EDACheckpoint_Group116_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If you lost points on the last checkpoint you can get them back by responding to TA/IA feedback**  \n",
    "\n",
    "Update/change the relevant sections where you lost those points, make sure you respond on GitHub Issues to your TA/IA to call their attention to the changes you made here.\n",
    "\n",
    "Please update your Timeline... no battle plan survives contact with the enemy, so make sure we understand how your plans have changed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - EDA Checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Benjamin Hinnant\n",
    "- Desmond Vu\n",
    "- Kayla Maldonado\n",
    "- Alexander Gao\n",
    "- Keshav Tiwari"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question\n",
    "\n",
    "What is the relationship between the racial backgrounds and urbanization levels of western and eastern states, and what is their effect on COVID-19 mortality rates in the USA?  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background & Prior Work\n",
    "\n",
    "During the COVID-19 pandemic, several analyses determined that people of color (POC) were experiencing a disproportionately larger COVID-19 mortality rate than Caucasians. One report from the APM Research Lab was written by Elisabeth Gawthrop, who used state-reported data from the U.S. Centers for Disease Control and Prevention, or CDC. She determined that as of September 27 of 2023, the COVID-19 mortality rate for Black individuals was around 55% higher than the rate for Caucasian individuals. The mortality rate for Latinos was nearly 65% higher than Caucasians (Gawthrop).  \n",
    "\n",
    "Another study done by Latoya Hill and Samantha Artiga used data from the CDC and the National Center for Health Statistics (NCHS). They determined that the largest differences in COVID-19 mortality rates between racial groups occurred during surges in the pandemic when the total mortality rate was particularly high. The first surge in COVID-19 deaths peaked in July 2020, and during this month, Hispanic individuals were five times more likely to die than Caucasians, American Indian or Alaska Natives (AIAN) were four times as likely to die, and Blacks were three times as likely to die. During the second surge in December 2020 and January 2021, all POC analyzed (Blacks, Hispanics, and AIAN) had higher rates of death than Caucasians. (Hill and Artiga).\n",
    "\n",
    "These previous studies have already assessed the relationship between racial background and COVID-19 mortality rates. As a result, we now want to determine the relationship between three variables: racial background, the level of urbanization, and whether the region of interest is an eastern or western state. We then want to determine the effect of these variables on COVID-19 mortality rates. By determining if any of these variables has a significant effect on mortality rates, we hope to potentially discover trends that can guide a future pandemic response.\n",
    "\n",
    "\n",
    "References \n",
    "\n",
    "Gawthrop, Elisabeth. “Color of Coronavirus: COVID-19 deaths analyzed by race and ethnicity — APM Research Lab.” APM Research Lab, 19 October 2023, https://www.apmresearchlab.org/covid/deaths-by-race. Accessed 25 February 2024. \n",
    "\n",
    "Hill, Latoya, and Samantha Artiga. “COVID-19 Cases and Deaths by Race/Ethnicity: Current Data and Changes Over Time.” KFF, 22 August 2022, https://www.kff.org/racial-equity-and-health-policy/issue-brief/covid-19-cases-and-deaths-by-race-ethnicity-current-data-and-changes-over-time/. Accessed 24 February 2024. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that areas with a higher urbanization level will have higher COVID-19 mortality rates. Within the confined spaces of an urban region, there are more opportunities for the uninfected population to make contact with COVID-19 infected individuals. We also predict that areas with a larger population of racial minorities will have higher COVID-19 mortality rates, since these communities often have less resources available and may find it more difficult to treat every infected patient optimally. Combining our previous predictions, we predict that urban areas with a larger population of racial minorities will have the highest COVID-19 mortality rates, while rural areas with a smaller population of racial minorities will have the lowest COVID-19 mortality rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Overview\n",
    "**Dataset #1**\n",
    "- Dataset name: Provisional COVID-19 Deaths by County, and Race and Hispanic Origin\n",
    "- [Link to dataset](https://data.cdc.gov/NCHS/Provisional-COVID-19-Deaths-by-County-and-Race-and/k8wy-p9cg/about_data)\n",
    "- Number of Observations: 3,687\n",
    "- Number of Variables: 21\n",
    "\n",
    "**Description:** The dataset we are utilizing to address our question is the Provisional COVID-19 Deaths by County and Race and Hispanic Origin dataset, directly sourced from the Centers for Disease Control and Prevention (CDC) website. This dataset covers the period from January 4, 2020, to September 27, 2023, collected weekly from counties across all 50 states. It comprises around 3,700 observations with string and integer data types. The dataset is mostly complete, with NaN values only present for different racial populations. Key columns in the dataset include county name, state name, total deaths from all causes, deaths involving COVID-19, a description of the level of urbanization, and percentages of each racial category (such as Asian, White, Hispanic, American Indian, etc.). Deaths involving COVID-19 are identified by the presence of an U07.1 diagnosis code assigned to a deceased patient, which is assigned only if a patient tests positive for COVID-19. The urbanization level is determined by the National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties, categorizing counties into 6 urban-rural categories suitable for health analyses.\n",
    "\n",
    "**Important Variables:**\n",
    "- Percentage of Race: Indicates the proportion of each racial category affected by COVID-19 in each county.\n",
    "- Urbanization level: Describes the level of urbanization of a county on a scale of 1 (large metropolitan city) to 6 (small non-core area), providing insights into how different counties address health access, resources, and disease prevention.\n",
    "- State Name: The state from which the data is collected, aiding in the analysis of regional differences in COVID-19.\n",
    "- Deaths by COVID-19: The number of deaths with a leading cause of COVID-19.\n",
    "\n",
    "**Data Wrangling:** As our focus is on COVID-19 deaths, affected populations, geographic locations, and urbanization levels, this dataset provides all the necessary information to address our research question. However, we do have concerns regarding missing data on certain races due to privacy concerns and unnecessary columns that are not required for this project. To address these, we cleaned the dataset by removing columns such as total deaths and ensured that we only considered counties that reported affected racial groups. This step was crucial to ensure the validity of our analysis by including as many individuals as possible in our observations. Despite the removal of some columns, we deemed this necessary given the ample data available from various counties that included racial populations for our analysis.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #1 Provisional COVID-19 Deaths by County, and Race and Hispanic Origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "from scipy.stats import ttest_ind\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics.regressionplots import plot_partregress_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the CSV file\n",
    "df = pd.read_csv('Provisional_COVID-19_Deaths_by_County__and_Race_and_Hispanic_Origin_20240224.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data as of</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "      <th>State</th>\n",
       "      <th>County Name</th>\n",
       "      <th>Urban Rural Code</th>\n",
       "      <th>FIPS State</th>\n",
       "      <th>FIPS County</th>\n",
       "      <th>FIPS Code</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>...</th>\n",
       "      <th>COVID-19 Deaths</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
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       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-Hispanic Native Hawaiian or Other Pacific Islander</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Other</th>\n",
       "      <th>Urban Rural Description</th>\n",
       "      <th>Footnote</th>\n",
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       "      <th>0</th>\n",
       "      <td>09/27/2023</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>09/23/2023</td>\n",
       "      <td>AK</td>\n",
       "      <td>Anchorage Municipality</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2020</td>\n",
       "      <td>Distribution of all-cause deaths (%)</td>\n",
       "      <td>...</td>\n",
       "      <td>787</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.050</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/27/2023</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>09/23/2023</td>\n",
       "      <td>AK</td>\n",
       "      <td>Anchorage Municipality</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2020</td>\n",
       "      <td>Distribution of COVID-19 deaths (%)</td>\n",
       "      <td>...</td>\n",
       "      <td>787</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.033</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>09/27/2023</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>09/23/2023</td>\n",
       "      <td>AK</td>\n",
       "      <td>Anchorage Municipality</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2020</td>\n",
       "      <td>Distribution of population (%)</td>\n",
       "      <td>...</td>\n",
       "      <td>787</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.077</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/27/2023</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>09/23/2023</td>\n",
       "      <td>AK</td>\n",
       "      <td>Fairbanks North Star Borough</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>2090</td>\n",
       "      <td>Distribution of all-cause deaths (%)</td>\n",
       "      <td>...</td>\n",
       "      <td>214</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.044</td>\n",
       "      <td>Small metro</td>\n",
       "      <td>One or more data cells have counts between 1-9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09/27/2023</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>09/23/2023</td>\n",
       "      <td>AK</td>\n",
       "      <td>Fairbanks North Star Borough</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>90</td>\n",
       "      <td>2090</td>\n",
       "      <td>Distribution of COVID-19 deaths (%)</td>\n",
       "      <td>...</td>\n",
       "      <td>214</td>\n",
       "      <td>0.626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056</td>\n",
       "      <td>Small metro</td>\n",
       "      <td>One or more data cells have counts between 1-9...</td>\n",
       "    </tr>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   Data as of  Start Date    End Date State                   County Name  \\\n",
       "0  09/27/2023  01/01/2020  09/23/2023    AK        Anchorage Municipality   \n",
       "1  09/27/2023  01/01/2020  09/23/2023    AK        Anchorage Municipality   \n",
       "2  09/27/2023  01/01/2020  09/23/2023    AK        Anchorage Municipality   \n",
       "3  09/27/2023  01/01/2020  09/23/2023    AK  Fairbanks North Star Borough   \n",
       "4  09/27/2023  01/01/2020  09/23/2023    AK  Fairbanks North Star Borough   \n",
       "\n",
       "   Urban Rural Code  FIPS State  FIPS County  FIPS Code  \\\n",
       "0                 3           2           20       2020   \n",
       "1                 3           2           20       2020   \n",
       "2                 3           2           20       2020   \n",
       "3                 4           2           90       2090   \n",
       "4                 4           2           90       2090   \n",
       "\n",
       "                              Indicator  ...  COVID-19 Deaths  \\\n",
       "0  Distribution of all-cause deaths (%)  ...              787   \n",
       "1   Distribution of COVID-19 deaths (%)  ...              787   \n",
       "2        Distribution of population (%)  ...              787   \n",
       "3  Distribution of all-cause deaths (%)  ...              214   \n",
       "4   Distribution of COVID-19 deaths (%)  ...              214   \n",
       "\n",
       "   Non-Hispanic White  Non-Hispanic Black  \\\n",
       "0               0.568               0.044   \n",
       "1               0.452               0.037   \n",
       "2               0.564               0.052   \n",
       "3               0.710               0.024   \n",
       "4               0.626                 NaN   \n",
       "\n",
       "   Non-Hispanic American Indian or Alaska Native  Non-Hispanic Asian  \\\n",
       "0                                          0.216               0.058   \n",
       "1                                          0.255               0.111   \n",
       "2                                          0.083               0.098   \n",
       "3                                          0.173               0.020   \n",
       "4                                          0.257                 NaN   \n",
       "\n",
       "   Non-Hispanic Native Hawaiian or Other Pacific Islander  Hispanic  Other  \\\n",
       "0                                              0.030          0.033  0.050   \n",
       "1                                              0.074          0.038  0.033   \n",
       "2                                              0.031          0.095  0.077   \n",
       "3                                                NaN          0.027  0.044   \n",
       "4                                                NaN            NaN  0.056   \n",
       "\n",
       "   Urban Rural Description                                           Footnote  \n",
       "0             Medium metro                                                NaN  \n",
       "1             Medium metro                                                NaN  \n",
       "2             Medium metro                                                NaN  \n",
       "3              Small metro  One or more data cells have counts between 1-9...  \n",
       "4              Small metro  One or more data cells have counts between 1-9...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How does the file look like\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean Data to only include rows for deaths from COVID-19\n",
    "df = df[df['Indicator'] == 'Distribution of COVID-19 deaths (%)']\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 22)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean Data to only include coastal states(Column with East, West)\n",
    "coast_states = ['WA', 'OR', 'CA', 'ME', 'NH', 'MA', 'RI', 'CT', 'NY', 'NJ', 'PA', 'DE', 'MD', 'VA', 'NC', 'SC', 'GA', 'FL']\n",
    "df['Coast'] = df['State'].apply(lambda x: 'West' if x in ['CA', 'OR', 'WA'] else 'East')\n",
    "df = df[df['State'].isin(coast_states)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 170 coastal counties where theres info for Non-Hispanic Whites, Non-Hispanic Blacks, Hispanics, Non-Hispanic Asians\n",
    "mask_white = df['Non-Hispanic White'].notna()\n",
    "mask_black = df['Non-Hispanic Black'].notna()\n",
    "mask_asian = df['Non-Hispanic Asian'].notna()\n",
    "mask_hispanic = df['Hispanic'].notna()\n",
    "combined_mask = mask_white & mask_black & mask_hispanic &mask_asian\n",
    "num_rows_non_nan = combined_mask.sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the biggest challenges was the differing levels of information for different ethnicities across counties. Some counties have information for every ethnicity, while others have missing data for one ethnicity only or for many ethnicities. Taking a look at the data, at least a significant number of counties had information for Non-Hispanic White, Black and Asians and Hispanics, which we felt was strong enough to analyze racial data trends for these counties. Thus we kept counties which had information for these ethnicities and dropped 3 ethnicities that often had missing values: Non-Hispanic American Indian or Alaska Native, Non-Hispanic Native Hawaiian or Other Pacific Islander, and Other. We also added a column composed of all ethnicities outside of the 4 primary ethnicities we decided to analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop counties where theres missing info in any of aforementioned columns\n",
    "df = df[combined_mask]\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df['Non-White, Non-Black, Non-Hispanic, Non-Asian'] = 1 - df['Non-Hispanic White'] - df['Non-Hispanic Black'] - df['Hispanic'] - df['Non-Hispanic Asian']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns, Remember Data is from 01/01/2020 to 09/23/2023\n",
    "df = df.drop(['Data as of', 'Start Date', 'End Date', 'Urban Rural Code', 'FIPS State', 'FIPS Code','FIPS County', 'Indicator'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rearrange the columns to have data arranged in desirable order\n",
    "df = df[[\"State\",\"County Name\", \"Coast\",\"Total deaths\", \"COVID-19 Deaths\",\"Non-Hispanic White\",\"Non-Hispanic Black\", \"Hispanic\", \"Non-Hispanic Asian\", \"Non-White, Non-Black, Non-Hispanic, Non-Asian\", \"Non-Hispanic American Indian or Alaska Native\",\"Non-Hispanic Native Hawaiian or Other Pacific Islander\",\"Other\",\"Urban Rural Description\",\"Footnote\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>West</td>\n",
       "      <td>41916</td>\n",
       "      <td>2628</td>\n",
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       "      <td>0.191</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.219</td>\n",
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       "      <td>Large central metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>West</td>\n",
       "      <td>9294</td>\n",
       "      <td>789</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.035</td>\n",
       "      <td>Small metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>Contra Costa County</td>\n",
       "      <td>West</td>\n",
       "      <td>31889</td>\n",
       "      <td>1754</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.028</td>\n",
       "      <td>Large fringe metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>Fresno County</td>\n",
       "      <td>West</td>\n",
       "      <td>33704</td>\n",
       "      <td>3278</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.019</td>\n",
       "      <td>Medium metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>West</td>\n",
       "      <td>27860</td>\n",
       "      <td>2711</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.024</td>\n",
       "      <td>Medium metro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State          County Name Coast  Total deaths  COVID-19 Deaths  \\\n",
       "0    CA       Alameda County  West         41916             2628   \n",
       "1    CA         Butte County  West          9294              789   \n",
       "2    CA  Contra Costa County  West         31889             1754   \n",
       "3    CA        Fresno County  West         33704             3278   \n",
       "4    CA          Kern County  West         27860             2711   \n",
       "\n",
       "   Non-Hispanic White  Non-Hispanic Black  Hispanic  Non-Hispanic Asian  \\\n",
       "0               0.312               0.191     0.239               0.219   \n",
       "1               0.790               0.015     0.119               0.041   \n",
       "2               0.536               0.127     0.198               0.111   \n",
       "3               0.393               0.046     0.450               0.092   \n",
       "4               0.373               0.058     0.505               0.040   \n",
       "\n",
       "   Non-White, Non-Black, Non-Hispanic, Non-Asian Urban Rural Description  \n",
       "0                                          0.039     Large central metro  \n",
       "1                                          0.035             Small metro  \n",
       "2                                          0.028      Large fringe metro  \n",
       "3                                          0.019            Medium metro  \n",
       "4                                          0.024            Medium metro  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Because we standardized the ethnicity data above, we decide to remove all data for specific ethnicities which we are not observing and only keep the ones that we described above.\n",
    "df = df.drop(['Non-Hispanic American Indian or Alaska Native','Non-Hispanic Native Hawaiian or Other Pacific Islander', 'Other', 'Footnote'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaned dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "\n",
    "To get the data into a usable format, we first had to keep only the parts of the data that were deaths specific to COVID, as the data also included non-COVID deaths. Based on our research question, we wanted to analyze states on the east and west coast. So, we only kept the remaining data that were for counties in east and west coast states. Then, we had to analyze which parts of the data were missing. Even though there were columns for specific ethnicities such as American Indian or Native Hawaiian, a lot of counties had missing information for these columns and only had data for few columns Non-Hispanic Whites and Non-Hispanic Blacks and NaN for others. In order to standardize the data, while still having a decent number of obvservations, we decided to see how many counties had data for Non-Hispanic Whites, Non-Hispanic Blacks, Non-Hispanic Asians, and Hispanics, which was 170, and removed those which didn’t. Then, we added essentially a column that was for the proportion of the population not in those ethnicities (the remainder/other column basically). Finally, we dropped a lot of columns which were unnecessary to our data, such as codes for counties and states (which do not provide as easily understandable info as county names and state names) and date-range, which was standard across the whole data set (1/2020 to 9/2023). We also removed total deaths, as we only cared about COVID-19 deaths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "Before we delve into the EDA, it is important to note that our data comprises of small units that cumulate together to answer our research question. Therefore, creating the characteristics table in order to understand our data better first requires the collation of county-wise mortality counts into state and coastal levels of granularity. Moreoever, all data must also be reorganized as per differing Rural Urban Descriptions, in order for us to better understand our dataset's ability to explore the relationships between different urbanization levels and racial backgrounds in the context of COVID-19 Deaths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, one aspect we need to be aware of is that because a lot of counties have a higher population than others, naturally, they will have a higher number of COVID deaths. Thus, we need to make sure that we take into account the covid death rate in order to truly understand what factors cause a higher percentage of COVID deaths. To this, we add a column called COVID death rate, which is the number of COVID deaths divided by the total number of deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>County Name</th>\n",
       "      <th>Coast</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>COVID-19 Deaths</th>\n",
       "      <th>Portion of Deaths due to COVID</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-White, Non-Black, Non-Hispanic, Non-Asian</th>\n",
       "      <th>Urban Rural Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>West</td>\n",
       "      <td>41916</td>\n",
       "      <td>2628</td>\n",
       "      <td>0.062697</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.039</td>\n",
       "      <td>Large central metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>West</td>\n",
       "      <td>9294</td>\n",
       "      <td>789</td>\n",
       "      <td>0.084893</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.035</td>\n",
       "      <td>Small metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>Contra Costa County</td>\n",
       "      <td>West</td>\n",
       "      <td>31889</td>\n",
       "      <td>1754</td>\n",
       "      <td>0.055003</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.028</td>\n",
       "      <td>Large fringe metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>Fresno County</td>\n",
       "      <td>West</td>\n",
       "      <td>33704</td>\n",
       "      <td>3278</td>\n",
       "      <td>0.097258</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.019</td>\n",
       "      <td>Medium metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>West</td>\n",
       "      <td>27860</td>\n",
       "      <td>2711</td>\n",
       "      <td>0.097308</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.024</td>\n",
       "      <td>Medium metro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State          County Name Coast  Total deaths  COVID-19 Deaths  \\\n",
       "0    CA       Alameda County  West         41916             2628   \n",
       "1    CA         Butte County  West          9294              789   \n",
       "2    CA  Contra Costa County  West         31889             1754   \n",
       "3    CA        Fresno County  West         33704             3278   \n",
       "4    CA          Kern County  West         27860             2711   \n",
       "\n",
       "   Portion of Deaths due to COVID  Non-Hispanic White  Non-Hispanic Black  \\\n",
       "0                        0.062697               0.312               0.191   \n",
       "1                        0.084893               0.790               0.015   \n",
       "2                        0.055003               0.536               0.127   \n",
       "3                        0.097258               0.393               0.046   \n",
       "4                        0.097308               0.373               0.058   \n",
       "\n",
       "   Hispanic  Non-Hispanic Asian  \\\n",
       "0     0.239               0.219   \n",
       "1     0.119               0.041   \n",
       "2     0.198               0.111   \n",
       "3     0.450               0.092   \n",
       "4     0.505               0.040   \n",
       "\n",
       "   Non-White, Non-Black, Non-Hispanic, Non-Asian Urban Rural Description  \n",
       "0                                          0.039     Large central metro  \n",
       "1                                          0.035             Small metro  \n",
       "2                                          0.028      Large fringe metro  \n",
       "3                                          0.019            Medium metro  \n",
       "4                                          0.024            Medium metro  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add COVID Death Rate column\n",
    "df.insert(5, \"Portion of Deaths due to COVID\", df[\"COVID-19 Deaths\"] / df[\"Total deaths\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 1: Shifting from county to coastal levels of granularity\n",
    "\n",
    "In order to compare east and west coast states, we must transform our data from county-wise proportions to a much higher level of granularity. By doing that, we can much easier observe patterns/comprehend if there is any preliminary relationships/distributions that can be seen in our cleaned dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>COVID-19 Deaths</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>Portion of Deaths due to COVID</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-White, Non-Black, Non-Hispanic, Non-Asian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East</th>\n",
       "      <td>308361</td>\n",
       "      <td>3058381</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West</th>\n",
       "      <td>116605</td>\n",
       "      <td>1308280</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      COVID-19 Deaths  Total deaths  Portion of Deaths due to COVID  \\\n",
       "East           308361       3058381                            0.10   \n",
       "West           116605       1308280                            0.09   \n",
       "\n",
       "      Non-Hispanic White  Non-Hispanic Black  Hispanic  Non-Hispanic Asian  \\\n",
       "East                0.60                0.20      0.15                0.04   \n",
       "West                0.41                0.07      0.38                0.11   \n",
       "\n",
       "      Non-White, Non-Black, Non-Hispanic, Non-Asian  \n",
       "East                                           0.01  \n",
       "West                                           0.03  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining all county wise data into statewise data, specifically summing together COVID-19 Deaths and Total deaths columns.  \n",
    "state_deaths = df.groupby('State').agg({'COVID-19 Deaths': 'sum'})\n",
    "state_total_deaths = df.groupby('State').agg({'Total deaths': 'sum'})\n",
    "# specifiying different a list of racial backgrounds for data handling later\n",
    "race_columns = ['Non-Hispanic White', 'Non-Hispanic Black', 'Hispanic', 'Non-Hispanic Asian', 'Non-White, Non-Black, Non-Hispanic, Non-Asian']\n",
    "race_deaths = pd.DataFrame()                # Creating a single intermediary dataframe to store death per race per state \n",
    "for col in race_columns:                    # iterating through the list of racial backgrounds\n",
    "    race_deaths[col] = df[col] * df['COVID-19 Deaths']            # multiplying proportions of death per race to total COVID-19 deaths in order to get number of deaths per race\n",
    "state_race_deaths = race_deaths.groupby(df['State']).sum()        # summing number of deaths per race per county into statewise (the first shift in granularity)\n",
    "state_race_death_proportions = state_race_deaths.div(state_deaths['COVID-19 Deaths'], axis=0)   # calculatiing death proportions per race at a state level of granularity\n",
    "state_df = pd.concat([state_deaths, state_race_death_proportions], axis=1)     # concatenating dataframes\n",
    "\n",
    "state_df.insert(1, 'Total deaths', state_total_deaths['Total deaths'])\n",
    "state_df.insert(2, 'Portion of Deaths due to COVID', state_deaths['COVID-19 Deaths']/state_total_deaths['Total deaths']) # creating a scaled calculation of how many deaths were due to COVID-19 \n",
    "\n",
    "\n",
    "# Combining all county wise data into coast data, specifically summing together COVID-19 Deaths and Total deaths columns.  \n",
    "coast_deaths = df.groupby('Coast').agg({'COVID-19 Deaths':'sum'})\n",
    "coast_total_deaths = df.groupby('Coast').agg({'Total deaths': 'sum'})\n",
    "race_deaths_coast = pd.DataFrame()         \n",
    "for col in race_columns:                    # iterating through the list of racial backgrounds\n",
    "    race_deaths_coast[col] = df[col] * df['COVID-19 Deaths']        # multiplying proportions of death per race to total COVID-19 deaths in order to get number of deaths per race   \n",
    "coast_race_deaths = race_deaths_coast.groupby(df['Coast']).sum()    # summing number of deaths per race per county into coastwide (the second shift in granularity)\n",
    "coast_race_death_proportions = coast_race_deaths.div(coast_deaths['COVID-19 Deaths'], axis=0)    # calculating death proportions per race at a coast level of granularity\n",
    "coast_df = pd.concat([coast_deaths, coast_race_death_proportions], axis=1)      # concatenating dataframes\n",
    "coast_df.insert(1, 'Total deaths', coast_total_deaths['Total deaths'])\n",
    "coast_df.insert(2, 'Portion of Deaths due to COVID', coast_deaths['COVID-19 Deaths']/coast_total_deaths['Total deaths']) # creating a scaled calculation of how many deaths were due to COVID-19\n",
    "\n",
    "\n",
    "location_df = pd.concat([coast_df, state_df], axis=0)\n",
    "location_df = location_df.round(2)          # rounding to easily comprehensible decimal places        \n",
    "\n",
    "coastal_mean = pd.DataFrame(coast_df[race_columns].mean())          # finding the mean for coastal data\n",
    "coastal_mean = coastal_mean.round(2)\n",
    "location_df.loc[['East', 'West']].head()    # display only coastal data \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 2 : Shifting from county to urbanization levels of granularity\n",
    "\n",
    "This section helps us look at different counties's central tendency when collated into distinct Urban Rural Descriptions. For it, we need to follow a similar process to the earlier shift in granularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>COVID-19 Deaths</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>Portion of Deaths due to COVID</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-White, Non-Black, Non-Hispanic, Non-Asian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban Rural Description</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Large central metro</th>\n",
       "      <td>191458</td>\n",
       "      <td>1898063</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large fringe metro</th>\n",
       "      <td>127965</td>\n",
       "      <td>1328164</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium metro</th>\n",
       "      <td>99183</td>\n",
       "      <td>1078031</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small metro</th>\n",
       "      <td>6360</td>\n",
       "      <td>62403</td>\n",
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       "                         COVID-19 Deaths  Total deaths  \\\n",
       "Urban Rural Description                                  \n",
       "Large central metro               191458       1898063   \n",
       "Large fringe metro                127965       1328164   \n",
       "Medium metro                       99183       1078031   \n",
       "Small metro                         6360         62403   \n",
       "\n",
       "                         Portion of Deaths due to COVID  Non-Hispanic White  \\\n",
       "Urban Rural Description                                                       \n",
       "Large central metro                                0.10                0.42   \n",
       "Large fringe metro                                 0.10                0.63   \n",
       "Medium metro                                       0.09                0.70   \n",
       "Small metro                                        0.10                0.71   \n",
       "\n",
       "                         Non-Hispanic Black  Hispanic  Non-Hispanic Asian  \\\n",
       "Urban Rural Description                                                     \n",
       "Large central metro                    0.18      0.30                0.09   \n",
       "Large fringe metro                     0.16      0.15                0.05   \n",
       "Medium metro                           0.13      0.14                0.03   \n",
       "Small metro                            0.08      0.17                0.03   \n",
       "\n",
       "                         Non-White, Non-Black, Non-Hispanic, Non-Asian  \n",
       "Urban Rural Description                                                 \n",
       "Large central metro                                               0.02  \n",
       "Large fringe metro                                                0.01  \n",
       "Medium metro                                                      0.01  \n",
       "Small metro                                                       0.01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combining all county wise data, specifically summing together COVID-19 Deaths and Total deaths columns, and categorizing it all into urbanization level data.  \n",
    "urban_deaths = df.groupby('Urban Rural Description').agg({'COVID-19 Deaths': 'sum'}) \n",
    "urban_total_deaths = df.groupby('Urban Rural Description').agg({'Total deaths': 'sum'})\n",
    "# specifiying different a list of racial backgrounds for data handling later\n",
    "race_columns = ['Non-Hispanic White', 'Non-Hispanic Black', 'Hispanic', 'Non-Hispanic Asian', 'Non-White, Non-Black, Non-Hispanic, Non-Asian']\n",
    "race_deaths = pd.DataFrame()        # Creating a single intermediary dataframe to store death per race per urbanization level\n",
    "for col in race_columns:            # iterating through the list of racial backgrounds\n",
    "    race_deaths[col] = df[col] * df['COVID-19 Deaths']              # multiplying proportions of death per race to total COVID-19 deaths in order to get number of deaths per race\n",
    "urban_race_deaths = race_deaths.groupby(df['Urban Rural Description']).sum()            # summing number of deaths per race per county into urbanization level of granularity (the third shift in granularity) \n",
    "urban_death_proportions = urban_race_deaths.div(urban_deaths['COVID-19 Deaths'], axis=0)        # using .div to divide in order to avoid division by null/zero value\n",
    "urban_df = pd.concat([urban_deaths, urban_death_proportions], axis=1)                   # concatenating dataframes \n",
    "\n",
    "urban_df.insert(1, 'Total deaths', urban_total_deaths['Total deaths'])\n",
    "urban_df.insert(2, 'Portion of Deaths due to COVID', urban_deaths['COVID-19 Deaths']/urban_total_deaths['Total deaths'])    # creating a scaled calculation of how many deaths were due to COVID-19\n",
    "\n",
    "\n",
    "\n",
    "urban_mean = pd.DataFrame(urban_df[race_columns].mean())         # finding the mean for urbanizaization-based data\n",
    "urban_mean = urban_mean.round(2)                    # rounding to easily comprehensible decimal places\n",
    "urban_df = urban_df.round(2)                    \n",
    "\n",
    "\n",
    "urban_df.head()     # display urbanization level data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon running some basic operations on our cleaned dataset, we now have a clearer picture of it. Through the operations, we were able to collate county-wide data into state and then eventually coast-based data on how differing racial backgrounds contribute to COVID-19 Deaths. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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       "      COVID-19 Deaths  Total deaths  Portion of Deaths due to COVID  \\\n",
       "East           308361       3058381                            0.10   \n",
       "West           116605       1308280                            0.09   \n",
       "\n",
       "      Non-Hispanic White  Non-Hispanic Black  Hispanic  Non-Hispanic Asian  \\\n",
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       "West                0.41                0.07      0.38                0.11   \n",
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       "      Non-White, Non-Black, Non-Hispanic, Non-Asian  \n",
       "East                                           0.01  \n",
       "West                                           0.03  "
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       "      <td>0.10</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.08</td>\n",
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      "text/plain": [
       "                         COVID-19 Deaths  Total deaths  \\\n",
       "Urban Rural Description                                  \n",
       "Large central metro               191458       1898063   \n",
       "Large fringe metro                127965       1328164   \n",
       "Medium metro                       99183       1078031   \n",
       "Small metro                         6360         62403   \n",
       "\n",
       "                         Portion of Deaths due to COVID  Non-Hispanic White  \\\n",
       "Urban Rural Description                                                       \n",
       "Large central metro                                0.10                0.42   \n",
       "Large fringe metro                                 0.10                0.63   \n",
       "Medium metro                                       0.09                0.70   \n",
       "Small metro                                        0.10                0.71   \n",
       "\n",
       "                         Non-Hispanic Black  Hispanic  Non-Hispanic Asian  \\\n",
       "Urban Rural Description                                                     \n",
       "Large central metro                    0.18      0.30                0.09   \n",
       "Large fringe metro                     0.16      0.15                0.05   \n",
       "Medium metro                           0.13      0.14                0.03   \n",
       "Small metro                            0.08      0.17                0.03   \n",
       "\n",
       "                         Non-White, Non-Black, Non-Hispanic, Non-Asian  \n",
       "Urban Rural Description                                                 \n",
       "Large central metro                                               0.02  \n",
       "Large fringe metro                                                0.01  \n",
       "Medium metro                                                      0.01  \n",
       "Small metro                                                       0.01  "
      ]
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   ],
   "source": [
    "display(location_df.loc[['East','West']])\n",
    "display(urban_df)                               \n",
    "# displaying both relevant databases together "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Size\n",
    "There have been 170 observations (rows) and 10 columns as established earlier upon the cleaning of our data. Then, all of that data is collated together to give us differing levels of granularity.\n",
    "#### Missingness\n",
    "No observations were missing, since it was ensured that through the cleaning process, there must remain a consistent number of observations across different racial backgrounds. This is reflected by the mathematicaaly sound proportion calculations made, that do not reflect NaN for any division by 0.\n",
    "#### Shape\n",
    "We will be exploring distributions for this data in the following section.\n",
    "#### Central Tendency and Variability\n",
    "By cumulating all county-based proportions into coast-based and urbanization-based proportions, the proportion values calculated were a fair indication of the central tendencies of the data. As for our cleaned dataset, we will explore central tendencies and variabilities through the following discussion on distributions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Relationships between variables and COVID-19 Mortality Rates\n",
    "\n",
    "In this section we will be looking at the correlations between our variables and the COVID-19 mortality rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total COVID Deaths vs Coast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the Geospatial Map with respect to states and portion of COVID 19 deaths to total deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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           "bgcolor": "#E5ECF6",
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
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           },
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           "zerolinewidth": 2
          },
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        },
        "title": {
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         "x": 0.5
        },
        "width": 900
       }
      }
     },
     "metadata": {},
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   ],
   "source": [
    "# Group the deaths by state\n",
    "df_grouped = df.groupby('State', as_index=False)['Portion of Deaths due to COVID'].sum()\n",
    "\n",
    "# Create the plots\n",
    "fig = px.scatter_geo(df_grouped, \n",
    "                     locations=\"State\", \n",
    "                     locationmode=\"USA-states\",\n",
    "                     color=\"Portion of Deaths due to COVID\",\n",
    "                     hover_name=\"State\",\n",
    "                     size=\"Portion of Deaths due to COVID\",\n",
    "                     projection=\"albers usa\",\n",
    "                     title=\"COVID-19 Deaths: West V.S. East Coast\",\n",
    "                     color_continuous_scale='Magma_r',\n",
    "                     width=900,  # Set width of the graph\n",
    "                     height=700  # Set height of the graph                     \n",
    "                    )\n",
    "\n",
    "# Fix the map\n",
    "fig.update_geos(\n",
    "    fitbounds=\"locations\",\n",
    "    visible=False,\n",
    "    showcountries=True, \n",
    "    showsubunits=True,  \n",
    "    subunitcolor=\"gray\",\n",
    "    showland=True,\n",
    "    landcolor='rgb(217, 217, 217)',\n",
    "    countrycolor=\"gray\"  # Color of country borders\n",
    ")\n",
    "\n",
    "# Put borders onto circles\n",
    "fig.update_traces(marker=dict(line=dict(width=1, color='rgb(0,0,0)')))\n",
    "\n",
    "# Center the title\n",
    "fig.update_layout(title_x=0.5)\n",
    "\n",
    "display(fig)"
   ]
  },
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   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![COVID-19 Deaths: West V.S. East Coast](attachment:covid19_deaths_map.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'COVID-19 Deaths for West and East Coast')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "\n",
    "boxplot = sns.boxplot(data = df, y = 'COVID-19 Deaths', x = 'Coast', hue = 'Coast')\n",
    "boxplot.set_xlabel('Coast')\n",
    "boxplot.set_title('COVID-19 Deaths for West and East Coast')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this graph, we can see the general distributions of COVID-19 Deaths between the West and East Coast. We can see that the East Coast has more outliers than the West Coast. We can also see that the interquartile range between the West and East Coast are fairly similar. However, based on this information alone, it is hard to visualize the actual difference or meaningful trends between these two populations. Perhaps there is a better way to quantify this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage of Total Deaths caused by COVID vs Coast\n",
    "\n",
    "In order to more accurately measure the relationship between coasts and COVID deaths, we also want to take a look at the covid death rates divided the total deaths in the population. Otherwise (whichever coast has the higher population would just end up having a higher number of COVID deaths). Also, this would negate the effect of the largest counties being outliers compared to the median county size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ratio of Total Deaths due to COVID for West and East Coast')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "\n",
    "boxplot = sns.boxplot(data = df, y = 'Portion of Deaths due to COVID', x = 'Coast', hue = 'Coast')\n",
    "boxplot.set_xlabel('Coast')\n",
    "boxplot.set_title('Ratio of Total Deaths due to COVID for West and East Coast')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this distribution, we can see that West Coast states have a much lower median COVID death rate than \n",
    "the East Coast, at around 0.75, whereas the East coast states have a higher median rate at around 0.95. Furthermore, the East distribution seems to have a few outliers at a significantly higher COVID \n",
    "death rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of COVID Death Rates in Relation to Coast\n",
    "\n",
    "In order to quantify that living in the West vs East Coast actually causes a significant difference in the portion of deaths due to COVID, let's use a t-test to test the difference between these two groups. This works well with the given data because we would like to see if there really is a statistical difference between East and West Coast COVID Rates and the null hypothesis of the trend being caused by random chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-statistic: -3.801220815269658\n",
      "p-value: 0.00020113242613057811\n"
     ]
    }
   ],
   "source": [
    "west_df = df[df['Coast'] == 'West']\n",
    "east_df = df[df['Coast'] == 'East']\n",
    "\n",
    "t_statistic, p_value = ttest_ind(west_df['Portion of Deaths due to COVID'], east_df['Portion of Deaths due to COVID'])\n",
    "print(\"t-statistic:\", t_statistic)\n",
    "print(\"p-value:\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our p_value is very small, below the threshold of 0.01, this means that there is a statistical difference between COVID death rates on the East Coast vs the West Coast and that the difference is not due to random chance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at our four different types of urbanization areas, which are Small Metro, Medium Metro, Large Fringe Metro and Large Central Metro and how they affect COVID total deaths and COVID death rates.\n",
    "\n",
    "### Total COVID Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "barplot = sns.barplot(data = df, y = 'COVID-19 Deaths', x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('COVID-19 Deaths for West and East Coast and their Urbanizations')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this graph, we can see that the West Coast has less COVID-19 Deaths for small metro, similar COVID-19 Deaths for medium metro and large fringe metro, and more COVID-19 Deaths for large central metro when compared to the East Coast. However, this does intituively make sense, because if we are only looking at total deaths, naturally large central metros will have a higher number of COVID deaths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage of Total Deaths caused by COVID vs Urbanization\n",
    "Again, though, we would like to plot the distribution of covid deaths over total deaths for our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot = sns.barplot(data = df, y = 'Portion of Deaths due to COVID', x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('Ratio of Total Deaths due to COVID for West and East Coast and their Urbanizations')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly enough, the East Coast seems to have a higher percent of deaths caused by COVID mostly except for small metros, where West Coast has a higher portion of COVID deaths. The difference between East and West Coast is most noticeable in the large fringe metro. Again, West Coast counties tend to have more outliers than East Coast.\n",
    "\n",
    "### Analysis of COVID Death Rates vs Urbanization\n",
    "\n",
    "To start off, we want to see if there is a significant level of difference WITHIN the same urbanization of counties but with different coasts (i.e. small metro West vs East, medium metro West vs East etc.). Therefore, we can run a t_test for all four of our urbanization types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small West Metro Count: 4\n",
      "Small East Metro Count: 5\n",
      "Medium West Metro Count: 13\n",
      "Medium East Metro Count: 46\n"
     ]
    }
   ],
   "source": [
    "#Small Metro\n",
    "west_small_df = df[(df['Urban Rural Description'] == 'Small metro') & (df['Coast'] == 'West')]\n",
    "east_small_df = df[(df['Urban Rural Description'] == 'Small metro') & (df['Coast'] == 'East')]\n",
    "\n",
    "west_small_count = west_small_df.shape[0]\n",
    "east_small_count = east_small_df.shape[0]\n",
    "print(\"Small West Metro Count:\", west_small_count)\n",
    "print(\"Small East Metro Count:\",  east_small_count)\n",
    "\n",
    "\n",
    "#Medium Metro\n",
    "west_medium_df = df[(df['Urban Rural Description'] == 'Medium metro') & (df['Coast'] == 'West')]\n",
    "east_medium_df = df[(df['Urban Rural Description'] == 'Medium metro') & (df['Coast'] == 'East')]\n",
    "\n",
    "west_medium_count = west_medium_df.shape[0]\n",
    "east_medium_count = east_medium_df.shape[0]\n",
    "print(\"Medium West Metro Count:\", west_medium_count)\n",
    "print(\"Medium East Metro Count:\", east_medium_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Large Fringe West Metro Count: 9\n",
      "Large Fringe East Metro Count: 54\n",
      "Large Central West Metro Count: 10\n",
      "Large Central East Metro Count: 29\n"
     ]
    }
   ],
   "source": [
    "#Large Fringe Metro\n",
    "west_large_fringe_df = df[(df['Urban Rural Description'] == 'Large fringe metro') & (df['Coast'] == 'West')]\n",
    "east_large_fringe_df = df[(df['Urban Rural Description'] == 'Large fringe metro') & (df['Coast'] == 'East')]\n",
    "\n",
    "west_large_fringe_count = west_large_fringe_df.shape[0]\n",
    "east_large_fringe_count = east_large_fringe_df.shape[0]\n",
    "print(\"Large Fringe West Metro Count:\", west_large_fringe_count)\n",
    "print(\"Large Fringe East Metro Count:\", east_large_fringe_count)\n",
    "\n",
    "# Large Central Metro\n",
    "west_large_central_df = df[(df['Urban Rural Description'] == 'Large central metro') & (df['Coast'] == 'West')]\n",
    "east_large_central_df = df[(df['Urban Rural Description'] == 'Large central metro') & (df['Coast'] == 'East')]\n",
    "\n",
    "west_large_central_count = west_large_central_df.shape[0]\n",
    "east_large_central_count = east_large_central_df.shape[0]\n",
    "print(\"Large Central West Metro Count:\", west_large_central_count)\n",
    "print(\"Large Central East Metro Count:\", east_large_central_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One slight issue with our analysis is that there is a large disparity in the number of observations for some of our urbanization levels. For example, there are only 4 small west metros and 5 small east metros. If we were to compare them against each other, the small number of obversations could be swung heavily by outliers. Thus, we can commbine our small and medium metros and large fringe and large central and compare these counties as \"Smaller vs Larger Metro\". This would result in a broader comparison, but a better understanding of these trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "west_smaller_df = pd.concat([west_small_df, west_medium_df])\n",
    "west_larger_df = pd.concat([west_large_fringe_df, west_large_central_df])\n",
    "\n",
    "east_smaller_df = pd.concat([east_small_df, east_medium_df])\n",
    "east_larger_df = pd.concat([east_large_fringe_df, east_large_central_df])\n",
    "\n",
    "west_smaller_df = west_smaller_df.reset_index(drop=True)\n",
    "west_larger_df = west_larger_df.reset_index(drop=True)\n",
    "east_smaller_df = east_smaller_df.reset_index(drop=True)\n",
    "east_larger_df = east_larger_df.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make our lives easier when plotting, we can modify the df directly in order to add a \"Smaller\" and \n",
    "\"Larger\" label to separate our urbanizations and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>County Name</th>\n",
       "      <th>Coast</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>COVID-19 Deaths</th>\n",
       "      <th>Portion of Deaths due to COVID</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-White, Non-Black, Non-Hispanic, Non-Asian</th>\n",
       "      <th>Urban Rural Description</th>\n",
       "      <th>General Urbanization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>West</td>\n",
       "      <td>41916</td>\n",
       "      <td>2628</td>\n",
       "      <td>0.062697</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.039</td>\n",
       "      <td>Large central metro</td>\n",
       "      <td>Large Fringe/Central Metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>West</td>\n",
       "      <td>9294</td>\n",
       "      <td>789</td>\n",
       "      <td>0.084893</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.035</td>\n",
       "      <td>Small metro</td>\n",
       "      <td>Small/Medium Metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>Contra Costa County</td>\n",
       "      <td>West</td>\n",
       "      <td>31889</td>\n",
       "      <td>1754</td>\n",
       "      <td>0.055003</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.028</td>\n",
       "      <td>Large fringe metro</td>\n",
       "      <td>Large Fringe/Central Metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>Fresno County</td>\n",
       "      <td>West</td>\n",
       "      <td>33704</td>\n",
       "      <td>3278</td>\n",
       "      <td>0.097258</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.019</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>Small/Medium Metro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>West</td>\n",
       "      <td>27860</td>\n",
       "      <td>2711</td>\n",
       "      <td>0.097308</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.024</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>Small/Medium Metro</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State          County Name Coast  Total deaths  COVID-19 Deaths  \\\n",
       "0    CA       Alameda County  West         41916             2628   \n",
       "1    CA         Butte County  West          9294              789   \n",
       "2    CA  Contra Costa County  West         31889             1754   \n",
       "3    CA        Fresno County  West         33704             3278   \n",
       "4    CA          Kern County  West         27860             2711   \n",
       "\n",
       "   Portion of Deaths due to COVID  Non-Hispanic White  Non-Hispanic Black  \\\n",
       "0                        0.062697               0.312               0.191   \n",
       "1                        0.084893               0.790               0.015   \n",
       "2                        0.055003               0.536               0.127   \n",
       "3                        0.097258               0.393               0.046   \n",
       "4                        0.097308               0.373               0.058   \n",
       "\n",
       "   Hispanic  Non-Hispanic Asian  \\\n",
       "0     0.239               0.219   \n",
       "1     0.119               0.041   \n",
       "2     0.198               0.111   \n",
       "3     0.450               0.092   \n",
       "4     0.505               0.040   \n",
       "\n",
       "   Non-White, Non-Black, Non-Hispanic, Non-Asian Urban Rural Description  \\\n",
       "0                                          0.039     Large central metro   \n",
       "1                                          0.035             Small metro   \n",
       "2                                          0.028      Large fringe metro   \n",
       "3                                          0.019            Medium metro   \n",
       "4                                          0.024            Medium metro   \n",
       "\n",
       "         General Urbanization  \n",
       "0  Large Fringe/Central Metro  \n",
       "1          Small/Medium Metro  \n",
       "2  Large Fringe/Central Metro  \n",
       "3          Small/Medium Metro  \n",
       "4          Small/Medium Metro  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['General Urbanization'] = df['Urban Rural Description'].apply(lambda x: 'Small/Medium Metro' if x in ['Small metro', 'Medium metro'] else 'Large Fringe/Central Metro')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small/Medium Metro'), Text(1, 0, 'Large Fringe/Central Metro')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot = sns.barplot(data = df, y = 'Portion of Deaths due to COVID', x = 'General Urbanization', hue = 'Coast', order = ['Small/Medium Metro', 'Large Fringe/Central Metro'])\n",
    "barplot.set_title('Ratio of Total Deaths due to COVID for West and East Coast and their Urbanizations')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have combined our data in order to reduce outliers, it seems that small/medium metros are very similar in terms of death rates on the East and West Coast. On the other hand, large fringe/central metros have a significantly higher death rate on the East Coast. Let's run a T-Test to see if these results are significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small/Medium Statistics\n",
      "t-statistic: -0.7216757965226612\n",
      "p-value: 0.473042849033364\n",
      "\n",
      "\n",
      "Large Fringe/Central Statistics\n",
      "t-statistic: -4.190952786211244\n",
      "p-value: 6.002924693847406e-05\n"
     ]
    }
   ],
   "source": [
    "#Small Medium Analysis\n",
    "t_statistic_1, p_value_1 = ttest_ind(west_smaller_df['Portion of Deaths due to COVID'], east_smaller_df['Portion of Deaths due to COVID'])\n",
    "print(\"Small/Medium Statistics\")\n",
    "print(\"t-statistic:\", t_statistic_1)\n",
    "print(\"p-value:\", p_value_1)\n",
    "\n",
    "#Large Fringe/Central Analysis\n",
    "t_statistic_2, p_value_2 = ttest_ind(west_larger_df['Portion of Deaths due to COVID'], east_larger_df['Portion of Deaths due to COVID'])\n",
    "print(\"\\n\\nLarge Fringe/Central Statistics\")\n",
    "print(\"t-statistic:\", t_statistic_2)\n",
    "print(\"p-value:\", p_value_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these t-values, our p-value for Large Metros in the East vs West Coast is very low, at around 6 e^-5, so there is a very big statistical difference between these groups. Also, our p-value for Small/Medium Metros in the East vs West Coast is only slightly smaller than the general threshold of 0.05 which means our p-value is very close to being significant. Thus, while it is hard to determine if small and medium metros are different in the East vs West Coast, there seems to be a lot higher portion of deaths in the Large metros in the East vs the West."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore even deeper by adding racial background and observe what their effect is to COVID-19 mortality rates.\n",
    "\n",
    "### Proportion of Racial Background COVID-19 Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will separate this by taking a look at each racial background's proportion to COVID-19 deaths to prevent cluttering and readability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of Non-Hispanic White COVID-19 Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Proportion of Non-Hispanic White contributed to COVID-19 Deaths\n",
    "barplot = sns.barplot(data = df, y = \"Non-Hispanic White\", x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('Non-Hispanic White COVID19 Death Ratio vs Urbanization and Coast')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this graph, the Non-Hispanic White COVID-19 Death ratios are higher in the East Coast for all urban rural descriptions with the exception of large fringe metro.\n",
    "\n",
    "What could this imply? (Answer if possible, remove if not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of Non-Hispanic Black COVID-19 Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Proportion of Non-Hispanic Black contributed to COVID-19 Deaths\n",
    "barplot = sns.barplot(data = df, y = \"Non-Hispanic Black\", x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('Non-Hispanic Black COVID19 Death Ratio vs Urbanization and Coast')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this graph, the Non-Hispanic Black COVID-19 death ratios are higher in the East Coast for all urban rural descriptions when compared to the West Coast.\n",
    "\n",
    "What could this possibly imply? (Answer the question if possible, remove if not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of Hispanic COVID-19 Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Proportion of Hispanic Black contributed to COVID-19 Deaths\n",
    "barplot = sns.barplot(data = df, y = \"Hispanic\", x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('Hispanic COVID19 Death Ratio vs Urbanization and Coast')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this graph, it is the opposite of the Non-Hispanic Black COVID-19 Death ratio where the West Coast is higher for all urban rural descriptions for Hispanic COVID-19 Death ratio.\n",
    "\n",
    "What does this imply? (Answer if possible, remove if not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of Non-Hispanic Asian COVID-19 Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Proportion of Non-Hispanic Asian contributed to COVID-19 Deaths\n",
    "barplot = sns.barplot(data = df, y = \"Non-Hispanic Asian\", x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('Non-Hispanic Asian COVID19 Death Ratio vs Urbanization and Coast')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the Hispanic COVID-19 Death ratio graph, the West Coast is higher in across all urban rural descriptions for Non-Hispanic Asian COVID-19 Death ratios.\n",
    "\n",
    "What does this imply? (Answer if possible, remove if not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Proportion of Non-White, Non-Black, Non-Hispanic, Non-Asian COVID-19 Deaths vs Urbanization and Coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Small metro'),\n",
       " Text(1, 0, 'Medium metro'),\n",
       " Text(2, 0, 'Large fringe metro'),\n",
       " Text(3, 0, 'Large central metro')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Proportion of Non-White, Non-Black, Non-Hispanic, Non-Asian contributed to COVID-19 Deaths\n",
    "barplot = sns.barplot(data = df, y = \"Non-White, Non-Black, Non-Hispanic, Non-Asian\", x = 'Urban Rural Description', hue = 'Coast', order = ['Small metro', 'Medium metro', 'Large fringe metro', 'Large central metro'],)\n",
    "barplot.set_title('Non-White, Non-Black, Non-Hispanic, Non-Asian COVID19 Death Ratio vs Urbanization and Coast')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the previous 2 graphs, the West Coast is higher in across all urban rural descriptions for Non-White, Non-Black, Non-Hispanic, Non-Asian COVID-19 Death ratios.\n",
    "\n",
    "What does this imply? (Answer if possible, remove if not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Racial Background COVID-19 Death Rate vs Coast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we do any visualizations, we need to modify our dataframe such that instead of the racial background COVID-19 death ratios are in multiple columns, we will put it in a singular column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Coast</th>\n",
       "      <th>Race</th>\n",
       "      <th>Racial Background Proportion to COVID-19 Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West</td>\n",
       "      <td>Non-Hispanic White</td>\n",
       "      <td>0.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>West</td>\n",
       "      <td>Non-Hispanic White</td>\n",
       "      <td>0.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West</td>\n",
       "      <td>Non-Hispanic White</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>Non-Hispanic White</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West</td>\n",
       "      <td>Non-Hispanic White</td>\n",
       "      <td>0.373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Coast                Race  Racial Background Proportion to COVID-19 Deaths\n",
       "0  West  Non-Hispanic White                                            0.312\n",
       "1  West  Non-Hispanic White                                            0.790\n",
       "2  West  Non-Hispanic White                                            0.536\n",
       "3  West  Non-Hispanic White                                            0.393\n",
       "4  West  Non-Hispanic White                                            0.373"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_columns = ['State', 'County Name', 'Total deaths', 'COVID-19 Deaths', 'Portion of Deaths due to COVID', 'Urban Rural Description', 'General Urbanization']\n",
    "df_clean = df.copy().drop(dropped_columns, axis = 1)\n",
    "dfm = pd.melt(df_clean, id_vars = ['Coast'], var_name = 'Race', value_name = 'Racial Background Proportion to COVID-19 Deaths')\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we do the visualization here with our modified dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'West'), Text(1, 0, 'East')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot = sns.barplot(data = dfm, y = \"Racial Background Proportion to COVID-19 Deaths\", x = 'Coast', hue = 'Race')\n",
    "barplot.set_title('Racial Background COVID19 Death Ratio vs Coast')\n",
    "barplot.set_xticklabels(barplot.get_xticklabels(), rotation=40, ha=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Relationship Between COVID-19 Death Rate vs Racial Background "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After comparing the difference in means between COVID-19 death rate and racial background, we wanted to determine the effect of racial backgrounds on COVID-19 death rates across counties using linear regression. Our null hypothesis is that being Non-Hispanic White has no effect on COVID-19 death rates, and our alternative hypothesis is that being Non-Hispanic White has a significant difference on COVID-19 death rates. For this portion of the converted our columns with percentages to mixed numbers for easy interpretation of our linear regression line. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>County Name</th>\n",
       "      <th>Coast</th>\n",
       "      <th>Total deaths</th>\n",
       "      <th>COVID-19 Deaths</th>\n",
       "      <th>Portion of Deaths due to COVID</th>\n",
       "      <th>Non-Hispanic White</th>\n",
       "      <th>Non-Hispanic Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Non-Hispanic Asian</th>\n",
       "      <th>Non-White, Non-Black, Non-Hispanic, Non-Asian</th>\n",
       "      <th>Urban Rural Description</th>\n",
       "      <th>General Urbanization</th>\n",
       "      <th>Non-White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>Alameda County</td>\n",
       "      <td>West</td>\n",
       "      <td>41916</td>\n",
       "      <td>2628</td>\n",
       "      <td>6.269682</td>\n",
       "      <td>31.2</td>\n",
       "      <td>19.1</td>\n",
       "      <td>23.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Large central metro</td>\n",
       "      <td>Large Fringe/Central Metro</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>Butte County</td>\n",
       "      <td>West</td>\n",
       "      <td>9294</td>\n",
       "      <td>789</td>\n",
       "      <td>8.489348</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>11.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Small metro</td>\n",
       "      <td>Small/Medium Metro</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>Contra Costa County</td>\n",
       "      <td>West</td>\n",
       "      <td>31889</td>\n",
       "      <td>1754</td>\n",
       "      <td>5.500329</td>\n",
       "      <td>53.6</td>\n",
       "      <td>12.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Large fringe metro</td>\n",
       "      <td>Large Fringe/Central Metro</td>\n",
       "      <td>46.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>Fresno County</td>\n",
       "      <td>West</td>\n",
       "      <td>33704</td>\n",
       "      <td>3278</td>\n",
       "      <td>9.725849</td>\n",
       "      <td>39.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>Small/Medium Metro</td>\n",
       "      <td>60.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Kern County</td>\n",
       "      <td>West</td>\n",
       "      <td>27860</td>\n",
       "      <td>2711</td>\n",
       "      <td>9.730797</td>\n",
       "      <td>37.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>50.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Medium metro</td>\n",
       "      <td>Small/Medium Metro</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State          County Name Coast  Total deaths  COVID-19 Deaths  \\\n",
       "0    CA       Alameda County  West         41916             2628   \n",
       "1    CA         Butte County  West          9294              789   \n",
       "2    CA  Contra Costa County  West         31889             1754   \n",
       "3    CA        Fresno County  West         33704             3278   \n",
       "4    CA          Kern County  West         27860             2711   \n",
       "\n",
       "   Portion of Deaths due to COVID  Non-Hispanic White  Non-Hispanic Black  \\\n",
       "0                        6.269682                31.2                19.1   \n",
       "1                        8.489348                79.0                 1.5   \n",
       "2                        5.500329                53.6                12.7   \n",
       "3                        9.725849                39.3                 4.6   \n",
       "4                        9.730797                37.3                 5.8   \n",
       "\n",
       "   Hispanic  Non-Hispanic Asian  \\\n",
       "0      23.9                21.9   \n",
       "1      11.9                 4.1   \n",
       "2      19.8                11.1   \n",
       "3      45.0                 9.2   \n",
       "4      50.5                 4.0   \n",
       "\n",
       "   Non-White, Non-Black, Non-Hispanic, Non-Asian Urban Rural Description  \\\n",
       "0                                            3.9     Large central metro   \n",
       "1                                            3.5             Small metro   \n",
       "2                                            2.8      Large fringe metro   \n",
       "3                                            1.9            Medium metro   \n",
       "4                                            2.4            Medium metro   \n",
       "\n",
       "         General Urbanization  Non-White  \n",
       "0  Large Fringe/Central Metro       68.8  \n",
       "1          Small/Medium Metro       21.0  \n",
       "2  Large Fringe/Central Metro       46.4  \n",
       "3          Small/Medium Metro       60.7  \n",
       "4          Small/Medium Metro       62.7  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the \"Non-Hispanic White\" and \"Portion of Deaths due to COVID\" column to whole numbers for easy interpretation\n",
    "reg_df = df.copy()\n",
    "reg_df[\"Portion of Deaths due to COVID\"] = df[\"Portion of Deaths due to COVID\"] * 100\n",
    "reg_df[\"Non-Hispanic White\"] = df[\"Non-Hispanic White\"] * 100\n",
    "reg_df[\"Non-Hispanic Black\"] = df[\"Non-Hispanic Black\"] * 100\n",
    "reg_df[\"Hispanic\"] = df[\"Hispanic\"] * 100\n",
    "reg_df[\"Non-Hispanic Asian\"] = df[\"Non-Hispanic Asian\"] * 100\n",
    "reg_df[\"Non-White, Non-Black, Non-Hispanic, Non-Asian\"] = df[\"Non-White, Non-Black, Non-Hispanic, Non-Asian\"] * 100\n",
    "reg_df[\"Non-White\"] = (1 - df[\"Non-Hispanic White\"]) * 100\n",
    "\n",
    "# Take a look at what changed\n",
    "reg_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's take a look at the data with a scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterplt = sns.scatterplot(data=reg_df, y='Portion of Deaths due to COVID', x='Non-Hispanic White')\n",
    "#plt.xlabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to visualize a line of best fit through the data, signifying that the data is roughly linear. Now let's take a look at the coefficients for our regression line using OLS regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     OLS Regression Results                                    \n",
      "===============================================================================================\n",
      "Dep. Variable:     Q(\"Portion of Deaths due to COVID\")   R-squared:                       0.050\n",
      "Model:                                             OLS   Adj. R-squared:                  0.044\n",
      "Method:                                  Least Squares   F-statistic:                     8.767\n",
      "Date:                                 Tue, 19 Mar 2024   Prob (F-statistic):            0.00351\n",
      "Time:                                         21:56:12   Log-Likelihood:                -389.14\n",
      "No. Observations:                                  170   AIC:                             782.3\n",
      "Df Residuals:                                      168   BIC:                             788.5\n",
      "Df Model:                                            1                                         \n",
      "Covariance Type:                             nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "Intercept                  11.1777      0.623     17.928      0.000       9.947      12.409\n",
      "Q(\"Non-Hispanic White\")    -0.0279      0.009     -2.961      0.004      -0.047      -0.009\n",
      "==============================================================================\n",
      "Omnibus:                        7.446   Durbin-Watson:                   1.414\n",
      "Prob(Omnibus):                  0.024   Jarque-Bera (JB):                7.161\n",
      "Skew:                           0.478   Prob(JB):                       0.0279\n",
      "Kurtosis:                       3.311   Cond. No.                         224.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Create model\n",
    "outcome, predictors = patsy.dmatrices('Q(\"Portion of Deaths due to COVID\") ~ Q(\"Non-Hispanic White\")', reg_df)\n",
    "model = sm.OLS(outcome, predictors)\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit()\n",
    "\n",
    "# Look at the results\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equation for our regression line is y = -0.028x - 11.18. This means that for every 1 percent increase in the percentage of Non-Hispanic Whites in a county, there is a 0.028 percent decrease in the percentage of deaths due to COVID-19. Now that we created a model, we can check if our data is homoscedastic enough for linear regression using a residual plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residual Plot')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_values = results.fittedvalues\n",
    "\n",
    "# Generate residuals\n",
    "residuals = results.resid\n",
    "sns.scatterplot(x=predicted_values, y=residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.title('Residual Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is mostly homoscedastic, although there are some larger residuals that start to appear on the right side of the graph. Although we believe the data is still homoscedastic enough to use linear regression, this is an important limitation of the model to keep in mind. We will now plot a visualization for the regression line. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recreate previous scatterplot\n",
    "sns.scatterplot(data=reg_df, y='Portion of Deaths due to COVID', x='Non-Hispanic White')\n",
    "\n",
    "# Plot the regression line\n",
    "xs = np.arange(reg_df['Non-Hispanic White'].min(), reg_df['Non-Hispanic White'].max())\n",
    "ys = -0.028 * xs + 11.18\n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "\n",
    "plt.xlabel('Non-Hispanic White')\n",
    "plt.ylabel('Percentage of Deaths due to COVID')\n",
    "plt.title('The Relationship Between the Percentage of Deaths Due to COVID-19 and Being Non-Hispanic White')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the relationship is not particularly strong, counties with a higher percentage of Whites dying from COVID-19 have less COVID-19 deaths relative to total deaths. We have confirmed our alternative hypothesis that being Non-Hispanic White has a significant difference on COVID-19 death rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can try to conduct a similar analysis on one of the other races in our dataset, Hispanics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterplt = sns.scatterplot(data=reg_df, y='Portion of Deaths due to COVID', x='Hispanic')\n",
    "#plt.xlabel('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately due to the heteroscedasticity of the data, we were unable to use a linear regression model for COVID-19 death rates vs. Hispanic deaths due to COVID-19, as seen below in the residual plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Residual Plot')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create model\n",
    "outcome, predictors = patsy.dmatrices('Q(\"Portion of Deaths due to COVID\") ~ Q(\"Hispanic\")', reg_df)\n",
    "model = sm.OLS(outcome, predictors)\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit()\n",
    "\n",
    "predicted_values = results.fittedvalues\n",
    "\n",
    "# Generate residuals\n",
    "residuals = results.resid\n",
    "sns.scatterplot(x=predicted_values, y=residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "plt.title('Residual Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not believe there would be issues regarding the confidentiality of patient/personal information as no names or specific data from one particular person would be used. All results were aggregated ensuring privacy at an individual level.\n",
    "\n",
    "Moreover, our data is sourced from reputable organizations such as the Health data organization, which adheres to strict ethical and data collection guidelines. Overall we believe our findings could prove to be beneficial to prepare for another pandemic in the future. \n",
    "\n",
    "However, it is also very crucial to approach any correlations with caution, avoiding misrepresentation of certain groups/populations of people when dealing with such diseases. Especially when dealing with our dataset that includes some missing values for race categories in a few counties. To account for this, we remove counties that lack proper representation of groups and make sure to only include those who include these values. By addressing these issues earlier during our cleaning portion of the data set we reduce biases before analyzing our data. To reduce bias after creating graphs, we plan on adding captions and carefully explain why we made certain choices: like what data to include, show or talk about for our project. Additionally, we took particular care in our project to ensure that no group is unfairly \"blamed\" or misrepresented based on their race. Especially given the increasing incidents of Asian hate during the COVID-19 pandemic, we are committed to presenting the data impartially and avoiding prejudice against any group. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Expectations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *Team Expectation 1*\n",
    "    * Have weekly meetings where everyone can attend. If someone cannot attend then they will get a summary, documenting our meeting’s goals and discussions. In each meeting we will talk about how we can make improvements on our previous checkpoint if needed. Then the rest of the meeting will go over the upcoming checkpoint, assigning what task everyone should do and our goals for that week.\n",
    "* *Team Expectation 2*\n",
    "    * Split up work from data wrangling, code, video, and write-up about data: we expect everyone to take a part in the project that plays to their strengths. Those who are good or confident at coding will split the coding portions among themselves (data, wranging, creating graphs etc), and those who are not as confident, would take up the majority of the writing or analysis portion. If all the parts are taken, we plan on having one or two people look over the entire project or step in when help is needed in the coding or writing parts of the project.\n",
    "* *Team Expecation 3*\n",
    "    * Overall expectations: We expect every member in our group to contribute equally and be able to respond to our group chat in a reasonable amount of time. We also expect everyone to try to go to our group meetings and if not possible to check in with the group after to see what they missed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Timeline Proposal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Week #      | Meeting Time | Weekly Goals |\n",
    "|------------|--------------|--------------------|\n",
    "| Week 6     | Sat. 6 PM    | For week 6, we found datasets that fit our research question. Once the best possible dataset was found for our specific topic, we divided the work to conduct some wrangling and cleaning on it, such as detecting redundant columns and tidying the data (e.g., converting age values to only years, weight to only pounds, etc.). |\n",
    "| Week 7     | Sat. 7 AM    | In week 7, we will start thinking about how to effectively present the data. We'll decide on the types of graphs to use, the reasons behind our choices, explore relationships within our dataset, and ensure the graphs are easily understandable to the viewer. Another words, discuss what we can do with the dataset |\n",
    "| Week 8     | Sat. 5 PM    | Week 8 will involve cleaning the data frame we chose and correcting any errors outlined by our TA/Professor from the proposal grading|\n",
    "| Week 9     | Fri. 10 AM   | During week 9, we will work on EDA- creating graphs based on our dataset and correct any additional errors that were pointed our in our previous checkpoint |\n",
    "| Week 10    |  Sat 5 PM        | Finally, in week 10, we will conduct a final review of our project, ensuring that we meet all the requirements outlined in the project rubric. |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "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": "3.9.18"
  }
 },
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}