Explore_NYC_highschool_SAT_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the datasets into a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "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": 34,
   "metadata": {
    "collapsed": true
   },
   "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.loc[:,survey_fields]\n",
    "data[\"survey\"] = survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add DBN columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "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": 36,
   "metadata": {
    "collapsed": true
   },
   "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": 37,
   "metadata": {
    "collapsed": true
   },
   "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": 38,
   "metadata": {
    "collapsed": true
   },
   "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": 39,
   "metadata": {
    "collapsed": true
   },
   "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": 40,
   "metadata": {
    "collapsed": true
   },
   "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n",
    "survey_fields.remove(\"DBN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate correlations between SAT scores and school surveys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_list = survey_fields\n",
    "var_list.append('sat_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_subset = combined[var_list]\n",
    "correlations = survey_subset.corr()\n",
    "correlations = correlations[\"sat_score\"]\n",
    "correlations = correlations[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7faa8d521080>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AW2+9hdu3byMkJKQg6kZERAZSPYKoUqUK510iIiqBVI8gOnTogHv37pmf3717F506dcrX\nShERkfFUE8Tt27dRuXJl8/MqVaogKSkpXytFRETGU00QpUqVynJb0NjYWE13liIioqJN9RrEl19+\niZYtW6JNmzYQQuDQoUP44YcfCqJuRERkINUE0blzZ5w8eRJHjx4FACxYsADVq1fP94oRUd7LqzuN\nUcmgmiAAoHr16ujWrVt+14WI8lle3WmMSgbVaxBERFQyMUFQnrCzs4MkSbIPOzs7o6tHRLmg6RRT\neno6EhMT8ezZM/MyJyenfKsUFT2JiZZPTSitI6LCSzVBfPPNN5g+fTpsbW1RqlTGAYckSThz5ky+\nV46IiIyjeopp4cKFiImJwfnz53H27FmcPXs2T5PDnj174Obmhvr162Pu3LnZ1sfExOD1119H2bJl\nMX/+/Dwrl4iIlKkeQTg6OqJSpUr5UrjJZEJQUBDCw8NRu3Zt+Pr6omfPnnBzczNvU61aNXzzzTcI\nDQ3NlzoQEZE8TdN9+/n5oWvXrihTpox5+dixY3UXHhUVhXr16sHZ2RkA0K9fP4SFhWVJENWrV0f1\n6tWxc+dO3eUREZF2qgnCyckJTk5OSE1NRWpqap4WnpCQAEdHR/NzBwcHREVF5WkZRESUO6oJYtq0\naQCAhw8fAgAqVKiQvzUiIiI42TkhPjFedp2jrSPibsbJrstLqgni3LlzGDhwIJKTkwFknPJZvXo1\nGjZsqLtwe3v7LBMBXr9+Hfb29rpeMzg4+LlnkQD8dL0eEZER4hPjEQH5Ye/+if6yy7U4dSrj36z7\nSnmqvZgCAwMxf/58xMbGIjY2FvPmzcPw4cNzXbnn+fr64tKlS4iNjUVqaio2bNiAHj16WNxeCKH6\nmsHBwc+9cb88qScRUXHh5ZXxb9Z9pTzVI4hHjx7B3///spWfnx8ePXqkq4KZrKyssHjxYnTs2BEm\nkwnDhg2Du7s7li5dCkmSEBgYiMTERPj4+ODBgwcoVaoUFi5ciOjoaJ7qIiLKZ5p6Mc2YMQMDBw4E\nAKxduxZ16tTJswp07twZMTExWZaNGDHC/H9bW1vEx8ufhyMiovyjeoopJCQEt27dQkBAAAICAnD7\n9m2EhIQURN2IiMhAqkcQVapUwaJFiwBkzMn06NEjVKxYMd8rRlSc2Tk5IdHCkbGtoyNuxuV/DxUi\nNaoJon///vj+++9hZWUFX19fpKSk4OOPP8Znn31WEPUjKpYS4+Nh6cYMif6576FClJdUTzFFR0ej\nYsWKCA0NRZcuXXD16lWsWbOmIOpGRFSk2Tk5WZ4GvwjMiK16BJGWloa0tDSEhoYiKCgI1tbWkCSp\nIOpGRFSkFfUjRdUjiJEjR8LFxQWPHj1C69atERsby2sQREQlgOIRhMlkgq2tLRISEszLnJycEKF0\nU1siIioWFI8gSpUqhX/9619ZlkmShNKlNd2IjoiIijDVU0zt27fH119/jfj4eCQnJ5sfRERUvKke\nCmzcuBEA8O2335qXSZKEK1eu5F+tiIjIcKoJ4urVqwVRDyIiKmQ0XUw4d+4coqOj8fTpU/OyQYMG\n5VuliIjIeKoJYvr06YiMjER0dDTefPNN7N69Gy1btmSCUOBiZ4fYxETZdc62trh282YB14iIKOdU\nL1Jv2bIF4eHhsLOzw4oVK3D69Gncv3+/IOpWZMUmJkIAsg9LiYOIqLBRTRAvv/wySpUqhdKlSyMl\nJQU1a9bk9NtERCWA6ikmHx8f3Lt3D8OHD0fTpk1RoUIFtGjRoiDqRkREBlJNEN999x2AjCk3Onfu\njJSUFHh6euZ7xYjym52DHRITLJ/ys7W3xc3rhe96kdLN7IGCu6E9FX+qCUIIgXXr1uHKlSuYOnUq\n4uLiEBUVhWbNmhVE/YjyTWJCIhCssD64cF4vUrqZPaDvhvZEz1O9BjFq1CgcOXIE69evBwDY2Nhg\n9OjR+V4xIiIyluoRxLFjx3Dy5Ek0adIEQMYd5lJTU/O9YkREZCzVIwhra2ukp6eb7wFx69YtlCql\nGkZEREWc6p5+zJgx6N27N5KSkjB58mS0bNkS//jHPwqibkSq7OxcLN6xS5Ik2Nm5GF1FoiJL9RTT\ne++9h6ZNmyI8PBxCCISGhsLd3b0g6kakKjExFhlDEC2t590PiXLLYoJ4+vQpvv/+e1y6dAkeHh4Y\nMWJEvtwHYs+ePfjkk09gMpkwbNgwTJw4Mds2Y8aMwe7du1G+fHmsXLkSXl5eeV4PIiLKyuIppsGD\nB+PEiRPw8PDA7t27MX78+Dwv3GQyISgoCHv37sX58+exfv16XLhwIcs2u3fvxuXLl/Hnn39i6dKl\nGDlyZJ7Xg4iIsrN4SBAdHY2zZ88CAIYNG5Yv4x6ioqJQr149ODs7AwD69euHsLAwuLm5mbcJCwsz\nTwz42muv4f79+0hMTIStrW2e14eIiP6PxSMIa2tr8//z6xajCQkJcHR0ND93cHDIcv9ruW3s7e2z\nbUNERHnP4p7/9OnTqFixIoCM0dRPnjxBxYoVIYSAJElISUkpsErmRHBwMACgfPlKePTI8gVKW1tn\nxddRmoZBbQoGZ1tbSArTfSuxdXREor/lkbC2zyXLFznaOiqOonW0VYh1tIW/v+WRw46OKvW2tUWi\nhfesdrRnZ+fy/y82W3ptZ9y8ec3iOqUL0UrtbGtvqzha2tZeud751c5KbQwY185KbZy5XolSW6n9\nPeqK1dHOSm2cuV6x7HxqZ6U2BtTbuWLF8uZ9pRJJCGG5C0g+O3r0KIKDg7Fnzx4AwJw5cyBJUpYL\n1SNHjoS/vz/eeecdAICbmxsOHjwo+2WUJAl59XYkSbI8DUMw8qwc+v+ftUJPJCDv2pWIslLabxo6\n4s3X1xeXLl1CbGwsUlNTsWHDBvTo0SPLNj169MDq1asBZCSUypUr8/oDEVEByJ+LCxpZWVlh8eLF\n6Nixo7mbq7u7O5YuXQpJkhAYGIg333wTu3btwquvvory5ctjxYoVRlaZiKjEMPQUU17jKaaiiaeY\niIxTaE8xERFR4cUEQUREspggiIhIlqEXqQszpb7Tav3jiYiKA16kJsPxIjWRcXiRmoiIcowJgoiI\nZDFBEBGRLCYIIiKSxQRBRESymCCIiEgWEwQREcligiAiIllMEEREJIsJgoiIZDFBEBGRLCYIIiKS\nxQRBRESymCCIiEgWEwQREcligiAiIllMEEREJIsJgoiIZBmWIO7evYuOHTvC1dUVnTp1wv3792W3\nGzZsGGxtbeHp6VnANSQiKtkMSxBz5sxB+/btERMTg7Zt22L27Nmy2w0ZMgR79+4t4NoREZEkDLob\nvJubGw4ePAhbW1vcvHkTfn5+uHDhguy2sbGx6N69O86cOaP4mko336bCS5IkAErtxnYlyi9K+03D\njiCSkpJga2sLALCzs0NSUpJRVSEiIhml8/PFO3TogMTERPNzIQQkScLMmTOzbZvxK1K/4OBg8//9\n/Pzg5+eXJ69LRFQcREZGIjIyUtO2hp1icnd3R2RkpPkUk7+/P/744w/ZbXmKqXjjKSYi4xTKU0w9\nevTAypUrAQCrVq1Cz549LW4rhOAOgoiogBmWICZOnIj9+/fD1dUV4eHh+PzzzwEAN27cQLdu3czb\n9e/fH6+//jouXrwIJycnrFixwqgqExGVKIadYsoPPMVUNPEUE5FxCuUpJqJMtrbOACSLj4z1RFTQ\neARBRFSC8QiCiIhyjAmCiIhkMUEQEZEsJggiIpLFBEFERLKYIIiISBYTBBERyWKCICIiWUwQREQk\niwmCiIhkMUEQEZEsJggiIpLFBEFERLKYIIiISBYTBBERyWKCICIiWUwQREQkiwmCiIhkMUEQEZEs\nJggiIpJlWIK4e/cuOnbsCFdXV3Tq1An379/Pts3169fRtm1bNGzYEB4eHli0aJEBNSUiKpkMSxBz\n5sxB+/btERMTg7Zt22L27NnZtildujTmz5+P8+fP48iRI/j2229x4cKFXJUXGRmZ67rqiTWy7KIY\na2TZfM9FI9bIskvaezYsQYSFhWHw4MEAgMGDByM0NDTbNnZ2dvDy8gIAVKhQAe7u7khISMhVeSWt\nYYtqrJFl8z0XjVgjyy5p79mwBJGUlARbW1sAGYkgKSlJcftr167h1KlTeO211wqiekREJV7p/Hzx\nDh06IDEx0fxcCAFJkjBz5sxs20qSZPF1Hj58iD59+mDhwoWoUKFCvtSViIheIAzi5uYmbt68KYQQ\n4saNG8LNzU12u7S0NNGpUyexYMEC1dcEwAcffPDBRw4fluTrEYSSHj16YOXKlZg4cSJWrVqFnj17\nym43dOhQNGjQAB9//LHqa2bkCCIiyguSMGivmpycjLfffhvx8fFwdnbGpk2bULlyZdy4cQPDhw/H\nzp078euvv6J169bw8PCAJEmQJAmzZs1C586djagyEVGJYliCICKiwo0jqYmISBYTBBERyWKCKOTu\n3r2LM2fO5Cjm6tWrmpZZkpqaijNnzuDs2bNITU3NUdkvevjwoa54KvzYxsVXsU0QEyZMQEpKCtLS\n0tCuXTvUqFEDa9eu1RTbrl07TcvkXLlyBd27d0f16tVRs2ZN9OzZE1euXMlR3f38/JCSkoLk5GR4\ne3tj+PDhGDt2rOb4t956K9uyPn36aIr9+eefUbduXYwZMwZBQUF49dVXsXv3bs1lv6hBgwa5js3t\ntCoA4OHhkevYLl265Do2MDAw17F6y/7nP/+Z69gVK1bkOtaoNgaMa2c9n7Xe74iettq/f3+Otjes\nm2t+27dvH/71r39h+/btcHFxwbZt29C6dWsMGDDAYszTp0/x+PFj3L59G3fv3jV3m01JSdE8xUf/\n/v0xevRobN++HQCwYcMGvPvuuzh27Jjmut+/fx8VK1bE8uXLMWjQIEyfPh2enp6qcRcuXMD58+dx\n//59bNu2zbw8JSUFT58+1VT2uHHjEBERgVdffRUAcPnyZXTt2lXxj2n+/Pmyy4UQun5dduzYEXFx\ncRbXP/8eXyz35s2biq998uRJi7GnTp1SjE1OTrYYu2vXLsVYvWUrWb58OaZOnZqr2GnTpmHIkCEW\n1xvVxoBx7axE7bPW+x1RotZWSoYNG6b6eT+v2CaIZ8+eAcj4Rdy3b19UqlRJNWbp0qVYsGAB/vrr\nLzRt2tScICpWrIigoCBN5T5+/BgDBw40Px8wYAC++uqrHNf9xo0b2LRpE7788kvNcTExMdi5cyfu\n3buHn376ybzcxsYGy5Yt0/QaNjY25uQAAHXq1IGNjY1izD/+8Q989tlnKF06+9fJZDIpxo4ZM0Z2\nuRAC9+7dU4x955138N5778mOwldLiL6+vmjTpo3s2Bm1cmvUqAFnZ+cssZIkQQihOmWM3rIrVqwo\nu1wIgSdPnijGWvqRIYTIMuOBHKPaGDCunfV81nq/I3raqkePHhZj79y5o1r2i0HF0sSJE4Wrq6vw\n8vISqampIikpSTRr1kxT7KJFixTX79u3z+K6CRMmiNmzZ4urV6+Ka9euiblz54rPP/9c3LlzR9y5\nc0dT+Zs2bRIeHh7iww8/FEIIcfnyZREQEKApVgghfvvtN8X1s2bNsrhu5MiRokuXLmLFihVi5cqV\nomvXruLDDz8UW7duFVu3bpWNadGihThx4oTsOgcHB8W6VKhQQSxdulSsXLky26NatWqKsd7e3uLs\n2bO5Krdhw4bi4sWLuYp99dVXRWxsbK5i9Zbt6OhonoEgp7E1a9YU//vf/8S1a9eyPK5evSpq1aql\nGGtUGwthXDvr+az1fkf0tFXlypXFzp07RWRkZJZHRESEqFmzpmrZzyu2CUIIIe7cuSOePXsmhBDi\n4cOH4saNG+Z1Sjt5NU2aNLG4zsXFxeLjlVdeyXWZz1PawWuhVP/333/f4mPIkCGyMRcuXBC3bt2S\nXWfpDyyTv7+/+PXXX2XXubi4KMb+8ssvFv8Ijx8/rhi7efNmceHCBdl127dvV4xdvHixOHXqlOw6\ntR8XesuePHmyOHbsmOy6CRMmKMYOHTpUHDp0SHbdu+++qxhrVBsLYVw76/ms9X5H9LRV586dxYED\nB2TXtWrVSrXs55XYgXLe3t4Wz0+qadKkCf73v//lKnb//v3o0KFDrmIz6ak7oK/+s2fPxqRJk3Jd\n9ouSk5PUvvSyAAAZE0lEQVRRtmxZlCtXLs9ekwoXtnHRVWx7ManRkxeVZp5VM3HixFzHZtKb0/XU\nf/PmzTnaXq3HRtWqVfNlx2FUj56c9hLJy7L19AjSc6HZqDYGjGtnPZ+13u9IQXYrLrEJQs9OUo+8\nOGDTW3c9dZCLTU5Oln3cuXNHV48NPd0Qly9fnuvYadOm5Tp22LBhuY7VW3bHjh1zHavWVbUwtjFg\nXDvr+az1fkf0dCvOabfgYtuLKbdMJhOOHj2K119/3eI2Li4uuX79vEhMepNM3759cx0rV389PTb0\ndEM0qkeP3l4iesrW0yNIT1dVo9oYMK6d9XzWer8jetpKT7fgFxXLBKFnJ1+qVCmMHj1a8Ry9pQYo\nKJZ28EqH25Ik4YsvvgCQ0WUxt+SSU506dRAeHg4nJ6ds6xwdHRVfT083xMqVK+P48ePmOxPmpNzE\nxETs3bsXVapUybJcCKH4vQGAQ4cOYe3atdluXiWEQFRUlGKs3rJXrFiBefPmoUyZMtnWrV+/XjFW\nT1dVo9oYMK6d9XzWer8jetpKT7fgFxXLBKF3J9+uXTts3boVAQEBOfrFr/foQ+8Ovnz58tmWPX78\nGMuXL8edO3fM8XrIJadPPvkEd+/eld15TJgwQfH13N3dsXTpUtSrVy/bOrU//kGDBiE2NlZ2x9G/\nf3/F2G7duuHhw4fme54/z8/PTzG2efPmKFeuHNq0aZNtnaurq2Ks3rJ9fX3RqFEj2e9YcHCwYqy3\ntzd69eqFpk2bZlundqrGqDYGjGtnPZ+13u+Inrby9PTE+PHj0ahRo2zr/vvf/6qW/bxi24tp/Pjx\naNGiRY538kIIWFlZAQBKly6NsmXLmm+VmpKSohqvp4fQvHnzsi17fgefk4tTDx48wMKFC/Gf//wH\nb7/9NsaNG4eaNWta3F5rctJDrgfXli1b4OHhIftHExoail69euku9/z582jYsGGuYu/evZvt12dB\nkStbT4+gmJgYVKtWDdWrV8+2LjExUXYHnFNGtTGQ9+1sZO8rPW116NAhODs7yybzEydOwMfHR3tF\nctQptogwmUxCkiQhSZKwtrYWNjY2okKFCsLGxkZTfMOGDXNd9rhx48SWLVuEyWTK9WsIIURKSoqY\nMWOGcHFxERMmTBCJiYma4u7cuSMmT54sXFxcxLRp00RycrKmuK+//jrb45///KdwcnIS5cuX1/NW\nzJTGX6hZuXKlIeXqiW3evHmuY/WWnZOBlS8KCgrKdaxRbay3bKM+a73fET1tpWU8VbHsxSRJEho0\naACTyYTU1FSkpKTgwYMHmo4AAKBp06Y4fvx4jssVQmD+/Pno27cvypQpg4oVK8LGxsbiRTY5ycnJ\nmDJlCjw9PfHs2TOcPHkSc+fOVfz1n+mzzz6Dr68vbGxscPbsWQQHB2v+9Ttu3DjzIzAwEE+ePEFI\nSAj69euX48kGLRE6DlYXLlxoSLl6YnN6vjcvy9bTZr/++muuY41qY71lG/VZ6/2O6GkrLV3Wi2WC\nAHK/kweAY8eOoUWLFqhbty48PT3h4eGhabI8vYlJzw4eyDhF9ddff2HmzJmoXbs2KlasmKMkpSc5\naaGnB5eeP2A95RoVa3TZRpSrp431ll0UY/XS8nkXy4vUQMZOft26dXB2dkb58uXN1xG03Fth7969\nuS43MzH5+vrmODazx8TMmTOzTNInNF4DUevdoOSzzz7Dtm3bEBgYiLNnz2brfWE0I/+QqGCwjQuW\nls+72CYIPTt5Z2fnXMfqSUx6dvB66U1OWugZP6Ln1+VLL71kSLl6fxEbVbaeWKPaGCia7VzovyO5\nvsJBsl6cfTHzURI8efJEzJs3T/Tu3VsEBASI+fPniydPnuTJa48ePVpx/datW8Wnn34qxo4dK7Zt\n25aj1/7999/FwoULxaJFi8Tvv/+eZZ3SDLxyE7Y9v8zSDKSZBgwYoLhMqewFCxYoLtu7d6/F2E2b\nNikuW7FihWJsSkqKEEKIGTNmiN69e2f7zHJLrY0PHz4sHj58KIQQYs2aNeLTTz/V/Ld16dIl8fTp\nUyGEEBEREWLhwoXi7t275vX59Vnr/Y7oaavDhw8rLvvyyy8VyxaimM/mSgWrb9++YujQoeLAgQPi\nwIED4oMPPhB9+vTRFHv79m0RFBQkmjRpIry9vcWYMWPE7du3NcV++OGHokOHDiIkJESEhISITp06\niVGjRmmKnT59umjUqJGYOnWqmDp1qvD09BQzZszQFCvX88XDw0NTrFz8s2fPhLu7e67L9vLyynWs\n1l48me/v0KFDok2bNmLnzp2ap9FfsGCBuH//vjCZTGLo0KGiSZMmijtXubJNJpM4deqU8PLyEosX\nLxatW7fWFNu4cWORlpYm/vzzT1GvXj0xfvx40aVLF02xef1Z6/mOWFqW17GZiu0pJip4586dQ3R0\ntPm5v7+/5nlj+vXrh9atW2Pr1q0AgHXr1uGdd97RNLDnwIED+OOPP8znVAcPHqy5P/y6detw+vRp\nlC1bFgDw+eefw8vLC1OmTLEYs2TJEnz33Xe4cuVKls4LDx48wBtvvKFa5uzZszFr1iw8efLE3HlA\nCIGXXnpJdeK79evX48cff8TVq1ezTOfw4MEDVK1aVTF29+7d2LVrFxISErJMI5GSkiI7YldO5hih\nn3/+GYGBgejataviZ/W8kJAQfPzxx9i7dy/u3r2LNWvWYODAgZrnNSpdujQkSUJYWBiCgoIwbNgw\n/Oc//9EUW6pUKZQuXRrbt2/HRx99hI8++ghNmjRRjNHzWev9juhpqyNHjuC3337DrVu3skzZkZKS\ngvT0dNWyn8cEQXnG29sbR48eRfPmzQFkXI/ROijnxo0bWQbjTZkyBRs3btQU++qrryIuLs587Sg+\nPj7LXfGU1K5dG0+fPjUniL///hv29vaKMf3790eXLl0wadIkzJkzx7zcxsYmy47D0iC7SZMmmR+z\nZ8+2WI7cwK/XX38dtWrVwu3btzFu3LgsZav1tKtduzZ8fHywY8eOLCN0bWxs8O9//1sxNpO9vT1G\njBiB/fv3Y+LEifj77781XzsT//+c965duzBw4EA0bNgwR+fQbWxsMHv2bKxduxa//PILTCYT0tLS\nNMVaW1tj/fr1WLVqlflui2qxej5rrd8RS/S0VWpqKh4+fIhnz57hwYMH5uUVK1bEli1bVMt+XrEd\nSU0Fz93dHTExMeYRnHFxcXB1dTX/8lO6UD927Fg0a9YMb7/9NoCM0bdRUVH4+uuvVctt06YNjh8/\njmbNmkGSJERFRcHHx8d8m9kdO3ZYjO3VqxeOHz+ODh06QJIk7N+/H82aNYODgwMAYNGiRZrf/4v0\n3rdDT3yLFi1w5MgR2XVpaWmwtra2GPvWW2+Zj+Re9PjxY+zZswceHh6oV68ebty4gbNnz5qPApRG\nng8ZMgQJCQm4evUqTp8+jfT0dPj5+eH333/X9J5u3ryJH3/8Eb6+vmjVqhXi4uIQGRmJQYMGqcZG\nR0fj+++/R4sWLfDuu+/i6tWr2LRpk+bp9xMTE83d5ps1a5ajrt+nT5/GoUOHAACtWrVC48aNNcem\npaVBCIGLFy8CyJimQ6ntnhcbGwtnZ2fzDAy56ZnIBEF5JjY2VnF9xYoVLe48bGxs8OjRI/MpjPT0\ndPPcUmq9qA4ePKhYrtx8OJlWrVqlGDt48GDF9Ur0TLuiN96oWKWkZjKZcOrUKdSpUweVK1fGnTt3\nkJCQYP41rmeqDEA5KapRSoqbN2/G+PHj4efnByEEDh06hK+++gp9+vRRfd1Fixbhhx9+QEBAAABg\n+/btCAwMxEcffaSpXgcPHsSgQYPg4uICIQTi4+OxatUqtG7dWjX23LlzGDhwIJKTkwEA1atXx6pV\nq2TnaLIoR1csiHTQM53BuXPnch2rZzoDPdMo6Hm/euONitV68Tavy9VbtlKsp6dnlqlukpKShKen\np6bX9fDwMPe8EiLj1sc5uUjt7e2d5ZapMTExwtvbW1NsixYtstx6NCIiQrRo0UJz2UIU06k2qHAS\nOg5WBw4cmOtYPdMZ5NU0IyVFcRxJbTKZspxSqlatWo6uu2QeFQMZF/lz8j7T0tKyTHJYv359zddd\nHj16BH9/f/NzPz8/PHr0SHPZAC9SUwEqLlNtXL16Fa+88opqrN4dXlEc+KVHYR1J3blzZ3Tq1Anv\nvvsuAGDjxo2a74Q3ZMgQvPbaa+jduzeAjNlrhw4dqrlsHx8ffPDBBxgwYACAjF53Wjt+1KlTBzNm\nzDD/uFq7di3q1KmjuWyACYKKiMK08+jTpw9+//13tGvXDuHh4Ra3U1qX6cyZM7h27RqePXtmXpZ5\nvvro0aO5ruOaNWtyHTt37txsywoqKeqRX0nxq6++wrZt23D48GEAGffgztzhqxk7diz8/PzMsStW\nrFDtXvu8JUuW4NtvvzV3lmjVqhVGjx6tKTYkJATTpk0z3/KgVatWOb8Pd45OSBHJuHLliqbtjDo/\nndfnpr28vMSXX34pHBwcxLx587I9tBoyZIho2rSpGDRokHj//ffF+++/L4YMGaIpNnP6+ucfDg4O\nolevXuLy5cuKsRcvXhRvvfWWcHd3F6+88or5oSTzvHfbtm0Vt1Makazmtddey3WsEOqjkpXoGQ2t\nRG20vBq1UdxK1EZha8FeTKRb06ZNNf2iTk5O1tQHXE7z5s1z/Yv63LlzOeu58Zx9+/ZlG8gVExOD\n0NBQLFiwACNHjswWM23aNE2v3aBBgywDC3Piiy++gIODA/r37w8hBDZs2IDLly/D29sbS5YsQWRk\npMXYli1bYvr06fj000/x008/YcWKFTCZTIo3jWrSpAn69u2LJUuW4NNPP822fuzYsZrqrXTEpMbG\nxibbkWSlSpXg4+ODefPmKZ4++fPPPzFp0iRER0dnuSal5RqTXM8sT09PTfOrvRibnp4ODw8Pze0u\nV7bWXmZysTntOs1TTKSbyWTCrFmzcPHiRdmbrWfuPNSSQ25Pt6jtOJSSg9qOQ26Ur6urKyZOnAhP\nT0/N56LlNGvWDNHR0ZpHmz9vx44dOH36tPl5YGAgvLy8MHfuXMyaNUsx9smTJ2jXrh2EEHB2dkZw\ncDCaNm2qmCA2bNiA0NDQbIOvcmLo0KE4c+YMGjZsiFKlMvrHSJKkOUF88sknFpPi0KFDFZPikCFD\nzEkxIiLCnBSV6BkNrWe0PGD8iPlMTBCkm9E7j4LecWTq0qULfv75Z5w/fz5Lcpk6daqm+Pfffx/N\nmzdHrVq1UKZMmRzN/FuuXDls2rTJ3Bd/y5Yt5tHgatdrypQpA5PJhHr16mHx4sWwt7dXvZ1tXiTF\no0eP5vqICSj4pKhnxLye0fKA8SPmzXJ0QopIwa5du3Idq3WSOjlyfdIbN25scd3zMs+tN2rUKNsy\nNSNGjBADBw4UDg4OIjg4WDRq1EgMHTpUa7VF3bp1RVhYmLhy5UqOZ/69fPmy6Natm6hWrZqoXr26\n6Natm/jzzz/F48ePxaFDhxRjo6KixIMHD0R8fLx4//33RUBAgDhy5Ijmeu/cuVPMnTtXTJ8+3fzQ\nYvDgweL8+fOay3lR8+bNxcaNG0V6erpIT08XGzduNF+3yGxvS1q0aCHS09NF7969xTfffCO2bdsm\n6tevn+u6PM+oMSdCKI/xSU1NVYzVMsaHRxCUZ/T8otZzuqWgf01n+u2333DmzBl4enpi2rRpGDdu\nXI5+XdeoUSPL6YOcqFOnjnlOoRe1bNlSMTbzZlYVKlTIca+WkSNH4vHjx4iIiMAHH3yALVu2oFmz\nZppi9RwxARldPD/++GOMGjUKkiShefPmWLt2LZ48eYLFixcrxi5cuBCPHz/GokWL8MUXXyAiIkJ1\nFL1WwsAuxUpjfNSm5NBy/YUJgvKMUTsPo3YcmUmoXLly+Ouvv1C1alXcuHFDUyyQcbGxf//+6N69\nO8qUKWNeruW02q1bt7Bs2bJs12xCQkJUYzt06IDNmzejcuXKADJOkfTr10/TTbb0JMUPPvgAa9eu\nhYeHh/k0Yk4YlRTVFOfb0jJBUJ4xaudh1I6je/fuuHfvHj777DN4e3tDkiQMHz5cc/yTJ09QpkwZ\n7Nu3z7xM63WXnj17olWrVmjfvn2Wkbpa3L5925wcAKBKlSpISkrSFKsnKeo5YgKMS4olGRME5Rmj\ndh5G7Tjc3NxgZWWFt956C9HR0Th58iR69eqlud56fsk+fvxYdkCbFqVKlUJcXJx51t1r165p/iWq\nJynqOWICCj4pFsTgQD2j5fWWrSWWCYLyjFE7D6N+Tc+YMQN9+/bF4cOHceDAAYwfPx4ffvghjh07\npin++vXr+Oijj/Drr78CyBglu3DhQvNU40q6deuGXbt24c0339RU1vO+/PJLtGzZEm3atDHPTvrD\nDz9oitWTFPUcMQEFnxTzasR8fo2WB/J+xPyLmCAozxi18zDq1/Tzd1cbPnx4ju6uBmR0se3fvz82\nb94MIGOunCFDhmD//v2qsQsXLsTs2bPx0ksvwdra2nzNRmla9EydO3fGiRMn8MMPP6BJkybo1asX\nXn75ZU111pMU9Z77L+ikmBfje/SO/SjoMT7ZaOxNRaTq+fsV+/n55eh+xXpMnjxZ/Pzzz7mK3b17\nt3B0dBQDBgwQ7733nnBychJ79uzRFNu1a1cRGBgoXnnlFXH37l3x9OlTzdNACyHfNVOtu2am9PR0\nsXr1anMX09jYWHH06FFNscuWLRONGjUSlStXFn5+fqJs2bLC399fU2zm1COff/65WLduXZZlauLj\n40WvXr1EjRo1RI0aNURAQICIj4/XFCtExvQipUqVEmXLlhU2Njbm6Ua0SkxMFDNmzBA7d+4Umzdv\nFgcPHlTc/sKFC2LOnDnCzs5OBAcHZ3tooaf7thBCTJkyRXz//fciJSVF3L9/XyxdulRMmDBBbNiw\nQbRp00Yx9o033hD//e9/hYeHh7h27ZqYNm2a+OKLL3JUPhME5Rmjdh4FvePI9OjRI7F161Zx8eJF\nIYQQf/31l+KcPi9q27atWLNmjXj27Jl49uyZWLNmjepcR5lGjhwpRo0aJdzc3IQQQiQnJwsfHx9N\nsY0aNRJPnjwxJ6M//vhD9O7dW1OsnqTYvn17ERISItLS0kRaWppYsWKFaN++vaZYIYxLinrG9+gd\n+2HUGJ9MTBCUZ4zaeRi149Dr2rVronv37qJ69eqiRo0aomfPniIuLk5TbOYAq+cTsNbPOjORNG7c\nWDx9+lQIIUSDBg00xepJinqOmIQwLikKkfvBgREREcLGxkbUr19feHh4iEaNGuXohkFGDw5kgqA8\nY9TOw8gdhx6DBg0SycnJ5ud37tzRPJtrs2bNxLNnz8yJIikpSfPRWq9evcTdu3fFtGnTRKtWrUSP\nHj1Ely5dcv4GckjPEZMQxiVFPSPm9YyWF8LYEfNCMEFQIaFn52HUjkMvS1OJa7F27VrRvXt3YW9v\nL/7xj3+I+vXr53gqZyGEiIyMFGFhYeLvv//OcWxO6TliEsK4pJj5iz/z3wcPHoiWLVtqitVzu9vC\ngL2YqFAICQnBRx99hE8//RSSJOH111/HypUrNcVaW1sjPT3d3Nvj1q1bmgfbOTg44N69e+jVqxc6\ndOiAKlWqwNnZObdvI0dMJlOWid6Sk5OzdIVU8t5776Fp06YIDw+HEAKhoaFwd3fPcR3atGmT45jc\nmjp1KlatWpXl/Y4fP17TeBUAGDNmDHr37o2kpCRMnjwZW7ZswcyZMzXFbt++HQAQHBwMf39/3L9/\nH507d9YUq2d8j96xH0YPDmSCoEJBz87DqB2HXuPGjUOLFi3Qt29fAMDmzZsxefJkzfFubm5wc3PL\nr+rluTNnzmSZ9bRq1aqa7muQyaikqGd8j96xH0aN8cnEBEGFgp6dR1H8NQ0AgwYNgo+PDw4cOAAA\n2LZtW64mKywq9BwxZTIiKeoZ36N37IdRY3zMDD7FRSSEyLhm8OIF2+e751HRt2rVKuHq6iqmTJki\npkyZIlxdXcXq1auNrpYqPeN79I79MGqMTybecpQKhdWrV2PWrFnZTrcMHDjQ4JpRXoqOjjYfMbVt\n27ZIHDFl3uJz0qRJ8PDwQP/+/TXf9rNDhw7o37+/+Xu8du1arFu3TtNoeSBjJPXjx49zNWIeAJKS\nkswj5p88eYKaNWuidevWmmIBgAmCCo2iuPOg4q9bt26wt7fH/v37cfLkSbz88sto1qxZlrvbWeLl\n5YVTp06pLrPEZDJh3bp1uHr1KqZOnYq4uDjcuHEDr732mmrs8uXLsXDhQly/fh1eXl44evQoWrRo\nYf4b0yRXxy5ERCWEnvE9esd+GD3GhxepiYgUlCtXLkuvo1q1aqFWrVqaYvV03waAY8eO4eTJk2jS\npAmAjJ5IqampmmLLli1r7qL7999/w83NDTExMZrLBtiLiYgo3+gd+2H0GB9egyAiyidyF7O1XuAG\nMm6nu3HjRpw8eRKDBw82j/HJ7Myh1cGDB81jfHJykyIeQRAR5RO9Yz+MHuPDBEFElE/0jpYHjB0x\nz1NMRET5qCh332aCICIiWdouhxMRUYnDBEFERLKYIIiISBYTBBERyWKCICIiWf8PJ4KPAiB+fbIA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa8d504f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = correlations.plot.bar()\n",
    "ax.set_ylabel(\"Pearson correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The SAT score is in general positively correlated with student survey results, less so with the teacher survey results, and not very correlated with the parent survey results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look more into correlations with saf_t_11 and saf_s_11 which measure how teachers and students perceive safety at school"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa8d546358>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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46qplXUavXuk2f8zl5eVuL4MZBCTM6ik9rHObWcueV915IM4YeWuvQiwG4ilj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\nYRiGcYn/D38PUaIFh114AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa8d56a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"saf_s_11\",\"sat_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7faa91448748>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4FjTXHcfvfnAXsmYXQO8KQXw+Hfc/8BQEKfo6VpTIuKAU19Z74RiBIdfM7uq4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\nFmLcg68AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa936b8470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# map out safety scores by district\n",
    "districts = combined.groupby(\"school_dist\").agg(numpy.mean)\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\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='blue', linewidth=.4)\n",
    "\n",
    "longitudes = districts[\"lon\"].tolist()\n",
    "latitudes = districts[\"lat\"].tolist()\n",
    "\n",
    "m.scatter(x=longitudes,y=latitudes,latlon=True,\n",
    "          s=20,zorder=2,c=districts[\"saf_s_11\"],\n",
    "          cmap=\"viridis\")\n",
    "# lighter colors have higher student safety ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Most lower safety ratings are in northern Brooklyn, with higher safety ratings in southern Brooklyn and in Manhattan (?...I'm not too familiar with the borough boundaries in NY) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Look into correlations with racial percentages and SAT score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7faa91b7c3c8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yyisQBAGbNm1CUFCQsSCeHQ5HZdOsWTPk5eUZC+Lx48dwcXFROJV6dezYEf36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\nksSCICIiSSwIIiKSxIIgIiJJ/wdwZjEGeL9xRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa8d5aecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of correlations\n",
    "var_list = [\"sat_score\",\"white_per\",\"asian_per\",\"black_per\",\"hispanic_per\"]\n",
    "survey_subset = combined[var_list]\n",
    "correlations = survey_subset.corr()\n",
    "correlations = correlations[\"sat_score\"]\n",
    "correlations = correlations[[\"white_per\",\"asian_per\",\"black_per\",\"hispanic_per\"]]\n",
    "ax = correlations.plot.bar()\n",
    "ax.set_ylabel(\"Pearson correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As suspected, whites and asians are strongly correlated to have higher SAT scores, and blacks and hispanics have lower SAT scores (the negative correlation with black percent is weak though).  Probably some complicated socioeconomics is at play."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa91bb6d30>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qg7vVXC0tLU7v+OhOdZgneOItuCNurrzG/F4tx7mThK7ABaRSJTstqvLt6VUU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\nhmE85v8H6p8SUse6awkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa91444780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at schools with low SAT scores and high hispanic_per by\n",
    "# making a scatterplot\n",
    "combined.plot.scatter(x=\"hispanic_per\",y=\"sat_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44             MANHATTAN BRIDGES HIGH SCHOOL\n",
      "82                                         0\n",
      "89     GREGORIO LUPERON HIGH SCHOOL FOR SCIE\n",
      "125                                        0\n",
      "141    INTERNATIONAL SCHOOL FOR LIBERAL ARTS\n",
      "176                                        0\n",
      "253                MULTICULTURAL HIGH SCHOOL\n",
      "286    PAN AMERICAN INTERNATIONAL HIGH SCHOO\n",
      "Name: School Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# get school names for schools with hispanic_per > 95%\n",
    "school_names = combined[combined[\"hispanic_per\"]>95][\"School Name\"]\n",
    "print(school_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looks like three of the schools with `hispanic_per` > 95% have missing names.  Based on google searches, the other schools specifically cater to recent immigrants so it makes sense that these students have lower SAT scores (since for example, they are adjusting to a new life in America and speaking English, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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 A\n",
      "356      STATEN ISLAND TECHNICAL HIGH SCHOOL\n",
      "Name: School Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# take a look at schools with hispanic_per < 10% and \n",
    "# sat_score > 1800\n",
    "school_names = combined[(combined[\"hispanic_per\"]<10) & (combined[\"sat_score\"]>1800)][\"School Name\"]\n",
    "print(school_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All of these schools seem to be elite technical schools that specialize in math and science."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate gender differences in SAT scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7faa916f2710>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n4ykpKVZFQwM3Y8YMLVq0SEFBQZKkXr16ad26dRanqt8oDrglPT1dOTk5CgkJ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\nAKZQHAAAUygOAIAp/wfqdU6wDjfcvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa91441128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of correlations\n",
    "var_list = [\"sat_score\",\"male_per\",\"female_per\"]\n",
    "survey_subset = combined[var_list]\n",
    "correlations = survey_subset.corr()\n",
    "correlations = correlations[\"sat_score\"]\n",
    "correlations = correlations[[\"male_per\",\"female_per\"]]\n",
    "ax = correlations.plot.bar()\n",
    "ax.set_ylabel(\"Pearson correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There is a very weak negative correlation of the SAT score with male percent and a very weak positive correlation with female percent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa916916a0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tra2Ii0tBW9th6Hps/RRAJ4AkAE0AVkAm+38IDJQhMDAOt2//C0AngoKSpeV5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\njKfGgoMznd4w09J72xXdfaj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tANvv7bhNTMLCwiCXy3Hr1i3cvXsXbW1tUKvVeOeddzB3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\nDMMwjMP8/6MLatq/BJjgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa93846668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a scatter plot of female_per vs. sat_score\n",
    "combined.plot.scatter(x=\"female_per\",y=\"sat_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## seems like schools with very low and very high female percentages seem to have low SAT scores!  Some of the schools with a female percent around 30-80% produce high average SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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\n",
      "302             TOWNSEND HARRIS HIGH SCHOOL\n",
      "Name: School Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# research schools with female_per > 60 and SAT score >\n",
    "# 1700\n",
    "school_names = combined[(combined[\"female_per\"]>60) & (combined[\"sat_score\"]>1700)][\"School Name\"]\n",
    "print(school_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In general these schools appear to be elite schools which cater toward a liberal arts/performing arts/humanities education."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate the relationship between the number of AP test-takers and SAT scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa9168cf28>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lmHjc/Ca9yiQmJaa/d0qrk/j4Utq6davd2IW7b/y2b/CNpFLFUlxcqWJygCf3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\nSE5ORkJCgtXzqQu758MwSnCMhLEgnLO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa9353e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total enrollment is highly correlated with SAT score\n",
    "# calculate percentage of students in each school that\n",
    "# took an AP exam and examine the correlation of this\n",
    "# percent with the SAT score\n",
    "\n",
    "#combined.columns\n",
    "combined[\"ap_per\"] = combined[\"AP Test Takers \"] / combined[\"total_enrollment\"]\n",
    "combined.plot.scatter(x=\"ap_per\",y=\"sat_score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  It looks like there is some positive correlation between the percent of students taking AP tests and the average SAT score.  For example, schools with an average SAT score > 1800 tend to have a percent of AP test takers >  20%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}