Add 217

Mission217Solutions.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read in the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas\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 = pandas.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": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "all_survey = pandas.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
+    "d75_survey = pandas.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
+    "survey = pandas.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_10\", \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": 3,
+   "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": 4,
+   "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] = pandas.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\"] = pandas.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n",
+    "data[\"hs_directory\"][\"lon\"] = pandas.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Condense datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 6,
+   "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] = pandas.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Combine the datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "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": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "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_10                                     NaN\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, 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": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1073d62b0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "N5RzcDczG66XZVP3XkX5eUvjdbOMmdlorTtrwfx2SnNwNzMbrajH8Xxys4yZWYWF2j/Dwd3MrIOa\n",
+       "PhfSzMwWEAd3M7MOcnA3M+sgB3czsw76X+mXGcuknf8uAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1081b9208>"
+      ]
+     },
+     "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": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1073b6ef0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "iFxIcAghhMiFBIcQQohcSHAIIYTIxX8AVU9wgdMGwSwAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1073a8550>"
+      ]
+     },
+     "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": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "RK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10976bda0>"
+      ]
+     },
+     "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",
+    "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 lower safety scores, whereas Brooklyn has high safety scores."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Racial differences in SAT scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1053baf60>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "1qYeAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1053a1c50>"
+      ]
+     },
+     "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": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x105436d30>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "8Mp4zCsIq8P9K/D3ZvZT7v611LWeC3ySsMDUQ9Z9OdgTCKtIHgbcZWZ/A5wIvNjdT42rz33BzE4n\n",
+       "zI/0YuACd6/d5H9ispASEdPGd93923F7lrCuRcITwNNm9mfA/4gl4RYAd3/AzP4ZeClhArs/NrOf\n",
+       "AOYICiphq7v/C4CZbYvX+Vr8zAgK4W/d/aF43ic6yOzAF9z9GeAZM/sKYbW504GzzOyuuN8igvJ4\n",
+       "GHhICkSMAikRMW08k9qeAxbEbXP3OTM7lbC+wr8HfoPmtRZaeTfwr+5+gZkdBKSn1G69TutvbVAf\n",
+       "RXL8H7r7J9MfRN/PkwOeX4hCyCciRCQuMzvj7v8TWEuYch3CyOEXLfBi4McIU/cvBh6N+/wqcFDB\n",
+       "SznwdeA1scPHzJZ1Eg0418zmR5PbasIaD7cDF0e5MbOjo5lMiJGhkYiYNvJGAA4cTvArHErouN+d\n",
+       "+uz/EDruxcB/in6QjwN/bWa/CmwCfljgOuFD911m9nbgc9Gf8RhwdgfZvg18BTgC+H0Pi1M9GsOF\n",
+       "/3cMwvoB8Na4v6KxxEhQiK8QXTCz6wmhwZ+r6PrvB37o7ldVcX0hOiFzlhDjgd72RC3RSESImmBm\n",
+       "bwMuban+O3f/LxWII0QhpESEEEL0jcxZQggh+kZKRAghRN9IiQghhOgbKREhhBB9IyUihBCib/4/\n",
+       "1OHIfC6BZooAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1053ce828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "combined.plot.scatter(\"hispanic_per\", \"sat_score\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "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": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "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": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x105483240>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
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+       "/6BtD8/ZU8vGg7uShvStJOcDa5OcDLyJ0bUnWkHO+CUddkk+2T39LqMLDH8OfIrR7Rv+Yqi6WuFZ\n",
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a6ee518>"
+      ]
+     },
+     "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": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x105436a20>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "/w9a4lwYoSdPtQAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1073cde80>"
+      ]
+     },
+     "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": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "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": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1097d27f0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "AmkHlRcAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1054a8860>"
+      ]
+     },
+     "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."
+   ]
+  }
+ ],
+ "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": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

README.md

@@ -13,4 +13,5 @@ Of course, there are always going to be multiple ways to solve any one problem,
 - [Guided Project: Predicting board game reviews](https://github.com/dataquestio/solutions/blob/master/Mission211Solution.ipynb)
 - [Guided Project: Predicting bike rentals](https://github.com/dataquestio/solutions/blob/master/Mission213Solution.ipynb)
 - [Guided Project: Preparing data for SQLite](https://github.com/dataquestio/solutions/blob/master/Mission215Solutions.ipynb)
-- [Guided Project: Creating relations in SQLite](https://github.com/dataquestio/solutions/blob/master/Mission216Solutions.ipynb)
+- [Guided Project: Creating relations in SQLite](https://github.com/dataquestio/solutions/blob/master/Mission216Solutions.ipynb)
+- [Guided Project: Analyzing NYC High School Data](https://github.com/dataquestio/solutions/blob/master/Mission217Solutions.ipynb)