analyzing_nyc_school_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Analyzing NYC High School Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAT, or Scholastic Aptitude Test, is a test given to graduating high schoolers in the US every year. The SAT has 3 sections, each of which is worth a maximum of 800 points. The SAT is used by colleges 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 the SAT scores of students, along with additional demographic datasets.\n",
    "\n",
    "- SAT scores by school -- SAT scores for each high school in New York City.\n",
    "- School attendance -- attendance information on every school in NYC.\n",
    "- Class size -- class size information for each school in NYC.\n",
    "- AP test results -- Advanced Placement exam results for each high school. Passing AP exams can get you college credit in the US.\n",
    "- Graduation outcomes -- percentage of students who graduated, and other outcome information.\n",
    "- Demographics -- demographic information for each school.\n",
    "- School survey -- 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 figure out if the SAT is a fair test. If certain racial groups consistently performed better on the SAT, we would have some evidence that the SAT is unfair, for example.\n",
    "\n",
    "The Dataframe combined contains all of our data, and is what we'll be using in our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read In The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll read each file into a Pandas Dataframe, and then store all of the Dataframes in a dictionary. This will make it easy to reference the Dataframes later on, while storing them all into a single variable for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "import re\n",
    "import matplotlib.pyplot\n",
    "matplotlib.style.use('fivethirtyeight')  \n",
    "from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "\n",
    "data_files = [\n",
    "    \"ap_2010.csv\",\n",
    "    \"class_size.csv\",\n",
    "    \"demographics.csv\",\n",
    "    \"graduation.csv\",\n",
    "    \"hs_directory.csv\",\n",
    "    \"sat_results.csv\"\n",
    "]\n",
    "\n",
    "data = {}\n",
    "\n",
    "for f in data_files:\n",
    "    d = pandas.read_csv(\"schools/{0}\".format(f))\n",
    "    data[f.replace(\".csv\", \"\")] = d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring The SAT Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main dataset that we're interested in is the SAT dataset, which corresponds to the dictionary key sat_results. This contains information about the SAT scores for each high school in New York City. We eventually want to correlate items from this dataset with items from the other datasets.\n",
    "\n",
    "Let's explore sat_results to see what we can discover. Exploring the Dataframe will enable us to understand the structure of the data, which will make it easier to analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </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</td>\n",
       "      <td>404</td>\n",
       "      <td>363</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</td>\n",
       "      <td>423</td>\n",
       "      <td>366</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</td>\n",
       "      <td>402</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>7</td>\n",
       "      <td>414</td>\n",
       "      <td>401</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390</td>\n",
       "      <td>433</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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  01M458                      FORSYTH SATELLITE ACADEMY   \n",
       "4  01M509                        MARTA VALLE HIGH SCHOOL   \n",
       "\n",
       "  Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score  \\\n",
       "0                     29                             355                 404   \n",
       "1                     91                             383                 423   \n",
       "2                     70                             377                 402   \n",
       "3                      7                             414                 401   \n",
       "4                     44                             390                 433   \n",
       "\n",
       "  SAT Writing Avg. Score  \n",
       "0                    363  \n",
       "1                    366  \n",
       "2                    370  \n",
       "3                    359  \n",
       "4                    384  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"sat_results\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring The Other Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From looking at the data, we can make a few observations:\n",
    "\n",
    "- The DBN appears to be a unique ID for each school.\n",
    "- From looking at the first few rows of names, we only have data about high schools.\n",
    "- There's only a single row for each high school, so each DBN is unique in the SAT data.\n",
    "- We may eventually want to combine the three columns that contain SAT scores -- SAT Critical Reading Avg., Score SAT Math Avg. Score, and SAT Writing Avg. Score into a single column to make it easier to analyze.\n",
    "\n",
    "Given these observations, let's explore the other datasets to see if we can get any insight into how to combine the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      DBN                             SchoolName AP Test Takers   \\\n",
      "0  01M448           UNIVERSITY NEIGHBORHOOD H.S.              39   \n",
      "1  01M450                 EAST SIDE COMMUNITY HS              19   \n",
      "2  01M515                    LOWER EASTSIDE PREP              24   \n",
      "3  01M539         NEW EXPLORATIONS SCI,TECH,MATH             255   \n",
      "4  02M296  High School of Hospitality Management               s   \n",
      "\n",
      "  Total Exams Taken Number of Exams with scores 3 4 or 5  \n",
      "0                49                                   10  \n",
      "1                21                                    s  \n",
      "2                26                                   24  \n",
      "3               377                                  191  \n",
      "4                 s                                    s  \n",
      "   CSD BOROUGH SCHOOL CODE                SCHOOL NAME GRADE  PROGRAM TYPE  \\\n",
      "0    1       M        M015  P.S. 015 Roberto Clemente     0K       GEN ED   \n",
      "1    1       M        M015  P.S. 015 Roberto Clemente     0K          CTT   \n",
      "2    1       M        M015  P.S. 015 Roberto Clemente     01       GEN ED   \n",
      "3    1       M        M015  P.S. 015 Roberto Clemente     01          CTT   \n",
      "4    1       M        M015  P.S. 015 Roberto Clemente     02       GEN ED   \n",
      "\n",
      "  CORE SUBJECT (MS CORE and 9-12 ONLY) CORE COURSE (MS CORE and 9-12 ONLY)  \\\n",
      "0                                    -                                   -   \n",
      "1                                    -                                   -   \n",
      "2                                    -                                   -   \n",
      "3                                    -                                   -   \n",
      "4                                    -                                   -   \n",
      "\n",
      "  SERVICE CATEGORY(K-9* ONLY)  NUMBER OF STUDENTS / SEATS FILLED  \\\n",
      "0                           -                               19.0   \n",
      "1                           -                               21.0   \n",
      "2                           -                               17.0   \n",
      "3                           -                               17.0   \n",
      "4                           -                               15.0   \n",
      "\n",
      "   NUMBER OF SECTIONS  AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  \\\n",
      "0                 1.0                19.0                    19.0   \n",
      "1                 1.0                21.0                    21.0   \n",
      "2                 1.0                17.0                    17.0   \n",
      "3                 1.0                17.0                    17.0   \n",
      "4                 1.0                15.0                    15.0   \n",
      "\n",
      "   SIZE OF LARGEST CLASS DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO  \n",
      "0                   19.0         ATS                             NaN  \n",
      "1                   21.0         ATS                             NaN  \n",
      "2                   17.0         ATS                             NaN  \n",
      "3                   17.0         ATS                             NaN  \n",
      "4                   15.0         ATS                             NaN  \n",
      "      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  01M458                      FORSYTH SATELLITE ACADEMY   \n",
      "4  01M509                        MARTA VALLE HIGH SCHOOL   \n",
      "\n",
      "  Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score  \\\n",
      "0                     29                             355                 404   \n",
      "1                     91                             383                 423   \n",
      "2                     70                             377                 402   \n",
      "3                      7                             414                 401   \n",
      "4                     44                             390                 433   \n",
      "\n",
      "  SAT Writing Avg. Score  \n",
      "0                    363  \n",
      "1                    366  \n",
      "2                    370  \n",
      "3                    359  \n",
      "4                    384  \n",
      "      dbn                                        school_name       boro  \\\n",
      "0  17K548                Brooklyn School for Music & Theatre   Brooklyn   \n",
      "1  09X543                   High School for Violin and Dance      Bronx   \n",
      "2  09X327        Comprehensive Model School Project M.S. 327      Bronx   \n",
      "3  02M280     Manhattan Early College School for Advertising  Manhattan   \n",
      "4  28Q680  Queens Gateway to Health Sciences Secondary Sc...     Queens   \n",
      "\n",
      "  building_code    phone_number    fax_number grade_span_min  grade_span_max  \\\n",
      "0          K440    718-230-6250  718-230-6262              9              12   \n",
      "1          X400    718-842-0687  718-589-9849              9              12   \n",
      "2          X240    718-294-8111  718-294-8109              6              12   \n",
      "3          M520  718-935-3477             NaN              9              10   \n",
      "4          Q695    718-969-3155  718-969-3552              6              12   \n",
      "\n",
      "  expgrade_span_min  expgrade_span_max  \\\n",
      "0               NaN                NaN   \n",
      "1               NaN                NaN   \n",
      "2               NaN                NaN   \n",
      "3                 9               14.0   \n",
      "4               NaN                NaN   \n",
      "\n",
      "                         ...                          \\\n",
      "0                        ...                           \n",
      "1                        ...                           \n",
      "2                        ...                           \n",
      "3                        ...                           \n",
      "4                        ...                           \n",
      "\n",
      "                                          priority02  \\\n",
      "0                    Then to New York City residents   \n",
      "1  Then to New York City residents who attend an ...   \n",
      "2  Then to Bronx students or residents who attend...   \n",
      "3  Then to New York City residents who attend an ...   \n",
      "4  Then to Districts 28 and 29 students or residents   \n",
      "\n",
      "                                          priority03  \\\n",
      "0                                                NaN   \n",
      "1                Then to Bronx students or residents   \n",
      "2  Then to New York City residents who attend an ...   \n",
      "3          Then to Manhattan students or residents     \n",
      "4               Then to Queens students or residents   \n",
      "\n",
      "                            priority04                       priority05  \\\n",
      "0                                  NaN                              NaN   \n",
      "1      Then to New York City residents                              NaN   \n",
      "2  Then to Bronx students or residents  Then to New York City residents   \n",
      "3      Then to New York City residents                              NaN   \n",
      "4      Then to New York City residents                              NaN   \n",
      "\n",
      "  priority06  priority07 priority08  priority09 priority10  \\\n",
      "0        NaN         NaN        NaN         NaN        NaN   \n",
      "1        NaN         NaN        NaN         NaN        NaN   \n",
      "2        NaN         NaN        NaN         NaN        NaN   \n",
      "3        NaN         NaN        NaN         NaN        NaN   \n",
      "4        NaN         NaN        NaN         NaN        NaN   \n",
      "\n",
      "                                          Location 1  \n",
      "0  883 Classon Avenue\\nBrooklyn, NY 11225\\n(40.67...  \n",
      "1  1110 Boston Road\\nBronx, NY 10456\\n(40.8276026...  \n",
      "2  1501 Jerome Avenue\\nBronx, NY 10452\\n(40.84241...  \n",
      "3  411 Pearl Street\\nNew York, NY 10038\\n(40.7106...  \n",
      "4  160-20 Goethals Avenue\\nJamaica, NY 11432\\n(40...  \n",
      "\n",
      "[5 rows x 58 columns]\n",
      "      DBN                       Name  schoolyear fl_percent  frl_percent  \\\n",
      "0  01M015  P.S. 015 ROBERTO CLEMENTE    20052006       89.4          NaN   \n",
      "1  01M015  P.S. 015 ROBERTO CLEMENTE    20062007       89.4          NaN   \n",
      "2  01M015  P.S. 015 ROBERTO CLEMENTE    20072008       89.4          NaN   \n",
      "3  01M015  P.S. 015 ROBERTO CLEMENTE    20082009       89.4          NaN   \n",
      "4  01M015  P.S. 015 ROBERTO CLEMENTE    20092010                    96.5   \n",
      "\n",
      "   total_enrollment prek   k grade1 grade2    ...     black_num black_per  \\\n",
      "0               281   15  36     40     33    ...            74      26.3   \n",
      "1               243   15  29     39     38    ...            68      28.0   \n",
      "2               261   18  43     39     36    ...            77      29.5   \n",
      "3               252   17  37     44     32    ...            75      29.8   \n",
      "4               208   16  40     28     32    ...            67      32.2   \n",
      "\n",
      "  hispanic_num hispanic_per white_num white_per male_num male_per female_num  \\\n",
      "0          189         67.3         5       1.8    158.0     56.2      123.0   \n",
      "1          153         63.0         4       1.6    140.0     57.6      103.0   \n",
      "2          157         60.2         7       2.7    143.0     54.8      118.0   \n",
      "3          149         59.1         7       2.8    149.0     59.1      103.0   \n",
      "4          118         56.7         6       2.9    124.0     59.6       84.0   \n",
      "\n",
      "  female_per  \n",
      "0       43.8  \n",
      "1       42.4  \n",
      "2       45.2  \n",
      "3       40.9  \n",
      "4       40.4  \n",
      "\n",
      "[5 rows x 38 columns]\n",
      "    Demographic     DBN                            School Name    Cohort  \\\n",
      "0  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2003   \n",
      "1  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2004   \n",
      "2  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2005   \n",
      "3  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2006   \n",
      "4  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL  2006 Aug   \n",
      "\n",
      "   Total Cohort Total Grads - n Total Grads - % of cohort Total Regents - n  \\\n",
      "0             5               s                         s                 s   \n",
      "1            55              37                     67.3%                17   \n",
      "2            64              43                     67.2%                27   \n",
      "3            78              43                     55.1%                36   \n",
      "4            78              44                     56.4%                37   \n",
      "\n",
      "  Total Regents - % of cohort Total Regents - % of grads  \\\n",
      "0                           s                          s   \n",
      "1                       30.9%                      45.9%   \n",
      "2                       42.2%                      62.8%   \n",
      "3                       46.2%                      83.7%   \n",
      "4                       47.4%                      84.1%   \n",
      "\n",
      "             ...            Regents w/o Advanced - n  \\\n",
      "0            ...                                   s   \n",
      "1            ...                                  17   \n",
      "2            ...                                  27   \n",
      "3            ...                                  36   \n",
      "4            ...                                  37   \n",
      "\n",
      "  Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads  \\\n",
      "0                                  s                                 s   \n",
      "1                              30.9%                             45.9%   \n",
      "2                              42.2%                             62.8%   \n",
      "3                              46.2%                             83.7%   \n",
      "4                              47.4%                             84.1%   \n",
      "\n",
      "  Local - n Local - % of cohort   Local - % of grads Still Enrolled - n  \\\n",
      "0         s                   s                    s                  s   \n",
      "1        20               36.4%                54.1%                 15   \n",
      "2        16                 25%  37.200000000000003%                  9   \n",
      "3         7                  9%                16.3%                 16   \n",
      "4         7                  9%                15.9%                 15   \n",
      "\n",
      "  Still Enrolled - % of cohort Dropped Out - n Dropped Out - % of cohort  \n",
      "0                            s               s                         s  \n",
      "1                        27.3%               3                      5.5%  \n",
      "2                        14.1%               9                     14.1%  \n",
      "3                        20.5%              11                     14.1%  \n",
      "4                        19.2%              11                     14.1%  \n",
      "\n",
      "[5 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "for k in data:\n",
    "    print(data[k].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading In The Survey Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the first few rows of each dataset, we can make some observations:\n",
    "\n",
    "- Each dataset appears to have either a DBN column, or the information to create a DBN column, so this is how we'll combine our datasets into one. We can match up rows from different datasets that have the same DBN and combine their columns.\n",
    "- Some fields look interesting for mapping, particularly Location 1, which contains coordinates inside a larger string.\n",
    "- Some of the datasets appear to contain multiple rows for each school (repeated DBN values), which means we’ll have to do some preprocessing to ensure that DBN is unique within each dataset. If we don't do this, it will cause issues when we combine the datasets, because we might be merging two rows in one dataset with one row in another dataset.\n",
    "\n",
    "Before we proceed with unifying the datasets, it will be useful to ensure we have all the data we want to unify. We mentioned the survey data, survey_all.txt and survey_d75.txt, earlier, but we didn't read them in, because they were in a slightly more complex format.\n",
    "\n",
    "The files are tab delimited, and are encoded with Windows-1252 encoding. An encoding defines how the contents of a file are stored in binary. The most common encodings are UTF-8 and ASCII.\n",
    "\n",
    "We'll need to specify the encoding and delimiter to the Pandas read_csv function in order to ensure the surveys are read in properly.\n",
    "\n",
    "After we read in the survey data, we'll want to combine it into a single Dataframe. We can do this with the Pandas concat function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
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    {
     "data": {
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       "      <th></th>\n",
       "      <th>N_p</th>\n",
       "      <th>N_s</th>\n",
       "      <th>N_t</th>\n",
       "      <th>aca_p_11</th>\n",
       "      <th>aca_s_11</th>\n",
       "      <th>aca_t_11</th>\n",
       "      <th>aca_tot_11</th>\n",
       "      <th>bn</th>\n",
       "      <th>com_p_11</th>\n",
       "      <th>com_s_11</th>\n",
       "      <th>...</th>\n",
       "      <th>t_q8c_1</th>\n",
       "      <th>t_q8c_2</th>\n",
       "      <th>t_q8c_3</th>\n",
       "      <th>t_q8c_4</th>\n",
       "      <th>t_q9</th>\n",
       "      <th>t_q9_1</th>\n",
       "      <th>t_q9_2</th>\n",
       "      <th>t_q9_3</th>\n",
       "      <th>t_q9_4</th>\n",
       "      <th>t_q9_5</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>M015</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>161.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.1</td>\n",
       "      <td>8.4</td>\n",
       "      <td>M019</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>M020</td>\n",
       "      <td>8.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>M034</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>M063</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2773 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     N_p    N_s   N_t  aca_p_11  aca_s_11  aca_t_11  aca_tot_11    bn  \\\n",
       "0   90.0    NaN  22.0       7.8       NaN       7.9         7.9  M015   \n",
       "1  161.0    NaN  34.0       7.8       NaN       9.1         8.4  M019   \n",
       "2  367.0    NaN  42.0       8.6       NaN       7.5         8.0  M020   \n",
       "3  151.0  145.0  29.0       8.5       7.4       7.8         7.9  M034   \n",
       "4   90.0    NaN  23.0       7.9       NaN       8.1         8.0  M063   \n",
       "\n",
       "   com_p_11  com_s_11   ...    t_q8c_1  t_q8c_2  t_q8c_3 t_q8c_4  t_q9  \\\n",
       "0       7.6       NaN   ...       29.0     67.0      5.0     0.0   NaN   \n",
       "1       7.6       NaN   ...       74.0     21.0      6.0     0.0   NaN   \n",
       "2       8.3       NaN   ...       33.0     35.0     20.0    13.0   NaN   \n",
       "3       8.2       5.9   ...       21.0     45.0     28.0     7.0   NaN   \n",
       "4       7.9       NaN   ...       59.0     36.0      5.0     0.0   NaN   \n",
       "\n",
       "   t_q9_1  t_q9_2  t_q9_3  t_q9_4  t_q9_5  \n",
       "0     5.0    14.0    52.0    24.0     5.0  \n",
       "1     3.0     6.0     3.0    78.0     9.0  \n",
       "2     3.0     5.0    16.0    70.0     5.0  \n",
       "3     0.0    18.0    32.0    39.0    11.0  \n",
       "4    10.0     5.0    10.0    60.0    15.0  \n",
       "\n",
       "[5 rows x 2773 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_survey = pandas.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "d75_survey = pandas.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n",
    "survey = pandas.concat([all_survey, d75_survey], axis=0)\n",
    "\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Up The Surveys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two immediate facts that we can see in the data:\n",
    "\n",
    "- There are over 2000 columns in the data, almost all of which we won't need. We'll need to filter this to remove columns to make it easier to work with. The fewer columns, the easier it is to print out the Dataframe, and to find correlations across the whole Dataframe.\n",
    "- The survey data has a dbn column that we'll want to convert to uppercase (DBN) for consistency with the other datasets.\n",
    "\n",
    "We'll need to filter the columns to remove the ones we don't need and there is a data dictionary where we took our files [here](https://data.cityofnewyork.us/Education/NYC-School-Survey-2011/mnz3-dyi8). \n",
    "\n",
    "Based on the dictionary, it looks like these are the relevant columns:\n",
    "\n",
    "[\"dbn\", \"rr_s\", \"rr_t\", \"rr_p\", \"N_s\", \"N_t\", \"N_p\", \"saf_p_11\", \"com_p_11\", \"eng_p_11\", \"aca_p_11\", \"saf_t_11\", \"com_t_11\", \"eng_t_11\", \"aca_t_11\", \"saf_s_11\", \"com_s_11\", \"eng_s_11\", \"aca_s_11\", \"saf_tot_11\", \"com_tot_11\", \"eng_tot_11\", \"aca_tot_11\"]\n",
    "\n",
    "These columns will give us aggregate survey data about how parents, teachers, and students feel about school safety, academic performance, and more. It will also give us the DBN, which allows us to uniquely identify the school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>rr_s</th>\n",
       "      <th>rr_t</th>\n",
       "      <th>rr_p</th>\n",
       "      <th>N_s</th>\n",
       "      <th>N_t</th>\n",
       "      <th>N_p</th>\n",
       "      <th>saf_p_11</th>\n",
       "      <th>com_p_11</th>\n",
       "      <th>eng_p_11</th>\n",
       "      <th>...</th>\n",
       "      <th>eng_t_10</th>\n",
       "      <th>aca_t_11</th>\n",
       "      <th>saf_s_11</th>\n",
       "      <th>com_s_11</th>\n",
       "      <th>eng_s_11</th>\n",
       "      <th>aca_s_11</th>\n",
       "      <th>saf_tot_11</th>\n",
       "      <th>com_tot_11</th>\n",
       "      <th>eng_tot_11</th>\n",
       "      <th>aca_tot_11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M034</td>\n",
       "      <td>89.0</td>\n",
       "      <td>73</td>\n",
       "      <td>50</td>\n",
       "      <td>145.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      DBN  rr_s  rr_t  rr_p    N_s   N_t    N_p  saf_p_11  com_p_11  eng_p_11  \\\n",
       "0  01M015   NaN    88    60    NaN  22.0   90.0       8.5       7.6       7.5   \n",
       "1  01M019   NaN   100    60    NaN  34.0  161.0       8.4       7.6       7.6   \n",
       "2  01M020   NaN    88    73    NaN  42.0  367.0       8.9       8.3       8.3   \n",
       "3  01M034  89.0    73    50  145.0  29.0  151.0       8.8       8.2       8.0   \n",
       "4  01M063   NaN   100    60    NaN  23.0   90.0       8.7       7.9       8.1   \n",
       "\n",
       "      ...      eng_t_10  aca_t_11  saf_s_11  com_s_11  eng_s_11  aca_s_11  \\\n",
       "0     ...           NaN       7.9       NaN       NaN       NaN       NaN   \n",
       "1     ...           NaN       9.1       NaN       NaN       NaN       NaN   \n",
       "2     ...           NaN       7.5       NaN       NaN       NaN       NaN   \n",
       "3     ...           NaN       7.8       6.2       5.9       6.5       7.4   \n",
       "4     ...           NaN       8.1       NaN       NaN       NaN       NaN   \n",
       "\n",
       "   saf_tot_11  com_tot_11  eng_tot_11  aca_tot_11  \n",
       "0         8.0         7.7         7.5         7.9  \n",
       "1         8.5         8.1         8.2         8.4  \n",
       "2         8.2         7.3         7.5         8.0  \n",
       "3         7.3         6.7         7.1         7.9  \n",
       "4         8.5         7.6         7.9         8.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make copy of dbn column.\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\n",
    "\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inserting DBN Fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we explored all the datasets, we noticed that not every dataset had a DBN column. Specifically, class_size and hs_directory do not have the column. In the case of hs_directory, it has a dbn column, so we can just rename it.\n",
    "\n",
    "However, class_size doesn't appear to have the column at all. From looking at these rows, it appears that the DBN is just a combination of the CSD and SCHOOL CODE columns in class_size. The main difference is that the DBN is padded so the CSD is always two digits -- we'll need to add a leading 0 to the CSD if the CSD is less than two digits long for consistency.\n",
    "\n",
    "As you can see, whenever the CSD is less than two digits long, we need to add a leading 0. We can accomplish this using the Pandas apply method, along with a custom function that:\n",
    "\n",
    "- Takes in a number.\n",
    "- Converts the number to a string using the str function.\n",
    "- Check the length of the string using the len function.\n",
    "    - If the string is two digits long, return the string.\n",
    "    - If the string is one digit long, add a 0 to the front of the string, then return it.\n",
    "    - If the string is b, you can do this with return \"0\" + b.\n",
    "\n",
    "Once we have the padded CSD, we can use the + operator to combine the values in the CSD and SCHOOL CODE columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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": [
    "## Combining The SAT Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're now almost ready to combine our datasets into one. Before we do, let's take some time to calculate variables that will be useful in our analysis. We've already discussed one such variable -- a column that combines all of the SAT scores into one number. This will make it much easier to correlate with demographic factors, because it's a single number, not three.\n",
    "\n",
    "In order to generate this column, we'll first need to convert the SAT Math Avg. Score, SAT Critical Reading Avg. Score, and SAT Writing Avg. Score in the sat_results dataset from the object (string) data type to a numeric datatype. We can use the to_numeric function to do this conversion. Without doing this conversion, we won't be able to add up the columns.\n",
    "\n",
    "It's important to pass the keyword argument errors=\"coerce\" when we call to_numeric, so that any invalid strings that can't be converted to numbers are instead treated as missing values.\n",
    "\n",
    "After the conversion, we can just use the + operator to add all three columns together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1122.0\n",
      "1    1172.0\n",
      "2    1149.0\n",
      "3    1174.0\n",
      "4    1207.0\n",
      "Name: sat_score, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']\n",
    "for c in cols:\n",
    "    data[\"sat_results\"][c] = pandas.to_numeric(data[\"sat_results\"][c], errors=\"coerce\")\n",
    "\n",
    "data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n",
    "\n",
    "data['sat_results'][cols[2]]\n",
    "# Print to see if everything went well.\n",
    "print(data['sat_results']['sat_score'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing Coordinates For Each School"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll want to parse the coordinates for each school. This will enable us to map out the schools, and figure out any geographic patterns in the data. Currently, the coordinates are in a text field called Location 1 in the hs_directory dataset.W e want to extract the latitude and the longitude. Together, the latitude and longitude make up a pair of coordinates, and allow us to uniquely locate any place on Earth.\n",
    "\n",
    "We can do this using a regular expression. We'll need to do further processing on this using the split and replace methods on strings to extract each coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>boro</th>\n",
       "      <th>building_code</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>fax_number</th>\n",
       "      <th>grade_span_min</th>\n",
       "      <th>grade_span_max</th>\n",
       "      <th>expgrade_span_min</th>\n",
       "      <th>expgrade_span_max</th>\n",
       "      <th>...</th>\n",
       "      <th>priority04</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>DBN</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17K548</td>\n",
       "      <td>Brooklyn School for Music &amp; Theatre</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>K440</td>\n",
       "      <td>718-230-6250</td>\n",
       "      <td>718-230-6262</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>883 Classon Avenue\\nBrooklyn, NY 11225\\n(40.67...</td>\n",
       "      <td>17K548</td>\n",
       "      <td>40.670299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09X543</td>\n",
       "      <td>High School for Violin and Dance</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>X400</td>\n",
       "      <td>718-842-0687</td>\n",
       "      <td>718-589-9849</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1110 Boston Road\\nBronx, NY 10456\\n(40.8276026...</td>\n",
       "      <td>09X543</td>\n",
       "      <td>40.827603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09X327</td>\n",
       "      <td>Comprehensive Model School Project M.S. 327</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>X240</td>\n",
       "      <td>718-294-8111</td>\n",
       "      <td>718-294-8109</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Then to Bronx students or residents</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1501 Jerome Avenue\\nBronx, NY 10452\\n(40.84241...</td>\n",
       "      <td>09X327</td>\n",
       "      <td>40.842414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02M280</td>\n",
       "      <td>Manhattan Early College School for Advertising</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M520</td>\n",
       "      <td>718-935-3477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>411 Pearl Street\\nNew York, NY 10038\\n(40.7106...</td>\n",
       "      <td>02M280</td>\n",
       "      <td>40.710679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28Q680</td>\n",
       "      <td>Queens Gateway to Health Sciences Secondary Sc...</td>\n",
       "      <td>Queens</td>\n",
       "      <td>Q695</td>\n",
       "      <td>718-969-3155</td>\n",
       "      <td>718-969-3552</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160-20 Goethals Avenue\\nJamaica, NY 11432\\n(40...</td>\n",
       "      <td>28Q680</td>\n",
       "      <td>40.718810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dbn                                        school_name       boro  \\\n",
       "0  17K548                Brooklyn School for Music & Theatre   Brooklyn   \n",
       "1  09X543                   High School for Violin and Dance      Bronx   \n",
       "2  09X327        Comprehensive Model School Project M.S. 327      Bronx   \n",
       "3  02M280     Manhattan Early College School for Advertising  Manhattan   \n",
       "4  28Q680  Queens Gateway to Health Sciences Secondary Sc...     Queens   \n",
       "\n",
       "  building_code    phone_number    fax_number grade_span_min  grade_span_max  \\\n",
       "0          K440    718-230-6250  718-230-6262              9              12   \n",
       "1          X400    718-842-0687  718-589-9849              9              12   \n",
       "2          X240    718-294-8111  718-294-8109              6              12   \n",
       "3          M520  718-935-3477             NaN              9              10   \n",
       "4          Q695    718-969-3155  718-969-3552              6              12   \n",
       "\n",
       "  expgrade_span_min  expgrade_span_max    ...      \\\n",
       "0               NaN                NaN    ...       \n",
       "1               NaN                NaN    ...       \n",
       "2               NaN                NaN    ...       \n",
       "3                 9               14.0    ...       \n",
       "4               NaN                NaN    ...       \n",
       "\n",
       "                            priority04                       priority05  \\\n",
       "0                                  NaN                              NaN   \n",
       "1      Then to New York City residents                              NaN   \n",
       "2  Then to Bronx students or residents  Then to New York City residents   \n",
       "3      Then to New York City residents                              NaN   \n",
       "4      Then to New York City residents                              NaN   \n",
       "\n",
       "  priority06 priority07 priority08  priority09 priority10  \\\n",
       "0        NaN        NaN        NaN         NaN        NaN   \n",
       "1        NaN        NaN        NaN         NaN        NaN   \n",
       "2        NaN        NaN        NaN         NaN        NaN   \n",
       "3        NaN        NaN        NaN         NaN        NaN   \n",
       "4        NaN        NaN        NaN         NaN        NaN   \n",
       "\n",
       "                                          Location 1     DBN        lat  \n",
       "0  883 Classon Avenue\\nBrooklyn, NY 11225\\n(40.67...  17K548  40.670299  \n",
       "1  1110 Boston Road\\nBronx, NY 10456\\n(40.8276026...  09X543  40.827603  \n",
       "2  1501 Jerome Avenue\\nBronx, NY 10452\\n(40.84241...  09X327  40.842414  \n",
       "3  411 Pearl Street\\nNew York, NY 10038\\n(40.7106...  02M280  40.710679  \n",
       "4  160-20 Goethals Avenue\\nJamaica, NY 11432\\n(40...  28Q680  40.718810  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_lat(loc):\n",
    "    coords = re.findall(\"\\(.+, .+\\)\", loc)\n",
    "    lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n",
    "    return lat\n",
    "\n",
    "data[\"hs_directory\"][\"lat\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lat)\n",
    "data[\"hs_directory\"][\"lat\"] = pandas.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n",
    "\n",
    "data[\"hs_directory\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting The Longitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We parsed the latitude from the Location 1 column. Now we'll just need to do the same for the longitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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\"][\"lon\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lon)\n",
    "data[\"hs_directory\"][\"lon\"] = pandas.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Condensing The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step that we'll need to take before we can combine the data is to condense some of the datasets. Previously, we noticed that while sat_results was unique on the DBN column, and only contained a single row for each unique DBN value, some of the other datasets, such as class_size had duplicated DBN values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condensing Class Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CSD</th>\n",
       "      <th>BOROUGH</th>\n",
       "      <th>SCHOOL CODE</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>PROGRAM TYPE</th>\n",
       "      <th>CORE SUBJECT (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>CORE COURSE (MS CORE and 9-12 ONLY)</th>\n",
       "      <th>SERVICE CATEGORY(K-9* ONLY)</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
       "      <th>SIZE OF SMALLEST CLASS</th>\n",
       "      <th>SIZE OF LARGEST CLASS</th>\n",
       "      <th>DATA SOURCE</th>\n",
       "      <th>SCHOOLWIDE PUPIL-TEACHER RATIO</th>\n",
       "      <th>padded_csd</th>\n",
       "      <th>DBN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 9</td>\n",
       "      <td>-</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 10</td>\n",
       "      <td>-</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 11</td>\n",
       "      <td>-</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>English 12</td>\n",
       "      <td>-</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>M292</td>\n",
       "      <td>Henry Street School for International Studies</td>\n",
       "      <td>09-12</td>\n",
       "      <td>GEN ED</td>\n",
       "      <td>MATH</td>\n",
       "      <td>Integrated Algebra</td>\n",
       "      <td>-</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>STARS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>01</td>\n",
       "      <td>01M292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CSD BOROUGH SCHOOL CODE                                    SCHOOL NAME  \\\n",
       "225    1       M        M292  Henry Street School for International Studies   \n",
       "226    1       M        M292  Henry Street School for International Studies   \n",
       "227    1       M        M292  Henry Street School for International Studies   \n",
       "228    1       M        M292  Henry Street School for International Studies   \n",
       "229    1       M        M292  Henry Street School for International Studies   \n",
       "\n",
       "    GRADE  PROGRAM TYPE CORE SUBJECT (MS CORE and 9-12 ONLY)  \\\n",
       "225  09-12       GEN ED                              ENGLISH   \n",
       "226  09-12       GEN ED                              ENGLISH   \n",
       "227  09-12       GEN ED                              ENGLISH   \n",
       "228  09-12       GEN ED                              ENGLISH   \n",
       "229  09-12       GEN ED                                 MATH   \n",
       "\n",
       "    CORE COURSE (MS CORE and 9-12 ONLY) SERVICE CATEGORY(K-9* ONLY)  \\\n",
       "225                           English 9                           -   \n",
       "226                          English 10                           -   \n",
       "227                          English 11                           -   \n",
       "228                          English 12                           -   \n",
       "229                  Integrated Algebra                           -   \n",
       "\n",
       "     NUMBER OF STUDENTS / SEATS FILLED  NUMBER OF SECTIONS  \\\n",
       "225                               63.0                 3.0   \n",
       "226                               79.0                 3.0   \n",
       "227                               38.0                 2.0   \n",
       "228                               69.0                 3.0   \n",
       "229                               53.0                 3.0   \n",
       "\n",
       "     AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  SIZE OF LARGEST CLASS  \\\n",
       "225                21.0                    19.0                   25.0   \n",
       "226                26.3                    24.0                   31.0   \n",
       "227                19.0                    16.0                   22.0   \n",
       "228                23.0                    13.0                   30.0   \n",
       "229                17.7                    16.0                   21.0   \n",
       "\n",
       "    DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO padded_csd     DBN  \n",
       "225       STARS                             NaN         01  01M292  \n",
       "226       STARS                             NaN         01  01M292  \n",
       "227       STARS                             NaN         01  01M292  \n",
       "228       STARS                             NaN         01  01M292  \n",
       "229       STARS                             NaN         01  01M292  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "class_size.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Average Class Sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't want to only select a certain type of class in which to base our class size data on. We want our class size data to be for every single class a school offers. What we can do is take the average across all the classes a school offers. This will give us unique DBN values, but also incorporate as much data as possible into the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>DBN</th>\n",
       "      <th>CSD</th>\n",
       "      <th>NUMBER OF STUDENTS / SEATS FILLED</th>\n",
       "      <th>NUMBER OF SECTIONS</th>\n",
       "      <th>AVERAGE CLASS SIZE</th>\n",
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       "      <td>88.0000</td>\n",
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       "      <td>46.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>21.00</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M378</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.00</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M448</td>\n",
       "      <td>1</td>\n",
       "      <td>105.6875</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>22.231250</td>\n",
       "      <td>18.25</td>\n",
       "      <td>27.062500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M450</td>\n",
       "      <td>1</td>\n",
       "      <td>57.6000</td>\n",
       "      <td>2.733333</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>19.40</td>\n",
       "      <td>22.866667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DBN  CSD  NUMBER OF STUDENTS / SEATS FILLED  NUMBER OF SECTIONS  \\\n",
       "0  01M292    1                            88.0000            4.000000   \n",
       "1  01M332    1                            46.0000            2.000000   \n",
       "2  01M378    1                            33.0000            1.000000   \n",
       "3  01M448    1                           105.6875            4.750000   \n",
       "4  01M450    1                            57.6000            2.733333   \n",
       "\n",
       "   AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  SIZE OF LARGEST CLASS  \\\n",
       "0           22.564286                   18.50              26.571429   \n",
       "1           22.000000                   21.00              23.500000   \n",
       "2           33.000000                   33.00              33.000000   \n",
       "3           22.231250                   18.25              27.062500   \n",
       "4           21.200000                   19.40              22.866667   \n",
       "\n",
       "   SCHOOLWIDE PUPIL-TEACHER RATIO  \n",
       "0                             NaN  \n",
       "1                             NaN  \n",
       "2                             NaN  \n",
       "3                             NaN  \n",
       "4                             NaN  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_size = class_size.groupby(\"DBN\").agg(numpy.mean)\n",
    "class_size.reset_index(inplace=True)\n",
    "data[\"class_size\"] = class_size\n",
    "data[\"class_size\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condensing Demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've finished condensing class_size, let's condense demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>Name</th>\n",
       "      <th>schoolyear</th>\n",
       "      <th>fl_percent</th>\n",
       "      <th>frl_percent</th>\n",
       "      <th>total_enrollment</th>\n",
       "      <th>prek</th>\n",
       "      <th>k</th>\n",
       "      <th>grade1</th>\n",
       "      <th>grade2</th>\n",
       "      <th>...</th>\n",
       "      <th>black_num</th>\n",
       "      <th>black_per</th>\n",
       "      <th>hispanic_num</th>\n",
       "      <th>hispanic_per</th>\n",
       "      <th>white_num</th>\n",
       "      <th>white_per</th>\n",
       "      <th>male_num</th>\n",
       "      <th>male_per</th>\n",
       "      <th>female_num</th>\n",
       "      <th>female_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01M015</td>\n",
       "      <td>P.S. 015 ROBERTO CLEMENTE</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.4</td>\n",
       "      <td>189</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>33.3</td>\n",
       "      <td>109</td>\n",
       "      <td>57.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>48.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>01M019</td>\n",
       "      <td>P.S. 019 ASHER LEVY</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.5</td>\n",
       "      <td>328</td>\n",
       "      <td>32</td>\n",
       "      <td>46</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>24.7</td>\n",
       "      <td>158</td>\n",
       "      <td>48.2</td>\n",
       "      <td>28</td>\n",
       "      <td>8.5</td>\n",
       "      <td>147.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>181.0</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>01M020</td>\n",
       "      <td>PS 020 ANNA SILVER</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.5</td>\n",
       "      <td>626</td>\n",
       "      <td>52</td>\n",
       "      <td>102</td>\n",
       "      <td>121</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>55</td>\n",
       "      <td>8.8</td>\n",
       "      <td>357</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2.6</td>\n",
       "      <td>330.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>296.0</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>01M034</td>\n",
       "      <td>PS 034 FRANKLIN D ROOSEVELT</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.7</td>\n",
       "      <td>401</td>\n",
       "      <td>14</td>\n",
       "      <td>34</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>22.4</td>\n",
       "      <td>275</td>\n",
       "      <td>68.6</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>50.9</td>\n",
       "      <td>197.0</td>\n",
       "      <td>49.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>01M063</td>\n",
       "      <td>PS 063 WILLIAM MCKINLEY</td>\n",
       "      <td>20112012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.9</td>\n",
       "      <td>176</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>23.3</td>\n",
       "      <td>110</td>\n",
       "      <td>62.5</td>\n",
       "      <td>15</td>\n",
       "      <td>8.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>55.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>44.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       DBN                                              Name  schoolyear  \\\n",
       "6   01M015  P.S. 015 ROBERTO CLEMENTE                           20112012   \n",
       "13  01M019  P.S. 019 ASHER LEVY                                 20112012   \n",
       "20  01M020  PS 020 ANNA SILVER                                  20112012   \n",
       "27  01M034  PS 034 FRANKLIN D ROOSEVELT                         20112012   \n",
       "35  01M063  PS 063 WILLIAM MCKINLEY                             20112012   \n",
       "\n",
       "   fl_percent  frl_percent  total_enrollment prek    k grade1 grade2  \\\n",
       "6         NaN         89.4               189   13   31     35     28   \n",
       "13        NaN         61.5               328   32   46     52     54   \n",
       "20        NaN         92.5               626   52  102    121     87   \n",
       "27        NaN         99.7               401   14   34     38     36   \n",
       "35        NaN         78.9               176   18   20     30     21   \n",
       "\n",
       "      ...     black_num black_per hispanic_num hispanic_per white_num  \\\n",
       "6     ...            63      33.3          109         57.7         4   \n",
       "13    ...            81      24.7          158         48.2        28   \n",
       "20    ...            55       8.8          357         57.0        16   \n",
       "27    ...            90      22.4          275         68.6         8   \n",
       "35    ...            41      23.3          110         62.5        15   \n",
       "\n",
       "   white_per male_num male_per female_num female_per  \n",
       "6        2.1     97.0     51.3       92.0       48.7  \n",
       "13       8.5    147.0     44.8      181.0       55.2  \n",
       "20       2.6    330.0     52.7      296.0       47.3  \n",
       "27       2.0    204.0     50.9      197.0       49.1  \n",
       "35       8.5     97.0     55.1       79.0       44.9  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n",
    "data[\"demographics\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condensing Graduation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graduation data, the Demographic and Cohort columns are what prevent DBN from being unique. A Cohort appears to be which year the data was recorded for, and the Demographic appears to be in which group the data was collected. In this case, we want to pick data from the most recent Cohort available, 2006. We also want data from the full cohort, so we'll only pick rows where Demographic is Total Cohort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Demographic</th>\n",
       "      <th>DBN</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Cohort</th>\n",
       "      <th>Total Cohort</th>\n",
       "      <th>Total Grads - n</th>\n",
       "      <th>Total Grads - % of cohort</th>\n",
       "      <th>Total Regents - n</th>\n",
       "      <th>Total Regents - % of cohort</th>\n",
       "      <th>Total Regents - % of grads</th>\n",
       "      <th>...</th>\n",
       "      <th>Regents w/o Advanced - n</th>\n",
       "      <th>Regents w/o Advanced - % of cohort</th>\n",
       "      <th>Regents w/o Advanced - % of grads</th>\n",
       "      <th>Local - n</th>\n",
       "      <th>Local - % of cohort</th>\n",
       "      <th>Local - % of grads</th>\n",
       "      <th>Still Enrolled - n</th>\n",
       "      <th>Still Enrolled - % of cohort</th>\n",
       "      <th>Dropped Out - n</th>\n",
       "      <th>Dropped Out - % of cohort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL</td>\n",
       "      <td>2006</td>\n",
       "      <td>78</td>\n",
       "      <td>43</td>\n",
       "      <td>55.1%</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2%</td>\n",
       "      <td>83.7%</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2%</td>\n",
       "      <td>83.7%</td>\n",
       "      <td>7</td>\n",
       "      <td>9%</td>\n",
       "      <td>16.3%</td>\n",
       "      <td>16</td>\n",
       "      <td>20.5%</td>\n",
       "      <td>11</td>\n",
       "      <td>14.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>124</td>\n",
       "      <td>53</td>\n",
       "      <td>42.7%</td>\n",
       "      <td>42</td>\n",
       "      <td>33.9%</td>\n",
       "      <td>79.2%</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>27.4%</td>\n",
       "      <td>64.2%</td>\n",
       "      <td>11</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>20.8%</td>\n",
       "      <td>46</td>\n",
       "      <td>37.1%</td>\n",
       "      <td>20</td>\n",
       "      <td>16.100000000000001%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>90</td>\n",
       "      <td>70</td>\n",
       "      <td>77.8%</td>\n",
       "      <td>67</td>\n",
       "      <td>74.400000000000006%</td>\n",
       "      <td>95.7%</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>74.400000000000006%</td>\n",
       "      <td>95.7%</td>\n",
       "      <td>3</td>\n",
       "      <td>3.3%</td>\n",
       "      <td>4.3%</td>\n",
       "      <td>15</td>\n",
       "      <td>16.7%</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>2006</td>\n",
       "      <td>84</td>\n",
       "      <td>47</td>\n",
       "      <td>56%</td>\n",
       "      <td>40</td>\n",
       "      <td>47.6%</td>\n",
       "      <td>85.1%</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>27.4%</td>\n",
       "      <td>48.9%</td>\n",
       "      <td>7</td>\n",
       "      <td>8.300000000000001%</td>\n",
       "      <td>14.9%</td>\n",
       "      <td>25</td>\n",
       "      <td>29.8%</td>\n",
       "      <td>5</td>\n",
       "      <td>6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Total Cohort</td>\n",
       "      <td>01M515</td>\n",
       "      <td>LOWER EAST SIDE PREPARATORY HIGH SCHO</td>\n",
       "      <td>2006</td>\n",
       "      <td>193</td>\n",
       "      <td>105</td>\n",
       "      <td>54.4%</td>\n",
       "      <td>91</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>86.7%</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>11.4%</td>\n",
       "      <td>21%</td>\n",
       "      <td>14</td>\n",
       "      <td>7.3%</td>\n",
       "      <td>13.3%</td>\n",
       "      <td>53</td>\n",
       "      <td>27.5%</td>\n",
       "      <td>35</td>\n",
       "      <td>18.100000000000001%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Demographic     DBN                            School Name Cohort  \\\n",
       "3   Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL   2006   \n",
       "10  Total Cohort  01M448    UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   2006   \n",
       "17  Total Cohort  01M450             EAST SIDE COMMUNITY SCHOOL   2006   \n",
       "24  Total Cohort  01M509                MARTA VALLE HIGH SCHOOL   2006   \n",
       "31  Total Cohort  01M515  LOWER EAST SIDE PREPARATORY HIGH SCHO   2006   \n",
       "\n",
       "    Total Cohort Total Grads - n Total Grads - % of cohort Total Regents - n  \\\n",
       "3             78              43                     55.1%                36   \n",
       "10           124              53                     42.7%                42   \n",
       "17            90              70                     77.8%                67   \n",
       "24            84              47                       56%                40   \n",
       "31           193             105                     54.4%                91   \n",
       "\n",
       "   Total Regents - % of cohort Total Regents - % of grads  \\\n",
       "3                        46.2%                      83.7%   \n",
       "10                       33.9%                      79.2%   \n",
       "17         74.400000000000006%                      95.7%   \n",
       "24                       47.6%                      85.1%   \n",
       "31                       47.2%                      86.7%   \n",
       "\n",
       "              ...            Regents w/o Advanced - n  \\\n",
       "3             ...                                  36   \n",
       "10            ...                                  34   \n",
       "17            ...                                  67   \n",
       "24            ...                                  23   \n",
       "31            ...                                  22   \n",
       "\n",
       "   Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads  \\\n",
       "3                               46.2%                             83.7%   \n",
       "10                              27.4%                             64.2%   \n",
       "17                74.400000000000006%                             95.7%   \n",
       "24                              27.4%                             48.9%   \n",
       "31                              11.4%                               21%   \n",
       "\n",
       "   Local - n Local - % of cohort Local - % of grads Still Enrolled - n  \\\n",
       "3          7                  9%              16.3%                 16   \n",
       "10        11                8.9%              20.8%                 46   \n",
       "17         3                3.3%               4.3%                 15   \n",
       "24         7  8.300000000000001%              14.9%                 25   \n",
       "31        14                7.3%              13.3%                 53   \n",
       "\n",
       "   Still Enrolled - % of cohort Dropped Out - n Dropped Out - % of cohort  \n",
       "3                         20.5%              11                     14.1%  \n",
       "10                        37.1%              20       16.100000000000001%  \n",
       "17                        16.7%               5                      5.6%  \n",
       "24                        29.8%               5                        6%  \n",
       "31                        27.5%              35       18.100000000000001%  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n",
    "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]\n",
    "data[\"graduation\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting AP Scores To Numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're almost ready to combine all of the datasets. The only remaining thing to do is to convert the AP test scores to numeric values from strings. The Advanced Placement, or AP, exams are taken by high school students. There are several AP exams, each corresponding to a school subject. If a high schooler passes a test with a high score, they may receive college credit. The AP is scored on a 1 to 5 scale, with anything 3 or higher being a \"passing\" score. Many high schoolers, particularly those who go to academically challenging high schools, take AP exams. AP exams are much rarer in schools that lack funding or don't have much academic rigor.\n",
    "\n",
    "It will be interesting to see if AP exam scores are correlated with SAT scores across high schools. In order to determine this, we'll need to convert the AP exam scores in the ap_2010 dataset to numeric values first.\n",
    "\n",
    "There are three columns we'll need to convert:\n",
    "\n",
    "- AP Test Takers (note that there's a trailing space)\n",
    "- Total Exams Taken\n",
    "- Number of Exams with scores 3 4 or 5\n",
    "\n",
    "Note that the first column above, AP Test Takers, has a trailing space at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DBN</th>\n",
       "      <th>SchoolName</th>\n",
       "      <th>AP Test Takers</th>\n",
       "      <th>Total Exams Taken</th>\n",
       "      <th>Number of Exams with scores 3 4 or 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD H.S.</td>\n",
       "      <td>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY HS</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M515</td>\n",
       "      <td>LOWER EASTSIDE PREP</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M539</td>\n",
       "      <td>NEW EXPLORATIONS SCI,TECH,MATH</td>\n",
       "      <td>255.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02M296</td>\n",
       "      <td>High School of Hospitality Management</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DBN                             SchoolName  AP Test Takers   \\\n",
       "0  01M448           UNIVERSITY NEIGHBORHOOD H.S.             39.0   \n",
       "1  01M450                 EAST SIDE COMMUNITY HS             19.0   \n",
       "2  01M515                    LOWER EASTSIDE PREP             24.0   \n",
       "3  01M539         NEW EXPLORATIONS SCI,TECH,MATH            255.0   \n",
       "4  02M296  High School of Hospitality Management              NaN   \n",
       "\n",
       "   Total Exams Taken  Number of Exams with scores 3 4 or 5  \n",
       "0               49.0                                  10.0  \n",
       "1               21.0                                   NaN  \n",
       "2               26.0                                  24.0  \n",
       "3              377.0                                 191.0  \n",
       "4                NaN                                   NaN  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n",
    "\n",
    "for col in cols:\n",
    "    data[\"ap_2010\"][col] = pandas.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")\n",
    "    \n",
    "data[\"ap_2010\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining The Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we merge our data, we'll need to decide on what kind of merge strategy we want to use. We'll be using the Pandas merge function, which supports four types of joins, left, right, inner, and outer. Each of these join types dictates how rows are combined.\n",
    "\n",
    "We'll be using the DBN to match up rows, which is how we'll know which row from the first dataset combines with the row from the second dataset. It's possible that even though the DBN is unique within each dataset, that there may be DBN values that exist in one dataset but not another. This is because the data is from different years, and each dataset has other inconsistencies in terms of how it was gathered. There may also be human and other errors in play. Therefore, we may not find matches in all of the datasets for the DBN values in sat_results, and other datasets may have DBN values that don't exist in sat_results.\n",
    "\n",
    "We'll merge two datasets at a time. So, for example, we'll merge sat_results and hs_directory, then merge the result with ap_2010, then merge the result with class_size, and so on until we've merged all of the datasets. Once we merge all of the datasets, we'll have roughly the same number of rows, but each row will have columns from all the datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing The Left Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the ap_2010 and the graduation datasets have many missing DBN values, so we'll use a left join when we join the sat_results dataset with them. A left join means that our final Dataframe will have all the same DBN values as the original sat_results Dataframe.\n",
    "\n",
    "We'll need to use the Pandas merge method to merge Dataframes. The \"left\" Dataframe is the Dataframe we call the method on, whereas the \"right\" Dataframe is the one we pass into merge.\n",
    "\n",
    "Because we're using the DBN column to join the Dataframes, we'll need to specify the keyword argument on=\"DBN\" when calling merge.\n",
    "\n",
    "We'll first assign data[\"sat_results\"] to the variable combined. We'll then merge all the other Dataframes with combined. At the end, combined will have all of the columns from all of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Regents w/o Advanced - n</th>\n",
       "      <th>Regents w/o Advanced - % of cohort</th>\n",
       "      <th>Regents w/o Advanced - % of grads</th>\n",
       "      <th>Local - n</th>\n",
       "      <th>Local - % of cohort</th>\n",
       "      <th>Local - % of grads</th>\n",
       "      <th>Still Enrolled - n</th>\n",
       "      <th>Still Enrolled - % of cohort</th>\n",
       "      <th>Dropped Out - n</th>\n",
       "      <th>Dropped Out - % of cohort</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>46.2%</td>\n",
       "      <td>83.7%</td>\n",
       "      <td>7</td>\n",
       "      <td>9%</td>\n",
       "      <td>16.3%</td>\n",
       "      <td>16</td>\n",
       "      <td>20.5%</td>\n",
       "      <td>11</td>\n",
       "      <td>14.1%</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>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>27.4%</td>\n",
       "      <td>64.2%</td>\n",
       "      <td>11</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>20.8%</td>\n",
       "      <td>46</td>\n",
       "      <td>37.1%</td>\n",
       "      <td>20</td>\n",
       "      <td>16.100000000000001%</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>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>74.400000000000006%</td>\n",
       "      <td>95.7%</td>\n",
       "      <td>3</td>\n",
       "      <td>3.3%</td>\n",
       "      <td>4.3%</td>\n",
       "      <td>15</td>\n",
       "      <td>16.7%</td>\n",
       "      <td>5</td>\n",
       "      <td>5.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>7</td>\n",
       "      <td>414.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>27.4%</td>\n",
       "      <td>48.9%</td>\n",
       "      <td>7</td>\n",
       "      <td>8.300000000000001%</td>\n",
       "      <td>14.9%</td>\n",
       "      <td>25</td>\n",
       "      <td>29.8%</td>\n",
       "      <td>5</td>\n",
       "      <td>6%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 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  01M458                      FORSYTH SATELLITE ACADEMY   \n",
       "4  01M509                        MARTA VALLE HIGH SCHOOL   \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                      7                            414.0   \n",
       "4                     44                            390.0   \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                401.0                   359.0     1174.0   \n",
       "4                433.0                   384.0     1207.0   \n",
       "\n",
       "                     SchoolName  AP Test Takers   Total Exams Taken  \\\n",
       "0                           NaN              NaN                NaN   \n",
       "1  UNIVERSITY NEIGHBORHOOD H.S.             39.0               49.0   \n",
       "2        EAST SIDE COMMUNITY HS             19.0               21.0   \n",
       "3                           NaN              NaN                NaN   \n",
       "4                           NaN              NaN                NaN   \n",
       "\n",
       "             ...             Regents w/o Advanced - n  \\\n",
       "0            ...                                   36   \n",
       "1            ...                                   34   \n",
       "2            ...                                   67   \n",
       "3            ...                                  NaN   \n",
       "4            ...                                   23   \n",
       "\n",
       "  Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads  \\\n",
       "0                              46.2%                             83.7%   \n",
       "1                              27.4%                             64.2%   \n",
       "2                74.400000000000006%                             95.7%   \n",
       "3                                NaN                               NaN   \n",
       "4                              27.4%                             48.9%   \n",
       "\n",
       "  Local - n  Local - % of cohort Local - % of grads Still Enrolled - n  \\\n",
       "0         7                   9%              16.3%                 16   \n",
       "1        11                 8.9%              20.8%                 46   \n",
       "2         3                 3.3%               4.3%                 15   \n",
       "3       NaN                  NaN                NaN                NaN   \n",
       "4         7   8.300000000000001%              14.9%                 25   \n",
       "\n",
       "  Still Enrolled - % of cohort Dropped Out - n Dropped Out - % of cohort  \n",
       "0                        20.5%              11                     14.1%  \n",
       "1                        37.1%              20       16.100000000000001%  \n",
       "2                        16.7%               5                      5.6%  \n",
       "3                          NaN             NaN                       NaN  \n",
       "4                        29.8%               5                        6%  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(479, 33)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing The Inner Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've done the left joins, we still have class_size, demographics, survey, and hs_directory left to merge into combined. Because these files contain information that's more valuable to our analysis, and because they have fewer missing DBN values, we'll use the inner join type when merging these into combined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>priority04</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",
       "    </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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Then to Manhattan students or residents</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </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>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </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>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </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>255.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 159 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",
       "  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",
       "   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",
       "                       SchoolName  AP Test Takers   Total Exams Taken  \\\n",
       "0                             NaN              NaN                NaN   \n",
       "1    UNIVERSITY NEIGHBORHOOD H.S.             39.0               49.0   \n",
       "2          EAST SIDE COMMUNITY HS             19.0               21.0   \n",
       "3                             NaN              NaN                NaN   \n",
       "4  NEW EXPLORATIONS SCI,TECH,MATH            255.0              377.0   \n",
       "\n",
       "     ...                                   priority04  \\\n",
       "0    ...      Then to Manhattan students or residents   \n",
       "1    ...                                          NaN   \n",
       "2    ...                                          NaN   \n",
       "3    ...                                          NaN   \n",
       "4    ...                                          NaN   \n",
       "\n",
       "                        priority05 priority06 priority07  priority08  \\\n",
       "0  Then to New York City residents        NaN        NaN         NaN   \n",
       "1                              NaN        NaN        NaN         NaN   \n",
       "2                              NaN        NaN        NaN         NaN   \n",
       "3                              NaN        NaN        NaN         NaN   \n",
       "4                              NaN        NaN        NaN         NaN   \n",
       "\n",
       "  priority09 priority10                                         Location 1  \\\n",
       "0        NaN        NaN  220 Henry Street\\nNew York, NY 10002\\n(40.7137...   \n",
       "1        NaN        NaN  200 Monroe Street\\nNew York, NY 10002\\n(40.712...   \n",
       "2        NaN        NaN  420 East 12 Street\\nNew York, NY 10009\\n(40.72...   \n",
       "3        NaN        NaN  145 Stanton Street\\nNew York, NY 10002\\n(40.72...   \n",
       "4        NaN        NaN  111 Columbia Street\\nNew York, NY 10002\\n(40.7...   \n",
       "\n",
       "         lat        lon  \n",
       "0  40.713764 -73.985260  \n",
       "1  40.712332 -73.984797  \n",
       "2  40.729783 -73.983041  \n",
       "3  40.720569 -73.985673  \n",
       "4  40.718725 -73.979426  \n",
       "\n",
       "[5 rows x 159 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "    \n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363, 159)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling In Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fill in missing data in Pandas using the fillna method. This method will replace any missing values in a Dataframe with values that we specify. We can compute the means of every column using the mean method. If we pass the results of the mean method into the fillna method, Pandas will fill in the missing values in each column with the mean of that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>priority04</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",
       "    </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>Then to Manhattan students or residents</td>\n",
       "      <td>Then to New York City residents</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </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</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 159 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",
       "  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",
       "   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",
       "                       SchoolName  AP Test Takers   Total Exams Taken  \\\n",
       "0                               0       129.028846         197.038462   \n",
       "1    UNIVERSITY NEIGHBORHOOD H.S.        39.000000          49.000000   \n",
       "2          EAST SIDE COMMUNITY HS        19.000000          21.000000   \n",
       "3                               0       129.028846         197.038462   \n",
       "4  NEW EXPLORATIONS SCI,TECH,MATH       255.000000         377.000000   \n",
       "\n",
       "     ...                                   priority04  \\\n",
       "0    ...      Then to Manhattan students or residents   \n",
       "1    ...                                            0   \n",
       "2    ...                                            0   \n",
       "3    ...                                            0   \n",
       "4    ...                                            0   \n",
       "\n",
       "                        priority05 priority06 priority07  priority08  \\\n",
       "0  Then to New York City residents          0          0           0   \n",
       "1                                0          0          0           0   \n",
       "2                                0          0          0           0   \n",
       "3                                0          0          0           0   \n",
       "4                                0          0          0           0   \n",
       "\n",
       "  priority09 priority10                                         Location 1  \\\n",
       "0          0          0  220 Henry Street\\nNew York, NY 10002\\n(40.7137...   \n",
       "1          0          0  200 Monroe Street\\nNew York, NY 10002\\n(40.712...   \n",
       "2          0          0  420 East 12 Street\\nNew York, NY 10009\\n(40.72...   \n",
       "3          0          0  145 Stanton Street\\nNew York, NY 10002\\n(40.72...   \n",
       "4          0          0  111 Columbia Street\\nNew York, NY 10002\\n(40.7...   \n",
       "\n",
       "         lat        lon  \n",
       "0  40.713764 -73.985260  \n",
       "1  40.712332 -73.984797  \n",
       "2  40.729783 -73.983041  \n",
       "3  40.720569 -73.985673  \n",
       "4  40.718725 -73.979426  \n",
       "\n",
       "[5 rows x 159 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = combined.fillna(combined.mean())\n",
    "combined = combined.fillna(0)\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding A School District Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've finished cleaning and combining our data! We now have a clean dataset on which we can base our analysis. One type of analysis that we might want to do is mapping out statistics on a school district level. In order to help us do this, it will be useful to add a column that specifies the school district to the dataset.\n",
    "\n",
    "The school district is just the first two characters of the DBN. We can apply a function over the DBN column of combined that pulls out the first two letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    01\n",
       "1    01\n",
       "2    01\n",
       "3    01\n",
       "4    01\n",
       "Name: school_dist, dtype: object"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_first_two_chars(dbn):\n",
    "    return dbn[0:2]\n",
    "\n",
    "combined[\"school_dist\"] = combined[\"DBN\"].apply(get_first_two_chars)\n",
    "\n",
    "combined[\"school_dist\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlations tell us how closely related two columns are. We'll be using the r value, also called Pearson's correlation coefficient, which measures how closely two sequences of numbers are correlated. \n",
    "\n",
    "An r value falls between -1 and 1, and tells you if the two columns are positively correlated, not correlated, or negatively correlated. The closer to 1 the r value is, the more strongly positively correlated the columns are. The closer to -1 the r value is, the more strongly negatively correlated the columns are. The closer to 0 the r value is, the less the columns are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb5650d630>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAMMJGgAAwHCCBgAAMJygAQAADCdoAAAAwwkaAADAcIIGAAAwnKABAAAMNyRoVNX9q+qi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\ne1bQw09V1Zs39PCY7p5kmN+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5650fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "combined.corr()[\"sat_score\"][survey_fields].plot.bar(figsize=(12,8), color = 'orange')"
   ]
  },
  {
   "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": [
    "## Plotting Enrollment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, SAT Critical Reading Avg. Score, SAT Math Avg. Score, SAT Writing Avg. Score, and sat_score are strongly correlated with sat_score.\n",
    "\n",
    "We can also make some other observations:\n",
    "\n",
    "- total_enrollment correlates strongly positively with sat_score, which is surprising, because you'd think smaller schools, which focused more on the student, would have higher scores. However, it looks like the opposite -- bigger schools tend to do better on the SAT.\n",
    "    - Other columns that are proxies for enrollment, like total_students, N_s, N_p, N_t, AP Test Takers, Total Exams Taken, and NUMBER OF SECTIONS correlate as well.\n",
    "- The percentage of females at a school (female_per), and the number of females (female_num) correlate positively with SAT score, whereas the percentage of males (male_per), and the number of males (male_num) correlate negatively. This potentially indicates than women do better on the SAT than men.\n",
    "- How highly teachers and students rated safety at the school (saf_t_11, and saf_s_11) correlates with sat_score.\n",
    "- How highly students rated academic standards (aca_s_11) correlates with sat_score, but this does not hold for teachers and parents (aca_p_11 and aca_t_11).\n",
    "- There is a significant racial inequality in SAT scores (white_per, asian_per, black_per, hispanic_per).\n",
    "- The percentage of English language learners at the school (ell_percent, frl_percent) correlates strongly negatively with SAT scores.\n",
    "\n",
    "Since enrollment seems to have such a strong correlation, let's make a scatterplot of total_enrollment vs sat_score. Each point in the scatterplot will represent a high school, so we'll be able to see if there are any interesting patterns.\n",
    "\n",
    "We can plot columns in a Dataframe using the plot accessor on a Dataframe. We can directly use different plot types. For example df.plot.scatter(x=\"A\", y=\"b\") will create a scatterplot of columns A and B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hU2+vqaHBqanJTX8DAAAA5AQzOkAehMNSZ2dAy5bVa+nSeVq+vF6dnQGFw8XuGQAAQGUg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\nERERkZyjQEdERERERHLO/wc+9xkLRc74mQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb56d37358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "combined.plot.scatter(x='total_enrollment', y='sat_score', figsize = (12, 8), s = 60, color ='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Schools With Low SAT Scores And Enrollment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From looking at the plot we just generated, it doesn't appear that there's an extremely strong correlation between sat_score and total_enrollment. If there was a very strong correlation, we'd expect all the points to be arranged in a line. Instead, there's a large cluster of schools, then a few schools going off in 3 different directions.\n",
    "\n",
    "However, there is an interesting cluster of points at the bottom left where total_enrollment and sat_score are both low. This cluster may be what is causing our r-value to be so high. It's worth extracting the names of the schools in this cluster, so we can research them more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91       INTERNATIONAL COMMUNITY HIGH SCHOOL\n",
      "125                                        0\n",
      "126          BRONX INTERNATIONAL HIGH SCHOOL\n",
      "139    KINGSBRIDGE INTERNATIONAL HIGH SCHOOL\n",
      "141    INTERNATIONAL SCHOOL FOR LIBERAL ARTS\n",
      "176                                        0\n",
      "179            HIGH SCHOOL OF WORLD CULTURES\n",
      "188       BROOKLYN INTERNATIONAL HIGH SCHOOL\n",
      "225    INTERNATIONAL HIGH SCHOOL AT PROSPECT\n",
      "237               IT TAKES A VILLAGE ACADEMY\n",
      "253                MULTICULTURAL HIGH SCHOOL\n",
      "286    PAN AMERICAN INTERNATIONAL HIGH SCHOO\n",
      "Name: School Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "low_enrollment = combined[combined[\"total_enrollment\"] < 1000]\n",
    "low_enrollment = low_enrollment[low_enrollment[\"sat_score\"] < 1000]\n",
    "print(low_enrollment[\"School Name\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Language Learning Percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From our research in the last screen, we found that most of the high schools with low total enrollment and low SAT scores are actually schools with a high percentage of English language learners enrolled. This indicates that it's actually ell_percent that correlates strongly with sat_score instead of total_enrollment. To explore this relationship more, let's plot out ell_percent vs sat_score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2VknZ5+EAMywcNr32WvLfv/1bpTo6yqZ935mRe8WkNTezVwwAAMB0muzIzZ8l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5b406710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(x='ell_percent', y='sat_score', figsize = (12,8), s=60, color = 'Blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mapping The Schools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like ell_percent correlates with sat_score more strongly, because the scatterplot is more linear. However, there's still the cluster with very high ell_percent and low sat_score, which is the same group of international high schools that we investigated earlier.\n",
    "\n",
    "In order to explore this relationship, we'll want to map out ell_percent by school district, so we can more easily see which parts of the city have a lot of English language learners.\n",
    "\n",
    "In order to do this, we'll need to be able to create maps. We extracted the coordinates of all the schools earlier, and stored them in the lat and lon columns. This will enable us to plot all of the schools onto a map of the US, centered on New York City.\n",
    "\n",
    "The code above will create a plot, centered on New York City (llcrnrlat, urcrnrlat, llcrnrlon, and urcrnrlon define the corners of the area that's shown), and will draw coastlines and rivers accordingly.\n",
    "\n",
    "Now, all we need to do is convert our lat and lon coordinates into x and y coordinates so we can plot them on top of the map. This will show us where all the schools in our dataset are located.\n",
    "\n",
    "In order to plot coordinates using basemap, we need to:\n",
    "\n",
    "- Convert the Pandas Series containing the latitude and longitude to lists using the tolist method.\n",
    "- Make a scatterplot using the longitudes and latitudes with the scatter method on the Basemap object.\n",
    "- Show the plot using the show method.\n",
    "\n",
    "Additionally, we need to make sure we pass a few keyword arguments to the scatter method:\n",
    "\n",
    "- s -- this affects the size of the point that represents each school on the map.\n",
    "- zorder -- this affects where the points that represent schools will be drawn in the z axis. If we set it to 2, the points will be drawn on top of the continents, which is where we want them.\n",
    "- latlon -- this is a Boolean that specifies whether we're passing in latitude and longitude coordinates instead of x/y plot coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4gRz19fKxfaKxGlcsabVaqDQ5PhsPyDM06O/ri8OoXDQaDXY+sQNdZ0/E/SSV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\nRCH4H+JNtmDcWC3pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5b58bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "plt.figure(figsize=(8,8))\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='LightBlue')\n",
    "m.drawcoastlines(color='grey', linewidth=1)\n",
    "m.fillcontinents(color='white',lake_color='#85A6D9')\n",
    "\n",
    "longitudes = combined[\"lon\"].tolist()\n",
    "latitudes = combined[\"lat\"].tolist()\n",
    "m.scatter(longitudes, latitudes, s=80, zorder=2, latlon=True, marker = '.')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Out Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the map above, we can see that schools are the most dense in Manhattan (the top of the map), and less dense in Brooklyn, The Bronx, Queens, and Staten Island.\n",
    "\n",
    "Now that we can plot out the positions of the schools, we can start to display meaningful information on maps, such as the percentage of English language learners by area.\n",
    "\n",
    "We can shade each point in the scatterplot by passing the keyword argument c into the scatter method. The c keyword argument will accept a sequence of numbers, and will shade points corresponding to lower numbers or higher numbers differently.\n",
    "\n",
    "Whatever sequence of numbers we pass into the c keyword argument will be converted to a range from 0 to 1. These values will then be mapped onto a color map. Matplotlib has quite a few default colormaps. In our case, we'll use the summer colormap, which results in green points when the associated number is low, and yellow when it's high.\n",
    "\n",
    "For example, let's say we plotted ell_percent by school. If we pass in the keyword argument c=combined[\"ell_percent\"], then any school with a high ell_percent would be shaded yellow, and any school with a low ell_percent would be shaded green. We can specify a colormap by passing the cmap keyword argument to the scatter method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Joqoqp8sL+bdP91DYUsej6cv4sPg86xNmkxASccPxxbVlbL/wOb/OOsT6mDTW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\nIhf8P8hF/M7QP5dfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5635d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\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='LightBlue')\n",
    "m.drawcoastlines(color='grey', linewidth=1)\n",
    "m.fillcontinents(color='white',lake_color='#85A6D9')\n",
    "\n",
    "longitudes = combined[\"lon\"].tolist()\n",
    "latitudes = combined[\"lat\"].tolist()\n",
    "\n",
    "m.scatter(longitudes, latitudes, s=80, zorder=2, latlon=True, marker = '.', c=combined[\"ell_percent\"], cmap=\"summer\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating District Level Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, due to the number of schools, it's hard to interpret the map we made in the last screen. It looks like uptown Manhattan and parts of Queens have a higher ell_percent, but we can't be sure. One way to make it easier to read very granular statistics is to aggregate them. In this case, we can aggregate based on district, which will enable us to plot ell_percent district by district instead of school by school.\n",
    "\n",
    "In the last mission, we used the groupby and agg methods to find the mean class size for every unique DBN. The principle is the exact same, except here we'd find the mean of each column for every unique value in school_dist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_dist</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>AP Test Takers</th>\n",
       "      <th>Total Exams Taken</th>\n",
       "      <th>Number of Exams with scores 3 4 or 5</th>\n",
       "      <th>Total Cohort</th>\n",
       "      <th>CSD</th>\n",
       "      <th>...</th>\n",
       "      <th>grade_span_max</th>\n",
       "      <th>expgrade_span_max</th>\n",
       "      <th>zip</th>\n",
       "      <th>total_students</th>\n",
       "      <th>number_programs</th>\n",
       "      <th>priority08</th>\n",
       "      <th>priority09</th>\n",
       "      <th>priority10</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>441.833333</td>\n",
       "      <td>473.333333</td>\n",
       "      <td>439.333333</td>\n",
       "      <td>1354.500000</td>\n",
       "      <td>116.681090</td>\n",
       "      <td>173.019231</td>\n",
       "      <td>135.800000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10003.166667</td>\n",
       "      <td>659.500000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.719022</td>\n",
       "      <td>-73.982377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>426.619092</td>\n",
       "      <td>444.186256</td>\n",
       "      <td>424.832836</td>\n",
       "      <td>1295.638184</td>\n",
       "      <td>128.908454</td>\n",
       "      <td>201.516827</td>\n",
       "      <td>157.495833</td>\n",
       "      <td>158.647849</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10023.770833</td>\n",
       "      <td>621.395833</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.739699</td>\n",
       "      <td>-73.991386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03</td>\n",
       "      <td>428.529851</td>\n",
       "      <td>437.997512</td>\n",
       "      <td>426.915672</td>\n",
       "      <td>1293.443035</td>\n",
       "      <td>156.183494</td>\n",
       "      <td>244.522436</td>\n",
       "      <td>193.087500</td>\n",
       "      <td>183.384409</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10023.750000</td>\n",
       "      <td>717.916667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.781574</td>\n",
       "      <td>-73.977370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>402.142857</td>\n",
       "      <td>416.285714</td>\n",
       "      <td>405.714286</td>\n",
       "      <td>1224.142857</td>\n",
       "      <td>129.016484</td>\n",
       "      <td>183.879121</td>\n",
       "      <td>151.035714</td>\n",
       "      <td>113.857143</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10029.857143</td>\n",
       "      <td>580.857143</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.793449</td>\n",
       "      <td>-73.943215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>427.159915</td>\n",
       "      <td>438.236674</td>\n",
       "      <td>419.666098</td>\n",
       "      <td>1285.062687</td>\n",
       "      <td>85.722527</td>\n",
       "      <td>115.725275</td>\n",
       "      <td>142.464286</td>\n",
       "      <td>143.677419</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10030.142857</td>\n",
       "      <td>609.857143</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.817077</td>\n",
       "      <td>-73.949251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school_dist  SAT Critical Reading Avg. Score  SAT Math Avg. Score  \\\n",
       "0          01                       441.833333           473.333333   \n",
       "1          02                       426.619092           444.186256   \n",
       "2          03                       428.529851           437.997512   \n",
       "3          04                       402.142857           416.285714   \n",
       "4          05                       427.159915           438.236674   \n",
       "\n",
       "   SAT Writing Avg. Score    sat_score  AP Test Takers   Total Exams Taken  \\\n",
       "0              439.333333  1354.500000       116.681090         173.019231   \n",
       "1              424.832836  1295.638184       128.908454         201.516827   \n",
       "2              426.915672  1293.443035       156.183494         244.522436   \n",
       "3              405.714286  1224.142857       129.016484         183.879121   \n",
       "4              419.666098  1285.062687        85.722527         115.725275   \n",
       "\n",
       "   Number of Exams with scores 3 4 or 5  Total Cohort  CSD    ...      \\\n",
       "0                            135.800000     93.500000  1.0    ...       \n",
       "1                            157.495833    158.647849  2.0    ...       \n",
       "2                            193.087500    183.384409  3.0    ...       \n",
       "3                            151.035714    113.857143  4.0    ...       \n",
       "4                            142.464286    143.677419  5.0    ...       \n",
       "\n",
       "   grade_span_max  expgrade_span_max           zip  total_students  \\\n",
       "0            12.0               12.0  10003.166667      659.500000   \n",
       "1            12.0               12.0  10023.770833      621.395833   \n",
       "2            12.0               12.0  10023.750000      717.916667   \n",
       "3            12.0               12.0  10029.857143      580.857143   \n",
       "4            12.0               12.0  10030.142857      609.857143   \n",
       "\n",
       "   number_programs  priority08  priority09  priority10        lat        lon  \n",
       "0         1.333333         0.0         0.0         0.0  40.719022 -73.982377  \n",
       "1         1.416667         0.0         0.0         0.0  40.739699 -73.991386  \n",
       "2         2.000000         0.0         0.0         0.0  40.781574 -73.977370  \n",
       "3         1.142857         0.0         0.0         0.0  40.793449 -73.943215  \n",
       "4         1.142857         0.0         0.0         0.0  40.817077 -73.949251  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "districts = combined.groupby(\"school_dist\").agg(numpy.mean)\n",
    "districts.reset_index(inplace=True)\n",
    "districts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Ell_percent By District"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've taken the mean of all the columns, we can plot out ell_percent by district. Not only did we find the mean of ell_percent, we also took the mean of the lon and lat columns, which will give us the coordinates for the center of each district."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WbIFYJk/pOrraRvReP4+enh40NTUl9VzGGJxOJ0ZHRzE2NoYRmw022ygmJ8YhlsrAS6So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27Qi4J7MWG2NiA9LQ1HjxxGVhb31vzFarXi8wtlSH7pVSTo8wMdDgWJUVMtUkIWcPb999f1eWdm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb561d7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\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",
    "m.drawmapboundary(fill_color='LightBlue')\n",
    "m.drawcoastlines(color='grey', linewidth=1)\n",
    "m.fillcontinents(color='white',lake_color='#85A6D9')\n",
    "\n",
    "\n",
    "longitudes = districts[\"lon\"].tolist()\n",
    "latitudes = districts[\"lat\"].tolist()\n",
    "m.scatter(longitudes, latitudes, s=80, zorder=2, latlon=True, c=districts[\"ell_percent\"], cmap=\"summer\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Safety And SAT Scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll investigate safety scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb561c1550>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KwQwAAKgLAwMt6ulZqGQy3b0ZGoqpp2ehBgZaqlwyAJVCMAMAAGpeImHasqXd87GtW9uV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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5632c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"saf_s_11\", \"sat_score\", figsize = (12,8), s =60, color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a correlation between SAT scores and safety, although it isn't that strong. 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": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PQ53aWOrr6/GrX/0KpaWli74HBSchJGEejwfvvf8+nn7qqUUNsX549CNE1DqU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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb561093c8>"
      ]
     },
     "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",
    "\n",
    "plt.figure(figsize=(8,8))\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",
    "m.drawmapboundary(fill_color='LightBlue')\n",
    "m.drawcoastlines(color='grey', linewidth=1)\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": {
    "collapsed": true
   },
   "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": "markdown",
   "metadata": {},
   "source": [
    "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",
    "By plotting out the correlations between these columns and sat_score, we can see if there are any racial differences in SAT performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb5610c828>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dApbO/OOdD+5uD6lky6qquye5a27+4Qd+w8uWU1WvS/Ls7v781LNwc85oLKmq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb56113278>"
      ]
     },
     "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(figsize = (12,8))"
   ]
  },
  {
   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb56397a90>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2rE39/RHF47UuGQAAQLAR3AAeGxiIqKenVfF46vIbGgqpp6dVAwORGpcMAAAg2AhuAA/F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\nRJ7w/wG2Xdlj8kTbZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5621c7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"hispanic_per\", \"sat_score\", figsize=(12,8), s =60, color = 'Blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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": 225,
   "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": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb561153c8>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVU+sqj/Zv6iq/jDJNUl+vKq+UVWvPNjxAMD2tK5nTLr7u0mev8b+W5O8aMX2Sw/neABg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb546af5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_fields = [\"male_per\", \"female_per\"]\n",
    "combined.corr()[\"sat_score\"][gender_fields].plot.bar(figsize = (8, 6))"
   ]
  },
  {
   "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": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb56a4cdd8>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAAqAmVrnMDAAA8Lhw2DQ+bmpudQiGX/wkAUCGM3AAAgIyGhqS+voDWrGnS6tXLtXZt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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5623cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot.scatter(\"female_per\", \"sat_score\", figsize = (12, 8), s = 60)"
   ]
  },
  {
   "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": 228,
   "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": "markdown",
   "metadata": {},
   "source": [
    "In the US, the Advanced Placement, or AP, exams, are exams that high schoolers take in order to gain college credit. AP exams can be taken in many different subjects, and passing the AP exam means that colleges may grant you credits.\n",
    "\n",
    "It makes sense that the number of students who took the AP exam in a school and SAT scores would be highly correlated, and let's dig into the relationship. Since total_enrollment is highly correlated with sat_score, we don't want to bias our results, so we'll instead look at the percentage of students in each school who took at least one AP exam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb54690278>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAADA1whqAAAAAPhatevUAACWKBo1jY6a2tudIhGXfwcAAOoMPTUA4FOxmDQ4GNLatW1a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\nNZw0uwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb5469c2e8>"
      ]
     },
     "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', figsize = (12, 8), s = 60)"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ideas For Further Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We've done quite a bit of investigation into demographics and SAT scores in this guided project. There's still quite a bit of analysis left to do, however. Here are some potential next steps:\n",
    "\n",
    "- Looking at class size and SAT scores.\n",
    "- Figuring out the best area to live in based on school performance.\n",
    "    - If we combine this with a property values dataset, we could find the cheapest place where there are good school.\n",
    "- Looking into the differences between parent, teacher, and student responses to surveys.\n",
    "- Assigning a score to schools based on sat_score and other attributes.\n"
   ]
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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