{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing NYC High School Data\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this project, we'll aim to explore relationships between SAT scores and demographic factors in New York City public schools. The SAT, or Scholastic Aptitude Test, is a test that high school seniors in the U.S. take every year. Colleges use the SAT to determine which students to admit. High average SAT scores are usually indicative of a good school.\n",
    "\n",
    "New York City has published data on student SAT scores by high school, along with additional demographic data sets. We will use  the following data sets in this analysis:\n",
    "- [SAT scores by school](https://data.cityofnewyork.us/Education/SAT-Results/f9bf-2cp4) - SAT scores for each high school in New York City\n",
    "- [School attendance](https://data.cityofnewyork.us/Education/School-Attendance-and-Enrollment-Statistics-by-Dis/7z8d-msnt) - Attendance information for each school in New York City\n",
    "- [Class size](https://data.cityofnewyork.us/Education/2010-2011-Class-Size-School-level-detail/urz7-pzb3) - Information on class size for each school\n",
    "- [AP test results](https://data.cityofnewyork.us/Education/AP-College-Board-2010-School-Level-Results/itfs-ms3e) - Advanced Placement (AP) exam results for each high school (passing an optional AP exam in a particular subject can earn a student college credit in that subject)\n",
    "- [Graduation outcomes](https://data.cityofnewyork.us/Education/Graduation-Outcomes-Classes-Of-2005-2010-School-Le/vh2h-md7a) - The percentage of students who graduated, and other outcome information\n",
    "- [Demographics](https://data.cityofnewyork.us/Education/School-Demographics-and-Accountability-Snapshot-20/ihfw-zy9j) - Demographic information for each school\n",
    "- [School survey](https://data.cityofnewyork.us/Education/NYC-School-Survey-2011/mnz3-dyi8) - Surveys of parents, teachers, and students at each school\n",
    "\n",
    "New York City has a significant immigrant population and is very diverse, so comparing demographic factors such as race, income, and gender with SAT scores is a good way to determine whether the SAT is a fair test.\n",
    "\n",
    "### Summary of results\n",
    "\n",
    "After analyzing the data, we reached that the SAT scores are determining by some of the demographic factors. we can highlight the following facts:\n",
    "- The safe schools present better results in this test, since a safe place is better for a learning environment.\n",
    "- The schools with high percentege of white and asian students achieve better results than the ones who present high percentege of black and hipanic students. \n",
    "- The gender does not present high influence in the SAT score results.\n",
    "\n",
    "### Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data\n",
    "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\diana\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Read in the surveys\n",
    "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": [
    "## Cleaning and Transforming datasets\n",
    "\n",
    "### Add DBN columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"hs_directory\"][\"DBN\"] = data[\"hs_directory\"][\"dbn\"]\n",
    "\n",
    "def pad_csd(num):\n",
    "    string_representation = str(num)\n",
    "    if len(string_representation) > 1:\n",
    "        return string_representation\n",
    "    else:\n",
    "        return \"0\" + string_representation\n",
    "    \n",
    "data[\"class_size\"][\"padded_csd\"] = data[\"class_size\"][\"CSD\"].apply(pad_csd)\n",
    "data[\"class_size\"][\"DBN\"] = data[\"class_size\"][\"padded_csd\"] + data[\"class_size\"][\"SCHOOL CODE\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert columns to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']\n",
    "for c in cols:\n",
    "    data[\"sat_results\"][c] = pd.to_numeric(data[\"sat_results\"][c], errors=\"coerce\")\n",
    "\n",
    "data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n",
    "\n",
    "def find_lat(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n",
    "    return lat\n",
    "\n",
    "def find_lon(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lon = coords[0].split(\",\")[1].replace(\")\", \"\").strip()\n",
    "    return lon\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lat)\n",
    "data[\"hs_directory\"][\"lon\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lon)\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = pd.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n",
    "data[\"hs_directory\"][\"lon\"] = pd.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condense datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_size = data[\"class_size\"]\n",
    "class_size = class_size[class_size[\"GRADE \"] == \"09-12\"]\n",
    "class_size = class_size[class_size[\"PROGRAM TYPE\"] == \"GEN ED\"]\n",
    "\n",
    "class_size = class_size.groupby(\"DBN\").agg(numpy.mean)\n",
    "class_size.reset_index(inplace=True)\n",
    "data[\"class_size\"] = class_size\n",
    "\n",
    "data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n",
    "\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert AP scores to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n",
    "\n",
    "for col in cols:\n",
    "    data[\"ap_2010\"][col] = pd.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine the datasets\n",
    "\n",
    "The dataframe combined contains all of the data we'll be using in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = data[\"sat_results\"]\n",
    "\n",
    "combined = combined.merge(data[\"ap_2010\"], on=\"DBN\", how=\"left\")\n",
    "combined = combined.merge(data[\"graduation\"], on=\"DBN\", how=\"left\")\n",
    "\n",
    "to_merge = [\"class_size\", \"demographics\", \"survey\", \"hs_directory\"]\n",
    "\n",
    "for m in to_merge:\n",
    "    combined = combined.merge(data[m], on=\"DBN\", how=\"inner\")\n",
    "\n",
    "combined = combined.fillna(combined.mean())\n",
    "combined = combined.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add a school district column for mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_first_two_chars(dbn):\n",
    "    return dbn[0:2]\n",
    "\n",
    "combined[\"school_dist\"] = combined[\"DBN\"].apply(get_first_two_chars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find correlations \n",
    "\n",
    "There are several fields in combined that originally came from a survey of parents, teachers, and students. Let's find correlations between these files and sat_score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n",
    "survey_fields.remove(\"DBN\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Surveys Fields')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "correlations[survey_fields].plot.bar()\n",
    "plt.title('Correlation between surveys fields and SAT Score')\n",
    "plt.ylabel('Correlation SAT Score')\n",
    "plt.xlabel('Surveys Fields')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are high correletaion between sat_score and the attributes N_s, N_p, N_t that represents the number of students respondents, parents respondents and total teacher respondents, respectively.\n",
    "\n",
    "The academic expectations score based on student responses or aca_s_11 correlates with sat_score. However, how teachers and parents perceive academic standards, or aca_p_11 and aca_t_11 present a weak correlation with sat_score.\n",
    "\n",
    "It is 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",
    "There are a high correlation between sat_score and the attributes related to safety anserwed by students and teachers (saf_s_11, saf_t_11), since a safe place is better for a learning environment. For this reason, we'll dig into this relationship a bit more, and try to figure out which schools have low safety scores.\n",
    "\n",
    "## Exploring safety"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Safety Score anserwed by students vs SAT score')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter('saf_s_11', 'sat_score')\n",
    "plt.title('Safety Score anserwed by students vs SAT score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This figure shows how saf_s_11 quite correlates with sat_score. There are a few schools with low safety scores and low SAT scores. Notice how for safety score higher than 7.0 the sat_score's values start to increase. There are not schools with a safety score lower than 6.5 that present an average SAT score higher than 1500. Then, the safe schools present better results in this test.\n",
    "\n",
    "Let's map out safety scores. To do so, we will compute the average safety score for each district and create a map that shows safety scores by district."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "#compute average safety score for each district\n",
    "districts = combined.groupby(\"school_dist\").agg(numpy.mean)\n",
    "districts.reset_index(inplace=True)\n",
    "\n",
    "# create a map that shows safety scores\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 higher safety scores, whereas Brooklyn has low safety scores.\n",
    "\n",
    "## Racial differences in SAT scores\n",
    "\n",
    "There are a few columns that indicate the percentage of each race at a given school:\n",
    "\n",
    "- white_per\n",
    "- asian_per\n",
    "- black_per\n",
    "- hispanic_per\n",
    "\n",
    "We will determine whether there are any racial differences in SAT performance by plotting out the correlations between these columns and sat_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Percentage of each race')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations[['white_per', 'asian_per','black_per', 'hispanic_per']].plot.bar()\n",
    "plt.title('Correlation between race fields and SAT Score')\n",
    "plt.ylabel('Correlation SAT Score')\n",
    "plt.xlabel('Percentage of each race')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are high positive correlation between the percentage of white and asian students with SAT score. However, the percentage of black (black_per) and hispanic (hispanic_per) students correlate negative with SAT score. A deeper rearch could be done to know why the white and asian students achieve better results than the black and hipanic ones.\n",
    "\n",
    "Let's explore schools with low SAT scores and high values for hispanic_per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Percentage of hispanic vs SAT score')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"hispanic_per\", \"sat_score\")\n",
    "plt.title('Percentage of hispanic vs SAT score')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see how when the percentage of hispanic students increase the SAT score values decreace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined[\"hispanic_per\"] > 95][\"SCHOOL NAME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The students from these schools are immigrants recently arrived from Spanish speaking countries, many of them are learning English, which would explain the lower SAT scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[(combined[\"hispanic_per\"] < 10) & (combined[\"sat_score\"] > 1800)][\"SCHOOL NAME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These schools are specialized schools that offer tuition-free accelerated academics to city residents, and only admit students who pass an entrance exam. It does explain why their students tend to do better SAT scores.\n",
    "\n",
    "## Gender differences in SAT scores\n",
    "\n",
    "There are two columns that indicate the percentage of each gender at a school: male_per, female_per\n",
    "We can plot out the correlations between each percentage and sat_score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Percentage of each gender')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations[['male_per', 'female_per']].plot.bar()\n",
    "plt.title('Correlation between gender fields and SAT Score')\n",
    "plt.ylabel('Correlation SAT Score')\n",
    "plt.xlabel('Percentage of each gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both attributes present a weak correlation with SAT score. However, notice how a high percentage of male at high scholls negatively correlate with SAT score, whereas the percentage of female positively correlate with SAT score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Percentage of female vs SAT score')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# schools with high SAT scores and a high female_per\n",
    "combined.plot.scatter(\"female_per\", \"sat_score\")\n",
    "plt.title('Percentage of female vs SAT score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, we can see a cluster with many schools with a percentage of female between 40 and 60 and SAT score between 1100 and 1500. However, there are many schools with a high percentage of females and high SAT scores. The correlation doesn't seem to be strong between these attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[(combined[\"female_per\"] > 60) & (combined[\"sat_score\"] > 1700)][\"SCHOOL NAME\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list above are specialiced schools that seems to be very selective, with academic standards. For example, the first one allows the students to begin their college studies two years early, graduating with a Bard Collage Associate in Arts degree in addition to their high school diploma.\n",
    "\n",
    "In the U.S., high school students take Advanced Placement (AP) exams to earn college credit. There are AP exams for many different subjects. It makes sense that the number of students at a school who took AP exams would be highly correlated with the school's SAT scores. We'll look at the percentage of students in each school who took at least one AP exam.\n",
    "\n",
    "## AP Exam Scores vs SAT Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Percentage of students who took AP exams vs SAT score')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined['ap_per'] = combined['AP Test Takers '] / combined['total_enrollment']\n",
    "combined.plot.scatter(\"ap_per\", \"sat_score\")\n",
    "plt.title('Percentage of students who took AP exams vs SAT score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the scatterplot, there doesn't seem to be an strong correlation between sat_score and ap_per. However, there is a cluster of schools with a low percentage of students that took at least one AP exam and low SAT scores.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "In this project, we explored relationships between SAT scores and demographic factors in New York City public schools. We can reach that safe schools present higher SAT scores than the unsafe ones. The schools with high percentege of white and asian students achieve better results than the ones who present high percentege of black and hipanic students. A deeper rearch could be done to know why the white and asian students achieve better results than the black and hipanic ones. The gender does not present high influence in the SAT score results. Finally, there doesn't seem to be an strong relation between SAT score and the number of students at a school who took AP exams.\n",
    "\n"
   ]
  }
 ],
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   "language": "python",
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