From c952744a647a778c4696a0945a8deb7031454571 Mon Sep 17 00:00:00 2001
From: tsb1995 <47466105+tsb1995@users.noreply.github.com>
Date: Fri, 21 Aug 2020 12:52:25 -0700
Subject: [PATCH] Add CovidUS Notebook

---
 CovidUS/USGeneral.ipynb | 607 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 607 insertions(+)
 create mode 100644 CovidUS/USGeneral.ipynb

diff --git a/CovidUS/USGeneral.ipynb b/CovidUS/USGeneral.ipynb
new file mode 100644
index 0000000..d7690b7
--- /dev/null
+++ b/CovidUS/USGeneral.ipynb
@@ -0,0 +1,607 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Covid US Case Data Analasys\n",
+    "\n",
+    "In this notebook, we will work with the open source [Case Surveilance Public Use Data](https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf  \"CDC Covid Data\") provided by the CDC on COVID-19 patients in the US. The data, as downloaded on 08/12/2020, contains information on 2,668,175 patients across the United States. The patients' names have been removed for privacy, but we are given information on their Sex, Age, Race/Ethnicity, and Fatality Status. We will look at the data broken down by each of these factors and use it to create informative and interesting visuals. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn; seaborn.set()\n",
+    "from pywaffle import Waffle\n",
+    "from sodapy import Socrata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Requests made without an app_token will be subject to strict throttling limits.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Unauthenticated client only works with public data sets. Note 'None'\n",
+    "# in place of application token, and no username or password:\n",
+    "client = Socrata(\"data.cdc.gov\", None)\n",
+    "\n",
+    "# Example authenticated client (needed for non-public datasets):\n",
+    "# client = Socrata(data.cdc.gov,\n",
+    "#                  MyAppToken,\n",
+    "#                  userame=\"user@example.com\",\n",
+    "#                  password=\"AFakePassword\")\n",
+    "\n",
+    "# First 2000 results, returned as JSON from API / converted to Python list of\n",
+    "# dictionaries by sodapy.\n",
+    "results = client.get(\"vbim-akqf\", limit=10000000)\n",
+    "\n",
+    "# Convert to pandas DataFrame\n",
+    "dfGeneralUS = pd.DataFrame.from_records(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2668175, 11)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfGeneralUS.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dfGeneralUS = pd.read_csv('COVID-19_Case_Surveillance_Public_Use_Data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Cleanup\n",
+    "\n",
+    "We start by cleaning up the data. Like most real world datasets, this data containts many missing or poorly formatted values. We start by removing features with an overwhelming proportion of NaN values, which includes the Initial Case Report Date and Date of First Positive Specimen Collection. \n",
+    "\n",
+    "Fortunately the remaining rows containing NaN are so few relative to the massive sample of patients that they can safely be dropped. If the features containing these rows were numerical, then some type of imputation could be more appealing. \n",
+    "\n",
+    "We then drop features with an overwhelming proportions of 'Missing' values. These are values that were not simply left blank as in NaN values, but rather we marked specifically as 'Missing' by whoever filled in the form. This includes ICU Admission Status, Hospitalization Status, and Presence of Underlying Condition or Comorbidity.\n",
+    "\n",
+    "Finally, we rename the Features so as to be easier to work with and flesh out the Death Status Feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop columns with too many nan valaues (massive proportions)\n",
+    "dfGeneralUS.drop(['pos_spec_dt', 'onset_dt'], axis = 1, inplace = True)\n",
+    "# Drop rows with nan values (not enough to be significant to dataset)\n",
+    "dfGeneralUS.dropna(inplace=True)\n",
+    " # Drop columns with too many 'Missing' values\n",
+    "dfGeneralUS.drop(['icu_yn', 'hosp_yn', 'medcond_yn'], axis = 1, inplace = True)\n",
+    "# Rename ethnic_group column\n",
+    "dfGeneralUS.columns = ['cdc_report_dt', 'current_status', 'sex', 'age_group',\n",
+    "       'ethnic_group', 'death_yn']\n",
+    "# Fill in missing or unknown death as No (Not a death if not reported as one)\n",
+    "filter = ['Missing', 'Unknown']\n",
+    "mask = dfGeneralUS['death_yn'].isin(filter)\n",
+    "dfGeneralUS.loc[mask ,'death_yn'] = 'No'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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>cdc_report_dt</th>\n",
+       "      <th>current_status</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age_group</th>\n",
+       "      <th>ethnic_group</th>\n",
+       "      <th>death_yn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-07-03T00:00:00.000</td>\n",
+       "      <td>Laboratory-confirmed case</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0 - 9 Years</td>\n",
+       "      <td>American Indian/Alaska Native, Non-Hispanic</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-05-27T00:00:00.000</td>\n",
+       "      <td>Laboratory-confirmed case</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0 - 9 Years</td>\n",
+       "      <td>American Indian/Alaska Native, Non-Hispanic</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-06-06T00:00:00.000</td>\n",
+       "      <td>Laboratory-confirmed case</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0 - 9 Years</td>\n",
+       "      <td>American Indian/Alaska Native, Non-Hispanic</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-06-28T00:00:00.000</td>\n",
+       "      <td>Laboratory-confirmed case</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0 - 9 Years</td>\n",
+       "      <td>American Indian/Alaska Native, Non-Hispanic</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-07-06T00:00:00.000</td>\n",
+       "      <td>Laboratory-confirmed case</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0 - 9 Years</td>\n",
+       "      <td>American Indian/Alaska Native, Non-Hispanic</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             cdc_report_dt             current_status     sex    age_group  \\\n",
+       "0  2020-07-03T00:00:00.000  Laboratory-confirmed case  Female  0 - 9 Years   \n",
+       "1  2020-05-27T00:00:00.000  Laboratory-confirmed case  Female  0 - 9 Years   \n",
+       "2  2020-06-06T00:00:00.000  Laboratory-confirmed case  Female  0 - 9 Years   \n",
+       "3  2020-06-28T00:00:00.000  Laboratory-confirmed case  Female  0 - 9 Years   \n",
+       "4  2020-07-06T00:00:00.000  Laboratory-confirmed case  Female  0 - 9 Years   \n",
+       "\n",
+       "                                  ethnic_group death_yn  \n",
+       "0  American Indian/Alaska Native, Non-Hispanic       No  \n",
+       "1  American Indian/Alaska Native, Non-Hispanic       No  \n",
+       "2  American Indian/Alaska Native, Non-Hispanic       No  \n",
+       "3  American Indian/Alaska Native, Non-Hispanic       No  \n",
+       "4  American Indian/Alaska Native, Non-Hispanic       No  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get a look at our cleaned up data\n",
+    "display(dfGeneralUS.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Death Tables\n",
+    "Here we organize information from the Death Status feature, in terms of the Total Population, Ethnicity/Race, Age Group, and Sex, into tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbf\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Total</th>        <th class=\"col_heading level0 col1\" >Proportion</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbflevel0_row0\" class=\"row_heading level0 row0\" >Survived</th>\n",
+       "                        <td id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbfrow0_col0\" class=\"data row0 col0\" >2554981</td>\n",
+       "                        <td id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbfrow0_col1\" class=\"data row0 col1\" >95.76%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbflevel0_row1\" class=\"row_heading level0 row1\" >Died</th>\n",
+       "                        <td id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbfrow1_col0\" class=\"data row1 col0\" >113194</td>\n",
+       "                        <td id=\"T_75923740_e3e5_11ea_9854_9061aebdbdbfrow1_col1\" class=\"data row1 col1\" >4.24%</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x264010a37f0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deathValueCounts = dfGeneralUS['death_yn'].value_counts()\n",
+    "deathProportions = dfGeneralUS['death_yn'].value_counts(normalize=True)\n",
+    "dfOverrallDeath = pd.concat([deathValueCounts, deathProportions], axis='columns')\n",
+    "dfOverrallDeath.columns = ['Total', 'Proportion']\n",
+    "dfOverrallDeath.index = ['Survived', 'Died']\n",
+    "dfOverrallDeath.style.format({'Proportion': '{:.2%}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to grab value counts and proportions for given column and return as a dataframe\n",
+    "\n",
+    "def getValueAndProp(column):\n",
+    "    dfDeathsUS = dfGeneralUS[dfGeneralUS['death_yn'] == 'Yes']\n",
+    "    valueCounts = dfDeathsUS[column].value_counts()\n",
+    "    proportions = dfDeathsUS[column].value_counts(normalize=True)\n",
+    "    df = pd.concat([valueCounts, proportions], axis='columns')\n",
+    "    df.columns = ['Raw Death Count', 'Proportion of Total Deaths']\n",
+    "    df.drop('Unknown', axis='rows',inplace=True)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DeathByAge = getValueAndProp('age_group')\n",
+    "DeathBySex = getValueAndProp('sex')\n",
+    "DeathByEthnicity = getValueAndProp('ethnic_group')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbf\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Raw Death Count</th>        <th class=\"col_heading level0 col1\" >Proportion of Total Deaths</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row0\" class=\"row_heading level0 row0\" >White, Non-Hispanic</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow0_col0\" class=\"data row0 col0\" >48,793</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow0_col1\" class=\"data row0 col1\" >43.11%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row1\" class=\"row_heading level0 row1\" >Black, Non-Hispanic</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow1_col0\" class=\"data row1 col0\" >21,605</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow1_col1\" class=\"data row1 col1\" >19.09%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row2\" class=\"row_heading level0 row2\" >Hispanic/Latino</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow2_col0\" class=\"data row2 col0\" >16,361</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow2_col1\" class=\"data row2 col1\" >14.45%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row3\" class=\"row_heading level0 row3\" >Asian, Non-Hispanic</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow3_col0\" class=\"data row3 col0\" >4,943</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow3_col1\" class=\"data row3 col1\" >4.37%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row4\" class=\"row_heading level0 row4\" >Multiple/Other, Non-Hispanic</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow4_col0\" class=\"data row4 col0\" >4,593</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow4_col1\" class=\"data row4 col1\" >4.06%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row5\" class=\"row_heading level0 row5\" >American Indian/Alaska Native, Non-Hispanic</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow5_col0\" class=\"data row5 col0\" >679</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow5_col1\" class=\"data row5 col1\" >0.60%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbflevel0_row6\" class=\"row_heading level0 row6\" >Native Hawaiian/Other Pacific Islander, Non-Hispanic</th>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow6_col0\" class=\"data row6 col0\" >151</td>\n",
+       "                        <td id=\"T_62722f80_e3e7_11ea_a273_9061aebdbdbfrow6_col1\" class=\"data row6 col1\" >0.13%</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2645f6c2be0>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Format floating point values to display percentage and add commas to integer values\n",
+    "DeathByEthnicity.style.format({'Proportion of Total Deaths': '{:.2%}',\n",
+    "                                'Raw Death Count': '{:,}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbf\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Raw Death Count</th>        <th class=\"col_heading level0 col1\" >Proportion of Total Deaths</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row0\" class=\"row_heading level0 row0\" >80+ Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow0_col0\" class=\"data row0 col0\" >52,057</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow0_col1\" class=\"data row0 col1\" >45.99%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row1\" class=\"row_heading level0 row1\" >70 - 79 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow1_col0\" class=\"data row1 col0\" >27,449</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow1_col1\" class=\"data row1 col1\" >24.25%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row2\" class=\"row_heading level0 row2\" >60 - 69 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow2_col0\" class=\"data row2 col0\" >18,979</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow2_col1\" class=\"data row2 col1\" >16.77%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row3\" class=\"row_heading level0 row3\" >50 - 59 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow3_col0\" class=\"data row3 col0\" >9,190</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow3_col1\" class=\"data row3 col1\" >8.12%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row4\" class=\"row_heading level0 row4\" >40 - 49 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow4_col0\" class=\"data row4 col0\" >3,518</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow4_col1\" class=\"data row4 col1\" >3.11%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row5\" class=\"row_heading level0 row5\" >30 - 39 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow5_col0\" class=\"data row5 col0\" >1,401</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow5_col1\" class=\"data row5 col1\" >1.24%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row6\" class=\"row_heading level0 row6\" >20 - 29 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow6_col0\" class=\"data row6 col0\" >498</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow6_col1\" class=\"data row6 col1\" >0.44%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row7\" class=\"row_heading level0 row7\" >10 - 19 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow7_col0\" class=\"data row7 col0\" >54</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow7_col1\" class=\"data row7 col1\" >0.05%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row8\" class=\"row_heading level0 row8\" >0 - 9 Years</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow8_col0\" class=\"data row8 col0\" >33</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow8_col1\" class=\"data row8 col1\" >0.03%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbflevel0_row9\" class=\"row_heading level0 row9\" >NA</th>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow9_col0\" class=\"data row9 col0\" >3</td>\n",
+       "                        <td id=\"T_62fa1e42_e3e7_11ea_b321_9061aebdbdbfrow9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2645f2a8dc0>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Format floating point values to be a decimal and display\n",
+    "DeathByAge.style.format({'Proportion of Total Deaths': '{:.2%}',\n",
+    "                                'Raw Death Count': '{:,}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbf\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Raw Death Count</th>        <th class=\"col_heading level0 col1\" >Proportion of Total Deaths</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbflevel0_row0\" class=\"row_heading level0 row0\" >Male</th>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow0_col0\" class=\"data row0 col0\" >60,473</td>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow0_col1\" class=\"data row0 col1\" >53.42%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbflevel0_row1\" class=\"row_heading level0 row1\" >Female</th>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow1_col0\" class=\"data row1 col0\" >52,033</td>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow1_col1\" class=\"data row1 col1\" >45.97%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbflevel0_row2\" class=\"row_heading level0 row2\" >Missing</th>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow2_col0\" class=\"data row2 col0\" >567</td>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow2_col1\" class=\"data row2 col1\" >0.50%</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbflevel0_row3\" class=\"row_heading level0 row3\" >Other</th>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow3_col0\" class=\"data row3 col0\" >1</td>\n",
+       "                        <td id=\"T_63360a81_e3e7_11ea_9a20_9061aebdbdbfrow3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x2645f6591c0>"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Format floating point values to be a decimal and display\n",
+    "DeathBySex.style.format({'Proportion of Total Deaths': '{:.2%}',\n",
+    "                                'Raw Death Count': '{:,}'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Death Charts\n",
+    "Here we use the information from our previously organized tables and the Matplotlib / Seaborn libraries to create interesting and informative charts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotDeathRaw(x, xlabel=None, title=None):\n",
+    "    x.plot(ylabel = 'Death Count', xlabel = xlabel, kind='bar', \n",
+    "                    figsize=(15,8), title = title)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# DeathByAge['Raw Death Count'].plot(ylabel = 'Death Count', xlabel = 'Age Group', kind='bar', \n",
+    "#                     figsize=(15,8), title = 'Total US Deaths By Age Group')\n",
+    "\n",
+    "plotDeathRaw(DeathByAge['Raw Death Count'], 'Age Group', 'Total US Deaths By Age Group')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotDeathRaw(DeathByEthnicity['Raw Death Count'], 'Race / Ethnicity', 'Total US Deaths By Race / Ethnicity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotDeathRaw(DeathBySex['Raw Death Count'], 'Sex', 'Total US Deaths By Sex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}