025</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow2_col15\" class=\"data row2 col15\" >0.293</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row3\" class=\"row_heading level0 row3\" >EP_AGE65</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col0\" class=\"data row3 col0\" >-0.077</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col1\" class=\"data row3 col1\" >-0.141</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col2\" class=\"data row3 col2\" >-0.286</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col4\" class=\"data row3 col4\" >-0.381</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col5\" class=\"data row3 col5\" >-0.149</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col6\" class=\"data row3 col6\" >-0.148</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col7\" class=\"data row3 col7\" >-0.584</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col8\" class=\"data row3 col8\" >0.404</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col9\" class=\"data row3 col9\" >-0.470</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col10\" class=\"data row3 col10\" >-0.336</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col11\" class=\"data row3 col11\" >0.137</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col12\" class=\"data row3 col12\" >-0.324</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col13\" class=\"data row3 col13\" >-0.203</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col14\" class=\"data row3 col14\" >-0.146</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow3_col15\" class=\"data row3 col15\" >-0.104</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row4\" class=\"row_heading level0 row4\" >EP_MINRTY</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col0\" class=\"data row4 col0\" >0.137</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col1\" class=\"data row4 col1\" >0.458</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col2\" class=\"data row4 col2\" >0.546</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col3\" class=\"data row4 col3\" >-0.381</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col5\" class=\"data row4 col5\" >0.462</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col6\" class=\"data row4 col6\" >0.499</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col7\" class=\"data row4 col7\" >0.299</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col8\" class=\"data row4 col8\" >-0.064</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col9\" class=\"data row4 col9\" >0.574</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col10\" class=\"data row4 col10\" >0.198</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col11\" class=\"data row4 col11\" >0.152</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col12\" class=\"data row4 col12\" >0.526</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col13\" class=\"data row4 col13\" >0.397</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col14\" class=\"data row4 col14\" >0.145</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow4_col15\" class=\"data row4 col15\" >0.583</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row5\" class=\"row_heading level0 row5\" >EP_UNEMP</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col0\" class=\"data row5 col0\" >0.029</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col1\" class=\"data row5 col1\" >0.647</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col2\" class=\"data row5 col2\" >0.034</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col3\" class=\"data row5 col3\" >-0.149</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col4\" class=\"data row5 col4\" >0.462</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col6\" class=\"data row5 col6\" >0.439</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col7\" class=\"data row5 col7\" >0.080</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col8\" class=\"data row5 col8\" >0.398</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col9\" class=\"data row5 col9\" >0.494</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col10\" class=\"data row5 col10\" >-0.052</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col11\" class=\"data row5 col11\" >0.320</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col12\" class=\"data row5 col12\" >0.346</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col13\" class=\"data row5 col13\" >0.460</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col14\" class=\"data row5 col14\" >0.059</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow5_col15\" class=\"data row5 col15\" >0.624</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row6\" class=\"row_heading level0 row6\" >EP_NOHSDP</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col0\" class=\"data row6 col0\" >0.010</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col1\" class=\"data row6 col1\" >0.613</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col2\" class=\"data row6 col2\" >0.452</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col3\" class=\"data row6 col3\" >-0.148</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col4\" class=\"data row6 col4\" >0.499</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col5\" class=\"data row6 col5\" >0.439</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col7\" class=\"data row6 col7\" >0.247</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col8\" class=\"data row6 col8\" >0.429</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col9\" class=\"data row6 col9\" >0.384</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col10\" class=\"data row6 col10\" >-0.256</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col11\" class=\"data row6 col11\" >0.551</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col12\" class=\"data row6 col12\" >0.355</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col13\" class=\"data row6 col13\" >0.266</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col14\" class=\"data row6 col14\" >0.088</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow6_col15\" class=\"data row6 col15\" >0.777</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row7\" class=\"row_heading level0 row7\" >EP_AGE17</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col0\" class=\"data row7 col0\" >-0.014</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col1\" class=\"data row7 col1\" >0.092</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col2\" class=\"data row7 col2\" >0.288</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col3\" class=\"data row7 col3\" >-0.584</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col4\" class=\"data row7 col4\" >0.299</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col5\" class=\"data row7 col5\" >0.080</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col6\" class=\"data row7 col6\" >0.247</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col7\" class=\"data row7 col7\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col8\" class=\"data row7 col8\" >-0.251</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col9\" class=\"data row7 col9\" >0.428</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col10\" class=\"data row7 col10\" >-0.085</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col11\" class=\"data row7 col11\" >-0.036</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col12\" class=\"data row7 col12\" >0.441</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col13\" class=\"data row7 col13\" >0.051</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col14\" class=\"data row7 col14\" >-0.275</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow7_col15\" class=\"data row7 col15\" >0.250</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row8\" class=\"row_heading level0 row8\" >EP_DISABL</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col0\" class=\"data row8 col0\" >-0.100</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col1\" class=\"data row8 col1\" >0.470</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col2\" class=\"data row8 col2\" >-0.217</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col3\" class=\"data row8 col3\" >0.404</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col4\" class=\"data row8 col4\" >-0.064</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col5\" class=\"data row8 col5\" >0.398</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col6\" class=\"data row8 col6\" >0.429</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col7\" class=\"data row8 col7\" >-0.251</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col8\" class=\"data row8 col8\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col9\" class=\"data row8 col9\" >0.033</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col10\" class=\"data row8 col10\" >-0.411</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col11\" class=\"data row8 col11\" >0.528</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col12\" class=\"data row8 col12\" >-0.091</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col13\" class=\"data row8 col13\" >0.122</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col14\" class=\"data row8 col14\" >0.007</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow8_col15\" class=\"data row8 col15\" >0.529</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row9\" class=\"row_heading level0 row9\" >EP_SNGPNT</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col0\" class=\"data row9 col0\" >0.043</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col1\" class=\"data row9 col1\" >0.528</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col2\" class=\"data row9 col2\" >0.209</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col3\" class=\"data row9 col3\" >-0.470</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col4\" class=\"data row9 col4\" >0.574</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col5\" class=\"data row9 col5\" >0.494</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col6\" class=\"data row9 col6\" >0.384</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col7\" class=\"data row9 col7\" >0.428</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col8\" class=\"data row9 col8\" >0.033</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col9\" class=\"data row9 col9\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col10\" class=\"data row9 col10\" >0.095</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col11\" class=\"data row9 col11\" >0.061</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col12\" class=\"data row9 col12\" >0.407</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col13\" class=\"data row9 col13\" >0.431</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col14\" class=\"data row9 col14\" >0.048</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow9_col15\" class=\"data row9 col15\" >0.548</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row10\" class=\"row_heading level0 row10\" >EP_MUNIT</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col0\" class=\"data row10 col0\" >0.356</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col1\" class=\"data row10 col1\" >-0.087</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col2\" class=\"data row10 col2\" >0.249</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col3\" class=\"data row10 col3\" >-0.336</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col4\" class=\"data row10 col4\" >0.198</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col5\" class=\"data row10 col5\" >-0.052</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col6\" class=\"data row10 col6\" >-0.256</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col7\" class=\"data row10 col7\" >-0.085</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col8\" class=\"data row10 col8\" >-0.411</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col9\" class=\"data row10 col9\" >0.095</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col10\" class=\"data row10 col10\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col11\" class=\"data row10 col11\" >-0.449</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col12\" class=\"data row10 col12\" >0.092</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col13\" class=\"data row10 col13\" >0.306</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col14\" class=\"data row10 col14\" >0.042</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow10_col15\" class=\"data row10 col15\" >-0.216</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row11\" class=\"row_heading level0 row11\" >EP_MOBILE</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col0\" class=\"data row11 col0\" >-0.112</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col1\" class=\"data row11 col1\" >0.470</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col2\" class=\"data row11 col2\" >-0.044</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col3\" class=\"data row11 col3\" >0.137</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col4\" class=\"data row11 col4\" >0.152</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col5\" class=\"data row11 col5\" >0.320</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col6\" class=\"data row11 col6\" >0.551</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col7\" class=\"data row11 col7\" >-0.036</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col8\" class=\"data row11 col8\" >0.528</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col9\" class=\"data row11 col9\" >0.061</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col10\" class=\"data row11 col10\" >-0.449</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col11\" class=\"data row11 col11\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col12\" class=\"data row11 col12\" >0.087</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col13\" class=\"data row11 col13\" >-0.017</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col14\" class=\"data row11 col14\" >0.057</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow11_col15\" class=\"data row11 col15\" >0.550</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row12\" class=\"row_heading level0 row12\" >EP_CROWD</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col0\" class=\"data row12 col0\" >0.114</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col1\" class=\"data row12 col1\" >0.290</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col2\" class=\"data row12 col2\" >0.456</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col3\" class=\"data row12 col3\" >-0.324</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col4\" class=\"data row12 col4\" >0.526</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col5\" class=\"data row12 col5\" >0.346</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col6\" class=\"data row12 col6\" >0.355</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col7\" class=\"data row12 col7\" >0.441</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col8\" class=\"data row12 col8\" >-0.091</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col9\" class=\"data row12 col9\" >0.407</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col10\" class=\"data row12 col10\" >0.092</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col11\" class=\"data row12 col11\" >0.087</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col12\" class=\"data row12 col12\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col13\" class=\"data row12 col13\" >0.439</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col14\" class=\"data row12 col14\" >-0.005</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow12_col15\" class=\"data row12 col15\" >0.448</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row13\" class=\"row_heading level0 row13\" >EP_NOVEH</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col0\" class=\"data row13 col0\" >0.350</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col1\" class=\"data row13 col1\" >0.432</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col2\" class=\"data row13 col2\" >0.084</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col3\" class=\"data row13 col3\" >-0.203</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col4\" class=\"data row13 col4\" >0.397</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col5\" class=\"data row13 col5\" >0.460</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col6\" class=\"data row13 col6\" >0.266</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col7\" class=\"data row13 col7\" >0.051</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col8\" class=\"data row13 col8\" >0.122</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col9\" class=\"data row13 col9\" >0.431</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col10\" class=\"data row13 col10\" >0.306</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col11\" class=\"data row13 col11\" >-0.017</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col12\" class=\"data row13 col12\" >0.439</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col13\" class=\"data row13 col13\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col14\" class=\"data row13 col14\" >0.115</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow13_col15\" class=\"data row13 col15\" >0.383</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row14\" class=\"row_heading level0 row14\" >EP_GROUPQ</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col0\" class=\"data row14 col0\" >-0.027</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col1\" class=\"data row14 col1\" >0.159</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col2\" class=\"data row14 col2\" >0.025</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col3\" class=\"data row14 col3\" >-0.146</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col4\" class=\"data row14 col4\" >0.145</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col5\" class=\"data row14 col5\" >0.059</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col6\" class=\"data row14 col6\" >0.088</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col7\" class=\"data row14 col7\" >-0.275</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col8\" class=\"data row14 col8\" >0.007</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col9\" class=\"data row14 col9\" >0.048</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col10\" class=\"data row14 col10\" >0.042</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col11\" class=\"data row14 col11\" >0.057</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col12\" class=\"data row14 col12\" >-0.005</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col13\" class=\"data row14 col13\" >0.115</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col14\" class=\"data row14 col14\" >1.000</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow14_col15\" class=\"data row14 col15\" >0.142</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3clevel0_row15\" class=\"row_heading level0 row15\" >RPL_THEMES</th>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col0\" class=\"data row15 col0\" >-0.006</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col1\" class=\"data row15 col1\" >0.754</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col2\" class=\"data row15 col2\" >0.293</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col3\" class=\"data row15 col3\" >-0.104</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col4\" class=\"data row15 col4\" >0.583</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col5\" class=\"data row15 col5\" >0.624</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col6\" class=\"data row15 col6\" >0.777</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col7\" class=\"data row15 col7\" >0.250</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col8\" class=\"data row15 col8\" >0.529</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col9\" class=\"data row15 col9\" >0.548</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col10\" class=\"data row15 col10\" >-0.216</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col11\" class=\"data row15 col11\" >0.550</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col12\" class=\"data row15 col12\" >0.448</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col13\" class=\"data row15 col13\" >0.383</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col14\" class=\"data row15 col14\" >0.142</td>\n",
       "                        <td id=\"T_3a03ee5c_8e52_11ea_bdbf_206a8aa20d3crow15_col15\" class=\"data row15 col15\" >1.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x199b9f7a6c8>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting a correlation matrix for all US counties\n",
    "svi_all_counties_deaths_corr = svi_all_counties_deaths[['deaths', 'EP_POV', 'EP_LIMENG', 'EP_AGE65', 'EP_MINRTY', 'EP_UNEMP', 'EP_NOHSDP', 'EP_AGE17', 'EP_DISABL', 'EP_SNGPNT', 'EP_MUNIT', 'EP_MOBILE', 'EP_CROWD', 'EP_NOVEH', 'EP_GROUPQ', 'RPL_THEMES']]\n",
    "corr = svi_all_counties_deaths_corr.corr()\n",
    "table = corr.style.background_gradient(cmap='Reds').format('{0:,.3f}')\n",
    "\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\r\n",
      "[>                                                           ] 0%\r",
      "[======>                                                     ] 10%\r",
      "[==============================>                             ] 50%\r",
      "[============================================================] 100%\r",
      "Rendering (2/2)                                                    \r\n",
      "[>                                                           ] 0%\r",
      "[===============>                                            ] 25%\r",
      "[============================================================] 100%\r",
      "Done                                                               \r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export Pandas style object to PNG\n",
    "html = table.render()\n",
    "\n",
    "path_wkthmltoimage = r'D:\\Tools\\wkthmltool\\bin\\wkhtmltoimage.exe'\n",
    "config = imgkit.config(wkhtmltoimage=path_wkthmltoimage)\n",
    "\n",
    "imgkit.from_string(html, './Media for Medium post/corr_deaths_all_counties.png',config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Analysis of the ten US counties with most deaths caused by Covid-19\n",
    "### B. Which are the ten US counties with most deaths caused by Covid-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      county_fips         county_name state_name  deaths\n",
      "1854        36047        Kings County         NY   896.0\n",
      "1871        36081       Queens County         NY   813.0\n",
      "1833        36005        Bronx County         NY   627.0\n",
      "1861        36061     New York County         NY   390.0\n",
      "1860        36059       Nassau County         NY   381.0\n",
      "1315        26163        Wayne County         MI   293.0\n",
      "1890        36119  Westchester County         NY   211.0\n",
      "2972        53033         King County         WA   208.0\n",
      "1778        34003       Bergen County         NJ   189.0\n",
      "613         17031         Cook County         IL   186.0\n"
     ]
    }
   ],
   "source": [
    "# Which are the 10 most affected US counties? \n",
    "us_deaths_top_10 = us_deaths_all_counties.head(10)\n",
    "print(us_deaths_top_10)\n",
    "us_deaths_top_10.to_csv('./Media for Medium post/us_deaths_top_10.csv')\n",
    "\n",
    "# Preparing a list with the 10 most affected counties: \n",
    "top_10_deaths_fips_list = us_deaths_top_10.county_fips.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>county_fips</th>\n",
       "      <th>county_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>state_fips</th>\n",
       "      <th>date</th>\n",
       "      <th>deaths</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>geometry</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46875</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46876</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46877</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46878</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46879</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook County</td>\n",
       "      <td>IL</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.841448</td>\n",
       "      <td>-87.816588</td>\n",
       "      <td>POINT (-87.81658794 41.84144849)</td>\n",
       "      <td>2020-01-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       county_fips  county_name state_name  state_fips        date  deaths  \\\n",
       "46875        17031  Cook County         IL          17  2020-01-22     0.0   \n",
       "46876        17031  Cook County         IL          17  2020-01-23     0.0   \n",
       "46877        17031  Cook County         IL          17  2020-01-24     0.0   \n",
       "46878        17031  Cook County         IL          17  2020-01-25     0.0   \n",
       "46879        17031  Cook County         IL          17  2020-01-26     0.0   \n",
       "\n",
       "             lat       long                          geometry   datetime  \n",
       "46875  41.841448 -87.816588  POINT (-87.81658794 41.84144849) 2020-01-22  \n",
       "46876  41.841448 -87.816588  POINT (-87.81658794 41.84144849) 2020-01-23  \n",
       "46877  41.841448 -87.816588  POINT (-87.81658794 41.84144849) 2020-01-24  \n",
       "46878  41.841448 -87.816588  POINT (-87.81658794 41.84144849) 2020-01-25  \n",
       "46879  41.841448 -87.816588  POINT (-87.81658794 41.84144849) 2020-01-26  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the timeseries statistics of the 10 most affected US counties\n",
    "us_deaths_top_10_datetime = us_deaths[us_deaths.county_fips.isin(top_10_deaths_fips_list) == True]\n",
    "us_deaths_top_10_datetime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Timeline of deaths\n",
    "\n",
    "f, ax = plt.subplots(figsize = (16, 10))\n",
    "ax = sns.lineplot(x = us_deaths_top_10_datetime.datetime, y = us_deaths_top_10_datetime.deaths, hue = us_deaths_top_10_datetime.county_name, ci = None)\n",
    "\n",
    "plt.xlabel('Timeline',fontsize=12)\n",
    "plt.ylabel('Deaths',fontsize=12)\n",
    "plt.title('Deaths caused by Covid-19 in US counties as of April 8th', fontsize = 16)\n",
    "plt.tick_params(labelsize=12)\n",
    "ax.yaxis.tick_right()\n",
    "plt.legend(loc=2, fontsize=12)\n",
    "plt.grid(False)\n",
    "ax.yaxis.grid()\n",
    "sns.despine()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Identify correlation between SVI and the ten US counties with most deaths caused by Covid-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>county_fips</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>EP_POV</th>\n",
       "      <th>EP_LIMENG</th>\n",
       "      <th>EP_AGE65</th>\n",
       "      <th>EP_MINRTY</th>\n",
       "      <th>EP_UNEMP</th>\n",
       "      <th>EP_NOHSDP</th>\n",
       "      <th>EP_AGE17</th>\n",
       "      <th>EP_DISABL</th>\n",
       "      <th>EP_SNGPNT</th>\n",
       "      <th>EP_MUNIT</th>\n",
       "      <th>EP_MOBILE</th>\n",
       "      <th>EP_CROWD</th>\n",
       "      <th>EP_NOVEH</th>\n",
       "      <th>EP_GROUPQ</th>\n",
       "      <th>RPL_THEMES</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36047</td>\n",
       "      <td>Kings</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>19.702270</td>\n",
       "      <td>11.425234</td>\n",
       "      <td>13.666622</td>\n",
       "      <td>62.745127</td>\n",
       "      <td>7.002670</td>\n",
       "      <td>17.972497</td>\n",
       "      <td>22.563418</td>\n",
       "      <td>10.203071</td>\n",
       "      <td>9.318692</td>\n",
       "      <td>34.845260</td>\n",
       "      <td>0.153805</td>\n",
       "      <td>10.716021</td>\n",
       "      <td>51.801068</td>\n",
       "      <td>1.593458</td>\n",
       "      <td>0.671966</td>\n",
       "      <td>896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36081</td>\n",
       "      <td>Queens</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>12.464752</td>\n",
       "      <td>13.553882</td>\n",
       "      <td>14.910714</td>\n",
       "      <td>74.194255</td>\n",
       "      <td>6.271273</td>\n",
       "      <td>18.169565</td>\n",
       "      <td>19.846894</td>\n",
       "      <td>9.428882</td>\n",
       "      <td>8.004348</td>\n",
       "      <td>24.628727</td>\n",
       "      <td>0.202174</td>\n",
       "      <td>9.048602</td>\n",
       "      <td>33.080124</td>\n",
       "      <td>1.169410</td>\n",
       "      <td>0.599525</td>\n",
       "      <td>813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36005</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>27.784337</td>\n",
       "      <td>15.004217</td>\n",
       "      <td>12.581928</td>\n",
       "      <td>88.284940</td>\n",
       "      <td>10.471084</td>\n",
       "      <td>27.526807</td>\n",
       "      <td>24.577711</td>\n",
       "      <td>14.728916</td>\n",
       "      <td>18.840361</td>\n",
       "      <td>61.236446</td>\n",
       "      <td>0.155422</td>\n",
       "      <td>11.897289</td>\n",
       "      <td>55.897892</td>\n",
       "      <td>2.735241</td>\n",
       "      <td>0.876122</td>\n",
       "      <td>627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36061</td>\n",
       "      <td>New York</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>16.366667</td>\n",
       "      <td>8.049821</td>\n",
       "      <td>15.774194</td>\n",
       "      <td>51.795699</td>\n",
       "      <td>5.816129</td>\n",
       "      <td>12.327957</td>\n",
       "      <td>13.768100</td>\n",
       "      <td>9.988172</td>\n",
       "      <td>5.775986</td>\n",
       "      <td>89.116846</td>\n",
       "      <td>0.057706</td>\n",
       "      <td>5.920789</td>\n",
       "      <td>77.127957</td>\n",
       "      <td>4.420072</td>\n",
       "      <td>0.517380</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36059</td>\n",
       "      <td>Nassau</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>5.569065</td>\n",
       "      <td>5.234532</td>\n",
       "      <td>17.138489</td>\n",
       "      <td>37.538489</td>\n",
       "      <td>4.215827</td>\n",
       "      <td>8.625540</td>\n",
       "      <td>21.671223</td>\n",
       "      <td>8.404317</td>\n",
       "      <td>5.633453</td>\n",
       "      <td>9.069784</td>\n",
       "      <td>0.181295</td>\n",
       "      <td>2.625180</td>\n",
       "      <td>6.764029</td>\n",
       "      <td>2.047842</td>\n",
       "      <td>0.278768</td>\n",
       "      <td>381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26163</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>MICHIGAN</td>\n",
       "      <td>26.183389</td>\n",
       "      <td>2.208804</td>\n",
       "      <td>14.871761</td>\n",
       "      <td>57.680399</td>\n",
       "      <td>12.413289</td>\n",
       "      <td>15.891196</td>\n",
       "      <td>23.297674</td>\n",
       "      <td>17.354651</td>\n",
       "      <td>13.248837</td>\n",
       "      <td>10.961960</td>\n",
       "      <td>1.559801</td>\n",
       "      <td>2.341030</td>\n",
       "      <td>16.699834</td>\n",
       "      <td>1.586711</td>\n",
       "      <td>0.618729</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36119</td>\n",
       "      <td>Westchester</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>9.673059</td>\n",
       "      <td>6.023288</td>\n",
       "      <td>17.057534</td>\n",
       "      <td>46.512329</td>\n",
       "      <td>5.882648</td>\n",
       "      <td>12.072146</td>\n",
       "      <td>22.125114</td>\n",
       "      <td>9.858447</td>\n",
       "      <td>7.752968</td>\n",
       "      <td>26.946575</td>\n",
       "      <td>0.141096</td>\n",
       "      <td>4.049772</td>\n",
       "      <td>14.765297</td>\n",
       "      <td>2.385845</td>\n",
       "      <td>0.459970</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>53033</td>\n",
       "      <td>King</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>9.835768</td>\n",
       "      <td>4.696725</td>\n",
       "      <td>13.087657</td>\n",
       "      <td>38.908816</td>\n",
       "      <td>4.556927</td>\n",
       "      <td>7.429975</td>\n",
       "      <td>20.496474</td>\n",
       "      <td>9.752393</td>\n",
       "      <td>6.558690</td>\n",
       "      <td>23.064987</td>\n",
       "      <td>2.125945</td>\n",
       "      <td>3.632494</td>\n",
       "      <td>8.898992</td>\n",
       "      <td>1.789421</td>\n",
       "      <td>0.394344</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34003</td>\n",
       "      <td>Bergen</td>\n",
       "      <td>NEW JERSEY</td>\n",
       "      <td>7.161453</td>\n",
       "      <td>6.894413</td>\n",
       "      <td>16.959777</td>\n",
       "      <td>42.912291</td>\n",
       "      <td>4.481564</td>\n",
       "      <td>7.659218</td>\n",
       "      <td>21.412291</td>\n",
       "      <td>8.102793</td>\n",
       "      <td>5.830168</td>\n",
       "      <td>15.555866</td>\n",
       "      <td>0.385475</td>\n",
       "      <td>2.316760</td>\n",
       "      <td>7.956425</td>\n",
       "      <td>1.210056</td>\n",
       "      <td>0.338170</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17031</td>\n",
       "      <td>Cook</td>\n",
       "      <td>ILLINOIS</td>\n",
       "      <td>17.136122</td>\n",
       "      <td>6.589886</td>\n",
       "      <td>13.754373</td>\n",
       "      <td>60.660608</td>\n",
       "      <td>9.353004</td>\n",
       "      <td>14.217947</td>\n",
       "      <td>22.228973</td>\n",
       "      <td>10.653004</td>\n",
       "      <td>10.195665</td>\n",
       "      <td>18.619468</td>\n",
       "      <td>0.650798</td>\n",
       "      <td>3.551483</td>\n",
       "      <td>19.018175</td>\n",
       "      <td>1.712700</td>\n",
       "      <td>0.544628</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   county_fips       COUNTY       STATE     EP_POV  EP_LIMENG   EP_AGE65  \\\n",
       "4        36047        Kings    NEW YORK  19.702270  11.425234  13.666622   \n",
       "7        36081       Queens    NEW YORK  12.464752  13.553882  14.910714   \n",
       "3        36005        Bronx    NEW YORK  27.784337  15.004217  12.581928   \n",
       "6        36061     New York    NEW YORK  16.366667   8.049821  15.774194   \n",
       "5        36059       Nassau    NEW YORK   5.569065   5.234532  17.138489   \n",
       "1        26163        Wayne    MICHIGAN  26.183389   2.208804  14.871761   \n",
       "8        36119  Westchester    NEW YORK   9.673059   6.023288  17.057534   \n",
       "9        53033         King  WASHINGTON   9.835768   4.696725  13.087657   \n",
       "2        34003       Bergen  NEW JERSEY   7.161453   6.894413  16.959777   \n",
       "0        17031         Cook    ILLINOIS  17.136122   6.589886  13.754373   \n",
       "\n",
       "   EP_MINRTY   EP_UNEMP  EP_NOHSDP   EP_AGE17  EP_DISABL  EP_SNGPNT  \\\n",
       "4  62.745127   7.002670  17.972497  22.563418  10.203071   9.318692   \n",
       "7  74.194255   6.271273  18.169565  19.846894   9.428882   8.004348   \n",
       "3  88.284940  10.471084  27.526807  24.577711  14.728916  18.840361   \n",
       "6  51.795699   5.816129  12.327957  13.768100   9.988172   5.775986   \n",
       "5  37.538489   4.215827   8.625540  21.671223   8.404317   5.633453   \n",
       "1  57.680399  12.413289  15.891196  23.297674  17.354651  13.248837   \n",
       "8  46.512329   5.882648  12.072146  22.125114   9.858447   7.752968   \n",
       "9  38.908816   4.556927   7.429975  20.496474   9.752393   6.558690   \n",
       "2  42.912291   4.481564   7.659218  21.412291   8.102793   5.830168   \n",
       "0  60.660608   9.353004  14.217947  22.228973  10.653004  10.195665   \n",
       "\n",
       "    EP_MUNIT  EP_MOBILE   EP_CROWD   EP_NOVEH  EP_GROUPQ  RPL_THEMES  deaths  \n",
       "4  34.845260   0.153805  10.716021  51.801068   1.593458    0.671966   896.0  \n",
       "7  24.628727   0.202174   9.048602  33.080124   1.169410    0.599525   813.0  \n",
       "3  61.236446   0.155422  11.897289  55.897892   2.735241    0.876122   627.0  \n",
       "6  89.116846   0.057706   5.920789  77.127957   4.420072    0.517380   390.0  \n",
       "5   9.069784   0.181295   2.625180   6.764029   2.047842    0.278768   381.0  \n",
       "1  10.961960   1.559801   2.341030  16.699834   1.586711    0.618729   293.0  \n",
       "8  26.946575   0.141096   4.049772  14.765297   2.385845    0.459970   211.0  \n",
       "9  23.064987   2.125945   3.632494   8.898992   1.789421    0.394344   208.0  \n",
       "2  15.555866   0.385475   2.316760   7.956425   1.210056    0.338170   189.0  \n",
       "0  18.619468   0.650798   3.551483  19.018175   1.712700    0.544628   186.0  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the rows in the SVI dataframe that contain the ten counties with most confirmed cases. \n",
    "svi_top_10_counties_deaths = svi[svi.county_fips.isin(top_10_deaths_fips_list) == True]\n",
    "\n",
    "# Drop rows with -999 in the top ten counties:\n",
    "svi_top_10_counties_deaths = svi_top_10_counties_deaths[svi_top_10_counties_deaths[svi_top_10_counties_deaths.columns] != -999]\n",
    "svi_top_10_counties_deaths = svi_top_10_counties_deaths.dropna()\n",
    "\n",
    "\n",
    "\n",
    "svi_top_10_counties_deaths = svi_top_10_counties_deaths.groupby(['county_fips', \n",
    "                                                                'COUNTY', \n",
    "                                                                'STATE']).agg({'EP_POV':'mean',\n",
    "                                                                              'EP_LIMENG':'mean',\n",
    "                                                                               'EP_AGE65':'mean',\n",
    "                                                                               'EP_MINRTY':'mean',\n",
    "                                                                               'EP_UNEMP':'mean',\n",
    "                                                                               'EP_NOHSDP':'mean',\n",
    "                                                                               'EP_AGE17':'mean',\n",
    "                                                                               'EP_DISABL':'mean',\n",
    "                                                                               'EP_SNGPNT':'mean',\n",
    "                                                                               'EP_MUNIT':'mean',\n",
    "                                                                               'EP_MOBILE':'mean',\n",
    "                                                                               'EP_CROWD':'mean',\n",
    "                                                                               'EP_NOVEH':'mean',\n",
    "                                                                               'EP_GROUPQ':'mean', \n",
    "                                                                               'RPL_THEMES': 'mean'\n",
    "                                                                              }\n",
    "                                                                             ).reset_index()\n",
    "\n",
    "\n",
    "# Merge county SVI with deaths caused by Covid-19\n",
    "svi_top_10_counties_deaths = svi_top_10_counties_deaths.merge(us_deaths_top_10, on = 'county_fips').sort_values('deaths', ascending = False)\n",
    "svi_top_10_counties_deaths = svi_top_10_counties_deaths.drop(['county_name', 'state_name'], axis = 1)\n",
    "svi_top_10_counties_deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #fc8060;\n",
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       "            background-color:  #ffece3;\n",
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       "            background-color:  #fee2d5;\n",
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       "            background-color:  #fff5f0;\n",
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       "            background-color:  #fff5f0;\n",
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       "            background-color:  #fff5f0;\n",
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       "            background-color:  #fff5f0;\n",
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       "            background-color:  #f03f2e;\n",
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       "            background-color:  #d11e1f;\n",
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       "            background-color:  #ea362a;\n",
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       "            background-color:  #8e0912;\n",
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       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col7 {\n",
       "            background-color:  #c8171c;\n",
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       "            background-color:  #a10e15;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #67000d;\n",
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       "            background-color:  #fb7252;\n",
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       "            background-color:  #fc8f6f;\n",
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       "            background-color:  #dc2924;\n",
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       "            background-color:  #fa6648;\n",
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       "            background-color:  #fb6d4d;\n",
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       "            background-color:  #f03d2d;\n",
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       "            background-color:  #fc9777;\n",
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       "            background-color:  #f03f2e;\n",
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       "            background-color:  #fff0e9;\n",
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       "            background-color:  #fb7d5d;\n",
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       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col3 {\n",
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       "            background-color:  #fdd2bf;\n",
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       "            background-color:  #fb7252;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col8 {\n",
       "            background-color:  #f75b40;\n",
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       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col9 {\n",
       "            background-color:  #fc8a6a;\n",
       "            color:  #000000;\n",
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       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #fee8dd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fff5f0;\n",
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       "            color:  #f1f1f1;\n",
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       "            background-color:  #d82422;\n",
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       "            color:  #f1f1f1;\n",
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       "            color:  #000000;\n",
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       "            background-color:  #9c0d14;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col7 {\n",
       "            background-color:  #fb7b5b;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col8 {\n",
       "            background-color:  #fc8464;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col9 {\n",
       "            background-color:  #d92523;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #d92523;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #fee6da;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col12 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col13 {\n",
       "            background-color:  #b91419;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #fb7252;\n",
       "            color:  #000000;\n",
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       "            background-color:  #a10e15;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #d92523;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #d42121;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col2 {\n",
       "            background-color:  #c8171c;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col3 {\n",
       "            background-color:  #fdc5ae;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col4 {\n",
       "            background-color:  #d21f20;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col5 {\n",
       "            background-color:  #fb7555;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col6 {\n",
       "            background-color:  #d01d1f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col7 {\n",
       "            background-color:  #fee4d8;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col8 {\n",
       "            background-color:  #fc8060;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col9 {\n",
       "            background-color:  #f6553c;\n",
       "            color:  #000000;\n",
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       "            color:  #f1f1f1;\n",
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       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col12 {\n",
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       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col13 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #b81419;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #c1161b;\n",
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       "            background-color:  #fcbba1;\n",
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       "            background-color:  #fc8464;\n",
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       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col3 {\n",
       "            background-color:  #fb7b5b;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fc8e6e;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fca689;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fc8060;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col7 {\n",
       "            background-color:  #fff5f0;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col8 {\n",
       "            background-color:  #fc997a;\n",
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       "            background-color:  #fc8e6e;\n",
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       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col14 {\n",
       "            background-color:  #67000d;\n",
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       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col15 {\n",
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       "            background-color:  #d42121;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #c1161b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col3 {\n",
       "            background-color:  #fff5f0;\n",
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       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col4 {\n",
       "            background-color:  #7a0510;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col5 {\n",
       "            background-color:  #b21218;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col6 {\n",
       "            background-color:  #71020e;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col7 {\n",
       "            background-color:  #f44d38;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col8 {\n",
       "            background-color:  #c3161b;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col9 {\n",
       "            background-color:  #900a12;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col10 {\n",
       "            background-color:  #e83429;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col11 {\n",
       "            background-color:  #fcb69b;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col12 {\n",
       "            background-color:  #a50f15;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col13 {\n",
       "            background-color:  #cb181d;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col14 {\n",
       "            background-color:  #fb7555;\n",
       "            color:  #000000;\n",
       "        }    #T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col15 {\n",
       "            background-color:  #67000d;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3c\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >deaths</th>        <th class=\"col_heading level0 col1\" >EP_POV</th>        <th class=\"col_heading level0 col2\" >EP_LIMENG</th>        <th class=\"col_heading level0 col3\" >EP_AGE65</th>        <th class=\"col_heading level0 col4\" >EP_MINRTY</th>        <th class=\"col_heading level0 col5\" >EP_UNEMP</th>        <th class=\"col_heading level0 col6\" >EP_NOHSDP</th>        <th class=\"col_heading level0 col7\" >EP_AGE17</th>        <th class=\"col_heading level0 col8\" >EP_DISABL</th>        <th class=\"col_heading level0 col9\" >EP_SNGPNT</th>        <th class=\"col_heading level0 col10\" >EP_MUNIT</th>        <th class=\"col_heading level0 col11\" >EP_MOBILE</th>        <th class=\"col_heading level0 col12\" >EP_CROWD</th>        <th class=\"col_heading level0 col13\" >EP_NOVEH</th>        <th class=\"col_heading level0 col14\" >EP_GROUPQ</th>        <th class=\"col_heading level0 col15\" >RPL_THEMES</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row0\" class=\"row_heading level0 row0\" >deaths</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col1\" class=\"data row0 col1\" >0.344</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col2\" class=\"data row0 col2\" >0.795</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col3\" class=\"data row0 col3\" >-0.354</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col4\" class=\"data row0 col4\" >0.650</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col5\" class=\"data row0 col5\" >0.101</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col6\" class=\"data row0 col6\" >0.650</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col7\" class=\"data row0 col7\" >0.060</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col8\" class=\"data row0 col8\" >0.050</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col9\" class=\"data row0 col9\" >0.272</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col10\" class=\"data row0 col10\" >0.269</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col11\" class=\"data row0 col11\" >-0.436</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col12\" class=\"data row0 col12\" >0.872</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col13\" class=\"data row0 col13\" >0.571</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col14\" class=\"data row0 col14\" >-0.076</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow0_col15\" class=\"data row0 col15\" >0.594</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row1\" class=\"row_heading level0 row1\" >EP_POV</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col0\" class=\"data row1 col0\" >0.344</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col2\" class=\"data row1 col2\" >0.305</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col3\" class=\"data row1 col3\" >-0.652</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col4\" class=\"data row1 col4\" >0.750</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col5\" class=\"data row1 col5\" >0.907</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col6\" class=\"data row1 col6\" >0.815</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col7\" class=\"data row1 col7\" >0.302</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col8\" class=\"data row1 col8\" >0.881</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col9\" class=\"data row1 col9\" >0.873</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col10\" class=\"data row1 col10\" >0.368</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col11\" class=\"data row1 col11\" >0.074</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col12\" class=\"data row1 col12\" >0.510</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col13\" class=\"data row1 col13\" >0.535</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col14\" class=\"data row1 col14\" >0.186</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow1_col15\" class=\"data row1 col15\" >0.899</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row2\" class=\"row_heading level0 row2\" >EP_LIMENG</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col0\" class=\"data row2 col0\" >0.795</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col1\" class=\"data row2 col1\" >0.305</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col3\" class=\"data row2 col3\" >-0.403</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col4\" class=\"data row2 col4\" >0.796</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col5\" class=\"data row2 col5\" >0.062</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col6\" class=\"data row2 col6\" >0.742</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col7\" class=\"data row2 col7\" >0.054</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col8\" class=\"data row2 col8\" >-0.047</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col9\" class=\"data row2 col9\" >0.400</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col10\" class=\"data row2 col10\" >0.472</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col11\" class=\"data row2 col11\" >-0.584</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col12\" class=\"data row2 col12\" >0.927</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col13\" class=\"data row2 col13\" >0.618</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col14\" class=\"data row2 col14\" >0.078</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow2_col15\" class=\"data row2 col15\" >0.659</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row3\" class=\"row_heading level0 row3\" >EP_AGE65</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col0\" class=\"data row3 col0\" >-0.354</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col1\" class=\"data row3 col1\" >-0.652</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col2\" class=\"data row3 col2\" >-0.403</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col4\" class=\"data row3 col4\" >-0.605</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col5\" class=\"data row3 col5\" >-0.500</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col6\" class=\"data row3 col6\" >-0.590</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col7\" class=\"data row3 col7\" >-0.278</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col8\" class=\"data row3 col8\" >-0.453</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col9\" class=\"data row3 col9\" >-0.625</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col10\" class=\"data row3 col10\" >-0.220</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col11\" class=\"data row3 col11\" >-0.376</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col12\" class=\"data row3 col12\" >-0.564</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col13\" class=\"data row3 col13\" >-0.332</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col14\" class=\"data row3 col14\" >0.060</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow3_col15\" class=\"data row3 col15\" >-0.696</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row4\" class=\"row_heading level0 row4\" >EP_MINRTY</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col0\" class=\"data row4 col0\" >0.650</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col1\" class=\"data row4 col1\" >0.750</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col2\" class=\"data row4 col2\" >0.796</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col3\" class=\"data row4 col3\" >-0.605</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col5\" class=\"data row4 col5\" >0.641</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col6\" class=\"data row4 col6\" >0.967</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col7\" class=\"data row4 col7\" >0.293</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col8\" class=\"data row4 col8\" >0.514</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col9\" class=\"data row4 col9\" >0.799</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col10\" class=\"data row4 col10\" >0.363</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col11\" class=\"data row4 col11\" >-0.306</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col12\" class=\"data row4 col12\" >0.815</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col13\" class=\"data row4 col13\" >0.560</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col14\" class=\"data row4 col14\" >0.021</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow4_col15\" class=\"data row4 col15\" >0.933</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row5\" class=\"row_heading level0 row5\" >EP_UNEMP</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col0\" class=\"data row5 col0\" >0.101</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col1\" class=\"data row5 col1\" >0.907</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col2\" class=\"data row5 col2\" >0.062</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col3\" class=\"data row5 col3\" >-0.500</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col4\" class=\"data row5 col4\" >0.641</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col6\" class=\"data row5 col6\" >0.686</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col7\" class=\"data row5 col7\" >0.463</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col8\" class=\"data row5 col8\" >0.927</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col9\" class=\"data row5 col9\" >0.851</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col10\" class=\"data row5 col10\" >0.043</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col11\" class=\"data row5 col11\" >0.180</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col12\" class=\"data row5 col12\" >0.215</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col13\" class=\"data row5 col13\" >0.201</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col14\" class=\"data row5 col14\" >-0.030</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow5_col15\" class=\"data row5 col15\" >0.744</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row6\" class=\"row_heading level0 row6\" >EP_NOHSDP</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col0\" class=\"data row6 col0\" >0.650</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col1\" class=\"data row6 col1\" >0.815</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col2\" class=\"data row6 col2\" >0.742</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col3\" class=\"data row6 col3\" >-0.590</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col4\" class=\"data row6 col4\" >0.967</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col5\" class=\"data row6 col5\" >0.686</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col7\" class=\"data row6 col7\" >0.377</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col8\" class=\"data row6 col8\" >0.606</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col9\" class=\"data row6 col9\" >0.871</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col10\" class=\"data row6 col10\" >0.385</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col11\" class=\"data row6 col11\" >-0.311</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col12\" class=\"data row6 col12\" >0.826</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col13\" class=\"data row6 col13\" >0.577</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col14\" class=\"data row6 col14\" >0.099</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow6_col15\" class=\"data row6 col15\" >0.964</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row7\" class=\"row_heading level0 row7\" >EP_AGE17</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col0\" class=\"data row7 col0\" >0.060</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col1\" class=\"data row7 col1\" >0.302</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col2\" class=\"data row7 col2\" >0.054</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col3\" class=\"data row7 col3\" >-0.278</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col4\" class=\"data row7 col4\" >0.293</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col5\" class=\"data row7 col5\" >0.463</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col6\" class=\"data row7 col6\" >0.377</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col7\" class=\"data row7 col7\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col8\" class=\"data row7 col8\" >0.418</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col9\" class=\"data row7 col9\" >0.612</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col10\" class=\"data row7 col10\" >-0.576</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col11\" class=\"data row7 col11\" >0.149</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col12\" class=\"data row7 col12\" >0.105</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col13\" class=\"data row7 col13\" >-0.452</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col14\" class=\"data row7 col14\" >-0.621</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow7_col15\" class=\"data row7 col15\" >0.310</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row8\" class=\"row_heading level0 row8\" >EP_DISABL</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col0\" class=\"data row8 col0\" >0.050</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col1\" class=\"data row8 col1\" >0.881</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col2\" class=\"data row8 col2\" >-0.047</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col3\" class=\"data row8 col3\" >-0.453</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col4\" class=\"data row8 col4\" >0.514</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col5\" class=\"data row8 col5\" >0.927</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col6\" class=\"data row8 col6\" >0.606</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col7\" class=\"data row8 col7\" >0.418</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col8\" class=\"data row8 col8\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col9\" class=\"data row8 col9\" >0.827</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col10\" class=\"data row8 col10\" >0.083</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col11\" class=\"data row8 col11\" >0.335</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col12\" class=\"data row8 col12\" >0.159</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col13\" class=\"data row8 col13\" >0.176</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col14\" class=\"data row8 col14\" >0.059</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow8_col15\" class=\"data row8 col15\" >0.675</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row9\" class=\"row_heading level0 row9\" >EP_SNGPNT</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col0\" class=\"data row9 col0\" >0.272</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col1\" class=\"data row9 col1\" >0.873</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col2\" class=\"data row9 col2\" >0.400</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col3\" class=\"data row9 col3\" >-0.625</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col4\" class=\"data row9 col4\" >0.799</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col5\" class=\"data row9 col5\" >0.851</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col6\" class=\"data row9 col6\" >0.871</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col7\" class=\"data row9 col7\" >0.612</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col8\" class=\"data row9 col8\" >0.827</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col9\" class=\"data row9 col9\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col10\" class=\"data row9 col10\" >0.174</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col11\" class=\"data row9 col11\" >0.028</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col12\" class=\"data row9 col12\" >0.514</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col13\" class=\"data row9 col13\" >0.280</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col14\" class=\"data row9 col14\" >0.009</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow9_col15\" class=\"data row9 col15\" >0.865</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row10\" class=\"row_heading level0 row10\" >EP_MUNIT</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col0\" class=\"data row10 col0\" >0.269</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col1\" class=\"data row10 col1\" >0.368</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col2\" class=\"data row10 col2\" >0.472</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col3\" class=\"data row10 col3\" >-0.220</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col4\" class=\"data row10 col4\" >0.363</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col5\" class=\"data row10 col5\" >0.043</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col6\" class=\"data row10 col6\" >0.385</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col7\" class=\"data row10 col7\" >-0.576</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col8\" class=\"data row10 col8\" >0.083</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col9\" class=\"data row10 col9\" >0.174</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col10\" class=\"data row10 col10\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col11\" class=\"data row10 col11\" >-0.384</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col12\" class=\"data row10 col12\" >0.529</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col13\" class=\"data row10 col13\" >0.917</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col14\" class=\"data row10 col14\" >0.872</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow10_col15\" class=\"data row10 col15\" >0.446</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row11\" class=\"row_heading level0 row11\" >EP_MOBILE</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col0\" class=\"data row11 col0\" >-0.436</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col1\" class=\"data row11 col1\" >0.074</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col2\" class=\"data row11 col2\" >-0.584</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col3\" class=\"data row11 col3\" >-0.376</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col4\" class=\"data row11 col4\" >-0.306</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col5\" class=\"data row11 col5\" >0.180</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col6\" class=\"data row11 col6\" >-0.311</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col7\" class=\"data row11 col7\" >0.149</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col8\" class=\"data row11 col8\" >0.335</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col9\" class=\"data row11 col9\" >0.028</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col10\" class=\"data row11 col10\" >-0.384</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col11\" class=\"data row11 col11\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col12\" class=\"data row11 col12\" >-0.441</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col13\" class=\"data row11 col13\" >-0.465</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col14\" class=\"data row11 col14\" >-0.304</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow11_col15\" class=\"data row11 col15\" >-0.162</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row12\" class=\"row_heading level0 row12\" >EP_CROWD</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col0\" class=\"data row12 col0\" >0.872</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col1\" class=\"data row12 col1\" >0.510</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col2\" class=\"data row12 col2\" >0.927</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col3\" class=\"data row12 col3\" >-0.564</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col4\" class=\"data row12 col4\" >0.815</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col5\" class=\"data row12 col5\" >0.215</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col6\" class=\"data row12 col6\" >0.826</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col7\" class=\"data row12 col7\" >0.105</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col8\" class=\"data row12 col8\" >0.159</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col9\" class=\"data row12 col9\" >0.514</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col10\" class=\"data row12 col10\" >0.529</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col11\" class=\"data row12 col11\" >-0.441</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col12\" class=\"data row12 col12\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col13\" class=\"data row12 col13\" >0.718</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col14\" class=\"data row12 col14\" >0.149</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow12_col15\" class=\"data row12 col15\" >0.799</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row13\" class=\"row_heading level0 row13\" >EP_NOVEH</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col0\" class=\"data row13 col0\" >0.571</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col1\" class=\"data row13 col1\" >0.535</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col2\" class=\"data row13 col2\" >0.618</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col3\" class=\"data row13 col3\" >-0.332</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col4\" class=\"data row13 col4\" >0.560</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col5\" class=\"data row13 col5\" >0.201</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col6\" class=\"data row13 col6\" >0.577</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col7\" class=\"data row13 col7\" >-0.452</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col8\" class=\"data row13 col8\" >0.176</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col9\" class=\"data row13 col9\" >0.280</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col10\" class=\"data row13 col10\" >0.917</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col11\" class=\"data row13 col11\" >-0.465</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col12\" class=\"data row13 col12\" >0.718</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col13\" class=\"data row13 col13\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col14\" class=\"data row13 col14\" >0.691</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow13_col15\" class=\"data row13 col15\" >0.632</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row14\" class=\"row_heading level0 row14\" >EP_GROUPQ</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col0\" class=\"data row14 col0\" >-0.076</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col1\" class=\"data row14 col1\" >0.186</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col2\" class=\"data row14 col2\" >0.078</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col3\" class=\"data row14 col3\" >0.060</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col4\" class=\"data row14 col4\" >0.021</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col5\" class=\"data row14 col5\" >-0.030</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col6\" class=\"data row14 col6\" >0.099</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col7\" class=\"data row14 col7\" >-0.621</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col8\" class=\"data row14 col8\" >0.059</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col9\" class=\"data row14 col9\" >0.009</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col10\" class=\"data row14 col10\" >0.872</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col11\" class=\"data row14 col11\" >-0.304</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col12\" class=\"data row14 col12\" >0.149</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col13\" class=\"data row14 col13\" >0.691</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col14\" class=\"data row14 col14\" >1.000</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow14_col15\" class=\"data row14 col15\" >0.138</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3clevel0_row15\" class=\"row_heading level0 row15\" >RPL_THEMES</th>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col0\" class=\"data row15 col0\" >0.594</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col1\" class=\"data row15 col1\" >0.899</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col2\" class=\"data row15 col2\" >0.659</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col3\" class=\"data row15 col3\" >-0.696</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col4\" class=\"data row15 col4\" >0.933</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col5\" class=\"data row15 col5\" >0.744</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col6\" class=\"data row15 col6\" >0.964</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col7\" class=\"data row15 col7\" >0.310</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col8\" class=\"data row15 col8\" >0.675</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col9\" class=\"data row15 col9\" >0.865</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col10\" class=\"data row15 col10\" >0.446</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col11\" class=\"data row15 col11\" >-0.162</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col12\" class=\"data row15 col12\" >0.799</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col13\" class=\"data row15 col13\" >0.632</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col14\" class=\"data row15 col14\" >0.138</td>\n",
       "                        <td id=\"T_3ab8c9e4_8e52_11ea_bad6_206a8aa20d3crow15_col15\" class=\"data row15 col15\" >1.000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x199b41a11c8>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting a correlation matrix for the ten most affected US counties\n",
    "\n",
    "svi_top_10_counties_deaths_corr = svi_top_10_counties_deaths[['deaths', 'EP_POV', 'EP_LIMENG', 'EP_AGE65', 'EP_MINRTY', 'EP_UNEMP', 'EP_NOHSDP', 'EP_AGE17', 'EP_DISABL', 'EP_SNGPNT', 'EP_MUNIT', 'EP_MOBILE', 'EP_CROWD', 'EP_NOVEH', 'EP_GROUPQ', 'RPL_THEMES']]\n",
    "corr = svi_top_10_counties_deaths_corr.corr()\n",
    "table = corr.style.background_gradient(cmap='Reds').format('{0:,.3f}')\n",
    "\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\r\n",
      "[>                                                           ] 0%\r",
      "[======>                                                     ] 10%\r",
      "[==============================>                             ] 50%\r",
      "[============================================================] 100%\r",
      "Rendering (2/2)                                                    \r\n",
      "[>                                                           ] 0%\r",
      "[===============>                                            ] 25%\r",
      "[============================================================] 100%\r",
      "Done                                                               \r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export Pandas style object to PNG\n",
    "html = table.render()\n",
    "\n",
    "path_wkthmltoimage = r'D:\\Tools\\wkthmltool\\bin\\wkhtmltoimage.exe'\n",
    "config = imgkit.config(wkhtmltoimage=path_wkthmltoimage)\n",
    "\n",
    "imgkit.from_string(html, './Media for Medium post/corr_deaths_top_10_counties.png',config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x199c473b148>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of Social Vulnerability Indicators and deaths caused by Covid-19 in the ten most affected counties\n",
    "# For the visualization, we take RPL_THEMES, which is defined as an overall ranking variable. \n",
    "svi_top_10_counties_deaths_RPL = svi_top_10_counties_deaths.sort_values('deaths')\n",
    "sns.set_palette('muted')\n",
    "\n",
    "f, ax = plt.subplots(figsize = (16, 10))\n",
    "c = sns.scatterplot(x = svi_top_10_counties_deaths_RPL.RPL_THEMES, y = svi_top_10_counties_deaths_RPL.deaths, size = svi_top_10_counties_deaths_RPL.RPL_THEMES, sizes = (100, 800), hue = svi_top_10_counties_deaths_RPL['COUNTY'])\n",
    "ax.yaxis.grid()\n",
    "\n",
    "plt.xlabel('Social Vulnerability Index, overall ranking variable PRL_THEMES', fontsize = 12)\n",
    "plt.ylabel('Deaths caused by Covid-19 as of April 8th, 2020', fontsize = 12)\n",
    "plt.title('Deaths caused by Covid-19 and overall SVI ranking in the ten most affected counties', fontsize = 14)\n",
    "sns.despine()\n",
    "plt.legend(scatterpoints=1, frameon=True, labelspacing=1.5, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building a Machine Learning model for deaths caused by Covid-19 \n",
    "### A. Multiple Linear Regression for deaths caused by Covid-19 in all US counties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2441006574888489\n"
     ]
    },
    {
     "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>Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.181673</td>\n",
       "      <td>EP_NOVEH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.781509</td>\n",
       "      <td>EP_LIMENG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.504375</td>\n",
       "      <td>EP_MUNIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.505521</td>\n",
       "      <td>EP_AGE17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.481979</td>\n",
       "      <td>EP_AGE65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.286014</td>\n",
       "      <td>EP_MOBILE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.049138</td>\n",
       "      <td>EP_UNEMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.002014</td>\n",
       "      <td>EP_DISABL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.004454</td>\n",
       "      <td>EP_MINRTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.104145</td>\n",
       "      <td>EP_NOHSDP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.281165</td>\n",
       "      <td>EP_GROUPQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.461475</td>\n",
       "      <td>EP_SNGPNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.522013</td>\n",
       "      <td>EP_POV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.258077</td>\n",
       "      <td>EP_CROWD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Coef   Features\n",
       "12  2.181673   EP_NOVEH\n",
       "1   1.781509  EP_LIMENG\n",
       "9   1.504375   EP_MUNIT\n",
       "6   0.505521   EP_AGE17\n",
       "2   0.481979   EP_AGE65\n",
       "10  0.286014  EP_MOBILE\n",
       "4   0.049138   EP_UNEMP\n",
       "7  -0.002014  EP_DISABL\n",
       "3  -0.004454  EP_MINRTY\n",
       "5  -0.104145  EP_NOHSDP\n",
       "13 -0.281165  EP_GROUPQ\n",
       "8  -0.461475  EP_SNGPNT\n",
       "0  -0.522013     EP_POV\n",
       "11 -1.258077   EP_CROWD"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the Multiple Linear Regression Model\n",
    "\n",
    "# Preparing the independent variable (X) and the dependent variables (y)\n",
    "y = svi_all_counties_deaths['deaths']\n",
    "X = svi_all_counties_deaths.drop(['deaths', 'county_fips', 'COUNTY', 'STATE', 'RPL_THEMES'], axis = 1)\n",
    "\n",
    "# Splitting the data in train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Initiate the model\n",
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, y_train)\n",
    "\n",
    "y_predict= lm.predict(X_test)\n",
    "\n",
    "\n",
    "# Score\n",
    "print(lm.score(X_test, y_test))\n",
    "# Score is very low, which may be due to the fact, that many US counties dont have confirmed cases yet. \n",
    "\n",
    "deaths_all_counties_coef = pd.DataFrame({'Coef' : model.coef_, 'Features' : X.columns}).sort_values('Coef', ascending = False)\n",
    "deaths_all_counties_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Multiple Linear Regression for the ten US counties with most deaths caused by Covid-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3985436152080033\n"
     ]
    },
    {
     "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>Coef</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37.216337</td>\n",
       "      <td>EP_SNGPNT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.491804</td>\n",
       "      <td>EP_LIMENG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.532342</td>\n",
       "      <td>EP_NOVEH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.820129</td>\n",
       "      <td>EP_DISABL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.689563</td>\n",
       "      <td>EP_NOHSDP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.267004</td>\n",
       "      <td>EP_CROWD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.548780</td>\n",
       "      <td>EP_GROUPQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.025137</td>\n",
       "      <td>EP_MOBILE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.931603</td>\n",
       "      <td>EP_AGE17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.563069</td>\n",
       "      <td>EP_POV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-9.270615</td>\n",
       "      <td>EP_AGE65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-16.697067</td>\n",
       "      <td>EP_MUNIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-19.976306</td>\n",
       "      <td>EP_MINRTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-28.705655</td>\n",
       "      <td>EP_UNEMP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Coef   Features\n",
       "8   37.216337  EP_SNGPNT\n",
       "1   30.491804  EP_LIMENG\n",
       "12  21.532342   EP_NOVEH\n",
       "7   20.820129  EP_DISABL\n",
       "5   16.689563  EP_NOHSDP\n",
       "11  14.267004   EP_CROWD\n",
       "13   5.548780  EP_GROUPQ\n",
       "10   5.025137  EP_MOBILE\n",
       "6    1.931603   EP_AGE17\n",
       "0   -7.563069     EP_POV\n",
       "2   -9.270615   EP_AGE65\n",
       "9  -16.697067   EP_MUNIT\n",
       "3  -19.976306  EP_MINRTY\n",
       "4  -28.705655   EP_UNEMP"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the Multiple Linear Regression Model\n",
    "\n",
    "# Preparing the independent variable (X) and the dependent variables (y)\n",
    "y = svi_top_10_counties_deaths['deaths']\n",
    "X = svi_top_10_counties_deaths.drop(['deaths', 'county_fips', 'COUNTY', 'STATE', 'RPL_THEMES'], axis = 1)\n",
    "\n",
    "# Splitting the data in train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, test_size = 0.2, random_state=42)\n",
    "\n",
    "\n",
    "# Initiate the model\n",
    "lm = LinearRegression()\n",
    "model = lm.fit(X_train, y_train)\n",
    "\n",
    "y_predict= lm.predict(X_test)\n",
    "\n",
    "\n",
    "# Score\n",
    "print(lm.score(X_test, y_test))\n",
    "\n",
    "deaths_top_10_counties_coef = pd.DataFrame({'Coef' : model.coef_, 'Features' : X.columns}).sort_values('Coef', ascending = False)\n",
    "deaths_top_10_counties_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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