pandasCovidProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "75aec70a",
   "metadata": {},
   "source": [
    "## Purpose: \n",
    "\n",
    "Examining the relationship between states with high covid-related death rates and states with high poverty, limited education, and no health insurance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "3110df69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f735f66",
   "metadata": {},
   "source": [
    "# TOTAL COVID DEATHS OVER 1 YEAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "15152884",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the file located on the desktop is opened and read as df\n",
    "df = pd.read_csv(\"/Users/morganchunn/Desktop/time_series_covid19_deaths (states).csv\")\n",
    "#the dataframe is grouped by the coulumn \"Province_State\" \n",
    "num_state = df.groupby(\"Province_State\").sum()\n",
    "#just the columns between the 2 dates are selected for\n",
    "num_state_year = num_state.loc[:,\"6/1/20\":\"6/1/21\"]\n",
    "#will find the number of cases between thhe 2 dates\n",
    "state_num_increase = num_state_year[\"6/1/21\"].sub(num_state_summer['6/1/20'], axis = 0)\n",
    "#selects for the top top 10 values \n",
    "deaths = state_num_increase.sort_values(ascending=False).head(10)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14aa032",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with the greatest number of covid related deaths over the span of 1 year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "c9c7b62d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'States with Greatest Number of Covid Related Deaths'}, ylabel='State'>"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plots a horizontal bar graph of data represented by deaths variable \n",
    "deaths.plot.barh(xlabel=\"State\", title= \"States with Greatest Number of Covid Related Deaths\", color = \"indianred\")\n",
    "                                                                                                                                                                                                                                 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7735e919",
   "metadata": {},
   "source": [
    "Normilizing Data to use for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "432c7f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province_State\n",
       "California      1.000000\n",
       "Texas           0.807728\n",
       "Florida         0.456240\n",
       "New York        0.180069\n",
       "Pennsylvania    0.161384\n",
       "Illinois        0.119723\n",
       "Georgia         0.094879\n",
       "Ohio            0.081361\n",
       "Arizona         0.047958\n",
       "Michigan        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#formula to normalize the data\n",
    "norm_death_data = (deaths-deaths.min())/(deaths.max()-deaths.min())  \n",
    "norm_death_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efef0f91",
   "metadata": {},
   "source": [
    "# HOUSES BELOW POVERTY LINE "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15bcac63",
   "metadata": {},
   "source": [
    "The below cell will show the top 10 states with the highest number of houses below the poverty line. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "4945c2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "poverty_data  = pd.read_csv(\"/Users/morganchunn/Desktop/Population_and_Poverty_Status (States).csv\")\n",
    "#the poverty data has its index set to \"State\" and the values are sorted descendingly by the given column name \n",
    "pov = poverty_data.set_index(\"State\").sort_values(by=\"Total Households Below the Poverty Level\", ascending=False)\n",
    "#just the top 10 values pertaining the specific column are selected for \n",
    "poverty_households= pov.loc[:,\"Total Households Below the Poverty Level\"].head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe5b59d5",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with the greatest number of houses below the poverty line. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "397f7c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'States with Greatest Number of Houses Below the Poverty Line'}, ylabel='State'>"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poverty_households.plot.barh(xlabel=\"State\", title= \"States with Greatest Number of Houses Below the Poverty Line\", color = \"lightseagreen\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc02bd4",
   "metadata": {},
   "source": [
    "Normilizing Data to use for comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "9e66a5c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "California        1.000000\n",
       "Texas             0.725761\n",
       "Florida           0.450282\n",
       "New York          0.441601\n",
       "Ohio              0.095119\n",
       "Pennsylvania      0.070198\n",
       "Illinois          0.055990\n",
       "North Carolina    0.014406\n",
       "Georgia           0.000056\n",
       "Puerto Rico       0.000000\n",
       "Name: Total Households Below the Poverty Level, dtype: float64"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#formula for normilization\n",
    "norm_poverty_data = (poverty_households-poverty_households.min())/(poverty_households.max()-poverty_households.min())\n",
    "norm_poverty_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba40a7d",
   "metadata": {},
   "source": [
    "# Comparing States with High Covid Related Deaths to States with High Poverty Rates:\n",
    "This depicts how many of the top 10 states with houses below the poverty line are in the top 10 states with the most covid deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "9a2063ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     8\n",
       "False    2\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the top 10 poverty states are selected for by the specific column\n",
    "compare_pov = poverty_data.sort_values(by=\"Total Households Below the Poverty Level\", ascending=False).head(10)\n",
    "num_state_summer['Covid Deaths'] = state_num_increase\n",
    "compare_deaths = num_state_summer.sort_values(by=\"Covid Deaths\", ascending=False).head(10).reset_index()\n",
    "#if the high poverty state is also a state found in the top 10 states with highest covid deaths, then the result is True\n",
    "compare_pov['State'].isin(compare_deaths['Province_State']).value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c14347",
   "metadata": {},
   "source": [
    "\"True\" means the state with high poverty is also a state with high covid related deaths. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be0fdd9",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with the greatest number of houses below the poverty line. States in red are also in the top 10 states who had the greatest number of covid related deaths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "842005a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'States with Greatest Number of Houses Below the Poverty Line'}, ylabel='State'>"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "poverty_households.plot.barh(xlabel=\"State\", title= \"States with Greatest Number of Houses Below the Poverty Line\", color = ['indianred', 'indianred', 'indianred', 'indianred', 'indianred', 'indianred', 'indianred', 'lightseagreen','indianred','lightseagreen'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea71d261",
   "metadata": {},
   "source": [
    "Since the majority of states who have high poverty are also states with high covid death rates, there is evidence to suggest high poverty rates could be an aiding factor in higher covid death rates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a71b21b1",
   "metadata": {},
   "source": [
    "# LIMITED EDUCATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "7dd336ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "California        2432994\n",
       "Texas             1482952\n",
       "New York           838231\n",
       "Florida            718909\n",
       "Illinois           419259\n",
       "Puerto Rico        358915\n",
       "Georgia            318461\n",
       "North Carolina     314545\n",
       "New Jersey         298624\n",
       "Pennsylvania       284062\n",
       "Name: Population 25 years and over - Less than 9th grade, dtype: int64"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education  = pd.read_csv(\"/Users/morganchunn/Desktop/Educational_Attainment (states).csv\")\n",
    "#index is set and the data is sorted by the specific column\n",
    "state_ed= education.set_index(\"State\").sort_values(by=\"Population 25 years and over - Less than 9th grade\", ascending=False)\n",
    "state_ed_less_9= state_ed.loc[:,\"Population 25 years and over - Less than 9th grade\"]\n",
    "#top 10 values from the specific column \n",
    "ed = state_ed_less_9.head(10)\n",
    "ed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2abd04a",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with limited education. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "4c41e1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top States with Limited Education'}, ylabel='State'>"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed.plot.barh(xlabel=\"State\", title= \"Top States with Limited Education\", color = \"orange\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12408ed3",
   "metadata": {},
   "source": [
    "Normilizing Data to use for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "e9bcc589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "California        1.000000\n",
       "Texas             0.557900\n",
       "New York          0.257881\n",
       "Florida           0.202355\n",
       "Illinois          0.062914\n",
       "Puerto Rico       0.034833\n",
       "Georgia           0.016007\n",
       "North Carolina    0.014185\n",
       "New Jersey        0.006776\n",
       "Pennsylvania      0.000000\n",
       "Name: Population 25 years and over - Less than 9th grade, dtype: float64"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#formula for normalization\n",
    "norm_ed_data = (ed-ed.min())/(ed.max()-ed.min())\n",
    "norm_ed_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80735930",
   "metadata": {},
   "source": [
    "# Comparing States with High Related Covid Deaths to States with Limited Education:\n",
    "This depicts how many of the top 10 states with limited education amoungst the population are in the top 10 states with the most covid deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "0ce42106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     7\n",
       "False    3\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_edu = education.sort_values(by=\"Population 25 years and over - Less than 9th grade\", ascending=False).head(10)\n",
    "#if the state with limited education is in the states with high covid deaths, true is the result.. otherwise false is given\n",
    "compare_edu['State'].isin(compare_deaths['Province_State']).value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31ffbc6",
   "metadata": {},
   "source": [
    "\"True\" means the state with limited education is also a state with high covid related deaths. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95883fc",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with limited education. States in red are also in the top 10 states who had the greatest number of covid related deaths. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "e8fca1ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top States with Limited Education'}, ylabel='State'>"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ed.plot.barh(xlabel=\"State\", title= \"Top States with Limited Education\", color = ['indianred', 'indianred', 'indianred', 'indianred', 'indianred', 'orange', 'indianred', 'orange','orange','indianred'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848607db",
   "metadata": {},
   "source": [
    "Since the majority of states who have limited education amongst the population are also states with high covid death rates, there is evidence to suggest that lack of education could be an aiding factor in higher covid death rates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a468f6",
   "metadata": {},
   "source": [
    "# POPULATION WITHOUT HEALTH INSURANCE COVERAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "22c523b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "insurance  = pd.read_csv(\"/Users/morganchunn/Desktop/Health_Insurance (States).csv\").set_index(\"State\")\n",
    "#values are sorted descendingly by the specific column name \n",
    "insurance.sort_values(by=\"Total Civilian Noninstitutionalized Population - No health insurance coverage\", ascending=False)\n",
    "without_in= insurance.loc[:,\"Total Civilian Noninstitutionalized Population - No health insurance coverage\"]\n",
    "ins = without_in.sort_values(ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62271ad6",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with the greatest number of uninsured individuals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "bc9d53cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top States with Limited Insurance Coverage Amongst the Population'}, ylabel='State'>"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ins.plot.barh(xlabel=\"State\", title= \"Top States with Limited Insurance Coverage Amongst the Population\", color = \"plum\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8fbd00",
   "metadata": {},
   "source": [
    "Normilizing Data to use for Comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "c789f9f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "Texas             1.000000\n",
       "California        0.540534\n",
       "Florida           0.472499\n",
       "Georgia           0.157508\n",
       "New York          0.100215\n",
       "North Carolina    0.090379\n",
       "Illinois          0.037140\n",
       "Arizona           0.003817\n",
       "Pennsylvania      0.001794\n",
       "Virginia          0.000000\n",
       "Name: Total Civilian Noninstitutionalized Population - No health insurance coverage, dtype: float64"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_in_data = (ins-ins.min())/(ins.max()-ins.min())\n",
    "norm_in_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfc419a",
   "metadata": {},
   "source": [
    "# Comparing States with High Covid Related Deaths to States without Health Insurance:\n",
    "This depicts how many of the top 10 states without health insurance are in the top 10 states with the most covid deaths.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "8160a748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     8\n",
       "False    2\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_ins = insurance.sort_values(by=\"Total Civilian Noninstitutionalized Population - No health insurance coverage\", ascending=False).head(10)\n",
    "compare_ins = com_ins.reset_index()\n",
    "#if the low insurance coverage state is also a state with high covid deaths, true is returned \n",
    "compare_ins['State'].isin(compare_deaths['Province_State']).value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fba432",
   "metadata": {},
   "source": [
    "\"True\" means the state with limited insurance coverage is also a state with high covid related deaths. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6904149f",
   "metadata": {},
   "source": [
    "Graph depicting the top 10 states with the greatest number of uninsured individuals. States in red are also in the top 10 states who had the greatest number of covid related deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "e7ab505d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top States with Limited Insurance Coverage Amongst the Population'}, ylabel='State'>"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ins.plot.barh(xlabel=\"State\", title= \"Top States with Limited Insurance Coverage Amongst the Population\", color = ['indianred', 'indianred', 'indianred', 'indianred', 'indianred', 'plum', 'indianred', 'indianred','indianred','plum'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5313b6",
   "metadata": {},
   "source": [
    "Since the majority of states who have limited health insurance amongst the population are also states with high covid death rates, there is evidence to suggest like lack of health insurance could be an aiding factor in higher covid death rates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a3458d",
   "metadata": {},
   "source": [
    "# Viewing the data in tabulated form:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0cc8434",
   "metadata": {},
   "source": [
    "# How the top 10 States in Covid Deaths compare to top 10 States Below Poverty line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "7e55c037",
   "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>Total Covid deaths over 1 Year</th>\n",
       "      <th>Houses Below Poverty Line</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>57981.0</td>\n",
       "      <td>1632747.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>49646.0</td>\n",
       "      <td>1331757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>34409.0</td>\n",
       "      <td>1029407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>22437.0</td>\n",
       "      <td>1019879.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>21627.0</td>\n",
       "      <td>612247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>19821.0</td>\n",
       "      <td>596654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>18744.0</td>\n",
       "      <td>535264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>18158.0</td>\n",
       "      <td>639599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>16710.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>14631.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>551013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>NaN</td>\n",
       "      <td>535202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Total Covid deaths over 1 Year  Houses Below Poverty Line \n",
       "State                                                                      \n",
       "California                              57981.0                   1632747.0\n",
       "Texas                                   49646.0                   1331757.0\n",
       "Florida                                 34409.0                   1029407.0\n",
       "New York                                22437.0                   1019879.0\n",
       "Pennsylvania                            21627.0                    612247.0\n",
       "Illinois                                19821.0                    596654.0\n",
       "Georgia                                 18744.0                    535264.0\n",
       "Ohio                                    18158.0                    639599.0\n",
       "Arizona                                 16710.0                         NaN\n",
       "Michigan                                14631.0                         NaN\n",
       "North Carolina                              NaN                    551013.0\n",
       "Puerto Rico                                 NaN                    535202.0\n",
       "New Jersey                                  NaN                         NaN\n",
       "Virginia                                    NaN                         NaN"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined  = pd.read_csv(\"/Users/morganchunn/Desktop/combined_data.csv\")\n",
    "#multiple columns are selected for and sorted by one specific column\n",
    "pov_deaths = combined.iloc[:,[0,1,2]].sort_values(by=\"Total Covid deaths over 1 Year\", ascending = False).set_index('State')\n",
    "pov_deaths\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6db7bb",
   "metadata": {},
   "source": [
    "Plot showing normalization of the above data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "008c97c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normilized_pov = normilized_com_all.iloc[:,[0,2]]\n",
    "#bar plot of the normalized data\n",
    "normilized_pov.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5bfa45",
   "metadata": {},
   "source": [
    "# How the top 10 States in Covid Deaths compare to top 10 States with limited Education  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "d3f1aaf3",
   "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>State</th>\n",
       "      <th>Total Covid deaths over 1 Year</th>\n",
       "      <th>Population 25 years and over - Less than 9th Grad Education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>57981.0</td>\n",
       "      <td>2432994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Texas</td>\n",
       "      <td>49646.0</td>\n",
       "      <td>1482952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>34409.0</td>\n",
       "      <td>718909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>22437.0</td>\n",
       "      <td>838231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>21627.0</td>\n",
       "      <td>284062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>19821.0</td>\n",
       "      <td>419259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>18744.0</td>\n",
       "      <td>318461.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>18158.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>16710.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>14631.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>358915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              State  Total Covid deaths over 1 Year  \\\n",
       "0        California                         57981.0   \n",
       "1             Texas                         49646.0   \n",
       "2           Florida                         34409.0   \n",
       "3          New York                         22437.0   \n",
       "4     Pennsylvania                          21627.0   \n",
       "5          Illinois                         19821.0   \n",
       "6           Georgia                         18744.0   \n",
       "7              Ohio                         18158.0   \n",
       "8          Arizona                          16710.0   \n",
       "9         Michigan                          14631.0   \n",
       "10  North Carolina                              NaN   \n",
       "11      Puerto Rico                             NaN   \n",
       "12       New Jersey                             NaN   \n",
       "13         Virginia                             NaN   \n",
       "\n",
       "    Population 25 years and over - Less than 9th Grad Education   \n",
       "0                                           2432994.0             \n",
       "1                                           1482952.0             \n",
       "2                                            718909.0             \n",
       "3                                            838231.0             \n",
       "4                                            284062.0             \n",
       "5                                            419259.0             \n",
       "6                                            318461.0             \n",
       "7                                                 NaN             \n",
       "8                                                 NaN             \n",
       "9                                                 NaN             \n",
       "10                                           314545.0             \n",
       "11                                           358915.0             \n",
       "12                                           298624.0             \n",
       "13                                                NaN             "
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#multiple columns are selected for by their position\n",
    "edu_deaths=combined.iloc[:,[0,1,3]] \n",
    "edu_deaths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ec82a8",
   "metadata": {},
   "source": [
    "Plot showing normalization of the above data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "ae42ae42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normilized_edu = normilized_com_all.iloc[:,[0,3]]\n",
    "normilized_edu.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff87abe3",
   "metadata": {},
   "source": [
    "# How the top 10 States in Covid Deaths over a year compare to the top 10 States lacking in Heath Insurance  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "e5d2a986",
   "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>State</th>\n",
       "      <th>Total Covid deaths over 1 Year</th>\n",
       "      <th>Population without Health Insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>57981.0</td>\n",
       "      <td>2915070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Texas</td>\n",
       "      <td>49646.0</td>\n",
       "      <td>4791164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>34409.0</td>\n",
       "      <td>2637268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>22437.0</td>\n",
       "      <td>1117159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>21627.0</td>\n",
       "      <td>715285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>19821.0</td>\n",
       "      <td>859612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>18744.0</td>\n",
       "      <td>1351097.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>18158.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>16710.0</td>\n",
       "      <td>723547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>14631.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1076994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>707960.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              State  Total Covid deaths over 1 Year  \\\n",
       "0        California                         57981.0   \n",
       "1             Texas                         49646.0   \n",
       "2           Florida                         34409.0   \n",
       "3          New York                         22437.0   \n",
       "4     Pennsylvania                          21627.0   \n",
       "5          Illinois                         19821.0   \n",
       "6           Georgia                         18744.0   \n",
       "7              Ohio                         18158.0   \n",
       "8          Arizona                          16710.0   \n",
       "9         Michigan                          14631.0   \n",
       "10  North Carolina                              NaN   \n",
       "11      Puerto Rico                             NaN   \n",
       "12       New Jersey                             NaN   \n",
       "13         Virginia                             NaN   \n",
       "\n",
       "    Population without Health Insurance    \n",
       "0                               2915070.0  \n",
       "1                               4791164.0  \n",
       "2                               2637268.0  \n",
       "3                               1117159.0  \n",
       "4                                715285.0  \n",
       "5                                859612.0  \n",
       "6                               1351097.0  \n",
       "7                                     NaN  \n",
       "8                                723547.0  \n",
       "9                                     NaN  \n",
       "10                              1076994.0  \n",
       "11                                    NaN  \n",
       "12                                    NaN  \n",
       "13                               707960.0  "
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_deaths = combined.iloc[:,[0,1,4]]\n",
    "ins_deaths\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e80c8bb",
   "metadata": {},
   "source": [
    "Plot showing normalization of the above data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "a04d4832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normilized_ins = normilized_com_all.iloc[:,[0,3]]\n",
    "normilized_ins.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c52b909",
   "metadata": {},
   "source": [
    "# Combined Data\n",
    "Top 10 States from each category "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "56bbbaeb",
   "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>Total Covid deaths over 1 Year</th>\n",
       "      <th>Houses Below Poverty Line</th>\n",
       "      <th>Population 25 years and over - Less than 9th Grad Education</th>\n",
       "      <th>Population without Health Insurance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>57981.0</td>\n",
       "      <td>1632747.0</td>\n",
       "      <td>2432994.0</td>\n",
       "      <td>2915070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>49646.0</td>\n",
       "      <td>1331757.0</td>\n",
       "      <td>1482952.0</td>\n",
       "      <td>4791164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>34409.0</td>\n",
       "      <td>1029407.0</td>\n",
       "      <td>718909.0</td>\n",
       "      <td>2637268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>22437.0</td>\n",
       "      <td>1019879.0</td>\n",
       "      <td>838231.0</td>\n",
       "      <td>1117159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>21627.0</td>\n",
       "      <td>612247.0</td>\n",
       "      <td>284062.0</td>\n",
       "      <td>715285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>19821.0</td>\n",
       "      <td>596654.0</td>\n",
       "      <td>419259.0</td>\n",
       "      <td>859612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>18744.0</td>\n",
       "      <td>535264.0</td>\n",
       "      <td>318461.0</td>\n",
       "      <td>1351097.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>18158.0</td>\n",
       "      <td>639599.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>16710.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>723547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>14631.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>551013.0</td>\n",
       "      <td>314545.0</td>\n",
       "      <td>1076994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>NaN</td>\n",
       "      <td>535202.0</td>\n",
       "      <td>358915.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298624.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>707960.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Total Covid deaths over 1 Year  Houses Below Poverty Line   \\\n",
       "State                                                                         \n",
       "California                              57981.0                   1632747.0   \n",
       "Texas                                   49646.0                   1331757.0   \n",
       "Florida                                 34409.0                   1029407.0   \n",
       "New York                                22437.0                   1019879.0   \n",
       "Pennsylvania                            21627.0                    612247.0   \n",
       "Illinois                                19821.0                    596654.0   \n",
       "Georgia                                 18744.0                    535264.0   \n",
       "Ohio                                    18158.0                    639599.0   \n",
       "Arizona                                 16710.0                         NaN   \n",
       "Michigan                                14631.0                         NaN   \n",
       "North Carolina                              NaN                    551013.0   \n",
       "Puerto Rico                                 NaN                    535202.0   \n",
       "New Jersey                                  NaN                         NaN   \n",
       "Virginia                                    NaN                         NaN   \n",
       "\n",
       "                 Population 25 years and over - Less than 9th Grad Education   \\\n",
       "State                                                                           \n",
       "California                                               2432994.0              \n",
       "Texas                                                    1482952.0              \n",
       "Florida                                                   718909.0              \n",
       "New York                                                  838231.0              \n",
       "Pennsylvania                                              284062.0              \n",
       "Illinois                                                  419259.0              \n",
       "Georgia                                                   318461.0              \n",
       "Ohio                                                           NaN              \n",
       "Arizona                                                        NaN              \n",
       "Michigan                                                       NaN              \n",
       "North Carolina                                            314545.0              \n",
       "Puerto Rico                                               358915.0              \n",
       "New Jersey                                                298624.0              \n",
       "Virginia                                                       NaN              \n",
       "\n",
       "                 Population without Health Insurance    \n",
       "State                                                   \n",
       "California                                   2915070.0  \n",
       "Texas                                        4791164.0  \n",
       "Florida                                      2637268.0  \n",
       "New York                                     1117159.0  \n",
       "Pennsylvania                                  715285.0  \n",
       "Illinois                                      859612.0  \n",
       "Georgia                                      1351097.0  \n",
       "Ohio                                               NaN  \n",
       "Arizona                                       723547.0  \n",
       "Michigan                                           NaN  \n",
       "North Carolina                               1076994.0  \n",
       "Puerto Rico                                        NaN  \n",
       "New Jersey                                         NaN  \n",
       "Virginia                                      707960.0  "
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_all = pd.read_csv(\"/Users/morganchunn/Desktop/combined_data.csv\").set_index(\"State\")\n",
    "com_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd228c1a",
   "metadata": {},
   "source": [
    "Below is the data normalized. The is done so all data can be observed from theh same scale. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "aa4736a1",
   "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>Total Covid deaths over 1 Year</th>\n",
       "      <th>Houses Below Poverty Line</th>\n",
       "      <th>Population 25 years and over - Less than 9th Grad Education</th>\n",
       "      <th>Population without Health Insurance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.540534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.807728</td>\n",
       "      <td>0.725761</td>\n",
       "      <td>0.557900</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>0.456240</td>\n",
       "      <td>0.450282</td>\n",
       "      <td>0.202355</td>\n",
       "      <td>0.472499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>0.180069</td>\n",
       "      <td>0.441601</td>\n",
       "      <td>0.257881</td>\n",
       "      <td>0.100215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>0.161384</td>\n",
       "      <td>0.070198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>0.119723</td>\n",
       "      <td>0.055990</td>\n",
       "      <td>0.062914</td>\n",
       "      <td>0.037140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>0.094879</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.016007</td>\n",
       "      <td>0.157508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.081361</td>\n",
       "      <td>0.095119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>0.047958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014406</td>\n",
       "      <td>0.014185</td>\n",
       "      <td>0.090379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034833</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Total Covid deaths over 1 Year  Houses Below Poverty Line   \\\n",
       "State                                                                         \n",
       "California                             1.000000                    1.000000   \n",
       "Texas                                  0.807728                    0.725761   \n",
       "Florida                                0.456240                    0.450282   \n",
       "New York                               0.180069                    0.441601   \n",
       "Pennsylvania                           0.161384                    0.070198   \n",
       "Illinois                               0.119723                    0.055990   \n",
       "Georgia                                0.094879                    0.000056   \n",
       "Ohio                                   0.081361                    0.095119   \n",
       "Arizona                                0.047958                         NaN   \n",
       "Michigan                               0.000000                         NaN   \n",
       "North Carolina                              NaN                    0.014406   \n",
       "Puerto Rico                                 NaN                    0.000000   \n",
       "New Jersey                                  NaN                         NaN   \n",
       "Virginia                                    NaN                         NaN   \n",
       "\n",
       "                 Population 25 years and over - Less than 9th Grad Education   \\\n",
       "State                                                                           \n",
       "California                                                1.000000              \n",
       "Texas                                                     0.557900              \n",
       "Florida                                                   0.202355              \n",
       "New York                                                  0.257881              \n",
       "Pennsylvania                                              0.000000              \n",
       "Illinois                                                  0.062914              \n",
       "Georgia                                                   0.016007              \n",
       "Ohio                                                           NaN              \n",
       "Arizona                                                        NaN              \n",
       "Michigan                                                       NaN              \n",
       "North Carolina                                            0.014185              \n",
       "Puerto Rico                                               0.034833              \n",
       "New Jersey                                                0.006776              \n",
       "Virginia                                                       NaN              \n",
       "\n",
       "                 Population without Health Insurance    \n",
       "State                                                   \n",
       "California                                    0.540534  \n",
       "Texas                                         1.000000  \n",
       "Florida                                       0.472499  \n",
       "New York                                      0.100215  \n",
       "Pennsylvania                                  0.001794  \n",
       "Illinois                                      0.037140  \n",
       "Georgia                                       0.157508  \n",
       "Ohio                                               NaN  \n",
       "Arizona                                       0.003817  \n",
       "Michigan                                           NaN  \n",
       "North Carolina                                0.090379  \n",
       "Puerto Rico                                        NaN  \n",
       "New Jersey                                         NaN  \n",
       "Virginia                                      0.000000  "
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normilized_com_all = pd.read_csv(\"/Users/morganchunn/Desktop/normilized_data.csv\").set_index(\"State\")\n",
    "normilized_com_all            \n",
    "                                 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3689faa",
   "metadata": {},
   "source": [
    "Plot showing normalization of the above data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "e33a3553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 427,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "normilized_com_all.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038fe0cb",
   "metadata": {},
   "source": [
    "# Comparing top states with high covid deaths to top states exhibiting no health insurance, limited education, and high poverty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbcdc06",
   "metadata": {},
   "source": [
    "To compare whether or not each condition (no health insurance, limited education, high poverty) had correlation with high covid death rates, the top ten states exhibiting each condition were selected for. Those ten states are then compared to the top 10 states with the most covid related deaths. If there is overlap, there is evidence to believe the given conditon is an aiding factor in higher covid death rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "5445a293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Condition'>"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creates a simple dataframe to show true/false data\n",
    "propotion_data = pd.DataFrame({'Condition': ['proportion of states with high poverty in top 10 states with highest number of covid deaths', 'proportion of states with limited education in top 10 states with highest number of covid deaths', 'proportion of states with a population lacking insurance in top 10 states with highest number of covid deaths'], 'conditon_val':[8,7,8], 'total': [2,3,2]})\n",
    "#\"Condition\" is set as the data index and a horizontal, stacked bar graph is made\n",
    "propotion_data.set_index(\"Condition\").plot.barh(stacked=True)\n",
    "\n",
    "                                             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee2b179",
   "metadata": {},
   "source": [
    "The above graph shows there is significant overlap between states with the highest covid related deaths and states exhibiting the given condition. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}