Deaths_Per_Cap_Scatter.ipynb

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    "def DeathsPerCapScatter():\n",
    "    #import libraries \n",
    "    import matplotlib.pyplot as plt\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    import requests\n",
    "    from scipy.stats import linregress\n",
    "    import scipy.stats as st\n",
    "    \n",
    "    #grabbing merged csv file \"AllByState-Daily.csv\" from team resources folder\n",
    "    allbystate_path = \"../George/Resources/AllByState-Daily.csv\"\n",
    "    allbystate_df = pd.read_csv(allbystate_path)\n",
    "\n",
    "    #grabbing the latest date's data for plots & calculations\n",
    "    date = pd.to_datetime(allbystate_df['Date']).max().date()\n",
    "    date_snapshot = allbystate_df.loc[allbystate_df[\"Date\"]==str(date),:]\n",
    "    \n",
    "    #cleaning extra columns for plot data\n",
    "    snapshot_df = date_snapshot[[\"Date\", \"Deaths\", \"State\",\"Median Household Income\", \"Per Capita Income\"]]\n",
    "    snapshot_df\n",
    "    \n",
    "    #setting x & y values for scatterplot\n",
    "    deaths = snapshot_df[\"Deaths\"]\n",
    "    per_cap = snapshot_df[\"Per Capita Income\"]\n",
    "    \n",
    "    #calculating slope & intercept for linear regression calculation\n",
    "    (slope, intercept, rvalue, pvalue, stderr) = linregress(per_cap, deaths)\n",
    "    regress_values = per_cap * slope + intercept\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    \n",
    "    #plotting scatter plot for Deaths & Median Income\n",
    "    plt.scatter(per_cap, deaths)\n",
    "    plt.plot(per_cap, regress_values, 'r-')\n",
    "    plt.annotate(line_eq, (30000,10000), fontsize=10, color='red')\n",
    "    plt.xlabel(\"Per Capita Income ($)\")\n",
    "    plt.ylabel(f\"Covid-19 Deaths\")\n",
    "    plt.title(f\"Deaths as of {date} & Per Capita Income by State\")\n",
    "    plt.grid(True)\n",
    "    correlation = st.pearsonr(per_cap, deaths)\n",
    "    corr = (f\"r^2 = {round(correlation[0],2)}\")\n",
    "    plt.annotate(corr, (30000, 12000), fontsize=10, color='green')\n",
    "    plt.savefig(\"Covid_Deaths_And_Per_Cap_Income_Plot.png\")\n",
    "    plt.show()\n",
    "    \n",
    "    return"
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