{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read 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": [
    "# Read 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_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] = 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": 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": [
    "# 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": {
      "image/png": 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Qp5mZNbBZ1UJJFwPbD1n0hv6ZiAhJUfE59wY+Abwyn8GbmdksUsTImFxdUFoLTEXEjyU9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\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": {
      "image/png": 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ZjNpOF4DBuJGU4MRcXmPDAa+xlEE/zegeTtXS/LOW95PKK78Fz31M2Z0u+wZ0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\niFxIcAghhMiFBIcQQohcSHAIIYTIxX8AVU9wgdMGwSwAAAAASUVORK5CYII=\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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lnn6hOTNevhv4PnBYDO/M2c2nn37MBx/87s71jOuZiJoG/NR9ITSwZsW5Pvnk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\nRK5CYII=\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",
    "# Temporary bug: if you run the following line of code in the Jupyter Guided Project interface on Dataquest, you'll get an error. \n",
    "# We're working on a fix, thanks for your patience! This should work fine locally on your own computer though.\n",
    "# m.fillcontinents(color='white',lake_color='#85A6D9')\n",
    "\n",
    "longitudes = districts[\"lon\"].tolist()\n",
    "latitudes = districts[\"lat\"].tolist()\n",
    "m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=districts[\"saf_s_11\"], cmap=\"summer\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like Upper Manhattan and parts of Queens and the Bronx tend to have 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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      "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": {
      "image/png": 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xtwclzN/R7OdJ+0uOiPuvSp1nYU/PpvP3LduxXuxlRI71Ksq4K5EvAv8hbv9X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\n1OHIfC6BZooAAAAASUVORK5CYII=\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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/+v0To4/OT07yLuDfgXcPW5L0iC8neRuwLslLGS37XDNwTVPPg7sNSPIs4MVd84aq2jFk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\n",
      "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": {
      "image/png": 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I3EeOVtJvnzMdXuajFc6sqGASg/jo+w1yzBwa2V4SwFjsJXZCbKtg94lKb8JK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\n/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": {
      "image/png": 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3hECAk4ANqfpXAN9q5Uvop1I8c1nhwYZxvBcC6NKzrpF9cNgTYbZ9vBeEzrrS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\nAmkHlRcAAAAASUVORK5CYII=\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": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
