NY high schools analysis project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying Biases in Standardized Tests.\n",
    "\n",
    "# Introduction: \n",
    "Our goal here is to identify whether SAT tests have some sort of bias towards people of certain demographics. The fairness of standardized testing has been a topic of debate for a while now and what this analysis looks to give insight to where this unfairness play a role. For the purpose of this analysis we will be looking at schools in New York.\n",
    "We are going to be working with 8 data sets, some of the data included are:\n",
    "* `ap_2010.csv`: This contains data on AP tests enrollment.\n",
    "* `demographics.csv`: This contains data on the different demographics of people in NY high scools.\n",
    "* `sat_results`: This contains data on the average SAT scores of different schools in New Yrok."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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": {},
   "outputs": [],
   "source": [
    "all_survey = pd.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "d75_survey = pd.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "survey = pd.concat([all_survey, d75_survey], axis=0)\n",
    "\n",
    "survey[\"DBN\"] = survey[\"dbn\"]\n",
    "\n",
    "survey_fields = [\n",
    "    \"DBN\", \n",
    "    \"rr_s\", \n",
    "    \"rr_t\", \n",
    "    \"rr_p\", \n",
    "    \"N_s\", \n",
    "    \"N_t\", \n",
    "    \"N_p\", \n",
    "    \"saf_p_11\", \n",
    "    \"com_p_11\", \n",
    "    \"eng_p_11\", \n",
    "    \"aca_p_11\", \n",
    "    \"saf_t_11\", \n",
    "    \"com_t_11\", \n",
    "    \"eng_t_11\", \n",
    "    \"aca_t_11\", \n",
    "    \"saf_s_11\", \n",
    "    \"com_s_11\", \n",
    "    \"eng_s_11\", \n",
    "    \"aca_s_11\", \n",
    "    \"saf_tot_11\", \n",
    "    \"com_tot_11\", \n",
    "    \"eng_tot_11\", \n",
    "    \"aca_tot_11\",\n",
    "]\n",
    "survey = survey.loc[:,survey_fields]\n",
    "data[\"survey\"] = survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add DBN columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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",
    "    ### uses the regex pattern to extract the latitude\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n",
    "    return lat\n",
    "\n",
    "def find_lon(loc):\n",
    "    ### uses the regex pattern to extract the longitude\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(np.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"
   ]
  },
  {
   "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"
   ]
  },
  {
   "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",
      "                                     ...   \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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "style.use('fivethirtyeight')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey_correlation = combined.corr()[\"sat_score\"][survey_fields]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#returns a bar plot of the r values between different survey response and SAT scores\n",
    "positive_corr = survey_correlation > 0\n",
    "corr_map = positive_corr.map({True:'#151b8d', False:'#ff4500'})\n",
    "survey_correlation.plot(kind='bar', legend=False, color=corr_map)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most imteresting things that we can find from our correlation plot is that SAT scores correlated most with the number of student respondents(N_s),  number of parent respondents(N_P), safetfy and respect score based on student response(saf_s_11), safety and respect score based on teachers response(saf_t_11) and academic expectation score based on student responses(aca_s_11)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Correlation between SAT Score and Safety Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(x='saf_s_11', y='sat_score', c='#3ea99f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at  our plot there doesn't seem to be a very strong correlation between the safety score and SAT scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping Data by Boroughs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groups data by a specific column\n",
    "boroughs = combined.groupby('boro').agg(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "boro_safety = boroughs['saf_s_11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "boro\n",
       "Bronx            6.606577\n",
       "Brooklyn         6.370755\n",
       "Manhattan        6.831370\n",
       "Queens           6.721875\n",
       "Staten Island    6.530000\n",
       "Name: saf_s_11, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boro_safety"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manhattan, Quuens and Bronx seem to have the highest average safety score amongst all of the boroughs in New York."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation with Race "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#returns a bar plot of the r values between race and SAT scores\n",
    "race_col = ['white_per', 'asian_per', 'black_per', 'hispanic_per']\n",
    "race_corr = combined.corr()['sat_score'][race_col]\n",
    "positive_corr = combined.corr()['sat_score'][race_col] > 0\n",
    "corr_map = positive_corr.map({True:'#151b8d', False:'#ff4500'})\n",
    "race_corr.plot(kind='bar', color=corr_map)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at our bar plot, we can see that both the white percentage and the Asian percentage has a strong positive correlation with the SAT scores while the black and especially the Hispanic percentage has a high negative correlation with the SAT scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Hispanic Percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <th>sat_score</th>\n",
       "      <th>SchoolName</th>\n",
       "      <th>AP Test Takers</th>\n",
       "      <th>Total Exams Taken</th>\n",
       "      <th>...</th>\n",
       "      <th>priority05</th>\n",
       "      <th>priority06</th>\n",
       "      <th>priority07</th>\n",
       "      <th>priority08</th>\n",
       "      <th>priority09</th>\n",
       "      <th>priority10</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>school_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>07X334</td>\n",
       "      <td>INTERNATIONAL COMMUNITY HIGH SCHOOL</td>\n",
       "      <td>34</td>\n",
       "      <td>310.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345 Brook Avenue\\nBronx, NY 10454\\n(40.8100361...</td>\n",
       "      <td>40.810036</td>\n",
       "      <td>-73.917812</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>09X365</td>\n",
       "      <td>ACADEMY FOR LANGUAGE AND TECHNOLOGY</td>\n",
       "      <td>54</td>\n",
       "      <td>315.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>Academy for Language and Technology</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1700 Macombs Road\\nBronx, NY 10453\\n(40.849102...</td>\n",
       "      <td>40.849102</td>\n",
       "      <td>-73.916088</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>09X403</td>\n",
       "      <td>BRONX INTERNATIONAL HIGH SCHOOL</td>\n",
       "      <td>49</td>\n",
       "      <td>314.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1110 Boston Road\\nBronx, NY 10456\\n(40.8276026...</td>\n",
       "      <td>40.827603</td>\n",
       "      <td>-73.904475</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>10X268</td>\n",
       "      <td>KINGSBRIDGE INTERNATIONAL HIGH SCHOOL</td>\n",
       "      <td>52</td>\n",
       "      <td>304.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>Kingsbridge International High School</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2780 Reservoir Avenue\\nBronx, NY 10468\\n(40.87...</td>\n",
       "      <td>40.870377</td>\n",
       "      <td>-73.898163</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>10X342</td>\n",
       "      <td>INTERNATIONAL SCHOOL FOR LIBERAL ARTS</td>\n",
       "      <td>49</td>\n",
       "      <td>300.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>934.0</td>\n",
       "      <td>International School for Liberal Arts</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2780 Reservoir Avenue\\nBronx, NY 10468\\n(40.87...</td>\n",
       "      <td>40.870377</td>\n",
       "      <td>-73.898163</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>12X388</td>\n",
       "      <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE</td>\n",
       "      <td>30</td>\n",
       "      <td>321.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300 Boynton Avenue\\nBronx, NY 10472\\n(40.8313...</td>\n",
       "      <td>40.831366</td>\n",
       "      <td>-73.878823</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>12X550</td>\n",
       "      <td>HIGH SCHOOL OF WORLD CULTURES</td>\n",
       "      <td>42</td>\n",
       "      <td>304.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>939.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300 Boynton Avenue\\nBronx, NY 10472\\n(40.8313...</td>\n",
       "      <td>40.831366</td>\n",
       "      <td>-73.878823</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>19K583</td>\n",
       "      <td>MULTICULTURAL HIGH SCHOOL</td>\n",
       "      <td>29</td>\n",
       "      <td>279.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>Multicultural High School</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>999 Jamaica Avenue\\nBrooklyn, NY 11208\\n(40.69...</td>\n",
       "      <td>40.691144</td>\n",
       "      <td>-73.868426</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>24Q296</td>\n",
       "      <td>PAN AMERICAN INTERNATIONAL HIGH SCHOOL</td>\n",
       "      <td>55</td>\n",
       "      <td>317.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45-10 94Th Street\\nElmhurst, NY 11373\\n(40.743...</td>\n",
       "      <td>40.743303</td>\n",
       "      <td>-73.870575</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 160 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DBN                                       SCHOOL NAME  \\\n",
       "91   07X334               INTERNATIONAL COMMUNITY HIGH SCHOOL   \n",
       "125  09X365               ACADEMY FOR LANGUAGE AND TECHNOLOGY   \n",
       "126  09X403                   BRONX INTERNATIONAL HIGH SCHOOL   \n",
       "139  10X268             KINGSBRIDGE INTERNATIONAL HIGH SCHOOL   \n",
       "141  10X342             INTERNATIONAL SCHOOL FOR LIBERAL ARTS   \n",
       "176  12X388  PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE   \n",
       "179  12X550                     HIGH SCHOOL OF WORLD CULTURES   \n",
       "253  19K583                         MULTICULTURAL HIGH SCHOOL   \n",
       "286  24Q296            PAN AMERICAN INTERNATIONAL HIGH SCHOOL   \n",
       "\n",
       "    Num of SAT Test Takers  SAT Critical Reading Avg. Score  \\\n",
       "91                      34                            310.0   \n",
       "125                     54                            315.0   \n",
       "126                     49                            314.0   \n",
       "139                     52                            304.0   \n",
       "141                     49                            300.0   \n",
       "176                     30                            321.0   \n",
       "179                     42                            304.0   \n",
       "253                     29                            279.0   \n",
       "286                     55                            317.0   \n",
       "\n",
       "     SAT Math Avg. Score  SAT Writing Avg. Score  sat_score  \\\n",
       "91                 324.0                   311.0      945.0   \n",
       "125                339.0                   297.0      951.0   \n",
       "126                312.0                   339.0      965.0   \n",
       "139                356.0                   302.0      962.0   \n",
       "141                333.0                   301.0      934.0   \n",
       "176                351.0                   298.0      970.0   \n",
       "179                323.0                   312.0      939.0   \n",
       "253                322.0                   286.0      887.0   \n",
       "286                323.0                   311.0      951.0   \n",
       "\n",
       "                                SchoolName  AP Test Takers   \\\n",
       "91                                       0       129.028846   \n",
       "125    Academy for Language and Technology        20.000000   \n",
       "126                                      0       129.028846   \n",
       "139  Kingsbridge International High School        32.000000   \n",
       "141  International School for Liberal Arts        55.000000   \n",
       "176                                      0       129.028846   \n",
       "179                                      0       129.028846   \n",
       "253              Multicultural High School        44.000000   \n",
       "286                                      0       129.028846   \n",
       "\n",
       "     Total Exams Taken  ...  priority05 priority06 priority07 priority08  \\\n",
       "91          197.038462  ...           0          0          0        0.0   \n",
       "125          20.000000  ...           0          0          0        0.0   \n",
       "126         197.038462  ...           0          0          0        0.0   \n",
       "139          37.000000  ...           0          0          0        0.0   \n",
       "141          73.000000  ...           0          0          0        0.0   \n",
       "176         197.038462  ...           0          0          0        0.0   \n",
       "179         197.038462  ...           0          0          0        0.0   \n",
       "253          44.000000  ...           0          0          0        0.0   \n",
       "286         197.038462  ...           0          0          0        0.0   \n",
       "\n",
       "     priority09 priority10                                         Location 1  \\\n",
       "91          0.0        0.0  345 Brook Avenue\\nBronx, NY 10454\\n(40.8100361...   \n",
       "125         0.0        0.0  1700 Macombs Road\\nBronx, NY 10453\\n(40.849102...   \n",
       "126         0.0        0.0  1110 Boston Road\\nBronx, NY 10456\\n(40.8276026...   \n",
       "139         0.0        0.0  2780 Reservoir Avenue\\nBronx, NY 10468\\n(40.87...   \n",
       "141         0.0        0.0  2780 Reservoir Avenue\\nBronx, NY 10468\\n(40.87...   \n",
       "176         0.0        0.0  1300 Boynton Avenue\\nBronx, NY 10472\\n(40.8313...   \n",
       "179         0.0        0.0  1300 Boynton Avenue\\nBronx, NY 10472\\n(40.8313...   \n",
       "253         0.0        0.0  999 Jamaica Avenue\\nBrooklyn, NY 11208\\n(40.69...   \n",
       "286         0.0        0.0  45-10 94Th Street\\nElmhurst, NY 11373\\n(40.743...   \n",
       "\n",
       "           lat        lon school_dist  \n",
       "91   40.810036 -73.917812          07  \n",
       "125  40.849102 -73.916088          09  \n",
       "126  40.827603 -73.904475          09  \n",
       "139  40.870377 -73.898163          10  \n",
       "141  40.870377 -73.898163          10  \n",
       "176  40.831366 -73.878823          12  \n",
       "179  40.831366 -73.878823          12  \n",
       "253  40.691144 -73.868426          19  \n",
       "286  40.743303 -73.870575          24  \n",
       "\n",
       "[9 rows x 160 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns result for schools with > 60% hispanic and average SAT socres less than 1000\n",
    "hispanic_filt = (combined['hispanic_per'] >= 60) & (combined['sat_score'] < 1000)\n",
    "hispanic_low_sat = combined[hispanic_filt]\n",
    "hispanic_low_sat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#shows a scatter plot of the hispanic percetage and SAT scores\n",
    "combined.plot.scatter('hispanic_per', 'sat_score', c='#3ea99f')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our scatter plot confirms the negative correlation between the hispanic percentages and SAT scores. Schools with lower hispanic percentages tend to have higher SAT scores. No school with over 60% of hispanic students have an SAT score up to 1600."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns schools with more than 95% hispanics\n",
    "filt = combined['hispanic_per'] > 95\n",
    "hispanic_more_than_95 = combined[filt]\n",
    "hispanic_more_than_95['SCHOOL NAME']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing common about the schools with over 95% of hisoancic students is that they all have a high percentage of minority enrollments. Usually 98% or more minority enrolllments  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns the result of schools with less than 10% hispanic and SAT scores > 1800\n",
    "filt = (combined['hispanic_per'] < 10) & (combined['sat_score'] > 1800)\n",
    "low_hisp_high_sat = combined[filt]\n",
    "low_hisp_high_sat['SCHOOL NAME']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sschools with less than 10% hispanic, What I have found out is that they are all part of the top 10 schools in New York snd these schools are generally geared towards sciences, This may Explain the high SAT scores. Although this schools have lower minority enrolment than schools with 95% hispanic, the difference is not as noticable as that of the economically disadvantaged people enrolled in these schools. Where schools with more than 95% hispanic had an economically disadvantaged enrollment of more than 90%, the schools with less than 10% hispanic on the other hand did not have an economically disadvantaged enrollment of more than 62%. So it is safe to infer that the rate of economically disadvantaged students enrolled in a school plays a part in why there are school's with a lot of hispanics and others with not so much."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation with Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#returns a bar plot of the r values between gender and SAT scores\n",
    "gender_col = ['female_per', 'male_per']\n",
    "gender_corr = combined.corr()['sat_score'][gender_col]\n",
    "positive_corr = combined.corr()['sat_score'][gender_col] > 0\n",
    "corr_map = positive_corr.map({True:'#151b8d', False:'#ff4500'})\n",
    "gender_corr.plot.bar(color=corr_map)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the r values do not show strong correlations, the male percentage correlates negatively with SAT scores while the female percentage correlates positively with SAT scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Female percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displays a scatter plot showing the correlation between female percentage and SAT scores\n",
    "combined.plot.scatter('female_per', 'sat_score', c='#3ea99f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our scatter plot shows us that there are little to no correlation between the female percentage and the SAT scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns schools with more than 60% female and SAT scores greater than 1700\n",
    "filt = (combined['female_per'] > 60) & (combined['sat_score'] > 1700)\n",
    "high_female_high_sat = combined[filt]\n",
    "high_female_high_sat['SCHOOL NAME']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common thing about these schools is the low enrollment rate for the economically challenged. They also seem to be more of liberal arts schools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation with Advanced Placement(AP) Exams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creates a new column with the AP percentages\n",
    "combined['ap_per'] = combined['AP Test Takers '] / combined['total_enrollment'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displays a scatter plot showing the correlation between AP percentages and SAT scores\n",
    "combined.plot.scatter('ap_per', 'sat_score', c='#3ea99f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our scatter plot shows us that there is little to no correlation between AP scores and SAT scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation with Class Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displays a scatter plot showing the correlayion between SAT scores and class size.\n",
    "combined.plot.scatter('AVERAGE CLASS SIZE', 'sat_score', c='#3ea99f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our plot shows us that there is also little to no correlation between class size and SAT scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assigning Points to School\n",
    "We are going to assign points to school based on how well they perform in SAT tests. There is going to be a total of 5 points. Schools with SAT scores above 1800 will get 5 points, while those with 1400 to 1800 wil get 4 points. Scores between 1200 to 1400 gets 3points, scores between 1000 to 1200 gets 2 points and scores below 1000 get 1 point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def point(val):\n",
    "    ###assigns points based on SAT scores\n",
    "    if val >= 1600:\n",
    "        return 5\n",
    "    elif val >= 1300 < 1600:\n",
    "        return 4\n",
    "    elif val >= 1200 < 1400:\n",
    "        return 3\n",
    "    elif val >= 1000 < 1200:\n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['sat_points'] = combined['sat_score'].apply(point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <th>sat_score</th>\n",
       "      <th>SchoolName</th>\n",
       "      <th>AP Test Takers</th>\n",
       "      <th>Total Exams Taken</th>\n",
       "      <th>...</th>\n",
       "      <th>priority07</th>\n",
       "      <th>priority08</th>\n",
       "      <th>priority09</th>\n",
       "      <th>priority10</th>\n",
       "      <th>Location 1</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>school_dist</th>\n",
       "      <th>ap_per</th>\n",
       "      <th>sat_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>29</td>\n",
       "      <td>355.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220 Henry Street\\nNew York, NY 10002\\n(40.7137...</td>\n",
       "      <td>40.713764</td>\n",
       "      <td>-73.985260</td>\n",
       "      <td>01</td>\n",
       "      <td>30.575556</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>91</td>\n",
       "      <td>383.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD H.S.</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200 Monroe Street\\nNew York, NY 10002\\n(40.712...</td>\n",
       "      <td>40.712332</td>\n",
       "      <td>-73.984797</td>\n",
       "      <td>01</td>\n",
       "      <td>9.898477</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>70</td>\n",
       "      <td>377.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>EAST SIDE COMMUNITY HS</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>420 East 12 Street\\nNew York, NY 10009\\n(40.72...</td>\n",
       "      <td>40.729783</td>\n",
       "      <td>-73.983041</td>\n",
       "      <td>01</td>\n",
       "      <td>3.177258</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145 Stanton Street\\nNew York, NY 10002\\n(40.72...</td>\n",
       "      <td>40.720569</td>\n",
       "      <td>-73.985673</td>\n",
       "      <td>01</td>\n",
       "      <td>35.157724</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M539</td>\n",
       "      <td>NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...</td>\n",
       "      <td>159</td>\n",
       "      <td>522.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>NEW EXPLORATIONS SCI,TECH,MATH</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>377.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111 Columbia Street\\nNew York, NY 10002\\n(40.7...</td>\n",
       "      <td>40.718725</td>\n",
       "      <td>-73.979426</td>\n",
       "      <td>01</td>\n",
       "      <td>15.809051</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>32K545</td>\n",
       "      <td>EBC HIGH SCHOOL FOR PUBLIC SERVICE–BUSHWICK</td>\n",
       "      <td>88</td>\n",
       "      <td>384.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>1154.0</td>\n",
       "      <td>EBC-HS FOR PUB SERVICE (BUSH)</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1155 Dekalb Avenue\\nBrooklyn, NY 11221\\n(40.69...</td>\n",
       "      <td>40.694482</td>\n",
       "      <td>-73.929154</td>\n",
       "      <td>32</td>\n",
       "      <td>7.755776</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>32K549</td>\n",
       "      <td>BUSHWICK SCHOOL FOR SOCIAL JUSTICE</td>\n",
       "      <td>75</td>\n",
       "      <td>348.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0</td>\n",
       "      <td>129.028846</td>\n",
       "      <td>197.038462</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400 Irving Avenue\\nBrooklyn, NY 11237\\n(40.696...</td>\n",
       "      <td>40.696962</td>\n",
       "      <td>-73.910816</td>\n",
       "      <td>32</td>\n",
       "      <td>29.730149</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>32K552</td>\n",
       "      <td>ACADEMY OF URBAN PLANNING</td>\n",
       "      <td>67</td>\n",
       "      <td>342.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>Academy of Urban Planning</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400 Irving Avenue\\nBrooklyn, NY 11237\\n(40.696...</td>\n",
       "      <td>40.696962</td>\n",
       "      <td>-73.910816</td>\n",
       "      <td>32</td>\n",
       "      <td>19.095477</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>32K554</td>\n",
       "      <td>ALL CITY LEADERSHIP SECONDARY SCHOOL</td>\n",
       "      <td>39</td>\n",
       "      <td>428.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>All City Leadership Secondary School</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>321 Palmetto Street\\nBrooklyn, NY 11237\\n(40.6...</td>\n",
       "      <td>40.697408</td>\n",
       "      <td>-73.913153</td>\n",
       "      <td>32</td>\n",
       "      <td>2.661597</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>32K556</td>\n",
       "      <td>BUSHWICK LEADERS HIGH SCHOOL FOR ACADEMIC EXCE...</td>\n",
       "      <td>23</td>\n",
       "      <td>347.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>Bushwick Leaders High School for Academic Exce...</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>797 Bushwick Avenue\\nBrooklyn, NY 11221\\n(40.6...</td>\n",
       "      <td>40.694996</td>\n",
       "      <td>-73.927986</td>\n",
       "      <td>32</td>\n",
       "      <td>7.359307</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>363 rows × 162 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        DBN                                        SCHOOL NAME  \\\n",
       "0    01M292      HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   \n",
       "1    01M448                UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "2    01M450                         EAST SIDE COMMUNITY SCHOOL   \n",
       "3    01M509                            MARTA VALLE HIGH SCHOOL   \n",
       "4    01M539  NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ...   \n",
       "..      ...                                                ...   \n",
       "358  32K545        EBC HIGH SCHOOL FOR PUBLIC SERVICE–BUSHWICK   \n",
       "359  32K549                 BUSHWICK SCHOOL FOR SOCIAL JUSTICE   \n",
       "360  32K552                          ACADEMY OF URBAN PLANNING   \n",
       "361  32K554               ALL CITY LEADERSHIP SECONDARY SCHOOL   \n",
       "362  32K556  BUSHWICK LEADERS HIGH SCHOOL FOR ACADEMIC EXCE...   \n",
       "\n",
       "    Num of SAT Test Takers  SAT Critical Reading Avg. Score  \\\n",
       "0                       29                            355.0   \n",
       "1                       91                            383.0   \n",
       "2                       70                            377.0   \n",
       "3                       44                            390.0   \n",
       "4                      159                            522.0   \n",
       "..                     ...                              ...   \n",
       "358                     88                            384.0   \n",
       "359                     75                            348.0   \n",
       "360                     67                            342.0   \n",
       "361                     39                            428.0   \n",
       "362                     23                            347.0   \n",
       "\n",
       "     SAT Math Avg. Score  SAT Writing Avg. Score  sat_score  \\\n",
       "0                  404.0                   363.0     1122.0   \n",
       "1                  423.0                   366.0     1172.0   \n",
       "2                  402.0                   370.0     1149.0   \n",
       "3                  433.0                   384.0     1207.0   \n",
       "4                  574.0                   525.0     1621.0   \n",
       "..                   ...                     ...        ...   \n",
       "358                409.0                   361.0     1154.0   \n",
       "359                353.0                   334.0     1035.0   \n",
       "360                364.0                   354.0     1060.0   \n",
       "361                465.0                   422.0     1315.0   \n",
       "362                358.0                   350.0     1055.0   \n",
       "\n",
       "                                            SchoolName  AP Test Takers   \\\n",
       "0                                                    0       129.028846   \n",
       "1                         UNIVERSITY NEIGHBORHOOD H.S.        39.000000   \n",
       "2                               EAST SIDE COMMUNITY HS        19.000000   \n",
       "3                                                    0       129.028846   \n",
       "4                       NEW EXPLORATIONS SCI,TECH,MATH       255.000000   \n",
       "..                                                 ...              ...   \n",
       "358                      EBC-HS FOR PUB SERVICE (BUSH)        47.000000   \n",
       "359                                                  0       129.028846   \n",
       "360                          Academy of Urban Planning        76.000000   \n",
       "361               All City Leadership Secondary School         7.000000   \n",
       "362  Bushwick Leaders High School for Academic Exce...        34.000000   \n",
       "\n",
       "     Total Exams Taken  ...                       priority07 priority08  \\\n",
       "0           197.038462  ...                                0        0.0   \n",
       "1            49.000000  ...                                0        0.0   \n",
       "2            21.000000  ...                                0        0.0   \n",
       "3           197.038462  ...                                0        0.0   \n",
       "4           377.000000  ...                                0        0.0   \n",
       "..                 ...  ...                              ...        ...   \n",
       "358          64.000000  ...                                0        0.0   \n",
       "359         197.038462  ...                                0        0.0   \n",
       "360         100.000000  ...                                0        0.0   \n",
       "361           8.000000  ...  Then to New York City residents        0.0   \n",
       "362          35.000000  ...                                0        0.0   \n",
       "\n",
       "    priority09 priority10                                         Location 1  \\\n",
       "0          0.0        0.0  220 Henry Street\\nNew York, NY 10002\\n(40.7137...   \n",
       "1          0.0        0.0  200 Monroe Street\\nNew York, NY 10002\\n(40.712...   \n",
       "2          0.0        0.0  420 East 12 Street\\nNew York, NY 10009\\n(40.72...   \n",
       "3          0.0        0.0  145 Stanton Street\\nNew York, NY 10002\\n(40.72...   \n",
       "4          0.0        0.0  111 Columbia Street\\nNew York, NY 10002\\n(40.7...   \n",
       "..         ...        ...                                                ...   \n",
       "358        0.0        0.0  1155 Dekalb Avenue\\nBrooklyn, NY 11221\\n(40.69...   \n",
       "359        0.0        0.0  400 Irving Avenue\\nBrooklyn, NY 11237\\n(40.696...   \n",
       "360        0.0        0.0  400 Irving Avenue\\nBrooklyn, NY 11237\\n(40.696...   \n",
       "361        0.0        0.0  321 Palmetto Street\\nBrooklyn, NY 11237\\n(40.6...   \n",
       "362        0.0        0.0  797 Bushwick Avenue\\nBrooklyn, NY 11221\\n(40.6...   \n",
       "\n",
       "           lat        lon school_dist     ap_per sat_points  \n",
       "0    40.713764 -73.985260          01  30.575556          2  \n",
       "1    40.712332 -73.984797          01   9.898477          2  \n",
       "2    40.729783 -73.983041          01   3.177258          2  \n",
       "3    40.720569 -73.985673          01  35.157724          3  \n",
       "4    40.718725 -73.979426          01  15.809051          5  \n",
       "..         ...        ...         ...        ...        ...  \n",
       "358  40.694482 -73.929154          32   7.755776          2  \n",
       "359  40.696962 -73.910816          32  29.730149          2  \n",
       "360  40.696962 -73.910816          32  19.095477          2  \n",
       "361  40.697408 -73.913153          32   2.661597          4  \n",
       "362  40.694996 -73.927986          32   7.359307          2  \n",
       "\n",
       "[363 rows x 162 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average School Points per Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displays a bar plot of the average school points for schools in each borough\n",
    "boro_pvt = combined.pivot_table(index='boro', values='sat_points')\n",
    "boro_pvt.plot.bar(rot=45, color='#3ea99f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from our plot that Staten Island is the borough with the most high performing schools on SATs while Bronx is the borough with the least performing schools on SATs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "Our goal initially in this analysis is to uncover whether standardized tests like the SATs has some sort of bias towards certain demographics and to find the correlation bewteen these demographics and the SAT scores. So far we have found out the following:\n",
    "1. There is a negative correlation between the percentage of hispanics and SAT scores in a school. While this looks to be a troubling trend, on futher investigation, we discovered that the schools which had low hispanic percentages and high sat scores were shcools with low enrollment for those with economical disadvantage. This could point to some kind of economic disparity between the hispanics and other demographics.\n",
    "\n",
    "2. Suprisingly there wasn't a very strong correlation between SAT scores and gender, so one can conclude that there isn't any gender bias in the SAT scores.\n",
    "\n",
    "3. There was also very little correlation between AP and SAT scores. ie: schools with high enrollment in AP tests didn't have any significant advantage over other schools\n",
    "\n",
    "4. Of the  high schools in New York, the shcools  at Saten Island seemed to perform the best in SAT scores, followed by Queens, Manhattan, Brroklyn and lastly Bronx."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
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}