Relationship+between+Demographic+Factors+and+SAT+scores+of+NYC+High+Schools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relationships between SAT scores and demographic factors\n",
    "\n",
    "Over the course of this project, survey data from NYC school departments will be analyzed to draw relationships between the average SAT score of a school, and its various demographic factors.\n",
    "\n",
    "The data is publicly available and [can be found here](https://data.cityofnewyork.us/Education/2011-NYC-School-Survey/mnz3-dyi8)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
    "# Reading in the surveys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "# Adding `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": [
    "# Converting 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] = 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": [
    "# Condensing 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] = pd.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining 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": [
    "# Finding 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_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": 10,
   "metadata": {
    "collapsed": false
   },
   "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d416a9278>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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crJ5t0sfNTbVeNy8vGKKsz1h0K7XDu5eXm5fqbEcvN5VYLzdERVn/heTlZWO93dys/sJy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\nozsYW2RG9ThrRJO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Ayt+xVcYEMJlZzbJ1a4llkiczzkoAzmrVzp07F8OGDcOXX36J/fv3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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d416a9978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "correlation = combined.corr()[\"sat_score\"]\n",
    "\n",
    "survey_fields_correlation = correlation[survey_fields]\n",
    "\n",
    "survey_fields_correlation.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`N_s`, `N_p`, `N_t`, and `rr_s` have very strong correlations with SAT scores. It makes sense to think that the average SAT score of a student might correlate with their likeliness to respond to the survey, since their willingness to submit a survey response could correlate to academic rigour. In addition to that, students might be especially more likely to participate in surveys if they know they're likely to do well, as they know that would improve their school's standing in the eyes of the public. \n",
    "\n",
    "`aca_t` and `aca_s` also correlate strongly to SAT scores. This is likely because if a students and teachers likely feel safest in environments conducive to studying. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Safety Scores\n",
    "\n",
    "In the above bar plot, we noticed that `saf_t` and `saf_s` correlated notably with `sat_score`. Now, we'll dig deeper into this relationship, and try to figure out which schools have low safety scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d416fb860>"
      ]
     },
     "execution_count": 12,
     "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 0x7f1d41702470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(x='saf_s_11', y='sat_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, there is a fair correlation with safety perceptions of students, and the average SAT score. This trend mostly holds true, except for 5 anomalous cases of schools with safety scores of more than 8, but average SAT scores of less than 1500. All schools with average SAT scores of 1600 or more had a safety score of more than 6.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['saf_p_11', 'saf_t_11', 'saf_s_11', 'saf_tot_11']\n",
      "\n",
      "\n",
      "  school_dist  saf_p_11  saf_t_11  saf_s_11  saf_tot_11\n",
      "0          01  8.233333  7.066667  6.768611    7.433333\n",
      "1          02  8.254167  7.404167  6.910660    7.520833\n",
      "2          03  8.316667  6.675000  6.716667    7.233333\n",
      "3          04  8.385714  7.871429  6.885714    7.714286\n",
      "4          05  8.085714  6.857143  6.314286    7.085714\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "by_district = combined.groupby('school_dist').agg(np.mean)\n",
    "\n",
    "#Using List Comprehension to identify safety related columns:\n",
    "safety_list = [each for each in by_district.columns if 'saf' in each]\n",
    "print(safety_list)\n",
    "print('\\n')\n",
    "\n",
    "by_district_saf = by_district[safety_list]\n",
    "by_district_saf = by_district_saf.reset_index()\n",
    "\n",
    "\n",
    "print(by_district_saf.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping out Safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hSZuAIBpxVe/q9H5WwTjayuvwNDuD7jXuKmPY6DkRkesMx/Zu5E9P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d41760080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\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",
    "\n",
    "longitudes = by_district[\"lon\"].tolist()\n",
    "latitudes = by_district[\"lat\"].tolist()\n",
    "m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=by_district[\"saf_s_11\"], cmap=\"summer\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Green spots indicate lower safety scores, and yellow spots indicate higher safety scores. \n",
    "\n",
    "From the map of NYC, it looks like Manhattan and the Bronx tend to have higher safety scores, whereas Brooklyn has lower safety scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigating Racial differences in SAT scores\n",
    "\n",
    "The following columns indicate the percentage of each of the following races in each school:\n",
    "- `white_per`\n",
    "- `asian_per`\n",
    "- `black_per`\n",
    "- `hispanic_per`\n",
    "\n",
    "Correlations between these columns and `sat_score` can be used to determine if there are significant discrepancies in performance based on race. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d4174c5c0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WPBGRxFjyREQSY8kTEUmMJU9EJDGWPBGRxFjyREQSY8kTEUmMJU9EJDGWPBGRxFjyREQS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d4175ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race_sat_corr = correlation[['white_per', 'asian_per', 'black_per', 'hispanic_per']]\n",
    "\n",
    "race_sat_corr.plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Schools with higher proportions of Asians and White students have higher SAT scores on average, whereas those with higher Black and Hispanic students perform more poorly. This is most likely correlated to socioeconomic factors that also happen to correlate to race. For instance, black and hispanic families tend to do less well financially. \n",
    "\n",
    "Let's look closer at the correlations between `hispanic_per` and `sat_score`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d40f45fd0>"
      ]
     },
     "execution_count": 16,
     "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 0x7f1d40f41eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(x='hispanic_per', y='sat_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, there's a clear negative correlation between `hispanic_per` and `sat_score`. No school with more than 30% hispanic students has an average SAT score higher than 1500.\n",
    "\n",
    "Let's investigate the schools that have Hispanic student enrollments of more than 95%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hispanic_students = combined[combined['hispanic_per'] > 95]\n",
    "\n",
    "hispanic_students[\"SCHOOL NAME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon doing some research, it looks as if the schools above are catering to students who might have recently immigrated to the US. \n",
    "\n",
    "Since these students are likely also less fluent in English, it makes sense to think that their avg SAT scores would be lower too.\n",
    "\n",
    "Now let's loot for schools with `hispanic_per` lower than 10%, and SAT scores above 1800:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_hispanic = combined[(combined['hispanic_per'] <10) & (combined['sat_score'] > 1800)]\n",
    "\n",
    "low_hispanic[\"SCHOOL NAME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above schools are mostly specialied schools that not only receive extra government funding, but also have entrance exams that they require their students to pass. One reason the `hispanic_per` might be lower in these schools is once again because of socioeconomic factors - it might be that school fees associated with such specialized technical schools are higher, and since hispanic families tend to have lower incomes on average, they're less financially equipped with the means to send their children to such schools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigating Gender Difference in SAT scores\n",
    "\n",
    "The `male_per` and `female_per` columns can be used to visualize difference in average scores based on gender percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male_per     -0.112062\n",
       "female_per    0.112108\n",
       "Name: sat_score, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_diff = combined.corr()[\"sat_score\"][['male_per', 'female_per']]\n",
    "\n",
    "gender_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d40ebb0b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d3f622128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_diff.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, there's a slight correlation in the no. of female students in the school, and the average SAT scores. There's a corresponding slight negative correlation in the no. of male students in the school and its average SAT scores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d3f63ee80>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tra2Ii0tBW9th6Hps/RRAJ4AkAE0AVkAm+38IDJQhMDAOt2//C0AngoKSpeV5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\nDMMwjMP8/6MLatq/BJjgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d3f5e2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot(kind='scatter', x='female_per', y='sat_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A scatter plot of `female_per` against `sat_score` reveals that there is no clear link between the percentage of female students in a school and its average SAT score after all.\n",
    "\n",
    "Let's look closer at schools with `female_per` greater than 60%, and average SAT scores of more than 1700:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_per_schools = combined[(combined['female_per'] >60) & (combined['sat_score'] > 1700)]\n",
    "\n",
    "female_per_schools['SCHOOL NAME']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High scoring schools with female percentages of above 60% look to be mostly liberal arts schools. These schools likely have high barriers to entry, making it so only academically stronger students may enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Investigating AP exam frequencies based on SAT scores\n",
    "\n",
    "High School students in the US take Advanced Placement (AP) exams to earn college credit. It's reasonable to expect that the percentage of students in a school who took AP exams would correlate positively to that school's avg SAT scores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d3f591978>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lmHjc/Ca9yiQmJaa/d0qrk/j4Utq6davd2IW7b/y2b/CNpFLFUlxcqWJygCf3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\nCwnDMAzjFSwkDMMwjFewkDAMwzBe8f8Bjpto6EowC/kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d3f566cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['ap_per'] = combined['AP Test Takers '] / combined['total_enrollment']\n",
    "\n",
    "combined.plot(kind='scatter', x = 'ap_per', y = 'sat_score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first glance, it seems like strangely, there isn't a clear correlation between the percentage of students who took an Advanced Placement exam, and the average SAT score of a school.\n",
    "\n",
    "There are about 20 schools whose average SAT score was less than 1250, and yet over 50% of their students took an AP exam. It might have been that in these schools, taking AP exams was a mandatory school requirements. Excluding these 20 or so schools, the correlation between AP exam-taker percentage and average SAT score becomes a bit clearer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05717081390766967"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_ap = combined.corr()['sat_score']\n",
    "\n",
    "correlation_ap['ap_per']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see above that the correlation between `sat_score` and `ap_per` is very insignificant as a result of the low SAT average schools with high AP-exam taking rates."
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}