Non_vs_Waiver_grad_rates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Corp Id</th>\n",
       "      <th>CORPORATION NAME</th>\n",
       "      <th>Cohort N</th>\n",
       "      <th>Grad N</th>\n",
       "      <th>State Grad Rate</th>\n",
       "      <th>Non-Waiver Grad N</th>\n",
       "      <th>State Non-Waiver Grad Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>Adams Central Community Schools</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "      <td>94.70%</td>\n",
       "      <td>87</td>\n",
       "      <td>91.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5265</td>\n",
       "      <td>Alexandria Community School Corporation</td>\n",
       "      <td>112</td>\n",
       "      <td>98</td>\n",
       "      <td>87.50%</td>\n",
       "      <td>97</td>\n",
       "      <td>86.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5275</td>\n",
       "      <td>Anderson Community School Corporation</td>\n",
       "      <td>394</td>\n",
       "      <td>353</td>\n",
       "      <td>89.60%</td>\n",
       "      <td>277</td>\n",
       "      <td>70.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5470</td>\n",
       "      <td>Argos Community Schools</td>\n",
       "      <td>46</td>\n",
       "      <td>34</td>\n",
       "      <td>73.90%</td>\n",
       "      <td>34</td>\n",
       "      <td>73.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2435</td>\n",
       "      <td>Attica Consolidated School Corporation</td>\n",
       "      <td>68</td>\n",
       "      <td>67</td>\n",
       "      <td>98.50%</td>\n",
       "      <td>64</td>\n",
       "      <td>94.10%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Corp Id                         CORPORATION NAME  Cohort N  Grad N  \\\n",
       "0       15          Adams Central Community Schools        95      90   \n",
       "1     5265  Alexandria Community School Corporation       112      98   \n",
       "2     5275    Anderson Community School Corporation       394     353   \n",
       "3     5470                  Argos Community Schools        46      34   \n",
       "4     2435   Attica Consolidated School Corporation        68      67   \n",
       "\n",
       "  State Grad Rate  Non-Waiver Grad N State Non-Waiver Grad Rate  \n",
       "0          94.70%                 87                     91.60%  \n",
       "1          87.50%                 97                     86.60%  \n",
       "2          89.60%                277                     70.30%  \n",
       "3          73.90%                 34                     73.90%  \n",
       "4          98.50%                 64                     94.10%  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad_rate_15_df = pd.read_csv(\"Resources/2015_grad_rates_revised.csv\")\n",
    "grad_rate_16_df = pd.read_csv(\"Resources/2016_grad_rates_revised.csv\")\n",
    "grad_rate_17_df = pd.read_csv(\"Resources/2017_grad_rates_revised.csv\")\n",
    "grad_rate_18_df = pd.read_csv(\"Resources/2018_grad_rates_revised.csv\")\n",
    "grad_rate_19_df = pd.read_csv(\"Resources/2019_grad_rates_revised.csv\")\n",
    "grad_rate_15_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient between poverty rate and graduation rate is0.84\n"
     ]
    }
   ],
   "source": [
    "# 2015 poverty rate vs 2015 graduation rate\n",
    "waiver_grad_15 = grad_rate_15_df[\"State Grad Rate\"].str.replace('%','').astype(np.float64)\n",
    "non_grad_15 = grad_rate_15_df[\"State Non-Waiver Grad Rate\"].str.replace('%','').astype(np.float64)\n",
    "# line regression for 2015 pov rate and grad rate.\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(waiver_grad_15,non_grad_15)\n",
    "regress_values = waiver_grad_15 * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "# Plot poverty rate vs. graduation rate.\n",
    "plt.scatter(waiver_grad_15,non_grad_15)\n",
    "plt.plot(waiver_grad_15,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(60,10),fontsize=15,color=\"red\")\n",
    "plt.show()\n",
    "print(f\"The correlation coefficient between poverty rate and graduation rate is{round(st.pearsonr(waiver_grad_15,non_grad_15)[0],2)}\")\n",
    "      "
   ]
  },
  {
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