Mission217Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "import re\n",
    "\n",
    "data_files = [\n",
    "    \"ap_2010.csv\",\n",
    "    \"class_size.csv\",\n",
    "    \"demographics.csv\",\n",
    "    \"graduation.csv\",\n",
    "    \"hs_directory.csv\",\n",
    "    \"sat_results.csv\"\n",
    "]\n",
    "\n",
    "data = {}\n",
    "\n",
    "for f in data_files:\n",
    "    d = pd.read_csv(\"schools/{0}\".format(f))\n",
    "    data[f.replace(\".csv\", \"\")] = d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the surveys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_survey = pd.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "d75_survey = pd.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "survey = pd.concat([all_survey, d75_survey], axis=0)\n",
    "\n",
    "survey[\"DBN\"] = survey[\"dbn\"]\n",
    "\n",
    "survey_fields = [\n",
    "    \"DBN\", \n",
    "    \"rr_s\", \n",
    "    \"rr_t\", \n",
    "    \"rr_p\", \n",
    "    \"N_s\", \n",
    "    \"N_t\", \n",
    "    \"N_p\", \n",
    "    \"saf_p_11\", \n",
    "    \"com_p_11\", \n",
    "    \"eng_p_11\", \n",
    "    \"aca_p_11\", \n",
    "    \"saf_t_11\", \n",
    "    \"com_t_11\", \n",
    "    \"eng_t_11\", \n",
    "    \"aca_t_11\", \n",
    "    \"saf_s_11\", \n",
    "    \"com_s_11\", \n",
    "    \"eng_s_11\", \n",
    "    \"aca_s_11\", \n",
    "    \"saf_tot_11\", \n",
    "    \"com_tot_11\", \n",
    "    \"eng_tot_11\", \n",
    "    \"aca_tot_11\",\n",
    "]\n",
    "survey = survey[survey_fields]\n",
    "data[\"survey\"] = survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add DBN columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"hs_directory\"][\"DBN\"] = data[\"hs_directory\"][\"dbn\"]\n",
    "\n",
    "def pad_csd(num):\n",
    "    string_representation = str(num)\n",
    "    if len(string_representation) > 1:\n",
    "        return string_representation\n",
    "    else:\n",
    "        return \"0\" + string_representation\n",
    "    \n",
    "data[\"class_size\"][\"padded_csd\"] = data[\"class_size\"][\"CSD\"].apply(pad_csd)\n",
    "data[\"class_size\"][\"DBN\"] = data[\"class_size\"][\"padded_csd\"] + data[\"class_size\"][\"SCHOOL CODE\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert columns to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']\n",
    "for c in cols:\n",
    "    data[\"sat_results\"][c] = pd.to_numeric(data[\"sat_results\"][c], errors=\"coerce\")\n",
    "\n",
    "data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n",
    "\n",
    "def find_lat(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n",
    "    return lat\n",
    "\n",
    "def find_lon(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lon = coords[0].split(\",\")[1].replace(\")\", \"\").strip()\n",
    "    return lon\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lat)\n",
    "data[\"hs_directory\"][\"lon\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lon)\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = pd.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n",
    "data[\"hs_directory\"][\"lon\"] = pd.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Condense datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_size = data[\"class_size\"]\n",
    "class_size = class_size[class_size[\"GRADE \"] == \"09-12\"]\n",
    "class_size = class_size[class_size[\"PROGRAM TYPE\"] == \"GEN ED\"]\n",
    "\n",
    "class_size = class_size.groupby(\"DBN\").agg(numpy.mean)\n",
    "class_size.reset_index(inplace=True)\n",
    "data[\"class_size\"] = class_size\n",
    "\n",
    "data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n",
    "\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert AP scores to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n",
    "\n",
    "for col in cols:\n",
    "    data[\"ap_2010\"][col] = pd.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = data[\"sat_results\"]\n",
    "\n",
    "combined = combined.merge(data[\"ap_2010\"], on=\"DBN\", how=\"left\")\n",
    "combined = combined.merge(data[\"graduation\"], on=\"DBN\", how=\"left\")\n",
    "\n",
    "to_merge = [\"class_size\", \"demographics\", \"survey\", \"hs_directory\"]\n",
    "\n",
    "for m in to_merge:\n",
    "    combined = combined.merge(data[m], on=\"DBN\", how=\"inner\")\n",
    "\n",
    "combined = combined.fillna(combined.mean())\n",
    "combined = combined.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a school district column for mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_first_two_chars(dbn):\n",
    "    return dbn[0:2]\n",
    "\n",
    "combined[\"school_dist\"] = combined[\"DBN\"].apply(get_first_two_chars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAT Critical Reading Avg. Score         0.986820\n",
      "SAT Math Avg. Score                     0.972643\n",
      "SAT Writing Avg. Score                  0.987771\n",
      "sat_score                               1.000000\n",
      "AP Test Takers                          0.523140\n",
      "Total Exams Taken                       0.514333\n",
      "Number of Exams with scores 3 4 or 5    0.463245\n",
      "Total Cohort                            0.325144\n",
      "CSD                                     0.042948\n",
      "NUMBER OF STUDENTS / SEATS FILLED       0.394626\n",
      "NUMBER OF SECTIONS                      0.362673\n",
      "AVERAGE CLASS SIZE                      0.381014\n",
      "SIZE OF SMALLEST CLASS                  0.249949\n",
      "SIZE OF LARGEST CLASS                   0.314434\n",
      "SCHOOLWIDE PUPIL-TEACHER RATIO               NaN\n",
      "schoolyear                                   NaN\n",
      "fl_percent                                   NaN\n",
      "frl_percent                            -0.722225\n",
      "total_enrollment                        0.367857\n",
      "ell_num                                -0.153778\n",
      "ell_percent                            -0.398750\n",
      "sped_num                                0.034933\n",
      "sped_percent                           -0.448170\n",
      "asian_num                               0.475445\n",
      "asian_per                               0.570730\n",
      "black_num                               0.027979\n",
      "black_per                              -0.284139\n",
      "hispanic_num                            0.025744\n",
      "hispanic_per                           -0.396985\n",
      "white_num                               0.449559\n",
      "                                          ...   \n",
      "rr_p                                    0.047925\n",
      "N_s                                     0.423463\n",
      "N_t                                     0.291463\n",
      "N_p                                     0.421530\n",
      "saf_p_11                                0.122913\n",
      "com_p_11                               -0.115073\n",
      "eng_p_11                                0.020254\n",
      "aca_p_11                                0.035155\n",
      "saf_t_11                                0.313810\n",
      "com_t_11                                0.082419\n",
      "eng_t_11                                0.036906\n",
      "aca_t_11                                0.132348\n",
      "saf_s_11                                0.337639\n",
      "com_s_11                                0.187370\n",
      "eng_s_11                                0.213822\n",
      "aca_s_11                                0.339435\n",
      "saf_tot_11                              0.318753\n",
      "com_tot_11                              0.077310\n",
      "eng_tot_11                              0.100102\n",
      "aca_tot_11                              0.190966\n",
      "grade_span_max                               NaN\n",
      "expgrade_span_max                            NaN\n",
      "zip                                    -0.063977\n",
      "total_students                          0.407827\n",
      "number_programs                         0.117012\n",
      "priority08                                   NaN\n",
      "priority09                                   NaN\n",
      "priority10                                   NaN\n",
      "lat                                    -0.121029\n",
      "lon                                    -0.132222\n",
      "Name: sat_score, Length: 67, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "correlations = combined.corr()\n",
    "correlations = correlations[\"sat_score\"]\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting survey correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n",
    "survey_fields.remove(\"DBN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f1194a8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fZ3x8fBiLNTPrhMnJSSYnJxvNq4jB87KktcB4RExJGgVWR8STS+ZdAVwYEXvVLDNy1sna\nIU0f+0vnwPvVbG5IIiLUb1ru5ZoLgFcX/78KOL9qPYrBzMzmSW6SPwU4UNLNwAHA3wFIeqyki6Zn\nknQ2cCXwREm3STo6s1wzM2sg63LNXPDlmsXJl2vM2jOXl2vMABgZWcHGK3KbD2m6mc03n8mbmS1y\nPpM3M9tCOcmbmXWYk7yZWYc5yZuZdZiTvJlZhznJm5l1mJO8mVmHOcmbmXWYk7yZWYc5yZuZdZiT\nvJlZhznJm5l1mJO8mVmHOcmbmXWYk7yZWYc5yZuZdZiTvJlZhznJm5l1mJO8mVmHOcmbmXVYVpKX\ntJOkSyXdLOlzknbsM88ySV+Q9A1JN0l6Q06ZZmbWXO6Z/FuAz0fE7sAXgBP7zPMr4I0RsSfwLODP\nJT1pkMImJycHXc+s2DbLXoyxbZbt97w4Ytsse0t7z7lJ/hDgrOL/s4BDZ84QEXdGxA3F/z8D1gK7\nDFLYlrZzFmtsm2X7PS+O2DbL3tLec26S3zkipiAlc2Dnqpkl7QrsDXwls1wzM2tg67oZJF0GjPSO\nAgI4qc/sUbGcRwCfAo4vzujNzGyOKaI0L9cHS2uB8YiYkjQKrI6IJ/eZb2vgIuDiiPhgzTIHXyEz\nsy1URKjf+Noz+RoXAK8GTgFeBZxfMt/HgG/WJXgoX1EzM5u93DP5pcB/AsuB9cDhEXG3pMcCH42I\nP5D0HOBLwE2kyzkBvDUiLsleezMzq5SV5M3MbGFzi1czsw5zkjcz6zAn+XlQdP+w1yxjdmsyriJ+\nG0l7SXqqpG1mU3afZT0iJ94WPu/j7lrQSV7SeyXtIOkhki6X9ENJRzWMvbzJuJLYx0m6UNKPJP1A\n0vmSHjfLdZ8s1n0pcB3wUUkfmMUiPt1n3Kcalv1C4L+BDwGnAt+S9PxZlD3TNwcNHLQLiyL2pozY\nizNizxg0dghlvz0j9uhBY2lpHxfxbe3nnG2d+xkZeF9JOnA28+dWoZxrvx8RfyXpxcB3gcNINXX+\ntSxA0sOAbYFHS9qJ1HgLYAead6dwNnAa8OLi9SuAfweeOYt13zEi7pH0GuDjEXGypBvrgoovzJ7A\njpIO65m0A/CwhmW/H1gZEd8qlvl44L+A0i+EpDeWTQJyzvIuBcYqyj2sbBIwWrVgSftUxO5dE7u0\nIvYFVbG5Zdd4DfDOAWPfAZxZNrGtfVyU3cp+rlG5rXM/IzUq91WNVdRs714LPclPr98LgU9GxE+l\n2mr0xwJ/AfwGcC0bk/w9pLPaJraNiE/0vP5XSW9uGDtt66Iq6eHAX88ibnfgD4BHAn/YM/5e4LUN\nl3HvdIIvfLuIr/K3wN+TOpSbqfIXn6QPlU0ivY8q/wH8G/1bS9cd1NYAX2TjPu5VV+4PSdV+e2Oj\neF3ZPUdu2ZLuKZsEPLwmtuxEQWzaMr2ftvYxtLSfc7Y1mZ+RnH0l6YKK2EfVld1roSf5iyStA34O\nvE7SY4BfVAUUDa4+KOn1EfHhsvkkHRgRl5VMvljSW4BzSDv15cBnp4/sEfGTBuv+TuBzwBURsaa4\n3HNrXVBEnA+cL+lZEXFVxfqfGBHvKZn8VUmfJbVhCOBlwJrps6mIOLdPzHXAeRFxbZ+yXlOz2kcD\nbwJ+2WfaETWxNwLvi4iv9yn392pi1wLHRsRm21XShprYbwMHRMRtA8Tmln038Izpfp9mGTsCHATc\nNTMUuLImtq19DO3t55xtnfsZydlXzwOOAmZ2ASNg3wZlbxQRC3oAlgJbFf9vB4z2TDswY7nXVUz7\nTsXw7SG9rxMz46vW/8yK4WMlMbsDjy6ZNlKzLl8Anl22LWtinweMlUx7ek3sS4HdS6YdWhP758DT\nSqa9vsH2zyn73cC+JdNOqYldBTy3ZNrZNbGt7OOW93POts79jOTsq4tJl1z7TftSXdm9w6JuDCXp\nuogou15XF3t9RPzWgLFVvwKaLmPgdS/ic9a/6lfAIMtbCvwiIu4b1jJtYfE+XrwWdO2aBnL6uck5\nup2SETstt4+enPV/2WxmrqtJEBE/mYsvf1s1TWZbe2HIZefURhr45mlb+7gou639nLOtcz8j81Zl\ndbEn+bZ+hgyjE7Xcdc9Zh81iJS0tGR5FRk2CnCpupNoPg3pHRuyqjNjcsi/NiK2sBrlA9zG0t59z\ntnXuZySnyuqsqpwu9BuvA5G0BNgvIqpubnw3o4hhHFxyDxSfzIjtt/4D1yTIrMrYSk2T3NoLmWUP\nXFMlsxpkK/u4iG9rP+ds69zPyMD7KqfK6UwLNsnnJOqI+LWk04DSa9YRUbYR50vfJF3z0zUi4l3F\nP3+bUXa/A0xOTYKcqoxt1TTJrb2QU3ZOTZWBq0HS3j6G9vZzzrbO/Yzk7KucKqebWLBJfgiJ+nJJ\nLwHOjVncXc79FTCEJP3/+ozblvST9lHAuyqW31S/A8w/AjsBmyUA4L01y8up4vZxYAWw2Zef1Cit\nykXAI6J4hvCMcidrYq8G7ouIL/aJvbkmNrfsNcDX+33GJE3UxOZUg2xrH0N7+zlnW+d+RnL2VU6V\n003nX8i1ayS9D7iK2SdqAQ8UL39FqlsvUpLdoUF8Ts2VN/UZ/WCSjojGN1wkbQ8cD/wJqc77+yPi\nBxXzNzrA5OhXs0jSS4GbImKzD76kQyPivCGUu2dEfGPA2J0iYuZZ4LzoV3ZOTRVJuwM/jogf9Zk2\n0u9MeYAyWtnHxbKGup/brBWUs68kPQ9YX/Kr6+kR8dXGKzKb+pbzOZCS8q+L4X9ILVbvBe5pGP/1\njLLfB7yE4iCYsZztSc/C/Q6pRs7ODeOWkur3fgeYAHZqGPemPsPbSNdhfzak/VJaP79B7KtaKjcn\n9qoWt9enM2I/vNj2ccv7OWdb535GcvZVbXubBVu7JtI7+GZELImIbSJih4jYPhqciReulfSM2ZZb\n/Ap4I+mSxi8l3SPp3oobR/2WsVTSu0k/ubYG9omIE6LiLLwn9u9JPzHvBZ4aERPR8Cw0It4/PQBn\nkG5oHUNquTurDtaqVjEj9viWys2JndX1zyGXnbPPnpMR29Y+zi27rW2d+xnJ2Ve11aEX7DX5wrWS\nnhERawaIfSZwpKT1pOvc05drKrv8jYiQ9M2IeMoAZU4n6cNISfapETHzpk2d6ZtEJwF/rY199TS6\n3FT8PH0jcCRwFukAM8xLFTnX99pq19BWbNtlt1Fum+0/FmNsrtrtvdCT/ECJunBQRrk5B5esJB0R\nA/+6GsIBZq4t3BtANizex/Ordnsv9CQ/cKKOiPUZ5Q58cMlJ0kOQdYBp6LsZsTlnef/TUrm5Z6Zt\nlZ0T+92WyoXFuZ8X9mck54ZBVwdSVa/NhrbXa57e+8NIl3vOJT245C+Bhw1p2afWTD8M+ACpP/wX\nz3LZ+wBvAF5PukTVO21pRdxmnVT1jgOeUlPuJ6rG1ZR9fNU40vMUymJfVjUOeHVVLLB98f9Jxb7e\np2z+Ie/j5wDbFf8fVezvFQ2X/XjgocX/48X+fuQ8bOvcz0jOvnpO1TjgrbXbbRg71kN3BlJVzVXA\nymL4KKkv/yaxjwI+TKoffC3wQVK10Sax/0RqZn50MVwCnNYw9u3ATaQm7u8Avgac1DB2sxoZwI2z\n2F7XzXi9FanCwKBlX58R26h2yfT7A54LTJKe1/CVhrHHkx5go+Jzcl1VguxXdhH7NOB6Uk+PX2wY\newPp6sMTgFtIDY0+29K2HvgzMst9NXDs9LDQL9fY/HtKROzR83q1pKb9bJxDenLXS4rXR5Ja7jVp\nvPG7wJOj+BRLOgtoWl/6SFKXsL8oYv+OlBDeXRYg6XXA/wIeN6PZ/PbA/60rUNKJwFuBh/fUvBLp\nckNlZ1+SjgD+CNhtRtP57YHKZxUoPcbxBcAuM5rs70D/lpX9TLcheSFwRkT8V1EbrIljIuKDkg4i\nNax6JfAJmvcD86uICEmHkM76V0n6k4axv46IXyk9Ke7DEfFhSddXBWRu69zPyMD7StKzgGcDj5nR\nPcIOpBOJxpzkbabrJO0XEVcDSHom0LThxWNj0wZX75b08oax3yI90mz6XsryYlwT3yNdZpp+oMxD\ngTtqYs4m9dn9HuAtPePvjZ6HwpQ1pIrUVfN7JL0nIk4sK6Skcc+VwPeBR5MuTT1YNulMt8r3SPvj\nRaRfS72xf1kTO+0OSacDBwKnSHoozTsrnL4G/ALSZalvSPWPa+tdz+IAeRTwO0UL84c0jL2/SNqv\nYuNT0+pic7Z1o89IhZx9tQ2pf5utSQeVafeQ+tdvbEG3eLX5J2kt6eES0y3txoCbSWceERU3n5Ue\nVH4N6ZIPpA/jvhHxvxuU+0XgGUV8kPoG+SrwU1LBL6qIPa+IvayIPbBYzu1F7Bvqyq9Ydm6//znP\nPLgqIp5VMu0hEXF/ReynI+IlJdO2BQ4mtWC9VekxlU+NiEuL6aUthCWdSXpW8m6kSy5bAZMR8dsN\n39Mo6cx6TUR8WdIYMB4RH28QuwfwZ6TGR/8uaTfg8Iho1PW3pBHS5wTgmmjQbqUn9mmkvmwAvhwR\nX5tF7ENIB8cnFqNurtp3M2JXRMR6FV0TxwA15pzkbROSVtTMck9FAriX9PSu6csBW7GxL56Iito9\nkvavKjT69B/SE/uqmtizqqZXyeniIje+xdjSA1Nx5r036Qlpdyt1U7xLRNxYTB+4W4IivvTA1iC2\n6sD2MlJL9klSwn0e8OaI+FSD5b4B+FPSDWqAF5Muc5U+XnRG/P6kvnu+W5S9nNQy+EsNYp9Cuhw2\n/VDxHxWxm/VpU2o2F/A9eCCv6fieGbEDNx0nr8n6wO93CNurrdhGNyTnaHvllF0aS7oZv3PP68cA\nX2u43BspagQVr7djdjder6Xn8YWkM/prG8ZeSc9jAEm1iq6czXZZsN0a2IKVU6f3ExmxOU3Hh9Wl\nw5aiiy1el8Sml2d+zOzuQzzQ8/oBZvc+HxI9HbtFxC00vw+xXUSs7omdJB1kGvONV5utTnRrIGm3\niPhOg9gtsXFPjoV6/fcSSZ8D/r14/XLSTdUmzgS+IukzxetDgY/NouyvSvoX4F+L10fSvDLDtyW9\njY0nSEeRngvQmJO8zaeFlAA+Bfy2pMsj4oCK+aqmASBpL2BXer5PEXFu8Xe/jHV8ZUbsCTNHzOOB\nLcecHNgi4s1KT1t6bjHqjIj4TNn8M2I/oNRv/XTs0RFRWXVzhteR2gNMVwD4MnBaw9hjSG0/ziV9\nf75MakfSmJO8AYsiAQz7y79E0luBJ6rPY9oi4gPF37q61B8D9iLV6f/1dDgbb9JVxd7L5ge+n5LO\n8t4UFTfXJP0mqWrfHvRcyoqIxxV/+9VbH9qBrULOLxcY8oFtmqRTIuIEevZLz7hKkj4REa8kNfya\nOa6JPys+Tx/oiT+e1Fiwzu/FjNphxU3kxo//dO0aA0DStRFRmwAkLa1LfBWxVw96ZivpKVVJryb2\n92cmPaUHOhwK/AXwkZkxEdHoAdFFj6V71M/ZN/ZdpGqeZ5MORK8gNd2/DnhdRIxXxF4BnAz8A6nO\n+NGk686lD44pGg59knRm+Q8zp08f2Bqsd+kvlwaxdQe20ksRdQe2mnI3qzEk6cZo0B/VzFhJW5Gq\nnzba7yVlN6r9VBI7q2q5PpO3acM6sx3o0sV8n9UWN8JOKb7oTa/N9nOVpD0iommr4F4vioin9bw+\nQ9INEXFCsS+qPDwiLpekSJ3xTUi6ltTFQ5lXkA5sMxvYNJbzy6Xwj5Qf2D5Gqj1S5kw2HthWUhzY\natZ34FarymjVXMS33bIZcJK3jdpOAPP65Z8WERdLeiGwJ5seIN7ZJJ5U//kqSXeSegCdTXfY90k6\nnHQZBVLjselWu3U/sX9Z1Fm/VdJxpBa+lY+WHNKBbb9Bf7kU5vvANnDL5shr1Qztt2xOZlPf0kP3\nB+D5GbGNOuYqid2szjJwQ9m0GfNdW/y9aea4BuV+hJSoN5AOFDcBq2ax3t8qvoi7McseS0lVOy8k\nNXD5YfH/E0hP9HpuTewzSEl9Gekg92lSAm663i8E/oqUIN8OvL1h3Cpgj4z9fBVwOOkgvKT4/+re\n/V0Re2URcy5wHKlR0s2DrsuMZbfSJmF6m1RMe0hNbG0bEJ/J2yYi78w259LFvJ7V9nh2ROxVnN2+\nQ9L7aV7qMZ4bAAAGbUlEQVS1DuCHEXFB/Wybi3T9+Q9LJl9REzv9QJufMcvaFpI+Qnq4/ErgX0jb\n+pqG4Tm/XCBVH/wgqdfRAK4GjpL0cFLirnJ8sd5vAN5VrH9la+dZaLO6amkbkKjv/qD2foSTvG2i\nxQTQ1pd/+kByn6TfIF0rfWzDWIDrJZ1NOgv/5fTIaHAjUtJjgNey+T2MYxrEXkbqk/zu4vVOwDkR\n0eRBOzkHtlWkGjA3sfGSXGNtHdgamKtGWK2X7SRvM7WSAFr88l8o6ZGkvsmvI31pPjqL+IeTkvvv\n964Sze5DnE+q9/x5Nm1R2cSjpxM8QETcJWnnhrE5B7aBf7lAqwe2LZaTvM3USgJo8cu/DnggIj6t\n1MvhPsB5Tdc7InLOKLeNBvW0S/xa0lhE3AYgaVeanxHmHNgG/uVSmNcD2zy1/8htGzCnl4qc5G2m\nthJAW2e1b4uIT0p6LunBJe8D/pn0nN9akpaRnob1nGLUl0mPlbu9QfhFkl4QEZ9tuK69/hq4QqmL\n5uleFf+0YWzOgS3nlwvM/4FtKA3ABq0a3NCcNACb5iRvM7WVANo6q+19StJHY3ZPSYJUs+Vs0nNT\nIfUtciapT/s6xwMnSvof4H423sOofeB6RFwi6emkxH49aR/9vOE6D3xgy/zlAvN/YMtu/5HbNmC+\n24DM5CRvM7WVANo6q815ShLAYyLizJ7X/0fSXzSM3ZF0w3m3iHin0gM0Gl0ak/Qa0kFiGelRh/uR\nqif+boPwgQ9smb9cYP4PbNntP8hvG9BKG5AH5dTv9NC9gaJPbtLZwx/1jmsQuwz4DPCDYvg0sKxh\n7L2k5PNz0iPO7iU9oKTpeu8MnERKXC8Ffqdh3LbAYcBvFq8fy+weTH056ex9q2I4Cri8Yew/kzqq\nWlu83on0xKQmsTeRzuym2xI8CTi3YexFwOmk3gwfSXpcYtO+1S8rEs3WxfBq4LJZbK8lpMsTby9e\njwHPbBj7muJ93wWsLj4rX2gYm9P+I7dtQCttQB6cf9AV99DNoa0E0NaXfwjbawVwAakx0w9IZ5fL\nG8ZeV/y9vmdc0229pvh7A/DQ4v9vNIwd+MBGnwZL/cZVxLdyYCvmH7QB2P6kyys3k1qq3sTsHhrS\nagOwOf8SeFhcQ1sJoM0vf+b2OgvYqef1UuBjDWO/Qjr7n072j6H5r6bPkA7CE8CXSDeuPzsP73fg\nXy5FfFsHtoFbNpPRqrmIb61lc4STvIchDjkJoK0v/xDe82ZJeRaJ+kjSr4Dbgb8hnSm+bIB12L9I\nQtvMw/sd+JdLEd/KgY3izLvn7yNID+RuEjvwoycXwuAbrzZMx5Buyv0DqTbBlaRLNk3cX3ThGvBg\nvfmmDapuL6p9ngdcJukuYP0s1jvHkt7OrSQtpWGFhoj4t6KDrQNIN+QOjYi1s12BqHjI+Rx4J+lB\n0r3v932kfd/Eh0jJemdJf0O6f3JSk8CIeHHx74Sk1aQb15c0LDen/UdW24C2G4A5ydsw5SSAtr78\nud5P6sph+iEOLyOdlTcSEetI1VYXi72ip7fGiPiJpNp+0Xvmb+vAltP+I7dtQFttQAAneRuugRPA\nIj2rJSI+LumrbKy6eFgM1kHbYjHwL5dpLR3YBm7/EfltA9pqAwI4ydtwZSWARXhWC0CR1Luc2Htl\n/XJp0cDtP4bQNqCtNiCAH/9nQyTpj0lP0tkkAUTEJ8qjbLEpzoSnf7l8YTH8clHxuD1J7yHVOT9b\nzR/BdxmpIdP05/go4MiIaNKqebrF67akPm5m1QCsiN+ZjQ3AHg78ICK+1CQWnORtyBZjArDuk3QR\n6TkDB5Iu1fwcuCY2fUpVWewNEbF33biK+CX0adkcEV9pENu3ZXNENGnZDMy2eaxZjYj4ZkScWgxO\n8LZQHA58DjiouJG5FHhzw9gfSzpK0lbFcBTw41mUfRopOR9RvL4XOLVh7PGkuvLrI2Il8FvA3dUh\nm/I1eTPrvIi4j57aMBHxfdLzV5vIqRoMqeX2PpKuL8q+S9I2DWN/ERG/kISkh0bEOkm7z6JsJ3kz\nsxq5bQNabQPia/JmZhX63aBtetO2mPdI4OWkewFnUbQBiYhPVgZuvpz9KdqARETjB5X4TN7MrFpu\n1eBW24A4yZuZVctuG9BmGxBfrjEzq7GYqwY7yZuZdZjryZuZdZiTvJlZhznJm5l1mJO8mVmHOcmb\nmXXY/weG6b6pbFmIxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e59d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "combined.corr()[\"sat_score\"][survey_fields].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are high correlations between `N_s`, `N_t`, `N_p` and `sat_score`.  Since these columns are correlated with `total_enrollment`, it makes sense that they would be high.  \n",
    "\n",
    "It is more interesting that `rr_s`, the student response rate, or the percentage of students that completed the survey, correlates with `sat_score`.  This might make sense because students who are more likely to fill out surveys may be more likely to also be doing well academically.\n",
    "\n",
    "How students and teachers percieved safety (`saf_t_11` and `saf_s_11`) correlate with `sat_score`.  This make sense, as it's hard to teach or learn in an unsafe environment.\n",
    "\n",
    "The last interesting correlation is the `aca_s_11`, which indicates how the student perceives academic standards, correlates with `sat_score`, but this is not true for `aca_t_11`, how teachers perceive academic standards, or `aca_p_11`, how parents perceive academic standards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f36de80>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6ZGZKZEdHB729q0kkFtHevoBEYhHLl1/DwYMKfAf4CfAdxscP88wzz2Qd29u7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f336748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"saf_s_11\", \"sat_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a correlation between SAT scores and safety, although it isn't thatstrong.  It looks like there are a few schools with extremely high SAT scores and high safety scores.  There are a few schools with low safety scores and low SAT scores.  No school with a safety score lower than `6.5` has an average SAT score higher than 1500 or so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eXcHd92zmsccPcfGtP434/KFgNJnJHT6RjLxRF5TSgr7iDioUZy3SOedb/1Y5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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11019ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "districts = combined.groupby(\"school_dist\").agg(numpy.mean)\n",
    "districts.reset_index(inplace=True)\n",
    "\n",
    "m = Basemap(\n",
    "    projection='merc', \n",
    "    llcrnrlat=40.496044, \n",
    "    urcrnrlat=40.915256, \n",
    "    llcrnrlon=-74.255735, \n",
    "    urcrnrlon=-73.700272,\n",
    "    resolution='i'\n",
    ")\n",
    "\n",
    "m.drawmapboundary(fill_color='#85A6D9')\n",
    "m.drawcoastlines(color='#6D5F47', linewidth=.4)\n",
    "m.drawrivers(color='#6D5F47', linewidth=.4)\n",
    "# Temporary bug: if you run the following line of code in the Jupyter Guided Project interface on Dataquest, you'll get an error. \n",
    "# We're working on a fix, thanks for your patience! This should work fine locally on your own computer though.\n",
    "# m.fillcontinents(color='white',lake_color='#85A6D9')\n",
    "\n",
    "longitudes = districts[\"lon\"].tolist()\n",
    "latitudes = districts[\"lat\"].tolist()\n",
    "m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=districts[\"saf_s_11\"], cmap=\"summer\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like Upper Manhattan and parts of Queens and the Bronx tend to have higher safety scores, whereas Brooklyn has low safety scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Racial differences in SAT scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d32f4e0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d33af28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race_fields = [\"white_per\", \"asian_per\", \"black_per\", \"hispanic_per\"]\n",
    "combined.corr()[\"sat_score\"][race_fields].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like a higher percentage of white or asian students at a school correlates positively with sat score, whereas a higher percentage of black or hispanic students correlates negatively with sat score.  This may be due to a lack of funding for schools in certain areas, which are more likely to have a higher percentage of black or hispanic students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aede240>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QVGqhZrPenJBs9pj5FOyoQHo2e6xmMsP6wQ9eotnsYpf6m1b4bMF3wa/unMvK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\nGIZhGDVjYmIYhmHUzP8PmV6dO5dlGx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f7d0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"hispanic_per\", \"sat_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44                         MANHATTAN BRIDGES HIGH SCHOOL\n",
      "82      WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL\n",
      "89     GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...\n",
      "125                  ACADEMY FOR LANGUAGE AND TECHNOLOGY\n",
      "141                INTERNATIONAL SCHOOL FOR LIBERAL ARTS\n",
      "176     PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE\n",
      "253                            MULTICULTURAL HIGH SCHOOL\n",
      "286               PAN AMERICAN INTERNATIONAL HIGH SCHOOL\n",
      "Name: SCHOOL NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(combined[combined[\"hispanic_per\"] > 95][\"SCHOOL NAME\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The schools listed above appear to primarily be geared towards recent immigrants to the US.  These schools have a lot of students who are learning English, which would explain the lower SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37                                STUYVESANT HIGH SCHOOL\n",
      "151                         BRONX HIGH SCHOOL OF SCIENCE\n",
      "187                       BROOKLYN TECHNICAL HIGH SCHOOL\n",
      "327    QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO...\n",
      "356                  STATEN ISLAND TECHNICAL HIGH SCHOOL\n",
      "Name: SCHOOL NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(combined[(combined[\"hispanic_per\"] < 10) & (combined[\"sat_score\"] > 1800)][\"SCHOOL NAME\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the schools above appear to be specialized science and technology schools that receive extra funding, and only admit students who pass an entrance exam.  This doesn't explain the low `hispanic_per`, but it does explain why their students tend to do better on the SAT -- they are students from all over New York City who did well on a standardized test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender differences in SAT scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f79f550>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5L/APgDePtp/Ag8DaHP4l8BrgbwCq6ijru0uwVskDvm15dVW9MsmXAKrqm0me1XVREvCd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\n0P8H0zUKgiI4dK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f39d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_fields = [\"male_per\", \"female_per\"]\n",
    "combined.corr()[\"sat_score\"][gender_fields].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, we can see that a high percentage of females at a school positively correlates with SAT score, whereas a high percentage of males at a school negatively correlates with SAT score.  Neither correlation is extremely strong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f378630>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/ZEThKIUWP1CNaLIvJV7PKXgfn1Jzn/WPHaulZbZedNHHvcgs8oKxSdvbF471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\n0CQrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f387cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"female_per\", \"sat_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the scatterplot, there doesn't seem to be any real correlation between `sat_score` and `female_per`.  However, there is a cluster of schools with a high percentage of females (`60` to `80`), and high SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5                         BARD HIGH SCHOOL EARLY COLLEGE\n",
      "26                         ELEANOR ROOSEVELT HIGH SCHOOL\n",
      "60                                    BEACON HIGH SCHOOL\n",
      "61     FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A...\n",
      "302                          TOWNSEND HARRIS HIGH SCHOOL\n",
      "Name: SCHOOL NAME, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(combined[(combined[\"female_per\"] > 60) & (combined[\"sat_score\"] > 1700)][\"SCHOOL NAME\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These schools appears to be very selective liberal arts schools that have high academic standards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AP Exam Scores vs SAT Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f7c3be0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NN9+sQ0NDunfvXt22bZveeeedIZZVr7s3ezRV6TglZrnua6GKy1H6Xcp+lY5D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\nGIZhlIUJiWEYhlEW/wdu8ayjgCEudgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd7cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined[\"ap_per\"] = combined[\"AP Test Takers \"] / combined[\"total_enrollment\"]\n",
    "\n",
    "combined.plot.scatter(x='ap_per', y='sat_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there is a relationship between the percentage of students in a school who take the AP exam, and their average SAT scores.  It's not an extremely strong correlation, though."
   ]
  }
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