Team12_Hackathon.ipynb

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  "metadata": {
    "colab": {
      "name": "Team12_Hackathon.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nizeyumukiza/BeyondLinearRegression/blob/master/Team12_Hackathon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIjZSqqho3ar"
      },
      "source": [
        "**Not having access to healthcare before the pandemic appears in how different group of population of US residents were affected with Covid-19**\n",
        "\n",
        "This notebook explores how covid-19 pandemic had impacted different races of American population. We investigated how disparities in health care access between races contributed to differences in how the pandemic affected those groups. We used data from The Commonwealth Fund and Health Policy Research- Kaiser Family Foundation. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpHBtBXj0sDK"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# The following two modules matplotlib and seaborn are for plots\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1uZXLal3CYP",
        "outputId": "a7588795-bde6-4193-ae4c-322ec1c388a3",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-fbe9f020-ce89-4268-a3df-812d67ba3236\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-fbe9f020-ce89-4268-a3df-812d67ba3236\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving HealthCareAccess_Corona_Race.csv to HealthCareAccess_Corona_Race.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCuHeEiv3NLV",
        "outputId": "f2ae7967-0c8b-48be-eec3-240993a2f902",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "path =\"\"\n",
        "df = pd.read_csv(path +'HealthCareAccess_Corona_Race.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>race</th>\n",
              "      <th>no_medicalcare_percent</th>\n",
              "      <th>Year</th>\n",
              "      <th>death_perc</th>\n",
              "      <th>death_out_race_population/(10^(-8))</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>White_only</td>\n",
              "      <td>10.1</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.62</td>\n",
              "      <td>5.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Black_or_African_American</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.17</td>\n",
              "      <td>89.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>American_Indian_or_Alaska_Native</td>\n",
              "      <td>9.3</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.11</td>\n",
              "      <td>870.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Asian</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.04</td>\n",
              "      <td>4.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hispanic_or_latino</td>\n",
              "      <td>10.3</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.05</td>\n",
              "      <td>44.70</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               race  ...  death_out_race_population/(10^(-8))\n",
              "0                        White_only  ...                                 5.97\n",
              "1         Black_or_African_American  ...                                89.00\n",
              "2  American_Indian_or_Alaska_Native  ...                               870.00\n",
              "3                             Asian  ...                                 4.60\n",
              "4                Hispanic_or_latino  ...                                44.70\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wR00Lniiuv4X"
      },
      "source": [
        "We extracted these data from raw data after downloading them. We found an average of all the death rates in different states for different races. From that, we used # of death of COVID * Percentage of certain races died of COVID / (total population * percentage of population of that race) to find the final answers at the right."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2Lk_KFNe9or",
        "outputId": "ca305a4b-ea72-4899-a212-74e0a77a3258",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.columns\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['race', 'no_medicalcare_percent', 'Year', 'death_perc',\n",
              "       'death_out_race_population/(10^(-8))'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81rsu2cHL2v6",
        "outputId": "7417d97d-4ecb-4e96-baef-2591c72f91ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "fig_dims=(20,10)\n",
        "fig, output=plt.subplots(figsize=fig_dims)\n",
        "sns.barplot(x=\"race\", y=\"no_medicalcare_percent\", ax=output, data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f15b011a1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V3SHJHbv7r6rqO5IcPr/RAAAAAFhmG3qNpSSpqp9J8mdJXjytOjbJX8xjKAAAAACW34bDUpKnJLl3ki8lSXd/JMmt5zEUAAAAAMtvlrD0z9399X0LVbUjSW/+SAAAAABsB7OEpbdW1a8kuXlVPTjJa5L89/mMBQAAAMCymyUsPSPJniTvT/LEJBck+bV5DAUAAADA8pvlXeG+WVXnJXlX9j4F7kPd7alwAAAAAIeoDYelqvp3Sc5O8rEkleT7quqJ3f2GeQ0HAAAAwPLacFhK8oIk9+/ujyZJVZ2Q5C+TCEsAAAAAh6BZXmPp2n1RafLxJNdu8jwAAAAAbBOzXLG0u6ouSPKn2fsaS49K8p6q+g9J0t2vncN8AAAAACypWcLSzZJ8Osl9p+U9SW6e5OHZG5qEJQAAAIBDyCzvCve4/W2vql/u7ufd9JEAAAAA2A5meY2lA3nUJp4LAAAAgCW3mWGpNvFcAAAAACy5zQxLvYnnAgAAAGDJuWIJAAAAgCGbGZZes4nnAgAAAGDJbTgsVdX/UlVvrqorpuW7VdWv7dve3b81jwEBAAAAWE6zXLH0R0l+Ock3kqS735fkMfMYCgAAAIDlN0tY+o7ufveqdddt5jAAAAAAbB+zhKXPVNUJmd79rapOS/KpuUwFAAAAwNLbMcO+T0lyTpIfqKqrk/xdkh+fy1TAlviH5/zrRY/AQea4Z75/0SMAAABbaENhqaoOT/Kz3f2gqrpFksO6+9r5jgYAAADAMttQWOru66vqPtPtr8x3JAAAAAC2g1meCvfeqjo/yWuS3BCXuvu1mz4VAAAAAEtvlrB0sySfTfKAFes6ibAEAAAAcAjacFjq7sfNcxAAAAAAtpcNh6WqulmSxye5S/ZevZQk6e6fnsNcAAAAACy5w2bY9xVJvjfJjyV5a5LbJfHOcAAAAACHqFnC0vd3939O8pXuPi/Jv0tyz/mMBQAAAMCymyUsfWP6/IWqumuS70py680fCQAAAIDtYJZ3hTunqo5K8mtJzk9yZJJnzmUqAAAAAJbeLO8K95Lp5sVJ/tV8xgEAAABgu9jwU+Gq6req6lYrlo+qqt+Yz1gAAAAALLtZXmPpod39hX0L3f35JKds/kgAAAAAbAezhKXDq+rb9y1U1c2TfPt+9gcAAADgIDbLi3e/Msmbq+qPp+XHJTlv80cCAAAAYDuY5cW7f7uq3pfkgdOq53b3hfMZCwAAAIBlN8sVS+nuNyR5w5xmAQAAAGAbOWBYqqprk/Ram5J0d3/npk8FAAAAwNI7YFjq7ltuxSAAAAAAbC8zPRUuSarq1klutm+5u/9hUycCAAAAYFs4bKM7VtUjquojSf4uyVuTfCJebwkAAADgkLXhsJTkuUnuleTD3f192fvucO+cy1QAAAAALL1Zngr3je7+bFUdVlWHdfdbqup393dAVZ2b5GFJrunuu07rvjvJq5Mcn71XPT26uz+/xrFnJPm1afE3uvu8GWa9yX7ol16+lXfHIeCS//JTix4BAAAANtUsVyx9oaqOTHJxkldW1e8l+coBjnlZkpNXrXtGkjd39x2TvHla/hZTfHpWknsmOSnJs6rqqBlmBQAAAGDOZglLpyb5apKfT/LGJB9L8vD9HdDdFyf53Brn2Xf10XlJHrnGoT+W5KLu/tx0NdNFuXGgAgAAAGCBZnkq3K2TfKq7//8k51XVzZMck+SzM97nMd39qen2P03nWO3YJJ9csXzVtO5GqurMJGcmyXHHHTfjKAAAAACMmuWKpdck+eaK5eundcO6u5P0TTzHOd29q7t37dy586acCgAAAIAZzBKWdnT31/ctTLePGLjPT1fVbZJk+nzNGvtcneT2K5ZvN60DAAAAYEnMEpb2VNUj9i1U1alJPjNwn+cnOWO6fUaS16+xz4VJHlJVR00v2v2QaR0AAAAAS2KW11h6Uva+G9xZSSp7XwNpv++fXlWvSnK/JEdX1VXZ+05vz0/yp1X1+CR/n+TR0767kjypu5/Q3Z+rqucmec90qud09+oXAQcAAABggTYclrr7Y0nuVVVHTstf3sAxp6+z6YFr7Ls7yRNWLJ+b5NyNzgcAAADA1jpgWKqqn+ju/1pVv7BqfZKku39nTrMBAAAAsMQ2csXSLabPt5znIAAAAABsLwcMS9394unzr89/HAAAAAC2i408Fe5F+9ve3U/bvHEAAAAA2C4O28A+l0wfN0tyjyQfmT5OTHLE/EYDAAAAYJlt5Klw5yVJVT05yX26+7pp+ewkb5vveAAAAAAsq41csbTPUUm+c8XykdM6AAAAAA5BG3lXuH2en+S9VfWWJJXkR5M8ex5DAQAAALD8NhyWuvuPq+oNSe45rfpP3f1P8xkLAAAAgGW34afCVVUleVCSu3f365McUVUnzW0yAAAAAJbaLK+x9P8k+eEkp0/L1yb5g02fCAAAAIBtYZbXWLpnd9+jqt6bJN39+ao6Yk5zAQAAALDkZglL36iqw5N0klTVziTfnMtUALBJ7v379170CBxk3vF/vGPRIwAALI1Zngr3oiSvS3LrqvrNJG9P8ltzmQoAAACApTfLu8K9sqouSfLAJJXkkd39wblNBgAAAMBSm+WpcEny6SRvm467eVXdo7sv3fyxAAAAAFh2Gw5LVfXcJI9N8rFMr7M0fX7A5o8FAAAAwLKb5YqlRyc5obu/Pq9hAAAAANg+Znnx7iuS3GpegwAAAACwvcxyxdLzkry3qq5I8s/7Vnb3IzZ9KgAAAACW3ixh6bwkv53k/Um+OZ9xAAAAANguZglLX+3uF81tEgAAAAC2lVnC0tuq6nlJzs+3PhXu0k2fCgAAAIClN0tY+sHp871WrOskD9i8cQAAAADYLjYclrr7/vvbXlVndPd5N30kAAAAALaDwzbxXD+3iecCAAAAYMltZliqTTwXAAAAAEtuM8NSb+K5AAAAAFhyrlgCAAAAYMhmhqV3bOK5AAAAAFhyGw5LVfVdVfXCqto9fbygqr5r3/bufup8RgQAAABgGc1yxdK5Sb6U5NHTx5eS/PE8hgIAAABg+e2YYd8Tuvs/rlj+9aq6bLMHAgAAAGB7mOWKpa9V1X32LVTVvZN8bfNHAgAAAGA7mOWKpScnOW/F6yp9PskZmz8SAAAAANvBLGHpg0n+7yQnJLlVki8meWSS981hLgAAAACW3Cxh6fVJvpDk0iRXz2ccAAAAALaLWcLS7br75LlNAgAAAMC2MsuLd/91Vf3ruU0CAAAAwLYyyxVL90ny2Kr6uyT/nKSSdHffbS6TAQAAALDUZglLD53bFAAAAABsOxsOS9399/McBAAAAIDtZZbXWAIAAACAGwhLAAAAAAwRlgAAAAAYIiwBAAAAMERYAgAAAGCIsAQAAADAEGEJAAAAgCHCEgAAAABDhCUAAAAAhghLAAAAAAwRlgAAAAAYIiwBAAAAMERYAgAAAGDIQsJSVd2pqi5b8fGlqnr6qn3uV1VfXLHPMxcxKwAAAABr27GIO+3uDyU5MUmq6vAkVyd53Rq7vq27H7aVswEAAACwMcvwVLgHJvlYd//9ogcBAAAAYOOWISw9Jsmr1tn2w1V1eVW9oarustYOVXVmVe2uqt179uyZ35QAAAAAfIuFhqWqOiLJI5K8Zo3Nlya5Q3ffPcnvJ/mLtc7R3ed0967u3rVz5875DQsAAADAt1j0FUsPTXJpd3969Ybu/lJ3f3m6fUGSb6uqo7d6QAAAAADWtuiwdHrWeRpcVX1vVdV0+6TsnfWzWzgbAAAAAPuxkHeFS5KqukWSByd54op1T0qS7j47yWlJnlxV1yX5WpLHdHcvYlYAAAAAbmxhYam7v5Lke1atO3vF7bOSnLXVcwEAAACwMYt+KhwAAAAA25SwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABiyY1F3XFWfSHJtkuuTXNfdu1ZtryS/l+SUJF9N8tjuvnSr5wQAWHZv/dH7LnoEDjL3vfitix4BgG1iYWFpcv/u/sw62x6a5I7Txz2T/OH0GQAAAIAlsMxPhTs1yct7r3cmuVVV3WbRQwEAAACw1yLDUid5U1VdUlVnrrH92CSfXLF81bTuW1TVmVW1u6p279mzZ06jAgAAALDaIsPSfbr7Htn7lLenVNWPjpyku8/p7l3dvWvnzp2bOyEAAAAA61pYWOruq6fP1yR5XZKTVu1ydZLbr1i+3bQOAAAAgCWwkLBUVbeoqlvuu53kIUmuWLXb+Ul+qva6V5IvdventnhUAAAAANaxqHeFOybJ66pq3wz/rbvfWFVPSpLuPjvJBUlOSfLRJF9N8rgFzQoAAADAGhYSlrr740nuvsb6s1fc7iRP2cq5AAAAANi4Rb54NwAAAADbmLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGCIsAQAAADBEWAIAAABgiLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGCIsAQAAADBEWAIAAABgiLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGCIsAQAAADBEWAIAAABgiLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGCIsAQAAADBEWAIAAABgiLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGCIsAQAAADBEWAIAAABgiLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGCIsAQAAADBEWAIAAABgiLAEAAAAwBBhCQAAAIAhwhIAAAAAQ4QlAAAAAIYISwAAAAAMEZYAAAAAGLKQsFRVt6+qt1TVB6rqyqr6uTX2uV9VfbGqLps+nrmIWQEAAABY244F3e91SX6xuy+tqlsmuaSqLuruD6za723d/bAFzAcAAADAASzkiqXu/lR3XzrdvjbJB5Mcu4hZAAAAABiz8NdYqqrjk/xgknetsfmHq+ryqnpDVd1lnePPrKrdVbV7z549c5wUAAAAgJUWGpaq6sgkf57k6d39pVWbL01yh+6+e5LfT/IXa52ju8/p7l3dvWvnzp3zHRgAAACAGywsLFXVt2VvVHpld7929fbu/lJ3f3m6fUGSb6uqo7d4TAAAAADWsah3haskL03ywe7+nXX2+d5pv1TVSdk762e3bkoAAAAA9mdR7wp37yQ/meT9VXXZtO5XkhyXJN19dpLTkjy5qq5L8rUkj+nuXsSwAAAAANzYQsJSd789SR1gn7OSnLU1EwEAAAAwq4W/KxwAAAAA25OwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACGCEsAAAAADBGWAAAAABgiLAEAAAAwRFgCAABy7ioAABVrSURBVAAAYIiwBAAAAMAQYQkAAACAIcISAAAAAEOEJQAAAACG7Fj0AAAAAEDymz9x2qJH4CDzq//1z+Z+H65YAgAAAGCIsAQAAADAEGEJAAAAgCHCEgAAAABDhCUAAAAAhghLAAAAAAwRlgAAAAAYIiwBAAAAMERYAgAAAGCIsAQAAADAEGEJAAAAgCHCEgAAAABDhCUAAAAAhghLAAAAAAwRlgAAAAAYIiwBAAAAMERYAgAAAGDIwsJSVZ1cVR+qqo9W1TPW2P7tVfXqafu7qur4rZ8SAAAAgPUsJCxV1eFJ/iDJQ5PcOcnpVXXnVbs9Psnnu/v7k7wwyW9v7ZQAAAAA7M+irlg6KclHu/vj3f31JH+S5NRV+5ya5Lzp9p8leWBV1RbOCAAAAMB+VHdv/Z1WnZbk5O5+wrT8k0nu2d1PXbHPFdM+V03LH5v2+cyqc52Z5Mxp8U5JPrQFXwL/4ugknzngXrC9eZxzKPA451Dgcc6hwOOcQ4HH+da7Q3fvXGvDjq2eZLN19zlJzln0HIeqqtrd3bsWPQfMk8c5hwKPcw4FHuccCjzOORR4nC+XRT0V7uokt1+xfLtp3Zr7VNWOJN+V5LNbMh0AAAAAB7SosPSeJHesqu+rqiOSPCbJ+av2OT/JGdPt05L8v72I5+0BAAAAsKaFPBWuu6+rqqcmuTDJ4UnO7e4rq+o5SXZ39/lJXprkFVX10SSfy974xPLxNEQOBR7nHAo8zjkUeJxzKPA451Dgcb5EFvLi3QAAAABsf4t6KhwAAAAA25ywBAAAAMAQYQkAgG2rqh5ZVV1VP3CA/S6oqltt1VwAcKgQlg5CVfXCqnr6iuULq+olK5ZfUFW/UFX/Y53jX1JVd55u/8r8J77hfp9dVf/nVt0fB1ZV11fVZVV1eVVdWlX/dlp/fFVdMXjO/6+qdm3upOve19FV9Y2qetKq9a+qqvdV1c+vccyTquqntmi+HVW1p6qeP+f7OSh/mNroD5M34fy7qupF8zj3qvt5bFWdNeMxL6uq06bbN/ydvSyq6ner6uqqOmzFupm/zum4+63379UM53hsVX2zqu62Yt0VVXX8AY57elV9x4rlg/LP0kHg9CRvnz6vq7tP6e4vbM1IHEyq6surlm/4+2yLv294TlU9aCvua7q/A36/N+3zv69Y3pJ/O9laK34muKKqXrPy38YZznHDv6FV9bSq+mBVvbKqHlFVzxg435cPsP0TVXX0rOcduS+EpYPVO5LsCwCHJTk6yV1WbP+3SY5Y7+DufkJ3f2Ba3LKwxFL6Wnef2N13T/LLSZ636IFWq6rD97P5UUnemRU/bFTV9yb5N919t+5+4apz7ejus7v75fOZ9kYenOTDSR5VVbXZJ6+9DjuIf5ja0A+TI6bHwu7uftpmn3uzrfo7e0tU1brvKjv9u/Pvk3wyyX23bKgDuyrJr854zNOT3PDN80H8Z2nbqqojk9wnyeMzvYNwVd2mqi5e8UPQj0zrb/gho6r+oqouqaorq+rMFef7clX95vQfKu+sqmMW8GWxjWzl9w3d/czu/qvNPu/+/k7fgOOT3BCWtsu/ncxs388Ed03y9SRPOtABq636N/Rnkzy4u3+8u8/v7rn+J+tW2vf996Ln2GqH3Bd8iPjrJD883b5LkiuSXFtVR1XVtyf5X5NcmuTIqvqzqvrbqRZX8i9XlExXUdx8+sbsldO2n6iqd0/rXry/H+qr6vSqev/0Td1vr1i/32/aquqEqrp0xfIdVy6zMN+Z5POrV07/U/W22ntF0w1XNU3b/tP0GLi8Vl2VU1WHTVdd/MZ6d3iAx9ALqury/MtjfS2nJ/nFJMdW1e2mdW+ali+rqh+ZHu+/W1W7k/xcrbhyrqq+v6r+qv7liq0TqurIqnrztPz+qjp1xa/DB6vqj6YfVN5UVTc/wK/p6Ul+L8k/rPw6ph9+njfNuLuq7lF7rzz8WK24+qqqfqmq3lN7r7769RVzfKiqXp69f/Zvv+qHqZ+a9r+8ql4xrXt4Vb2rqt47fb3HTOufXVXnTr9GH6+qpflGcZ0fJu9XVW+tqtdP8z6/qn58+jvr/VV1wrTfzqr68+nX7j1Vde9p/bOr6hVV9Y4kr6gVV8pMv+9/PJ3nfVX1H6f1fzj9Hl257/dgWv+Jqvr1FY+TDV1VNf2ZeFFV/fX0Ney7Kqmq6qzp9/avktx6xTE3XAW4GfNU1XfX3h+631d7/46+21q/Pvv5Mu6X5Mokf5h1ot9+HnP3nR73l03bbrnquH8zrT9hvXPsx/9IcpequtMa89zo1216vN82yVuq6i3Tuk/U3ishn19VT1lx/Mq/N27055K5OjXJG7v7w0k+W1U/lL0/5F7Y3ScmuXuSy9Y47qe7+4eS7ErytKr6nmn9LZK8c/oPlYuT/MzcvwK2tVV//p9WVR+Y/vz/yYrtr6iqv6mqj/zP9u4/yK6yvuP4+0tCSSLJAkElieBGBdEpCEIqSqQJIkot0+BkyGQQiNXKUIZfA4511BrHqlAlrSBMhVBSEG1EGomiJGkCSBMCAcPugkKlRuRHBkEwQAgRkm//+H6PObm55+69d3ezm83nNXNnzz17fjznnOec5znPr2tmf5fzW85P2PatVadkWtGV6dzYivCNKqVfa81ses6fY2aLzWwFsLyJ46zK710CvD+f2xfa9mlnZT7CovfEg/m5oN4+Zci6C3hbg7S8Ks9UpKH/BrwF+GnGmXILwDea2aKM112leFbJKioTapZpqTLBzCbnPdtjNe8q9dJ5q5P/bvPc7rrcXZ9h+AHWAQcBZxElyl8G/go4lngYTAM2AG8iChjvBqbmuncAR+f0S6VtvgP4EbBnfr8KOKNi/xOJl+XXAyOBFcCM/J8DJ+f0PwOfz+m5wMU5fTtwRE5/FTh3sM/p7vgBthAZ8oczvhyV8zuBB3N6DDAqpw8G7svpk4hCzjH5fb9S/DoG+B7wuQb77i0OndpL2A8EflWKQxfVhr0UnqtK38vx8B7glJwelcc6EhiX8/YHHgUst/taKd5+H/hYg/CNAp4CRgOfAq4o/e83wNk5/S9ANzA2z8XTOf9E4Orc9x7ES/NxGY6twDE12ytaLv4vsH/NNdkXsJz+JHBZ6VysAvbK9X9P3v+D/QFOA67N6VXAUcRz7Q/AhAzzk8CXcpnzgX/N6e+y7Xl3EPDL0vHeD4zO79OAH+f0pcX6xTmrOYcjMi4dXjrn5+b03wPzGxzLHOBbOb0AuCmv6TuBR3P+R4FluZ+JeZwzS3H46H4MzxXAF3P6eOCBeuenwfrXAKcThdFPsi3NKB9nVZz7EXBsTu9N3G/TiPj9vtz/QY220egcA2cA/5HzHgQ6mzhv+9e5l44E7izN/wXxzKl7Xw72/TKcP3mOP5jT5wHfIJ6Fj2acPaL2+pXic1d+NpDPTGBzKV7NanSv6LP7fNiWHyo+vy09z+ayLd/wFLBXTu9T+n8Xkd7vT7TmnEgb+QkijZhJ9Dz4NdECG+J5O7Ii7BcB/57Th2bYR+Vz8Yni+Vexbie95/emkWll7Xcq8hFEmt1DFOTuTVRGHDnY11mfhvfAS/l3JHALcDbVaXlVnqn8DC5PzyndTwuBC3J6BNDRRJguIt8pcp2xdfZRpPOjifR/fH6vei9dTL7nAueU9tV0/nt3+6jF0vC1isiEv48oNLq79H1lLnOvuz/h7luJRLKzl21+gEgI1pjZA/n9LRXLTgHucPdn3P014EbipoNoPlmMl3F/xX7nAx+3aBE1i3gRlJ2vaPZ6KPBh4HqzHbps7QlcY2Y9xAtxMdbLCcB17v4ygLs/V1rn20RG5SsN9t0oDm0Bbu4l7LOIzBjAf9K4u9TC2hlZ8zfJ3Rdl+F/JYzHgq2bWDfw3MAkoWkqsc/eiZrwqbhf+Grjd3Tflscyw7VsALs6/PcA97v6iuz8DbLbon35iftYSLRAPJTJ6AI+5++o6+zweuMndn81jKq7Jm4AleQ0/zfZdZ2919825zu9KxzrYZhPXFba/vmvcfb27bwb+j2ihBnEeO3P6BOBb+RxbDIyzaAEFsDivSa0TgCuLL+5etN471aJF5VrivJXHOvqv/NtbXKj1Q3ff6tG9rTjfxwHfc/ct7v4UUdBaT3+EZyrZIsndVwDjzWxc/q/q/ABgZn9GVGL80N1fIApnP1Rn0ao4txKYl7Xa++S9D1GxcTWR+fttL9to5LvAMWY2uWZ+o/O2A3dfC7zBzCaa2buA5939cRrfl9LPzGw/4rk238x+Q8SDU4kKtOOIgs0FVjP+jZlNI+7p93q0TFpLvGgDvOr59kCkNX3pIiTDR5EfOsKjJdw/VizXDdxoZh8jCocKt7j7pkxLbwf+gr7lJ94OrHf3NQDu/kLpeVlrKvCdXO5h4DHgkPzfspr8WSNV+b3e1MtHTAUWuftGd3+JSJ92aGUiQ8rozDfdRxROXkt1OlyVZ2rG8USLZzLPs6GJddYQ741zgcPc/cU6y5xn0dNhNVERVKTNVe+lxxKV4LB9K+128t+7BSWWw1cxztJhRKns40Rp7gvAdbnM5tLyzWSejKjp/Wwfw9ZMpu1m4IvEy9P97v77Pu5T+sjd77boTvX6mn9dCDxNdDfYA3ilic2tAqab2WXu3szytV5x9y29LDMbOMDMTsvvE83sYODVOstubGHfpxHn4Ch3fzVfZooXktp7qlFXuNnA1FwfYDyRmC6r2dbWmu1uJe4ZA77m7t8ub9RiMOJWjgeihco8d1+cL1xzS/9r9Tkx4Eovk4eZmRO1Uw7cyo7nqnwei7DvQdQobRf3ssy06XOXhRMXEzXGz5vZArbFBUr7bvW8lY+h6bG3BjA8Zb2dnw8B+wA9eT7HAJvYlmkr1I1z7n6Jmd1KFE6tNLOiUGo9cSxHEi0CKrfRiLu/ZmaXAZ8p5jVx3qrcRLQcOIBthdN170sZMDOBG9z9rGKGmd1JFCr9j7tfYzEEwLuB8hg4HURh4MsW3UKP2ZmBlmHtI0T8Oxn4nJkdlvO9Zjmn//ITfdFKfqGd/B4MwXyEtGVTFqr+iZm1nA4PBHf/mZkdR9x/C8xsnpfGPaupTHjZzO6gucqE2vsW+jf/PayoxdLwtYpoEfFclvY+R2T235v/a9arZrZnTi8HZprZG+BP43C8uWK9e4G/zH60I4iX6Dub3Wm+8C0hSqyv62Vx2Qky8z2CaMZc1kHUmm0lur8UrW6WEbUHY3L9/UrrXAv8BPi+VQ8Y2XYcMrNDgL3dfZK7d7p7JzHweNODPGdtxxNmNiO3uVceSwfwu8wETgeq7oFG4RtH1MwdVArfOa2Ej7g//rZoaWNmk4p7s4EVxEDh43Od4pp0EDX7AGe2EIbBUrxMvjnP34FE999mazuXAucWX8zsiAbLFpYR16hYZ1+i68FGYEP2yT+pyf2342fALDMbYWYTgOl1lumv8NxFvPAUmbFns/VRM2YDnyzF68nAB23HX4+pG+fM7K3u3uPulxI1kMVYUH8gMoxfyzBVbqMJC4gMZlFI3ui8vUh0Q61nITG+10yikAnauy+lfbOBRTXzbiaucZeZrSVar36zZpnbgJFm9ktifJjdtoZZ+o/FYL0HuvvtROF1B9HNC+BvLMY6Gk90FVtD3/ITjwATzGxK7ntsg/xU+Zl+CNEF/JGWDi5U5fcaPSer3EW01B5jZq8jfvDhrjbCJIOrKh2ul2dq1nKimx2Z5+nobYV8H33a3a8her28u044W61MWEmO4UneP0npfAUVLA1fPURf5tU18zYU3WCadDXQbWY3ZreMzwNLs9nuMmIskx24+3rgH4jmvl1Eq6NbWjyGG4lWBkt7W1AGTDF4+wPES9SZdVoKXQWcmc1LDyVL6939NqKb0X25/sXlldx9HtGM9Aar88sJfYxDVS8brf562OlE09luokD2ACJeHp3Nfs8gxp9q1SnAiuyuVbgFODlr13vl7kuJbj13Z1h+QC8ZO3d/CPgKcGder3n5r7nATWZ2P9DK82Gw9PX6nkdcw24z+wXN/bLJPwH7WgwK2QVMd/cuIg4/TFyLlY020EeLgF8RY/lcT3Rv3k4/hmcucFTG+0tostAmC48+TLQcK8K0kfjlvpPr7KNenLsgz3E30brwp6VtPU1UmFxpZu9psI2G3P2PwOXkAOi9nLergdssB++u2c5DxD33ZD6v2rovpX3uPj3TmvK8y919srv/ubsf6e7vd/d1+b9Od382u+Wc5O7vcPcZ7j7N3e/IZfYubesH7j5nZx6T7NJGAN/Je38tcLlv+wWsbiI/sxr4skeX5rbzE/kcmwVckWnSMqpbWl4F7JH7WQjMqcl/NKtufo84ti0Wgx9f2GT4f04UAN9LdJmen12MZdcyl/rp8A55pha2eT7Rq6GH6JrWTJfLafR/ZcL5wDkZjknFTKXz1YrBtkSGHItf2Ohw9y8MdlhERERERFplMe7LS+7+jcEOi4jIQFEfVxmSzGwR8FZiHBURERERERERGYLUYkn6zMzuIX5GtOx0d+8ZjPDIrqcvcSgLIWt/4ekz7r6kv8LXF2Z2JfHLEmXfdHeNHbabMbOPE02ry1a6+zn1lh/K4clBtS+tmb3O3U/pr/C1Y6idYxGRnaUvz+UcZPyGmtmb3f09/RU+kXbkuGTL6/zrA/pxp6FFBUsiIiIiIiIiItIWDd4tIiIiIiIiIiJtUcGSiIiIiIiIiIi0RQVLIiIiIiIiIiLSFhUsiYiIiAwQC8pviYiIyLCljI6IiIhIPzKzTjN7xMyuBx4ErjWz+8zsITP7Umm5KWa2ysy6zOxeMxtrZiPM7OtmtsbMus3srME7EhEREZHejRzsAIiIiIgMQwcDZ7r7ajPbz92fM7MRwHIzOxx4GFgIzHL3NWY2DtgEfALY4O5TzGwvYKWZLXX3dYN2JCIiIiINqGBJREREpP895u6rc/pUM/sUke+aALwTcGC9u68BcPcXAMzsROBwM5uZ63YQhVQqWBIREZEhSQVLIiIiIv1vI4CZTQYuBqa4+/NmtgAY1WA9A8519yUDH0QRERGRvtMYSyIiIiIDZxxRyLTBzN4InJTzHwEmmNkUgBxfaSSwBDjbzPbM+YeY2esGIdwiIiIiTVGLJREREZEB4u5dZraWGFPpcWBlzv+jmc0CrjCz0cT4SicA84FO4OdmZsAzwIzBCLuIiIhIM8zdBzsMIiIiIiIiIiKyC1JXOBERERERERERaYsKlkREREREREREpC0qWBIRERERERERkbaoYElERERERERERNqigiUREREREREREWmLCpZERERERERERKQtKlgSEREREREREZG2/D80aE5CCAecWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCq56Wtdlr4T"
      },
      "source": [
        "This graph shows the percentage of people that do not have medicare and catagorize them by race."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY5FQxbRcwIl",
        "outputId": "c0cee55e-c658-4af0-9e5a-af880c00c308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "fig_dims=(20,10)\n",
        "fig, output2=plt.subplots(figsize=fig_dims)\n",
        "sns.barplot(x=\"race\", y=\"death_out_race_population/(10^(-8))\", ax=output2, data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f15b016fd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBIWdWd2icFr"
      },
      "source": [
        "This graph shows the number of people of each race who died from COVID if they all had the same population.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p76QnMytdxmh",
        "outputId": "77f1a5af-265e-4965-e287-bfa9c2b42f3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "fig_dims=(20,10)\n",
        "fig, output2=plt.subplots(figsize=fig_dims)\n",
        "sns.barplot(x=\"race\", y=\"death_perc\", ax=output2, data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f15b0057438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klYqscCSj-wa"
      },
      "source": [
        "The graphs above shows the percentage of COVID deaths differenciated by race in America. As one can see, the highest number of deaths are White people, who also have a majority population in America. \n",
        "\n",
        "Looking at all three graphs, we noticed that the lack of medicare does increase the death percentage of some races. In the case of American Indians or Pacific Islanders, because they are such a small portion of the population, when calculating how much people would die if all ethinic groups have the same number of population, the number is really high. However, Black or Hispanic do have some population about 20% in America, yet they suffer the most death rates, which corresponds to their lack of medicare in the first graph."
      ]
    }
  ]
}