Data Table

Exploring SAT Scores of New York City: Data Cleaning & Preparation

This project takes a look at the 2012 SAT scores from New York City high schools, and explores insights and correlations found in related data. For ease of reading and to allow for a more in-depth discussion of the data cleaning processes, this project is split into two separate Jupyter notebooks: the first for data cleaning/preparation, and the second covering visualization and analysis.


Although inspired by — and running largely parallel to — this blog post from Dataquest, these two notebooks seek to better explain steps and reasoning that were glossed over (or left out entirely) in the original post.


In addition, there are a number of variances from the Dataquest version. In the first notebook, notable differences are the inclusion of the attendance dataset, the exclusion of the (entirely inapplicable) math_test_results dataset, and a different approach to cleaning the class_size and graduation data. Furthermore, a more thorough look at the final data reveals several other features that have a stronger correlation to SAT scores than those outlined in the blog post. These will be examined in the second notebook, which dives far deeper into the analysis of the data, including several custom visualizations and some basic significance and hypothesis testing.


The datasets used for this project can be found at:

Data Cleaning and Exploration

To begin, we will import Pandas and NumPy and set up Pandas to display up to 500 rows and columns. This avoids the notorious truncated format, where Pandas only shows the first few and last few columns (or rows) of a table, with elipses (...) in between.

In [1]:
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)

If the .csv files do not yet have clear, short names, now is a good time to rename them for convenience and clarity. After renaming the files, we convert each to a Pandas DataFrame and add each DataFrame to a Python dictionary. Doing so will make looping through each dataset easier in the long run.

In [2]:
files = ['ap_2010.csv', 
         'attendance.csv', 
         'class_size.csv', 
         'demographics.csv',
         'graduation.csv',
         'hs_directory.csv',
         'sat_results.csv']
In [3]:
data = {}
for f in files:
    d = pd.read_csv('data/{0}'.format(f))
    data[f.replace('.csv', '')] = d

Now, let's take a look at the data. First we'll examine the column names of each dataset, and then we'll briefly scan the values to get an idea of the information in each. In particular, we want to look for any similarities in the datasets that will allow us to directly compare specific qualities of a given school to its students' SAT performance.

In [4]:
for key, value in data.items():
    print('\n' + key + '\n')
    print(data[key].columns)
ap_2010

Index(['DBN', 'SchoolName', 'AP Test Takers ', 'Total Exams Taken',
       'Number of Exams with scores 3 4 or 5'],
      dtype='object')

attendance

Index(['District', 'YTD % Attendance (Avg)', 'YTD Enrollment(Avg)'], dtype='object')

class_size

Index(['CSD', 'BOROUGH', 'SCHOOL CODE', 'SCHOOL NAME', 'GRADE ',
       'PROGRAM TYPE', 'CORE SUBJECT (MS CORE and 9-12 ONLY)',
       'CORE COURSE (MS CORE and 9-12 ONLY)', 'SERVICE CATEGORY(K-9* ONLY)',
       'NUMBER OF STUDENTS / SEATS FILLED', 'NUMBER OF SECTIONS',
       'AVERAGE CLASS SIZE', 'SIZE OF SMALLEST CLASS', 'SIZE OF LARGEST CLASS',
       'DATA SOURCE', 'SCHOOLWIDE PUPIL-TEACHER RATIO'],
      dtype='object')

demographics

Index(['DBN', 'Name', 'schoolyear', 'fl_percent', 'frl_percent',
       'total_enrollment', 'prek', 'k', 'grade1', 'grade2', 'grade3', 'grade4',
       'grade5', 'grade6', 'grade7', 'grade8', 'grade9', 'grade10', 'grade11',
       'grade12', 'ell_num', 'ell_percent', 'sped_num', 'sped_percent',
       'ctt_num', 'selfcontained_num', 'asian_num', 'asian_per', 'black_num',
       'black_per', 'hispanic_num', 'hispanic_per', 'white_num', 'white_per',
       'male_num', 'male_per', 'female_num', 'female_per'],
      dtype='object')

graduation

Index(['Demographic', 'DBN', 'School Name', 'Cohort', 'Total Cohort',
       'Total Grads - n', 'Total Grads - % of cohort', 'Total Regents - n',
       'Total Regents - % of cohort', 'Total Regents - % of grads',
       'Advanced Regents - n', 'Advanced Regents - % of cohort',
       'Advanced Regents - % of grads', 'Regents w/o Advanced - n',
       'Regents w/o Advanced - % of cohort',
       'Regents w/o Advanced - % of grads', 'Local - n', 'Local - % of cohort',
       'Local - % of grads', 'Still Enrolled - n',
       'Still Enrolled - % of cohort', 'Dropped Out - n',
       'Dropped Out - % of cohort'],
      dtype='object')

hs_directory

Index(['dbn', 'school_name', 'borough', 'building_code', 'phone_number',
       'fax_number', 'grade_span_min', 'grade_span_max', 'expgrade_span_min',
       'expgrade_span_max', 'bus', 'subway', 'primary_address_line_1', 'city',
       'state_code', 'postcode', 'website', 'total_students', 'campus_name',
       'school_type', 'overview_paragraph', 'program_highlights',
       'language_classes', 'advancedplacement_courses', 'online_ap_courses',
       'online_language_courses', 'extracurricular_activities',
       'psal_sports_boys', 'psal_sports_girls', 'psal_sports_coed',
       'school_sports', 'partner_cbo', 'partner_hospital', 'partner_highered',
       'partner_cultural', 'partner_nonprofit', 'partner_corporate',
       'partner_financial', 'partner_other', 'addtl_info1', 'addtl_info2',
       'start_time', 'end_time', 'se_services', 'ell_programs',
       'school_accessibility_description', 'number_programs', 'priority01',
       'priority02', 'priority03', 'priority04', 'priority05', 'priority06',
       'priority07', 'priority08', 'priority09', 'priority10', 'Location 1',
       'Community Board', 'Council District', 'Census Tract', 'BIN', 'BBL',
       'NTA'],
      dtype='object')

sat_results

Index(['DBN', 'SCHOOL NAME', 'Num of SAT Test Takers',
       'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score',
       'SAT Writing Avg. Score'],
      dtype='object')
In [5]:
for key, value in data.items():
    print('\n' + key + '\n')
    print(value.head())
ap_2010

      DBN                             SchoolName  AP Test Takers   \
0  01M448           UNIVERSITY NEIGHBORHOOD H.S.             39.0   
1  01M450                 EAST SIDE COMMUNITY HS             19.0   
2  01M515                    LOWER EASTSIDE PREP             24.0   
3  01M539         NEW EXPLORATIONS SCI,TECH,MATH            255.0   
4  02M296  High School of Hospitality Management              NaN   

   Total Exams Taken  Number of Exams with scores 3 4 or 5  
0               49.0                                  10.0  
1               21.0                                   NaN  
2               26.0                                  24.0  
3              377.0                                 191.0  
4                NaN                                   NaN  

attendance

      District  YTD % Attendance (Avg)  YTD Enrollment(Avg)
0  DISTRICT 01                   91.18                12367
1  DISTRICT 02                   89.01                60823
2  DISTRICT 03                   89.28                21962
3  DISTRICT 04                   91.13                14252
4  DISTRICT 05                   89.08                13170

class_size

   CSD BOROUGH SCHOOL CODE                SCHOOL NAME GRADE  PROGRAM TYPE  \
0    1       M        M015  P.S. 015 Roberto Clemente     0K       GEN ED   
1    1       M        M015  P.S. 015 Roberto Clemente     0K          CTT   
2    1       M        M015  P.S. 015 Roberto Clemente     01       GEN ED   
3    1       M        M015  P.S. 015 Roberto Clemente     01          CTT   
4    1       M        M015  P.S. 015 Roberto Clemente     02       GEN ED   

  CORE SUBJECT (MS CORE and 9-12 ONLY) CORE COURSE (MS CORE and 9-12 ONLY)  \
0                                    -                                   -   
1                                    -                                   -   
2                                    -                                   -   
3                                    -                                   -   
4                                    -                                   -   

  SERVICE CATEGORY(K-9* ONLY)  NUMBER OF STUDENTS / SEATS FILLED  \
0                           -                               19.0   
1                           -                               21.0   
2                           -                               17.0   
3                           -                               17.0   
4                           -                               15.0   

   NUMBER OF SECTIONS  AVERAGE CLASS SIZE  SIZE OF SMALLEST CLASS  \
0                 1.0                19.0                    19.0   
1                 1.0                21.0                    21.0   
2                 1.0                17.0                    17.0   
3                 1.0                17.0                    17.0   
4                 1.0                15.0                    15.0   

   SIZE OF LARGEST CLASS DATA SOURCE  SCHOOLWIDE PUPIL-TEACHER RATIO  
0                   19.0         ATS                             NaN  
1                   21.0         ATS                             NaN  
2                   17.0         ATS                             NaN  
3                   17.0         ATS                             NaN  
4                   15.0         ATS                             NaN  

demographics

      DBN                       Name  schoolyear fl_percent  frl_percent  \
0  01M015  P.S. 015 ROBERTO CLEMENTE    20052006       89.4          NaN   
1  01M015  P.S. 015 ROBERTO CLEMENTE    20062007       89.4          NaN   
2  01M015  P.S. 015 ROBERTO CLEMENTE    20072008       89.4          NaN   
3  01M015  P.S. 015 ROBERTO CLEMENTE    20082009       89.4          NaN   
4  01M015  P.S. 015 ROBERTO CLEMENTE    20092010                    96.5   

   total_enrollment prek   k grade1 grade2 grade3 grade4 grade5 grade6 grade7  \
0               281   15  36     40     33     38     52     29     38    NaN   
1               243   15  29     39     38     34     42     46    NaN    NaN   
2               261   18  43     39     36     38     47     40    NaN    NaN   
3               252   17  37     44     32     34     39     49    NaN    NaN   
4               208   16  40     28     32     30     24     38    NaN    NaN   

  grade8 grade9 grade10 grade11 grade12  ell_num  ell_percent  sped_num  \
0    NaN    NaN     NaN     NaN     NaN     36.0         12.8      57.0   
1    NaN    NaN     NaN     NaN     NaN     38.0         15.6      55.0   
2    NaN    NaN     NaN     NaN     NaN     52.0         19.9      60.0   
3    NaN    NaN     NaN     NaN     NaN     48.0         19.0      62.0   
4    NaN    NaN     NaN     NaN     NaN     40.0         19.2      46.0   

   sped_percent ctt_num selfcontained_num  asian_num  asian_per  black_num  \
0          20.3      25                 9         10        3.6         74   
1          22.6      19                15         18        7.4         68   
2          23.0      20                14         16        6.1         77   
3          24.6      21                17         16        6.3         75   
4          22.1      14                14         16        7.7         67   

   black_per  hispanic_num  hispanic_per  white_num  white_per  male_num  \
0       26.3           189          67.3          5        1.8     158.0   
1       28.0           153          63.0          4        1.6     140.0   
2       29.5           157          60.2          7        2.7     143.0   
3       29.8           149          59.1          7        2.8     149.0   
4       32.2           118          56.7          6        2.9     124.0   

   male_per  female_num  female_per  
0      56.2       123.0        43.8  
1      57.6       103.0        42.4  
2      54.8       118.0        45.2  
3      59.1       103.0        40.9  
4      59.6        84.0        40.4  

graduation

    Demographic     DBN                            School Name    Cohort  \
0  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2003   
1  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2004   
2  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2005   
3  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL      2006   
4  Total Cohort  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL  2006 Aug   

   Total Cohort Total Grads - n  Total Grads - % of cohort Total Regents - n  \
0             5               s                        NaN                 s   
1            55              37                       67.3                17   
2            64              43                       67.2                27   
3            78              43                       55.1                36   
4            78              44                       56.4                37   

   Total Regents - % of cohort  Total Regents - % of grads  \
0                          NaN                         NaN   
1                         30.9                        45.9   
2                         42.2                        62.8   
3                         46.2                        83.7   
4                         47.4                        84.1   

  Advanced Regents - n  Advanced Regents - % of cohort  \
0                    s                             NaN   
1                    0                             0.0   
2                    0                             0.0   
3                    0                             0.0   
4                    0                             0.0   

   Advanced Regents - % of grads Regents w/o Advanced - n  \
0                            NaN                        s   
1                            0.0                       17   
2                            0.0                       27   
3                            0.0                       36   
4                            0.0                       37   

   Regents w/o Advanced - % of cohort  Regents w/o Advanced - % of grads  \
0                                 NaN                                NaN   
1                                30.9                               45.9   
2                                42.2                               62.8   
3                                46.2                               83.7   
4                                47.4                               84.1   

  Local - n  Local - % of cohort  Local - % of grads Still Enrolled - n  \
0         s                  NaN                 NaN                  s   
1        20                 36.4                54.1                 15   
2        16                 25.0                37.2                  9   
3         7                  9.0                16.3                 16   
4         7                  9.0                15.9                 15   

   Still Enrolled - % of cohort Dropped Out - n  Dropped Out - % of cohort  
0                           NaN               s                        NaN  
1                          27.3               3                        5.5  
2                          14.1               9                       14.1  
3                          20.5              11                       14.1  
4                          19.2              11                       14.1  

hs_directory

      dbn                                        school_name    borough  \
0  21K540                             John Dewey High School   Brooklyn   
1  15K429                 Brooklyn School for Global Studies   Brooklyn   
2  24Q530  International High School at LaGuardia Communi...     Queens   
3  05M367  Academy for Social Action: A College Board School  Manhattan   
4  27Q260          Frederick Douglass Academy VI High School     Queens   

  building_code  phone_number    fax_number  grade_span_min  grade_span_max  \
0          K540  718-373-6400  718-266-4385             9.0              12   
1          K293  718-694-9741  718-694-9745             6.0              12   
2          Q520  718-392-3433  718-392-3443             9.0              12   
3          M043  212-234-3102  212-234-8597             9.0              12   
4          Q465  718-471-2154  718-471-2890             9.0              12   

   expgrade_span_min  expgrade_span_max  \
0                NaN                NaN   
1                NaN                NaN   
2                NaN                NaN   
3                NaN                NaN   
4                NaN                NaN   

                                                 bus  \
0                                   B1, B4, B64, B82   
1                 B103, B45, B57, B61, B62, B63, B65   
2          B24, Q100, Q101, Q102, Q32, Q39, Q60, Q67   
3  Bx15, Bx33, M10, M100, M101, M104, M11, M3, M4...   
4                                          Q113, Q22   

                                              subway primary_address_line_1  \
0  D to Bay 50th St ; F to Ave X ; N to Gravesend...            50 Avenue X   
1    A, C to Hoyt & Schermerhorn ; F, G to Bergen St      284 Baltic Street   
2   7 to Rawson St-33rd St ; E, M, R to Queens Plaza   45-35 Van Dam Street   
3                          1, A, B, C, D to 125th St    509 West 129 Street   
4                       A to Beach 25th St-Wavecrest     8-21 Bay 25 Street   

               city state_code  postcode  \
0          Brooklyn         NY     11223   
1          Brooklyn         NY     11201   
2  Long Island City         NY     11101   
3          New York         NY     10027   
4      Far Rockaway         NY     11691   

                                        website  total_students  \
0                http://johndeweyhighschool.org          1937.0   
1                                www.mybsgs.com           275.0   
2                                www.ihsnyc.org           503.0   
3  http://schools.nyc.gov/schoolportals/05/M367           309.0   
4  http://schools.nyc.gov/schoolportals/27/Q260           412.0   

                       campus_name                       school_type  \
0                              NaN                               NaN   
1                              NaN                 Consortium School   
2            Middle College Campus  Consortium, International School   
3                              NaN                               NaN   
4  Far Rockaway Educational Campus                               NaN   

                                  overview_paragraph  \
0  We offer an innovative form of education that ...   
1  At The Brooklyn School for Global Studies, all...   
2  We are a small multicultural high school on th...   
3  In collaboration with the College Board, Acade...   
4  Frederick Douglass Academy (FDA) VI High Schoo...   

                                  program_highlights  \
0  Computer Science Institute, Medical & Health P...   
1  ILearn NYC and iZone School (Online Blended Le...   
2  English instruction in all classes with Native...   
3  Career and Technical Education courses in Digi...   
4  Advisory, Graphic Arts Design, Teaching Intern...   

                                    language_classes  \
0         Chinese, French, Italian, Russian, Spanish   
1                                            Spanish   
2  American Sign Language, Arabic, Bengali, Chine...   
3                                            Spanish   
4                                            Spanish   

                           advancedplacement_courses  \
0  Art History, Biology, Calculus AB, Calculus BC...   
1  Art History, Calculus AB, English Language and...   
2  Biology, Calculus AB, Chemistry, Chinese Langu...   
3  Art History, Calculus AB, English Language and...   
4  Calculus AB, English Language and Composition,...   

          online_ap_courses  \
0                       NaN   
1  Art History, Calculus AB   
2                       NaN   
3                       NaN   
4        Biology, Physics B   

                             online_language_courses  \
0                                                NaN   
1  Arabic, Bengali, Chinese, Chinese (Cantonese),...   
2                                                NaN   
3                                                NaN   
4                                    French, Spanish   

                          extracurricular_activities  \
0  Anime, Asian-American, ASPIRA of New York Lead...   
1  Academic Enrichment, Cheerleading, Art Classes...   
2  Access to LaGuardia Community College faciliti...   
3  Sports and Arts Foundation, Digital Video Prod...   
4  After-school Program, Book, Writing, Homework ...   

                                    psal_sports_boys  \
0  Basketball, Cross Country, Football, Outdoor T...   
1                             Basketball, Volleyball   
2  Basketball, Golf, Handball, Outdoor Track, Soc...   
3                                                NaN   
4  Basketball, Cross Country, Indoor Track, Outdo...   

                                   psal_sports_girls psal_sports_coed  \
0  Basketball, Cross Country, Football, Outdoor T...              NaN   
1                             Basketball, Volleyball              NaN   
2  Basketball, Golf, Handball, Outdoor Track, Soc...              NaN   
3                                                NaN              NaN   
4  Basketball, Cross Country, Indoor Track, Outdo...              NaN   

                                       school_sports  \
0  We also offer a variety of after-school sports...   
1                                Rugby, Cheerleading   
2  International High School athletes join with s...   
3  Boys Varsity Basketball, Girls Varsity Softbal...   
4             Step Team, Modern Dance, Hip Hop Dance   

                                         partner_cbo  \
0  Jewish Board of Family and Children’s Services...   
1  Catholic Charities Out of School Time (OST) Hi...   
2  Global Kids, Young Citizens’ Center of New Yor...   
3  College Board, Turnaround for Children, Sports...   
4                                                NaN   

                                    partner_hospital  \
0  Coney Island Hospital Center, JASA Scheuer Hou...   
1          SUNY Downstate School-Based Health Clinic   
2                                                NaN   
3              Harlem Hospital, Foundling Foundation   
4  Jamaica Hospital Medical Center, Peninsula Hos...   

                                    partner_highered  \
0  Kingsborough Community College, Medgar Evers C...   
1  Young Women’s Leadership Network: College Boun...   
2                        LaGuardia Community College   
3  The City College of New York (CUNY), Wheelock ...   
4  York College, Brooklyn College, St. John's Col...   

                                    partner_cultural  \
0  Theatre Development Fund (TDF), Center for Art...   
1  Palazzo Strozzi Renaissance Award Essay Compet...   
2                                Earsay Theater Arts   
3        ArtsConnection, Music Theatre International   
4                                                NaN   

                                   partner_nonprofit    partner_corporate  \
0  National Academy Foundation (NAF), Prospect Pa...                  NaN   
1                                                NaN                  NaN   
2  Internationals Network for Public Schools (INP...  Shearman & Sterling   
3  The College Board, College Bound Initiative,Tu...                  NaN   
4  Queens District Attorney, Sports and Arts Foun...   Replications, Inc.   

                                   partner_financial  \
0  Citigroup, Ernst & Young , Federal Reserve Ban...   
1                                                NaN   
2                                                NaN   
3                                                NaN   
4                                           Citibank   

                                       partner_other  \
0  National Aeronautics Space Administration (NAS...   
1                                                NaN   
2                                    YMCA (Swimming)   
3                                                NaN   
4           New York Road Runners Foundation (NYRRF)   

                                         addtl_info1  \
0                      Community Service Requirement   
1  Saturday School Test Prep, Dedicated 9th Grade...   
2       Portfolio Assessment required for graduation   
3  Uniform Required: school shirt with logo, gray...   
4  Uniform Required: plain white collared shirt, ...   

                                         addtl_info2 start_time end_time  \
0                                                NaN    8:13 AM  3:05 PM   
1             Extended Day Program, Uniform Required    8:45 AM  3:10 PM   
2  Community Service Requirement, Extended Day Pr...    8:00 AM  3:30 PM   
3                                                NaN    8:35 AM  3:45 PM   
4  Extended Day Program, Student Summer Orientati...    7:45 AM  2:05 PM   

                                         se_services  \
0  This school will provide students with disabil...   
1  This school will provide students with disabil...   
2  This school will provide students with disabil...   
3  This school will provide students with disabil...   
4  This school will provide students with disabil...   

                                   ell_programs  \
0  ESL; Transitional Bilingual Program: Chinese   
1                                           ESL   
2                                           ESL   
3                                           ESL   
4                                           ESL   

  school_accessibility_description  number_programs  \
0      Not Functionally Accessible                8   
1      Not Functionally Accessible                1   
2          Functionally Accessible                1   
3      Not Functionally Accessible                1   
4      Not Functionally Accessible                1   

                                          priority01  \
0         Priority to Brooklyn students or residents   
1                 Priority to continuing 8th graders   
2  Open only to New York City residents living in...   
3                 Priority to continuing 8th graders   
4  Priority to Queens students or residents who a...   

                                          priority02  \
0                    Then to New York City residents   
1  Then to Districts 13, 14, 15 and 16 students o...   
2                                                NaN   
3  Then to New York City residents who attend an ...   
4  Then to New York City residents who attend an ...   

                                          priority03  \
0  For K56B only: Open only to students whose hom...   
1             Then to Brooklyn students or residents   
2                                                NaN   
3                    Then to New York City residents   
4               Then to Queens students or residents   

                        priority04 priority05 priority06 priority07  \
0                              NaN        NaN        NaN        NaN   
1  Then to New York City residents        NaN        NaN        NaN   
2                              NaN        NaN        NaN        NaN   
3                              NaN        NaN        NaN        NaN   
4  Then to New York City residents        NaN        NaN        NaN   

  priority08 priority09 priority10  \
0        NaN        NaN        NaN   
1        NaN        NaN        NaN   
2        NaN        NaN        NaN   
3        NaN        NaN        NaN   
4        NaN        NaN        NaN   

                                          Location 1  Community Board  \
0  50 Avenue\nX Brooklyn, NY 11223\n(40.589238098...             13.0   
1  284 Baltic Street\nBrooklyn, NY 11201\n(40.685...              6.0   
2  45 35 Van Dam Street\nLong Island City, NY 111...              2.0   
3  509 West 129 Street\nNew York, NY 10027\n(40.8...              9.0   
4  8 21 Bay 25 Street\nFar Rockaway, NY 11691\n(4...             14.0   

   Council District  Census Tract        BIN           BBL  \
0              47.0         308.0  3194998.0  3.071850e+09   
1              33.0          69.0  3006401.0  3.004020e+09   
2              26.0         179.0  4003442.0  4.002490e+09   
3               7.0         219.0  1059723.0  1.019840e+09   
4              31.0      100802.0  4300730.0  4.157360e+09   

                                                 NTA  
0  Gravesend                                     ...  
1  DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hi...  
2  Hunters Point-Sunnyside-West Maspeth          ...  
3  Manhattanville                                ...  
4  Far Rockaway-Bayswater                        ...  

sat_results

      DBN                                    SCHOOL NAME  \
0  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   
1  01M448            UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   
2  01M450                     EAST SIDE COMMUNITY SCHOOL   
3  01M458                      FORSYTH SATELLITE ACADEMY   
4  01M509                        MARTA VALLE HIGH SCHOOL   

  Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score  \
0                     29                             355                 404   
1                     91                             383                 423   
2                     70                             377                 402   
3                      7                             414                 401   
4                     44                             390                 433   

  SAT Writing Avg. Score  
0                    363  
1                    366  
2                    370  
3                    359  
4                    384  

From a cursory look at each dataset, we see that most have a column named DBN. A quick Google search tells us that this is a unique identifier for each NYC school, so this is likely our best column to join our data on. However, before we can do that, we will need to create a DBN column for class_size and hs_directory. Because our attendance dataset lists districts and not individual schools we will need to add it in seperately at the end.

At first glance, each DBN appears to be a combination of the CSD and SCHOOL CODE columns of our class_size dataset. Since CSD seems likely to be the school district number, we should examine this further to see if we have an easy way of creating a new column for joining attendance.

To do this, we'll take a look at the unique values for the CSD column of class_size and the District column of attendance.

In [6]:
np.sort(data['class_size']['CSD'].unique())
Out[6]:
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],
      dtype=int64)
In [7]:
data['attendance']['District'].unique()
Out[7]:
array(['DISTRICT 01', 'DISTRICT 02', 'DISTRICT 03', 'DISTRICT 04',
       'DISTRICT 05', 'DISTRICT 06', 'DISTRICT 07', 'DISTRICT 08',
       'DISTRICT 09', 'DISTRICT 10', 'DISTRICT 11', 'DISTRICT 12',
       'DISTRICT 13', 'DISTRICT 14', 'DISTRICT 15', 'DISTRICT 16',
       'DISTRICT 17', 'DISTRICT 18', 'DISTRICT 19', 'DISTRICT 20',
       'DISTRICT 21', 'DISTRICT 22', 'DISTRICT 23', 'DISTRICT 24',
       'DISTRICT 25', 'DISTRICT 26', 'DISTRICT 27', 'DISTRICT 28',
       'DISTRICT 29', 'DISTRICT 30', 'DISTRICT 31', 'DISTRICT 32',
       'SPECIAL ED DISTRICT 75', 'ALTERNATIVE HIGH SCHOOLS', 'Citywide'],
      dtype=object)

Although it appears CSD represents the district, we see that our attendance dataset includes three additional districts.

We know we do not need the citywide attendance numbers, but what about SPECIAL ED DISTRICT 75 AND ALTERNATIVE HIGH SCHOOLS? Since what we are really concerned with is SAT scores, we look to that dataset to determine whether to include the extra districts:

In [8]:
s = data['sat_results']['DBN']
print(s[s.str.contains('7.\w{4}', regex=True)])
468    75K371
469    75M035
470    75Q256
471    75Q811
472    75R025
473    75X012
474    75X754
475    79M645
476    79Q950
477    79X490
Name: DBN, dtype: object

sat_results contains data for two districts that begin with 7: District 75 and District 79. A Google search confirms that District 79 is the ALTERNATIVE HIGH SCHOOLS value in our attendance dataset.

For uniformity, we can update attendance to reflect this and drop the Citywide row since we will not be using that data.

In [9]:
print(data['attendance'].tail())
                    District  YTD % Attendance (Avg)  YTD Enrollment(Avg)
30               DISTRICT 31                   90.98                59373
31               DISTRICT 32                   89.28                15297
32    SPECIAL ED DISTRICT 75                   83.21                21435
33  ALTERNATIVE HIGH SCHOOLS                   63.81                 7288
34                  Citywide                   89.99              1002463
In [10]:
data['attendance'].loc[33, 'District'] = 'ALTERNATIVE DISTRICT 79'

data['attendance'] = data['attendance'].drop(index=34)
print(data['attendance'].tail())
                   District  YTD % Attendance (Avg)  YTD Enrollment(Avg)
29              DISTRICT 30                   92.79                39742
30              DISTRICT 31                   90.98                59373
31              DISTRICT 32                   89.28                15297
32   SPECIAL ED DISTRICT 75                   83.21                21435
33  ALTERNATIVE DISTRICT 79                   63.81                 7288

To keep in mind while we move forward, it is useful to know which datasets include districts 75 and 79. The easiest way of doing this is looping through each and printing out the max DBN.

In [11]:
for k, v in data.items():
    if 'DBN' in data[k].columns:
        print('\n' + k + ':')
        print(data[k]['DBN'].max())
    elif 'dbn' in data[k].columns:
        print('\n' + k + ':')
        print(data[k]['dbn'].max())
    else:
        print('\n' + k + ':')
        print('No DBN column')
ap_2010:
32K556

attendance:
No DBN column

class_size:
No DBN column

demographics:
32K564

graduation:
32K564

hs_directory:
32K556

sat_results:
79X490

Now we will add a DBN column to class_size and hs_directory. In doing so, we make sure to add a zero padding to the beginning of the CSD column for districts 1 through 9.

In [12]:
data['hs_directory'].rename(columns={'dbn' : 'DBN'}, inplace=True)
data['class_size']['DBN'] = data['class_size'].apply(lambda x: '{0:02d}{1}'.format(x['CSD'], x['SCHOOL CODE']), axis=1)

print(data['class_size']['DBN'].max())
32K564

Next, we need to add in the surveys before combining our datasets. However, if we simply try to read these files in using pd.read_csv we run into a an error: UnicodeDecodeError: 'utf-8' codec can't decode byte 0x96 in position 34: invalid start byte.

Since the characters in the range 0x80-0x9F are specific to Windows-1252, that is likely the encoding we should use. Just to double check, we can look up the Windows-1252 character at code point 0x96 (-) and search for it in gened.txt. (It turns out there is one - in the text file that is likely throwing the error.)

Note: For more information on debugging encoding problems between UTF-8, Windows-1252 and ISO-8859-1, check out this incredibly helpful resource by Unicode expert Tex Texin.
In [13]:
survey1 = pd.read_csv('2011_School_Survey/gened.txt', delimiter='\t', encoding='windows-1252')
survey2 = pd.read_csv('2011_School_Survey/d75.txt', delimiter='\t', encoding='windows-1252')
survey = pd.concat([survey1, survey2], axis=0, sort=False)

Now that we have the surveys accessible as DataFrames, we will need to pare them down to only what we need. As a first step, we should update survey to only include the columns that we need. This is where the Survey Data Dictionary file becomes invaluable, as it tells us that most of the columns are individual survey questions/responses as opposed to the score each school received.

In [14]:
#We do not want to add an additional ~3000 columns to our data unless we can use it.
print(survey.shape) 
print(survey.columns)
(1702, 2773)
Index(['dbn', 'bn', 'schoolname', 'd75', 'studentssurveyed', 'highschool',
       'schooltype', 'rr_s', 'rr_t', 'rr_p',
       ...
       's_q14_2', 's_q14_3', 's_q14_4', 's_q14_5', 's_q14_6', 's_q14_7',
       's_q14_8', 's_q14_9', 's_q14_10', 's_q14_11'],
      dtype='object', length=2773)
In [15]:
survey.rename(columns={'dbn' : 'DBN'}, inplace=True)
survey_fields = ['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']
survey = survey.loc[:, survey_fields]
data['survey'] = survey
print(survey.shape)
(1702, 23)

Handling Duplicate DBNs

With the surveys added to our data dictionary, we now have to get rid of rows with duplicate DBNs since our sat_results data has one row per school. To quickly identify which datasets need this, we can print the number of rows that have a duplicate DBN:

In [16]:
for k, v in data.items():
    if k != 'attendance':
        print('\n'+ k + ':')
        print('Duplicate schools: ')
        print(data[k].duplicated(['DBN']).sum())
    
ap_2010:
Duplicate schools: 
1

class_size:
Duplicate schools: 
26124

demographics:
Duplicate schools: 
8481

graduation:
Duplicate schools: 
24673

hs_directory:
Duplicate schools: 
0

sat_results:
Duplicate schools: 
0

survey:
Duplicate schools: 
0

Since ap_2010 has only one duplicate, we should find it and see whether it is disposable.

In [17]:
prev = ''
for dbn in data['ap_2010']['DBN']:
    if dbn == prev:
        print(data['ap_2010'][data['ap_2010']['DBN'] == dbn])
    else:
        prev = dbn
       DBN                                         SchoolName  \
51  04M610  THE YOUNG WOMEN'S LEADERSHIP SCHOOL OF EAST HA...   
52  04M610                       YOUNG WOMEN'S LEADERSHIP SCH   

    AP Test Takers   Total Exams Taken  Number of Exams with scores 3 4 or 5  
51             41.0               55.0                                  29.0  
52              NaN                NaN                                   NaN  

This particular duplicate seems to be a mistake and doesn't appear to serve any purpose, so we can simply drop it and move on to the next dataset.

In [18]:
data['ap_2010'] = data['ap_2010'].drop(index=52)
data['ap_2010'][data['ap_2010']['DBN'] == '04M610']
Out[18]:
DBN SchoolName AP Test Takers Total Exams Taken Number of Exams with scores 3 4 or 5
51 04M610 THE YOUNG WOMEN'S LEADERSHIP SCHOOL OF EAST HA... 41.0 55.0 29.0

When we examine the class_size dataset we can see that each school is split into multiple rows by three criteria — the grade or grade-range, the program type, and finally the class subject. Since we are interested in class size from all subjects, but only for high school, we will begin by limiting the data to only those rows represeting grades 9 through 12.


It is worthwile to note here that there is no good way to limit the data by PROGRAM TYPE. While it could be argued that we only want to include class size for gen ed classes (as alternate programs may have small classes, bringing down the mean), the author of this notebook believes there are two persuasive reasons to include alternative program types.
  1. The sat_results data only distinguishes between special ed districts, but not special ed programs within a school. Thus, some of the scores from a regular district school will be from students in a special ed program.
  2. The CTT program type stands for "Collaborative Team-Teaching", a program type that has a roughly 60/40 split of gen ed and special ed students. To exclude this program type would be to exclude a sizeable portion of potential test-taking students.
In [19]:
class_size = data['class_size']
class_size = class_size[class_size['GRADE '] == '09-12']

By grouping the data by DBN and aggregating by the mean, we are left with only one row per DBN and only the columns containing numerical values.

In [20]:
class_size = class_size.groupby('DBN').mean().reset_index()
class_size.head()
Out[20]:
DBN CSD NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS SCHOOLWIDE PUPIL-TEACHER RATIO
0 01M292 1 65.384615 2.923077 22.700000 20.115385 25.307692 NaN
1 01M332 1 29.833333 1.666667 18.250000 17.666667 19.000000 NaN
2 01M378 1 33.000000 1.000000 33.000000 33.000000 33.000000 NaN
3 01M448 1 103.923077 4.423077 23.800000 20.115385 28.000000 NaN
4 01M450 1 53.535714 2.464286 21.928571 20.464286 23.250000 NaN

Looking at the head of class_size, we see that the student-teacher ratio was not calculated. Although we could add this back in, the ratios are schoolwide and likely combine data for lower grades. Dropping the column in this case is appropriate.

In [21]:
class_size = class_size.drop(columns=['SCHOOLWIDE PUPIL-TEACHER RATIO'])
class_size.head()
Out[21]:
DBN CSD NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS
0 01M292 1 65.384615 2.923077 22.700000 20.115385 25.307692
1 01M332 1 29.833333 1.666667 18.250000 17.666667 19.000000
2 01M378 1 33.000000 1.000000 33.000000 33.000000 33.000000
3 01M448 1 103.923077 4.423077 23.800000 20.115385 28.000000
4 01M450 1 53.535714 2.464286 21.928571 20.464286 23.250000
In [22]:
data['class_size'] = class_size

The demographics data appears to be split by schoolyear and also contains a number of columns for lower grades. We will drop the unnecessary columns and select only the 2011-2012 school year.

In [23]:
demographics = data['demographics']
demographics.head(10)
Out[23]:
DBN Name schoolyear fl_percent frl_percent total_enrollment prek k grade1 grade2 grade3 grade4 grade5 grade6 grade7 grade8 grade9 grade10 grade11 grade12 ell_num ell_percent sped_num sped_percent ctt_num selfcontained_num asian_num asian_per black_num black_per hispanic_num hispanic_per white_num white_per male_num male_per female_num female_per
0 01M015 P.S. 015 ROBERTO CLEMENTE 20052006 89.4 NaN 281 15 36 40 33 38 52 29 38 NaN NaN NaN NaN NaN NaN 36.0 12.8 57.0 20.3 25 9 10 3.6 74 26.3 189 67.3 5 1.8 158.0 56.2 123.0 43.8
1 01M015 P.S. 015 ROBERTO CLEMENTE 20062007 89.4 NaN 243 15 29 39 38 34 42 46 NaN NaN NaN NaN NaN NaN NaN 38.0 15.6 55.0 22.6 19 15 18 7.4 68 28.0 153 63.0 4 1.6 140.0 57.6 103.0 42.4
2 01M015 P.S. 015 ROBERTO CLEMENTE 20072008 89.4 NaN 261 18 43 39 36 38 47 40 NaN NaN NaN NaN NaN NaN NaN 52.0 19.9 60.0 23.0 20 14 16 6.1 77 29.5 157 60.2 7 2.7 143.0 54.8 118.0 45.2
3 01M015 P.S. 015 ROBERTO CLEMENTE 20082009 89.4 NaN 252 17 37 44 32 34 39 49 NaN NaN NaN NaN NaN NaN NaN 48.0 19.0 62.0 24.6 21 17 16 6.3 75 29.8 149 59.1 7 2.8 149.0 59.1 103.0 40.9
4 01M015 P.S. 015 ROBERTO CLEMENTE 20092010 96.5 208 16 40 28 32 30 24 38 NaN NaN NaN NaN NaN NaN NaN 40.0 19.2 46.0 22.1 14 14 16 7.7 67 32.2 118 56.7 6 2.9 124.0 59.6 84.0 40.4
5 01M015 P.S. 015 ROBERTO CLEMENTE 20102011 96.5 203 13 37 35 33 30 30 25 NaN NaN NaN NaN NaN NaN NaN 30.0 14.8 46.0 22.7 21 9 13 6.4 75 36.9 110 54.2 4 2.0 113.0 55.7 90.0 44.3
6 01M015 P.S. 015 ROBERTO CLEMENTE 20112012 NaN 89.4 189 13 31 35 28 25 28 29 20.0 10.6 40.0 21.2 23 7 12 6.3 63 33.3 109 57.7 4 2.1 97.0 51.3 92.0 48.7
7 01M019 P.S. 019 ASHER LEVY 20052006 61.5 NaN 402 15 43 55 53 68 59 64 45 NaN NaN NaN NaN NaN NaN 37.0 9.2 93.0 23.1 7 37 40 10.0 103 25.6 207 51.5 39 9.7 214.0 53.2 188.0 46.8
8 01M019 P.S. 019 ASHER LEVY 20062007 61.5 NaN 312 13 37 45 52 47 61 57 NaN NaN NaN NaN NaN NaN NaN 30.0 9.6 72.0 23.1 13 22 30 9.6 70 22.4 172 55.1 19 6.1 157.0 50.3 155.0 49.7
9 01M019 P.S. 019 ASHER LEVY 20072008 61.5 NaN 338 28 48 46 47 53 48 68 NaN NaN NaN NaN NaN NaN NaN 40.0 11.8 75.0 22.2 12 19 42 12.4 72 21.3 186 55.0 22 6.5 162.0 47.9 176.0 52.1
In [24]:
demographics.columns
Out[24]:
Index(['DBN', 'Name', 'schoolyear', 'fl_percent', 'frl_percent',
       'total_enrollment', 'prek', 'k', 'grade1', 'grade2', 'grade3', 'grade4',
       'grade5', 'grade6', 'grade7', 'grade8', 'grade9', 'grade10', 'grade11',
       'grade12', 'ell_num', 'ell_percent', 'sped_num', 'sped_percent',
       'ctt_num', 'selfcontained_num', 'asian_num', 'asian_per', 'black_num',
       'black_per', 'hispanic_num', 'hispanic_per', 'white_num', 'white_per',
       'male_num', 'male_per', 'female_num', 'female_per'],
      dtype='object')

As the data dictionary provided on the download page for this dataset was not very thorough, significant internet digging is required to fully understand the columns. In particular, the fl_percent, frl_percent and selfcontained_num columns were not self-explanatory.

Luckily, the Kaggle Column Info table found here sheds some light. We now see that selfcontained_num represents the number of special ed students who are in special ed-only classes as opposed to the mixed CTT classes.

We also find that fl_percent represents the percentage of students who qualified for, and received, free school lunches annually through the 2008-2009 school year. From the 2009-2010 school year onward, fl_percent was not recorded and free and reduced-price lunches were recorded together under the column frl_percent. Thus, for the year we are interested in (2011-2012) fl_percent contains only NaN values and should be dropped.

In [25]:
demographics = demographics.drop(columns=['fl_percent', 'prek', 'k', 'grade1', 'grade2', 'grade3', 'grade4', 'grade5', 'grade6', 'grade7', 'grade8'])
demographics = demographics[demographics['schoolyear'] == 20112012]
demographics.dtypes
Out[25]:
DBN                   object
Name                  object
schoolyear             int64
frl_percent          float64
total_enrollment       int64
grade9                object
grade10               object
grade11               object
grade12               object
ell_num              float64
ell_percent          float64
sped_num             float64
sped_percent         float64
ctt_num               object
selfcontained_num     object
asian_num              int64
asian_per            float64
black_num              int64
black_per            float64
hispanic_num           int64
hispanic_per         float64
white_num              int64
white_per            float64
male_num             float64
male_per             float64
female_num           float64
female_per           float64
dtype: object

When we take a look at the remaining data types for each demographics column, we see that some columns we expected to contain numbers are Pandas' object type. On further examination, we see that these numbers are saved as strings. Converting these columns into a numerical data type now will save us headaches down the road when we have to handle missing data.

In [26]:
demographics['selfcontained_num'].unique()
Out[26]:
array(['7', '16', '31', '4', '28', '9', '21', '25', '36', '29', '0', '12',
       '35', '24', '11', '13', '18', '10', '19', '2', '14', '26', '17',
       '20', '1', '6', '33', '8', '44', '3', '5', '    ', '15', '47',
       '45', '80', '108', '30', '42', '66', '78', '32', '43', '22', '38',
       '34', '60', '53', '63', '23', '127', '49', '52', '41', '54', '39',
       '51', '48', '61', '27', '46', '55', '57', '74', '59', '50', '87',
       '120', '88', '56', '65', '85', '90', '37', '92', '347', '69', '64',
       '73', '62', '76', '82', '119', '77', '110', '124', '81', '72',
       '79', '352', '153', '75', '67', '95', '40', '258', '70', '113',
       '58', '97', '167', '68', '133', '71', '93', '125', '182', '270',
       '186', '83', '126', '249', '100', '118', '122', '89', '96', '112',
       '162', '137', '173', '214', '138', '91', '103', '109', '135',
       '147', '114', '102', '111', '176', '121', '98', '86', '101', '151',
       '172', '308'], dtype=object)
In [27]:
string_nums = ['grade9', 'grade10', 'grade11', 'grade12', 'ctt_num', 'selfcontained_num']
for col in string_nums:
    demographics[col] = pd.to_numeric(demographics[col], errors='coerce')

data['demographics'] = demographics
data['demographics'].dtypes
Out[27]:
DBN                   object
Name                  object
schoolyear             int64
frl_percent          float64
total_enrollment       int64
grade9               float64
grade10              float64
grade11              float64
grade12              float64
ell_num              float64
ell_percent          float64
sped_num             float64
sped_percent         float64
ctt_num              float64
selfcontained_num    float64
asian_num              int64
asian_per            float64
black_num              int64
black_per            float64
hispanic_num           int64
hispanic_per         float64
white_num              int64
white_per            float64
male_num             float64
male_per             float64
female_num           float64
female_per           float64
dtype: object

Finally, we take a look at our graduation dataset, which we know has over 24,000 duplicate DBNs. It appears to be split by cohort and Demographic, so we first check out the values for those two columns.

In [28]:
graduation = data['graduation']
print(graduation.shape)
graduation.head(10)
(25096, 23)
Out[28]:
Demographic DBN School Name Cohort Total Cohort Total Grads - n Total Grads - % of cohort Total Regents - n Total Regents - % of cohort Total Regents - % of grads Advanced Regents - n Advanced Regents - % of cohort Advanced Regents - % of grads Regents w/o Advanced - n Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads Local - n Local - % of cohort Local - % of grads Still Enrolled - n Still Enrolled - % of cohort Dropped Out - n Dropped Out - % of cohort
0 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2003 5 s NaN s NaN NaN s NaN NaN s NaN NaN s NaN NaN s NaN s NaN
1 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2004 55 37 67.3 17 30.9 45.9 0 0.0 0.0 17 30.9 45.9 20 36.4 54.1 15 27.3 3 5.5
2 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2005 64 43 67.2 27 42.2 62.8 0 0.0 0.0 27 42.2 62.8 16 25.0 37.2 9 14.1 9 14.1
3 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2006 78 43 55.1 36 46.2 83.7 0 0.0 0.0 36 46.2 83.7 7 9.0 16.3 16 20.5 11 14.1
4 Total Cohort 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL 2006 Aug 78 44 56.4 37 47.4 84.1 0 0.0 0.0 37 47.4 84.1 7 9.0 15.9 15 19.2 11 14.1
5 Total Cohort 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 2001 64 46 71.9 32 50.0 69.6 7 10.9 15.2 25 39.1 54.3 14 21.9 30.4 10 15.6 6 9.4
6 Total Cohort 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 2002 52 33 63.5 19 36.5 57.6 8 15.4 24.2 11 21.2 33.3 14 26.9 42.4 16 30.8 1 1.9
7 Total Cohort 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 2003 87 67 77.0 39 44.8 58.2 11 12.6 16.4 28 32.2 41.8 28 32.2 41.8 9 10.3 11 12.6
8 Total Cohort 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 2004 112 75 67.0 36 32.1 48.0 6 5.4 8.0 30 26.8 40.0 39 34.8 52.0 33 29.5 4 3.6
9 Total Cohort 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 2005 121 64 52.9 35 28.9 54.7 4 3.3 6.3 31 25.6 48.4 29 24.0 45.3 41 33.9 11 9.1
In [29]:
print(graduation['Demographic'].unique())
['Total Cohort' 'Asian' 'Male' 'Black' 'English Language Learners'
 'Hispanic' 'White' 'English Proficient Students'
 'Special Education Students' 'General Education Students' 'Female']
In [30]:
print(graduation['Cohort'].unique())
['2003' '2004' '2005' '2006' '2006 Aug' '2001' '2002']

We only care about the graduation statistics for the whole school, so we limit our data to the Demographic Total Cohort. It is also useful to check the number of individual DBNs at this point, since this is the number of rows we want after cleaning the dataset.

In [31]:
graduation = data['graduation'][data['graduation']['Demographic'] == 'Total Cohort']
graduation.DBN.nunique()
Out[31]:
423

As we only have data on the cohorts from 2001-2006 (graduating classes of 2005-2010), selecting a single cohort would not be appropriate here — particularly since the SAT results are from 2012. However, a historic graduation average for the included schools may indicate a broader, schoolwide trend. To achieve this, we will group by DBN and take the mean of the combined cohorts. Just like with our class_size dataset, this will leave us with one line per DBN and only the relevant numeric columns.

In [32]:
graduation = graduation.groupby('DBN').mean().reset_index()
print(graduation.shape)
data['graduation'] = graduation
(423, 13)

If we run our test for duplicate rows now, we should have nothing but zeros.

In [33]:
for k, v in data.items():
    if k != 'attendance':
        print('\n'+ k + ':')
        print('Duplicate schools: ')
        print(data[k].duplicated(['DBN']).sum())
ap_2010:
Duplicate schools: 
0

class_size:
Duplicate schools: 
0

demographics:
Duplicate schools: 
0

graduation:
Duplicate schools: 
0

hs_directory:
Duplicate schools: 
0

sat_results:
Duplicate schools: 
0

survey:
Duplicate schools: 
0

Feature Engineering

Now that our data is clean and ready to process, we need to create any columns needed for analysis and combine everything into one DataFrame.

Given that we are interested in correlations to SAT scores, we should start by creating a column that lists the average total score for each school. To do this, we simply convert the Reading, Math and Writing scores to numbers and add them together.

In [34]:
data['sat_results'].dtypes
Out[34]:
DBN                                object
SCHOOL NAME                        object
Num of SAT Test Takers             object
SAT Critical Reading Avg. Score    object
SAT Math Avg. Score                object
SAT Writing Avg. Score             object
dtype: object
In [35]:
sat_results = data['sat_results']
scores = [
    'Num of SAT Test Takers', #while not a score, we want this column to be numeric
    'SAT Critical Reading Avg. Score', 
    'SAT Math Avg. Score', 
    'SAT Writing Avg. Score'
    ]

for score in scores:
    sat_results[score] = pd.to_numeric(sat_results[score], errors='coerce')

sat_results['sat_score'] = sat_results[scores[1]] + sat_results[scores[2]] + sat_results[scores[3]]
In [36]:
print(sat_results.dtypes)
print(sat_results['sat_score'].head())
DBN                                 object
SCHOOL NAME                         object
Num of SAT Test Takers             float64
SAT Critical Reading Avg. Score    float64
SAT Math Avg. Score                float64
SAT Writing Avg. Score             float64
sat_score                          float64
dtype: object
0    1122.0
1    1172.0
2    1149.0
3    1174.0
4    1207.0
Name: sat_score, dtype: float64

When converting the columns to numeric values, we specified errors='coerce', which allowed Pandas to change values labeled 's' to Nan values. The NYC OpenData download pages note that an 's' signifies the record contained fewer than five students and was suppressed. Unfortunately, nearly 60 New York schools in sat_results had fewer than five students take the SAT in 2012, which leaves a significant gap in our data.

In [37]:
print(sat_results['sat_score'].isna().sum())
57

Since sat_results is our primary dataset, we won't drop these rows. Instead, we will fill the missing data with the mean of each score, fill in 2 for the number of test takers (the mean of the numbers below five), and flag these rows by adding a new column is_suppressed.

In [38]:
lambda1 = lambda x: 0 if pd.notnull(x) else 1
sat_results['is_suppressed'] = sat_results['sat_score'].apply(lambda1)

sat_results[scores[0]] = sat_results[scores[0]].fillna(value=2)
sat_results = sat_results.fillna(sat_results.mean()) 

There is no need for the extra decimal places, so we can round all numeric values to zero decimal places and save this DataFrame back into our data dictionary.

In [39]:
sat_results = sat_results.round(0)
data['sat_results'] = sat_results
data['sat_results'].head(25)
Out[39]:
DBN SCHOOL NAME Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score SAT Writing Avg. Score sat_score is_suppressed
0 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 29.0 355.0 404.0 363.0 1122.0 0
1 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 91.0 383.0 423.0 366.0 1172.0 0
2 01M450 EAST SIDE COMMUNITY SCHOOL 70.0 377.0 402.0 370.0 1149.0 0
3 01M458 FORSYTH SATELLITE ACADEMY 7.0 414.0 401.0 359.0 1174.0 0
4 01M509 MARTA VALLE HIGH SCHOOL 44.0 390.0 433.0 384.0 1207.0 0
5 01M515 LOWER EAST SIDE PREPARATORY HIGH SCHOOL 112.0 332.0 557.0 316.0 1205.0 0
6 01M539 NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ... 159.0 522.0 574.0 525.0 1621.0 0
7 01M650 CASCADES HIGH SCHOOL 18.0 417.0 418.0 411.0 1246.0 0
8 01M696 BARD HIGH SCHOOL EARLY COLLEGE 130.0 624.0 604.0 628.0 1856.0 0
9 02M047 47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECO... 16.0 395.0 400.0 387.0 1182.0 0
10 02M288 FOOD AND FINANCE HIGH SCHOOL 62.0 409.0 393.0 392.0 1194.0 0
11 02M294 ESSEX STREET ACADEMY 53.0 394.0 384.0 378.0 1156.0 0
12 02M296 HIGH SCHOOL OF HOSPITALITY MANAGEMENT 58.0 374.0 375.0 362.0 1111.0 0
13 02M298 PACE HIGH SCHOOL 85.0 423.0 438.0 432.0 1293.0 0
14 02M300 URBAN ASSEMBLY SCHOOL OF DESIGN AND CONSTRUCTI... 48.0 404.0 449.0 416.0 1269.0 0
15 02M303 FACING HISTORY SCHOOL, THE 76.0 353.0 358.0 340.0 1051.0 0
16 02M305 URBAN ASSEMBLY ACADEMY OF GOVERNMENT AND LAW, THE 50.0 375.0 388.0 385.0 1148.0 0
17 02M308 LOWER MANHATTAN ARTS ACADEMY 40.0 403.0 392.0 405.0 1200.0 0
18 02M313 JAMES BALDWIN SCHOOL, THE: A SCHOOL FOR EXPEDI... 69.0 408.0 390.0 390.0 1188.0 0
19 02M316 URBAN ASSEMBLY SCHOOL OF BUSINESS FOR YOUNG WO... 42.0 373.0 370.0 384.0 1127.0 0
20 02M374 GRAMERCY ARTS HIGH SCHOOL 60.0 391.0 391.0 394.0 1176.0 0
21 02M376 NYC ISCHOOL 92.0 473.0 483.0 479.0 1435.0 0
22 02M392 MANHATTAN BUSINESS ACADEMY 2.0 401.0 413.0 394.0 1208.0 1
23 02M393 BUSINESS OF SPORTS SCHOOL 2.0 401.0 413.0 394.0 1208.0 1
24 02M394 EMMA LAZARUS HIGH SCHOOL 79.0 319.0 512.0 357.0 1188.0 0

We also want to take advantage of the Location 1 variable listed in the hs_directory dataset.

If we print out the first value in the Location 1 column, we see it is a string containing the address, followed by the coordinates. To make the best use of this information, we will split it into two columns for the latitude and longitude of each school and ensure those values are numeric.

In [40]:
data['hs_directory']['Location 1'][0]
Out[40]:
'50 Avenue\nX Brooklyn, NY 11223\n(40.589238098, -73.981746524)'
In [41]:
lat_lambda = lambda x: x.split('\n')[-1].replace('(', '').replace(')', '').split(', ')[0]
lon_lambda = lambda x: x.split('\n')[-1].replace('(', '').replace(')', '').split(', ')[1]

data['hs_directory']['lat'] = data['hs_directory']['Location 1'].apply(lat_lambda)
data['hs_directory']['lon'] = data['hs_directory']['Location 1'].apply(lon_lambda)
In [42]:
for c in ['lat', 'lon']:
    data['hs_directory'][c] = pd.to_numeric(data['hs_directory'][c])
print(data['hs_directory'].dtypes)
data['hs_directory'].head()
DBN                                  object
school_name                          object
borough                              object
building_code                        object
phone_number                         object
fax_number                           object
grade_span_min                      float64
grade_span_max                        int64
expgrade_span_min                   float64
expgrade_span_max                   float64
bus                                  object
subway                               object
primary_address_line_1               object
city                                 object
state_code                           object
postcode                              int64
website                              object
total_students                      float64
campus_name                          object
school_type                          object
overview_paragraph                   object
program_highlights                   object
language_classes                     object
advancedplacement_courses            object
online_ap_courses                    object
online_language_courses              object
extracurricular_activities           object
psal_sports_boys                     object
psal_sports_girls                    object
psal_sports_coed                     object
school_sports                        object
partner_cbo                          object
partner_hospital                     object
partner_highered                     object
partner_cultural                     object
partner_nonprofit                    object
partner_corporate                    object
partner_financial                    object
partner_other                        object
addtl_info1                          object
addtl_info2                          object
start_time                           object
end_time                             object
se_services                          object
ell_programs                         object
school_accessibility_description     object
number_programs                       int64
priority01                           object
priority02                           object
priority03                           object
priority04                           object
priority05                           object
priority06                           object
priority07                           object
priority08                           object
priority09                           object
priority10                           object
Location 1                           object
Community Board                     float64
Council District                    float64
Census Tract                        float64
BIN                                 float64
BBL                                 float64
NTA                                  object
lat                                 float64
lon                                 float64
dtype: object
Out[42]:
DBN school_name borough building_code phone_number fax_number grade_span_min grade_span_max expgrade_span_min expgrade_span_max bus subway primary_address_line_1 city state_code postcode website total_students campus_name school_type overview_paragraph program_highlights language_classes advancedplacement_courses online_ap_courses online_language_courses extracurricular_activities psal_sports_boys psal_sports_girls psal_sports_coed school_sports partner_cbo partner_hospital partner_highered partner_cultural partner_nonprofit partner_corporate partner_financial partner_other addtl_info1 addtl_info2 start_time end_time se_services ell_programs school_accessibility_description number_programs priority01 priority02 priority03 priority04 priority05 priority06 priority07 priority08 priority09 priority10 Location 1 Community Board Council District Census Tract BIN BBL NTA lat lon
0 21K540 John Dewey High School Brooklyn K540 718-373-6400 718-266-4385 9.0 12 NaN NaN B1, B4, B64, B82 D to Bay 50th St ; F to Ave X ; N to Gravesend... 50 Avenue X Brooklyn NY 11223 http://johndeweyhighschool.org 1937.0 NaN NaN We offer an innovative form of education that ... Computer Science Institute, Medical & Health P... Chinese, French, Italian, Russian, Spanish Art History, Biology, Calculus AB, Calculus BC... NaN NaN Anime, Asian-American, ASPIRA of New York Lead... Basketball, Cross Country, Football, Outdoor T... Basketball, Cross Country, Football, Outdoor T... NaN We also offer a variety of after-school sports... Jewish Board of Family and Children’s Services... Coney Island Hospital Center, JASA Scheuer Hou... Kingsborough Community College, Medgar Evers C... Theatre Development Fund (TDF), Center for Art... National Academy Foundation (NAF), Prospect Pa... NaN Citigroup, Ernst & Young , Federal Reserve Ban... National Aeronautics Space Administration (NAS... Community Service Requirement NaN 8:13 AM 3:05 PM This school will provide students with disabil... ESL; Transitional Bilingual Program: Chinese Not Functionally Accessible 8 Priority to Brooklyn students or residents Then to New York City residents For K56B only: Open only to students whose hom... NaN NaN NaN NaN NaN NaN NaN 50 Avenue\nX Brooklyn, NY 11223\n(40.589238098... 13.0 47.0 308.0 3194998.0 3.071850e+09 Gravesend ... 40.589238 -73.981747
1 15K429 Brooklyn School for Global Studies Brooklyn K293 718-694-9741 718-694-9745 6.0 12 NaN NaN B103, B45, B57, B61, B62, B63, B65 A, C to Hoyt & Schermerhorn ; F, G to Bergen St 284 Baltic Street Brooklyn NY 11201 www.mybsgs.com 275.0 NaN Consortium School At The Brooklyn School for Global Studies, all... ILearn NYC and iZone School (Online Blended Le... Spanish Art History, Calculus AB, English Language and... Art History, Calculus AB Arabic, Bengali, Chinese, Chinese (Cantonese),... Academic Enrichment, Cheerleading, Art Classes... Basketball, Volleyball Basketball, Volleyball NaN Rugby, Cheerleading Catholic Charities Out of School Time (OST) Hi... SUNY Downstate School-Based Health Clinic Young Women’s Leadership Network: College Boun... Palazzo Strozzi Renaissance Award Essay Compet... NaN NaN NaN NaN Saturday School Test Prep, Dedicated 9th Grade... Extended Day Program, Uniform Required 8:45 AM 3:10 PM This school will provide students with disabil... ESL Not Functionally Accessible 1 Priority to continuing 8th graders Then to Districts 13, 14, 15 and 16 students o... Then to Brooklyn students or residents Then to New York City residents NaN NaN NaN NaN NaN NaN 284 Baltic Street\nBrooklyn, NY 11201\n(40.685... 6.0 33.0 69.0 3006401.0 3.004020e+09 DUMBO-Vinegar Hill-Downtown Brooklyn-Boerum Hi... 40.685452 -73.993491
2 24Q530 International High School at LaGuardia Communi... Queens Q520 718-392-3433 718-392-3443 9.0 12 NaN NaN B24, Q100, Q101, Q102, Q32, Q39, Q60, Q67 7 to Rawson St-33rd St ; E, M, R to Queens Plaza 45-35 Van Dam Street Long Island City NY 11101 www.ihsnyc.org 503.0 Middle College Campus Consortium, International School We are a small multicultural high school on th... English instruction in all classes with Native... American Sign Language, Arabic, Bengali, Chine... Biology, Calculus AB, Chemistry, Chinese Langu... NaN NaN Access to LaGuardia Community College faciliti... Basketball, Golf, Handball, Outdoor Track, Soc... Basketball, Golf, Handball, Outdoor Track, Soc... NaN International High School athletes join with s... Global Kids, Young Citizens’ Center of New Yor... NaN LaGuardia Community College Earsay Theater Arts Internationals Network for Public Schools (INP... Shearman & Sterling NaN YMCA (Swimming) Portfolio Assessment required for graduation Community Service Requirement, Extended Day Pr... 8:00 AM 3:30 PM This school will provide students with disabil... ESL Functionally Accessible 1 Open only to New York City residents living in... NaN NaN NaN NaN NaN NaN NaN NaN NaN 45 35 Van Dam Street\nLong Island City, NY 111... 2.0 26.0 179.0 4003442.0 4.002490e+09 Hunters Point-Sunnyside-West Maspeth ... 40.744150 -73.933627
3 05M367 Academy for Social Action: A College Board School Manhattan M043 212-234-3102 212-234-8597 9.0 12 NaN NaN Bx15, Bx33, M10, M100, M101, M104, M11, M3, M4... 1, A, B, C, D to 125th St 509 West 129 Street New York NY 10027 http://schools.nyc.gov/schoolportals/05/M367 309.0 NaN NaN In collaboration with the College Board, Acade... Career and Technical Education courses in Digi... Spanish Art History, Calculus AB, English Language and... NaN NaN Sports and Arts Foundation, Digital Video Prod... NaN NaN NaN Boys Varsity Basketball, Girls Varsity Softbal... College Board, Turnaround for Children, Sports... Harlem Hospital, Foundling Foundation The City College of New York (CUNY), Wheelock ... ArtsConnection, Music Theatre International The College Board, College Bound Initiative,Tu... NaN NaN NaN Uniform Required: school shirt with logo, gray... NaN 8:35 AM 3:45 PM This school will provide students with disabil... ESL Not Functionally Accessible 1 Priority to continuing 8th graders Then to New York City residents who attend an ... Then to New York City residents NaN NaN NaN NaN NaN NaN NaN 509 West 129 Street\nNew York, NY 10027\n(40.8... 9.0 7.0 219.0 1059723.0 1.019840e+09 Manhattanville ... 40.815230 -73.955201
4 27Q260 Frederick Douglass Academy VI High School Queens Q465 718-471-2154 718-471-2890 9.0 12 NaN NaN Q113, Q22 A to Beach 25th St-Wavecrest 8-21 Bay 25 Street Far Rockaway NY 11691 http://schools.nyc.gov/schoolportals/27/Q260 412.0 Far Rockaway Educational Campus NaN Frederick Douglass Academy (FDA) VI High Schoo... Advisory, Graphic Arts Design, Teaching Intern... Spanish Calculus AB, English Language and Composition,... Biology, Physics B French, Spanish After-school Program, Book, Writing, Homework ... Basketball, Cross Country, Indoor Track, Outdo... Basketball, Cross Country, Indoor Track, Outdo... NaN Step Team, Modern Dance, Hip Hop Dance NaN Jamaica Hospital Medical Center, Peninsula Hos... York College, Brooklyn College, St. John's Col... NaN Queens District Attorney, Sports and Arts Foun... Replications, Inc. Citibank New York Road Runners Foundation (NYRRF) Uniform Required: plain white collared shirt, ... Extended Day Program, Student Summer Orientati... 7:45 AM 2:05 PM This school will provide students with disabil... ESL Not Functionally Accessible 1 Priority to Queens students or residents who a... Then to New York City residents who attend an ... Then to Queens students or residents Then to New York City residents NaN NaN NaN NaN NaN NaN 8 21 Bay 25 Street\nFar Rockaway, NY 11691\n(4... 14.0 31.0 100802.0 4300730.0 4.157360e+09 Far Rockaway-Bayswater ... 40.601989 -73.762834

At this point, we see that we can pare down the number of columns in hs_directory. While more data is usually better, columns such as phone_number and fax_number will not yield any useful information for our purposes. Removing them from our hs_directory data and creating our own boolean features such as has_ap will help focus our analysis.

In [43]:
directory_cols = ['DBN', 'school_name', 'borough', 'building_code', 'grade_span_min', 'grade_span_max',
                 'city', 'postcode', 'total_students', 'school_type', 'language_classes', 'advancedplacement_courses',
                 'online_ap_courses', 'online_language_courses', 'start_time', 'end_time', 'number_programs', 
                 'Location 1', 'Community Board', 'Council District', 'lat', 'lon']

data['hs_directory'] = data['hs_directory'][directory_cols]
data['hs_directory'].columns
Out[43]:
Index(['DBN', 'school_name', 'borough', 'building_code', 'grade_span_min',
       'grade_span_max', 'city', 'postcode', 'total_students', 'school_type',
       'language_classes', 'advancedplacement_courses', 'online_ap_courses',
       'online_language_courses', 'start_time', 'end_time', 'number_programs',
       'Location 1', 'Community Board', 'Council District', 'lat', 'lon'],
      dtype='object')
In [44]:
# Creating boolean features for AP and language courses
data['hs_directory']['has_lang'] = data['hs_directory']['language_classes'].apply(lambda x: 0 if pd.isnull(x) else 1)
data['hs_directory']['has_ap'] = data['hs_directory']['advancedplacement_courses'].apply(lambda x: 0 if pd.isnull(x) else 1)
data['hs_directory']['has_online_lang'] = data['hs_directory']['online_language_courses'].apply(lambda x: 0 if pd.isnull(x) else 1)
data['hs_directory']['has_online_ap'] = data['hs_directory']['online_ap_courses'].apply(lambda x: 0 if pd.isnull(x) else 1)
In [45]:
data['hs_directory'].head(10)
Out[45]:
DBN school_name borough building_code grade_span_min grade_span_max city postcode total_students school_type language_classes advancedplacement_courses online_ap_courses online_language_courses start_time end_time number_programs Location 1 Community Board Council District lat lon has_lang has_ap has_online_lang has_online_ap
0 21K540 John Dewey High School Brooklyn K540 9.0 12 Brooklyn 11223 1937.0 NaN Chinese, French, Italian, Russian, Spanish Art History, Biology, Calculus AB, Calculus BC... NaN NaN 8:13 AM 3:05 PM 8 50 Avenue\nX Brooklyn, NY 11223\n(40.589238098... 13.0 47.0 40.589238 -73.981747 1 1 0 0
1 15K429 Brooklyn School for Global Studies Brooklyn K293 6.0 12 Brooklyn 11201 275.0 Consortium School Spanish Art History, Calculus AB, English Language and... Art History, Calculus AB Arabic, Bengali, Chinese, Chinese (Cantonese),... 8:45 AM 3:10 PM 1 284 Baltic Street\nBrooklyn, NY 11201\n(40.685... 6.0 33.0 40.685452 -73.993491 1 1 1 1
2 24Q530 International High School at LaGuardia Communi... Queens Q520 9.0 12 Long Island City 11101 503.0 Consortium, International School American Sign Language, Arabic, Bengali, Chine... Biology, Calculus AB, Chemistry, Chinese Langu... NaN NaN 8:00 AM 3:30 PM 1 45 35 Van Dam Street\nLong Island City, NY 111... 2.0 26.0 40.744150 -73.933627 1 1 0 0
3 05M367 Academy for Social Action: A College Board School Manhattan M043 9.0 12 New York 10027 309.0 NaN Spanish Art History, Calculus AB, English Language and... NaN NaN 8:35 AM 3:45 PM 1 509 West 129 Street\nNew York, NY 10027\n(40.8... 9.0 7.0 40.815230 -73.955201 1 1 0 0
4 27Q260 Frederick Douglass Academy VI High School Queens Q465 9.0 12 Far Rockaway 11691 412.0 NaN Spanish Calculus AB, English Language and Composition,... Biology, Physics B French, Spanish 7:45 AM 2:05 PM 1 8 21 Bay 25 Street\nFar Rockaway, NY 11691\n(4... 14.0 31.0 40.601989 -73.762834 1 1 1 1
5 21K559 Life Academy High School for Film and Music Brooklyn K400 9.0 12 Brooklyn 11214 260.0 NaN Spanish NaN Biology, English Literature and Composition, E... NaN 8:15 AM 3:00 PM 1 2630 Benson Avenue\nBrooklyn, NY 11214\n(40.59... 13.0 47.0 40.593594 -73.984729 1 0 0 1
6 16K393 Frederick Douglass Academy IV Secondary School Brooklyn K026 9.0 12 Brooklyn 11221 155.0 NaN French, Spanish English Language and Composition, United State... French Language and Culture NaN 8:00 AM 2:20 PM 1 1014 Lafayette Avenue\nBrooklyn, NY 11221\n(40... 3.0 36.0 40.692134 -73.931503 1 1 0 1
7 08X305 Pablo Neruda Academy Bronx X450 9.0 12 Bronx 10473 335.0 NaN Spanish Art History, English Language and Composition,... NaN Spanish 8:00 AM 3:50 PM 1 1980 Lafayette Avenue\nBronx, NY 10473\n(40.82... 9.0 18.0 40.822304 -73.855961 1 1 1 0
8 03M485 Fiorello H. LaGuardia High School of Music & A... Manhattan M485 9.0 12 New York 10023 2730.0 Specialized School French, Italian, Japanese, Spanish Art History, Biology, Calculus AB, Calculus BC... NaN Spanish 8:00 AM 4:00 PM 6 100 Amsterdam Avenue\nNew York, NY 10023\n(40.... 7.0 6.0 40.773671 -73.985269 1 1 1 0
9 02M305 Urban Assembly Academy of Government and Law, The Manhattan M445 9.0 12 New York 10002 326.0 NaN Spanish English Literature and Composition, Microecono... NaN NaN 8:32 AM 3:45 PM 1 350 Grand Street\nNew York, NY 10002\n(40.7168... 3.0 1.0 40.716867 -73.989532 1 1 0 0

Features representing the existence of a particular language program may also lead to some interesting insights. To create these, we must first fill in the Nan values to avoid throwing an error.

In [46]:
data['hs_directory']['language_classes'] = data['hs_directory']['language_classes'].fillna(value='No Language Classes')

data['hs_directory']['has_spanish'] = data['hs_directory']['language_classes'].apply(lambda x: 1 if 'Spanish' in x else 0)
data['hs_directory']['has_french'] = data['hs_directory']['language_classes'].apply(lambda x: 1 if 'French' in x else 0)
data['hs_directory']['has_chinese'] = data['hs_directory']['language_classes'].apply(lambda x: 1 if 'Chinese' in x else 0)
data['hs_directory']['has_russian'] = data['hs_directory']['language_classes'].apply(lambda x: 1 if 'Russian' in x else 0)
In [47]:
print(data['hs_directory'].dtypes)
data['hs_directory'].head(15)
DBN                           object
school_name                   object
borough                       object
building_code                 object
grade_span_min               float64
grade_span_max                 int64
city                          object
postcode                       int64
total_students               float64
school_type                   object
language_classes              object
advancedplacement_courses     object
online_ap_courses             object
online_language_courses       object
start_time                    object
end_time                      object
number_programs                int64
Location 1                    object
Community Board              float64
Council District             float64
lat                          float64
lon                          float64
has_lang                       int64
has_ap                         int64
has_online_lang                int64
has_online_ap                  int64
has_spanish                    int64
has_french                     int64
has_chinese                    int64
has_russian                    int64
dtype: object
Out[47]:
DBN school_name borough building_code grade_span_min grade_span_max city postcode total_students school_type language_classes advancedplacement_courses online_ap_courses online_language_courses start_time end_time number_programs Location 1 Community Board Council District lat lon has_lang has_ap has_online_lang has_online_ap has_spanish has_french has_chinese has_russian
0 21K540 John Dewey High School Brooklyn K540 9.0 12 Brooklyn 11223 1937.0 NaN Chinese, French, Italian, Russian, Spanish Art History, Biology, Calculus AB, Calculus BC... NaN NaN 8:13 AM 3:05 PM 8 50 Avenue\nX Brooklyn, NY 11223\n(40.589238098... 13.0 47.0 40.589238 -73.981747 1 1 0 0 1 1 1 1
1 15K429 Brooklyn School for Global Studies Brooklyn K293 6.0 12 Brooklyn 11201 275.0 Consortium School Spanish Art History, Calculus AB, English Language and... Art History, Calculus AB Arabic, Bengali, Chinese, Chinese (Cantonese),... 8:45 AM 3:10 PM 1 284 Baltic Street\nBrooklyn, NY 11201\n(40.685... 6.0 33.0 40.685452 -73.993491 1 1 1 1 1 0 0 0
2 24Q530 International High School at LaGuardia Communi... Queens Q520 9.0 12 Long Island City 11101 503.0 Consortium, International School American Sign Language, Arabic, Bengali, Chine... Biology, Calculus AB, Chemistry, Chinese Langu... NaN NaN 8:00 AM 3:30 PM 1 45 35 Van Dam Street\nLong Island City, NY 111... 2.0 26.0 40.744150 -73.933627 1 1 0 0 1 1 1 0
3 05M367 Academy for Social Action: A College Board School Manhattan M043 9.0 12 New York 10027 309.0 NaN Spanish Art History, Calculus AB, English Language and... NaN NaN 8:35 AM 3:45 PM 1 509 West 129 Street\nNew York, NY 10027\n(40.8... 9.0 7.0 40.815230 -73.955201 1 1 0 0 1 0 0 0
4 27Q260 Frederick Douglass Academy VI High School Queens Q465 9.0 12 Far Rockaway 11691 412.0 NaN Spanish Calculus AB, English Language and Composition,... Biology, Physics B French, Spanish 7:45 AM 2:05 PM 1 8 21 Bay 25 Street\nFar Rockaway, NY 11691\n(4... 14.0 31.0 40.601989 -73.762834 1 1 1 1 1 0 0 0
5 21K559 Life Academy High School for Film and Music Brooklyn K400 9.0 12 Brooklyn 11214 260.0 NaN Spanish NaN Biology, English Literature and Composition, E... NaN 8:15 AM 3:00 PM 1 2630 Benson Avenue\nBrooklyn, NY 11214\n(40.59... 13.0 47.0 40.593594 -73.984729 1 0 0 1 1 0 0 0
6 16K393 Frederick Douglass Academy IV Secondary School Brooklyn K026 9.0 12 Brooklyn 11221 155.0 NaN French, Spanish English Language and Composition, United State... French Language and Culture NaN 8:00 AM 2:20 PM 1 1014 Lafayette Avenue\nBrooklyn, NY 11221\n(40... 3.0 36.0 40.692134 -73.931503 1 1 0 1 1 1 0 0
7 08X305 Pablo Neruda Academy Bronx X450 9.0 12 Bronx 10473 335.0 NaN Spanish Art History, English Language and Composition,... NaN Spanish 8:00 AM 3:50 PM 1 1980 Lafayette Avenue\nBronx, NY 10473\n(40.82... 9.0 18.0 40.822304 -73.855961 1 1 1 0 1 0 0 0
8 03M485 Fiorello H. LaGuardia High School of Music & A... Manhattan M485 9.0 12 New York 10023 2730.0 Specialized School French, Italian, Japanese, Spanish Art History, Biology, Calculus AB, Calculus BC... NaN Spanish 8:00 AM 4:00 PM 6 100 Amsterdam Avenue\nNew York, NY 10023\n(40.... 7.0 6.0 40.773671 -73.985269 1 1 1 0 1 1 0 0
9 02M305 Urban Assembly Academy of Government and Law, The Manhattan M445 9.0 12 New York 10002 326.0 NaN Spanish English Literature and Composition, Microecono... NaN NaN 8:32 AM 3:45 PM 1 350 Grand Street\nNew York, NY 10002\n(40.7168... 3.0 1.0 40.716867 -73.989532 1 1 0 0 1 0 0 0
10 11X509 High School of Language and Innovation Bronx X415 9.0 12 Bronx 10469 255.0 NaN Chinese, Korean, Russian Calculus AB NaN NaN 8:25 AM 3:15 PM 1 925 Astor Avenue\nBronx, NY 10469\n(40.8596983... 11.0 13.0 40.859698 -73.860741 1 1 0 0 0 0 1 1
11 17K122 Pathways in Technology Early College High Scho... Brooklyn K625 9.0 12 Brooklyn 11213 330.0 NYC P-Tech 9-14 School No Language Classes NaN NaN NaN 8:15 AM 4:30 PM 1 150 Albany Avenue\nBrooklyn, NY 11213\n(40.675... 8.0 36.0 40.675402 -73.938881 0 0 0 0 0 0 0 0
12 08X348 Schuylerville Preparatory High School Bronx X405 9.0 11 Bronx 10461 95.0 NaN Latin, Spanish Native Language Arts NaN NaN NaN 8:00 AM 3:40 PM 1 3000 East Tremont Avenue\nBronx, NY 10461\n(40... 10.0 13.0 40.840514 -73.838121 1 0 0 0 1 0 0 0
13 24Q236 International High School for Health Sciences Queens Q455 9.0 11 Elmhurst 11373 50.0 International School No Language Classes NaN NaN NaN 8:30 AM 3:30 PM 1 48 01 90 Street\nElmhurst, NY 11373\n(40.74120... 4.0 25.0 40.741205 -73.874730 0 0 0 0 0 0 0 0
14 23K644 Eagle Academy for Young Men II Brooklyn K271 6.0 12 Brooklyn 11233 513.0 All-Boys School Spanish Biology, Calculus AB, Chemistry, English Liter... NaN NaN 8:45 AM 3:00 PM 1 1137 Herkimer Street\nBrooklyn, NY 11233\n(40.... 16.0 41.0 40.677826 -73.915823 1 1 0 0 1 0 0 0

Finally, we see that our start_time and end_time columns are not numerical data. In the off-chance of a correlation with start and end times, we will convert them into numerical values and fill any null values with the mean.

Note: While we could convert these times into datetime objects, we would not be able to calculate their means within our DataFrame. Converting the times to numbers is simply easier in this case.
In [48]:
times = ['start_time', 'end_time']
for c in times:
    data['hs_directory'][c] = data['hs_directory'][c].str.split(' ').str[0].str.replace(':', '')
    data['hs_directory'][c] = pd.to_numeric(data['hs_directory'][c])
    data['hs_directory'][c] = data['hs_directory'][c].fillna(data['hs_directory'][c].mean())
    data['hs_directory'][c] = data['hs_directory'][c].round(0)

data['hs_directory'][times].head()
Out[48]:
start_time end_time
0 813.0 305.0
1 845.0 310.0
2 800.0 330.0
3 835.0 345.0
4 745.0 205.0

Combining our Data

Our data is now ready to be consolidated into one DataFrame for analysis. There are a number of ways we could do this, but for this project we are only really interested in the schools for which we have SAT data. What this means is that we want to join the data in a way that preserves all rows of sat_results, but only keeps rows from our other datasets that correspond to those schools.

To start off, we can take a look at the shapes of our data sets to understand what our final DataFrame should look like.

In [49]:
for k, v in data.items():
    print(k)
    print(v.shape)
ap_2010
(257, 5)
attendance
(34, 3)
class_size
(590, 7)
demographics
(1509, 27)
graduation
(423, 13)
hs_directory
(435, 30)
sat_results
(478, 8)
survey
(1702, 23)

We know it should have the same number of rows as sat_results (478), and that our final column count will be six less than the sum of our current column count (we are joining six datasets to sat_results on the DBN column). Now we can combine our data and check to see if we lose any columns.

To begin combining our data, we start with the sat_results dataset and join each dataset that has a DBN column using a left join. This will preserve all rows in sat_results as well as all columns from all datasets, but drop rows where the DBN is not in our SAT data.

In [50]:
names = [k for k, v in data.items()]
full = data['sat_results']
for name in names:
    if name not in ['sat_results', 'attendance']:
        full = full.merge(data[name], on='DBN', how='left')
        
full.shape
Out[50]:
(478, 107)

Now we will create a school_dist feature for our full and attendance datasets and left join attenance into full.

In [51]:
full['school_dist'] = full['DBN'].apply(lambda x: x[:2])

dist_lambda = lambda x: x.split(' ')[-1]
data['attendance']['school_dist'] = data['attendance']['District'].apply(dist_lambda)

full = full.merge(data['attendance'], on='school_dist', how='left')
In [52]:
full.head()
Out[52]:
DBN SCHOOL NAME Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score SAT Writing Avg. Score sat_score is_suppressed SchoolName AP Test Takers Total Exams Taken Number of Exams with scores 3 4 or 5 CSD NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS Name schoolyear frl_percent total_enrollment grade9 grade10 grade11 grade12 ell_num ell_percent sped_num sped_percent ctt_num selfcontained_num asian_num asian_per black_num black_per hispanic_num hispanic_per white_num white_per male_num male_per female_num female_per Total Cohort Total Grads - % of cohort Total Regents - % of cohort Total Regents - % of grads Advanced Regents - % of cohort Advanced Regents - % of grads Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads Local - % of cohort Local - % of grads Still Enrolled - % of cohort Dropped Out - % of cohort school_name borough building_code grade_span_min grade_span_max city postcode total_students school_type language_classes advancedplacement_courses online_ap_courses online_language_courses start_time end_time number_programs Location 1 Community Board Council District lat lon has_lang has_ap has_online_lang has_online_ap has_spanish has_french has_chinese has_russian 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 school_dist District YTD % Attendance (Avg) YTD Enrollment(Avg)
0 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 29.0 355.0 404.0 363.0 1122.0 0 NaN NaN NaN NaN 1.0 65.384615 2.923077 22.700000 20.115385 25.307692 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 20112012.0 88.6 422.0 98.0 79.0 80.0 50.0 94.0 22.3 105.0 24.9 34.0 35.0 59.0 14.0 123.0 29.1 227.0 53.8 7.0 1.7 259.0 61.4 163.0 38.6 56.000000 61.500000 41.675000 69.125000 0.000000 0.000000 41.675000 69.125000 19.850000 30.875000 20.275000 11.950000 Henry Street School for International Studies Manhattan M056 6.0 12.0 New York 10002.0 323.0 NaN Chinese (Mandarin), Spanish Psychology Chinese Language and Culture, Spanish Literatu... Chinese (Mandarin), Spanish 830.0 330.0 1.0 220 Henry Street\nNew York, NY 10002\n(40.7137... 3.0 1.0 40.713764 -73.985260 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 89.0 70.0 39.0 379.0 26.0 151.0 7.8 7.7 7.4 7.6 6.3 5.3 6.1 6.5 6.0 5.6 6.1 6.7 6.7 6.2 6.6 7.0 01 DISTRICT 01 91.18 12367
1 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 91.0 383.0 423.0 366.0 1172.0 0 UNIVERSITY NEIGHBORHOOD H.S. 39.0 49.0 10.0 1.0 103.923077 4.423077 23.800000 20.115385 28.000000 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 20112012.0 71.8 394.0 109.0 97.0 93.0 95.0 83.0 21.1 86.0 21.8 55.0 10.0 115.0 29.2 89.0 22.6 181.0 45.9 9.0 2.3 226.0 57.4 168.0 42.6 97.714286 60.485714 37.157143 62.471429 8.657143 14.071429 28.528571 48.385714 23.314286 37.528571 26.957143 9.828571 University Neighborhood High School Manhattan M446 9.0 12.0 New York 10002.0 299.0 NaN Chinese, Spanish Calculus AB, Chinese Language and Culture, Eng... NaN Chinese (Cantonese), Chinese (Mandarin), Spanish 815.0 315.0 3.0 200 Monroe Street\nNew York, NY 10002\n(40.712... 3.0 1.0 40.712332 -73.984797 1.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 84.0 95.0 10.0 385.0 37.0 46.0 7.9 7.4 7.2 7.3 6.6 5.8 6.6 7.3 6.0 5.7 6.3 7.0 6.8 6.3 6.7 7.2 01 DISTRICT 01 91.18 12367
2 01M450 EAST SIDE COMMUNITY SCHOOL 70.0 377.0 402.0 370.0 1149.0 0 EAST SIDE COMMUNITY HS 19.0 21.0 NaN 1.0 53.535714 2.464286 21.928571 20.464286 23.250000 EAST SIDE COMMUNITY HIGH SCHOOL 20112012.0 71.8 598.0 101.0 93.0 77.0 86.0 30.0 5.0 158.0 26.4 91.0 19.0 58.0 9.7 143.0 23.9 331.0 55.4 62.0 10.4 327.0 54.7 271.0 45.3 79.571429 70.385714 66.000000 93.828571 0.000000 0.000000 66.000000 93.828571 4.357143 6.171429 17.614286 10.742857 East Side Community School Manhattan M060 6.0 12.0 New York 10009.0 649.0 Consortium School No Language Classes Calculus AB, English Literature and Composition NaN American Sign Language, Arabic, Chinese (Manda... 830.0 330.0 1.0 420 East 12 Street\nNew York, NY 10009\n(40.72... 3.0 2.0 40.729783 -73.983041 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 98.0 28.0 NaN 42.0 150.0 8.7 8.2 8.1 8.4 7.3 8.0 8.0 8.8 NaN NaN NaN NaN 7.9 7.9 7.9 8.4 01 DISTRICT 01 91.18 12367
3 01M458 FORSYTH SATELLITE ACADEMY 7.0 414.0 401.0 359.0 1174.0 0 NaN NaN NaN NaN 1.0 28.600000 1.200000 23.000000 22.600000 23.400000 SATELLITE ACADEMY HS @ FORSYTHE STREET 20112012.0 72.8 224.0 131.0 49.0 44.0 NaN 9.0 4.0 20.0 8.9 3.0 0.0 5.0 2.2 77.0 34.4 133.0 59.4 8.0 3.6 97.0 43.3 127.0 56.7 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 40.0 100.0 23.0 66.0 10.0 37.0 8.1 7.0 6.7 7.6 8.5 8.2 8.9 8.9 6.8 6.1 6.1 6.8 7.8 7.1 7.2 7.8 01 DISTRICT 01 91.18 12367
4 01M509 MARTA VALLE HIGH SCHOOL 44.0 390.0 433.0 384.0 1207.0 0 NaN NaN NaN NaN 1.0 49.851852 2.296296 19.370370 17.370370 21.481481 MARTA VALLE SECONDARY SCHOOL 20112012.0 80.7 367.0 143.0 100.0 51.0 73.0 41.0 11.2 95.0 25.9 28.0 36.0 34.0 9.3 116.0 31.6 209.0 56.9 6.0 1.6 170.0 46.3 197.0 53.7 73.571429 49.914286 31.385714 61.157143 10.571429 19.628571 20.814286 41.514286 18.514286 38.842857 29.857143 14.342857 Marta Valle High School Manhattan M025 9.0 12.0 New York 10002.0 401.0 NaN French, Spanish English Literature and Composition, Studio Art... NaN Spanish 800.0 330.0 1.0 145 Stanton Street\nNew York, NY 10002\n(40.72... 3.0 1.0 40.720569 -73.985673 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 90.0 100.0 21.0 306.0 29.0 69.0 7.7 7.4 7.2 7.3 6.4 5.3 6.1 6.8 6.4 5.9 6.4 7.0 6.9 6.2 6.6 7.0 01 DISTRICT 01 91.18 12367

To verify that we lost no columns, we print out the number of columns in full and the combined total of all columns from our data dictionary. If we merged our data successfully, full should have six columns fewer.

In [53]:
full_columns = [full.columns[x] for x in range(len(full.columns))]

all_columns = []
for name in names:
    all_columns += [data[name].columns[x] for x in range(len(data[name].columns))]
    
print(len(full_columns), len(all_columns))
111 117

Filling in missing values

Our last step before analyzing the data is to fill in any missing values that we have left. Although there is never a perfect way to address this, we can use our knowledge of the data to determine the best method for each case. For this data, we will use a combination of dropping columns we don't need, filling Nans with values extracted from elsewhere in the dataset, and filling empty values with either 0 or the column's mean for the remainder.

To begin, we look at the total number of missing values in each column and look for redundant or otherwise unhelpful columns that we missed in our earlier cleaning of the datasets.

In [54]:
full.isna().sum()
Out[54]:
DBN                                       0
SCHOOL NAME                               0
Num of SAT Test Takers                    0
SAT Critical Reading Avg. Score           0
SAT Math Avg. Score                       0
SAT Writing Avg. Score                    0
sat_score                                 0
is_suppressed                             0
SchoolName                              225
AP Test Takers                          246
Total Exams Taken                       246
Number of Exams with scores 3 4 or 5    327
CSD                                      44
NUMBER OF STUDENTS / SEATS FILLED        44
NUMBER OF SECTIONS                       44
AVERAGE CLASS SIZE                       44
SIZE OF SMALLEST CLASS                   44
SIZE OF LARGEST CLASS                    44
Name                                     28
schoolyear                               28
frl_percent                              28
total_enrollment                         28
grade9                                   41
grade10                                  29
grade11                                  44
grade12                                  60
ell_num                                  28
ell_percent                              28
sped_num                                 28
sped_percent                             28
ctt_num                                  38
selfcontained_num                        38
asian_num                                28
asian_per                                28
black_num                                28
black_per                                28
hispanic_num                             28
hispanic_per                             28
white_num                                28
white_per                                28
male_num                                 28
male_per                                 28
female_num                               28
female_per                               28
Total Cohort                             89
Total Grads - % of cohort               110
Total Regents - % of cohort             110
Total Regents - % of grads              110
Advanced Regents - % of cohort          110
Advanced Regents - % of grads           110
Regents w/o Advanced - % of cohort      110
Regents w/o Advanced - % of grads       110
Local - % of cohort                     110
Local - % of grads                      110
Still Enrolled - % of cohort            110
Dropped Out - % of cohort               110
school_name                             109
borough                                 109
building_code                           109
grade_span_min                          111
grade_span_max                          109
city                                    109
postcode                                109
total_students                          109
school_type                             406
language_classes                        109
advancedplacement_courses               170
online_ap_courses                       425
online_language_courses                 422
start_time                              109
end_time                                109
number_programs                         109
Location 1                              109
Community Board                         111
Council District                        111
lat                                     109
lon                                     109
has_lang                                109
has_ap                                  109
has_online_lang                         109
has_online_ap                           109
has_spanish                             109
has_french                              109
has_chinese                             109
has_russian                             109
rr_s                                      8
rr_t                                      8
rr_p                                      8
N_s                                      12
N_t                                       8
N_p                                       9
saf_p_11                                  9
com_p_11                                  9
eng_p_11                                  9
aca_p_11                                  9
saf_t_11                                  8
com_t_11                                  8
eng_t_11                                  8
aca_t_11                                  8
saf_s_11                                 12
com_s_11                                 12
eng_s_11                                 12
aca_s_11                                 12
saf_tot_11                                8
com_tot_11                                8
eng_tot_11                                8
aca_tot_11                                8
school_dist                               0
District                                  0
YTD % Attendance (Avg)                    0
YTD Enrollment(Avg)                       0
dtype: int64
In [55]:
full['building_code'].unique()[:100]
Out[55]:
array(['M056', 'M446', 'M060', nan, 'M025', 'M022', 'M097', 'M047',
       'M535', 'M445', 'M131', 'M282', 'M460', 'M615', 'M440', 'M625',
       'M834', 'M475', 'M017', 'M874', 'M070', 'M660', 'M855', 'M824',
       'M894', 'M620', 'M873', 'M480', 'M477', 'M833', 'M520', 'M486',
       'M896', 'M877', 'M600', 'M488', 'M645', 'M490', 'M470', 'M088',
       'M837', 'M485', 'M045', 'M435', 'M495', 'M013', 'M895', 'M107',
       'M136', 'M125', 'M043', 'M010', 'M970', 'M812', 'M218', 'M814',
       'M143', 'M052', 'M465', 'M540', 'M876', 'X149', 'X139', 'X884',
       'X470', 'X162', 'X155', 'X790', 'X183', 'X600', 'X392', 'X174',
       'X405', 'X450', 'X039', 'X972', 'X410', 'X465', 'X970', 'X002',
       'X004', 'X400', 'X166', 'X082', 'X460', 'X148', 'X876', 'X141',
       'X475', 'X137', 'X143', 'X435', 'X430', 'X368', 'X440', 'X445',
       'X905', 'X455', 'X425', 'X362'], dtype=object)

Immediately, we see that there are a handful of repeat school name columns and a CSD column that are missing data. As these are redundant, we will simply drop these columns. There is also a column titled building_code that has over 100 missing values. Upon further inspection, we see that this is nearly identical to the SCHOOL CODE column from our class_size dataset that we already dropped. As this information is already in DBN, we can drop this column as well.

In [56]:
cols_to_drop = ['SchoolName', 'Name', 'school_name', 'CSD', 'building_code']
full = full.drop(columns=cols_to_drop)

One column with a large number of missing values is school_type. Although this is a categorical column, we can use it to generate more numeric columns for our analysis. To do this, we can fill in the null values with a generic term and use each school type as new boolean column for our full dataset.

In [57]:
print(full['school_type'].unique())
[nan 'Consortium School' 'CTE School' 'All-Girls School'
 'International School' 'Consortium, International School'
 'Specialized School' 'All-Boys School']
In [58]:
full['school_type'] = full['school_type'].fillna(value='Traditional')

full['is_consort'] = full['school_type'].apply(lambda x: 1 if x == 'Consortium School' else 0)
full['is_CTE'] = full['school_type'].apply(lambda x: 1 if x == 'CTE School' else 0)
full['is_allgirls'] = full['school_type'].apply(lambda x: 1 if x == 'All-Girls School' else 0)
full['is_intl'] = full['school_type'].apply(lambda x: 1 if x == 'International School' else 0)
full['is_consort_intl'] = full['school_type'].apply(lambda x: 1 if x == 'Consortium, International School' else 0)
full['is_specialized'] = full['school_type'].apply(lambda x: 1 if x == 'Specialized School' else 0)
full['is_allboys'] = full['school_type'].apply(lambda x: 1 if x == 'All-Boys School' else 0)

We are also missing a significant amount of data on AP test takers. For this data, we will only look for correlations in those schools that have AP Test data, and fill the missing values with 0.

In [59]:
ap_cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']
print(full[ap_cols].dtypes)
full[ap_cols] = full[ap_cols].fillna(value=0)
#full.isna().sum()
AP Test Takers                          float64
Total Exams Taken                       float64
Number of Exams with scores 3 4 or 5    float64
dtype: object

Our language and AP class columns also have over 100 missing values. Just as with the AP columns above, we will not assume any schools missing this data have those courses. We will fill in the categorical columns with a value of 'No courses' and the boolean columns with a value of 0.

In [60]:
ap_lang = ['language_classes', 'advancedplacement_courses', 'online_ap_courses', 'online_language_courses']
full[ap_lang] = full[ap_lang].fillna(value='No courses')

bools = ['has_lang', 'has_ap', 'has_online_lang', 'has_online_ap', 'has_spanish', 'has_french', 'has_chinese', 'has_russian']
full[bools] = full[bools].fillna(value=0)

full.isna().sum()
Out[60]:
DBN                                       0
SCHOOL NAME                               0
Num of SAT Test Takers                    0
SAT Critical Reading Avg. Score           0
SAT Math Avg. Score                       0
SAT Writing Avg. Score                    0
sat_score                                 0
is_suppressed                             0
AP Test Takers                            0
Total Exams Taken                         0
Number of Exams with scores 3 4 or 5      0
NUMBER OF STUDENTS / SEATS FILLED        44
NUMBER OF SECTIONS                       44
AVERAGE CLASS SIZE                       44
SIZE OF SMALLEST CLASS                   44
SIZE OF LARGEST CLASS                    44
schoolyear                               28
frl_percent                              28
total_enrollment                         28
grade9                                   41
grade10                                  29
grade11                                  44
grade12                                  60
ell_num                                  28
ell_percent                              28
sped_num                                 28
sped_percent                             28
ctt_num                                  38
selfcontained_num                        38
asian_num                                28
asian_per                                28
black_num                                28
black_per                                28
hispanic_num                             28
hispanic_per                             28
white_num                                28
white_per                                28
male_num                                 28
male_per                                 28
female_num                               28
female_per                               28
Total Cohort                             89
Total Grads - % of cohort               110
Total Regents - % of cohort             110
Total Regents - % of grads              110
Advanced Regents - % of cohort          110
Advanced Regents - % of grads           110
Regents w/o Advanced - % of cohort      110
Regents w/o Advanced - % of grads       110
Local - % of cohort                     110
Local - % of grads                      110
Still Enrolled - % of cohort            110
Dropped Out - % of cohort               110
borough                                 109
grade_span_min                          111
grade_span_max                          109
city                                    109
postcode                                109
total_students                          109
school_type                               0
language_classes                          0
advancedplacement_courses                 0
online_ap_courses                         0
online_language_courses                   0
start_time                              109
end_time                                109
number_programs                         109
Location 1                              109
Community Board                         111
Council District                        111
lat                                     109
lon                                     109
has_lang                                  0
has_ap                                    0
has_online_lang                           0
has_online_ap                             0
has_spanish                               0
has_french                                0
has_chinese                               0
has_russian                               0
rr_s                                      8
rr_t                                      8
rr_p                                      8
N_s                                      12
N_t                                       8
N_p                                       9
saf_p_11                                  9
com_p_11                                  9
eng_p_11                                  9
aca_p_11                                  9
saf_t_11                                  8
com_t_11                                  8
eng_t_11                                  8
aca_t_11                                  8
saf_s_11                                 12
com_s_11                                 12
eng_s_11                                 12
aca_s_11                                 12
saf_tot_11                                8
com_tot_11                                8
eng_tot_11                                8
aca_tot_11                                8
school_dist                               0
District                                  0
YTD % Attendance (Avg)                    0
YTD Enrollment(Avg)                       0
is_consort                                0
is_CTE                                    0
is_allgirls                               0
is_intl                                   0
is_consort_intl                           0
is_specialized                            0
is_allboys                                0
dtype: int64

Among the remaining columns there are many missing values that we simply cannot infer from the rest of the data. However, there are a few more columns that we can fill in from the information we already have. In particular, the borough, lat and lon columns are not too diffucult to fill in to some degree of accuracy with merely what we have in our full dataset.

For borough we will isolate the letter in each school's DBN and fill in the corresponding borough if it is missing. Each of the five letters in the DBNs represent a different borough of NYC: M for Manhattan, K for Brooklyn (Kings County), X for Bronx, Q for Queens and R for Staten Island (Richmond County).

In [61]:
def find_borough(x):
    if x[2] == 'M':
        return 'Manhattan'
    elif x[2] == 'K':
        return 'Brooklyn'
    elif x[2] == 'X':
        return 'Bronx'
    elif x[2] == 'Q':
        return 'Queens'
    else:
        return 'Staten Island'
    
full['borough'] = full['DBN'].apply(find_borough)

For our lat and lon columns, we could theoretically look up the addresses of all 109 missing schools on Google and find the coordinates from there. However, for our purposes, we don't need their locations to be so accurate as to justify the time that would take. Instead, we will fill in each latitude and longitude with the mean coordinates of the school district it is in. Since districts 75 and 79 have no physical boundaries, we will fill those missing values with the mean coordinates of the borough instead.

In [62]:
dist_coords = full[['lat', 'lon']].groupby(full['school_dist']).mean().reset_index()
boro_coords = full[['lat', 'lon']].groupby(full['borough']).mean().reset_index()
print(boro_coords)
dist_coords
         borough        lat        lon
0          Bronx  40.844908 -73.890242
1       Brooklyn  40.665784 -73.947509
2      Manhattan  40.765282 -73.975853
3         Queens  40.715683 -73.835808
4  Staten Island  40.595680 -74.125726
Out[62]:
school_dist lat lon
0 01 40.719022 -73.982377
1 02 40.739540 -73.991099
2 03 40.781574 -73.977370
3 04 40.793572 -73.942534
4 05 40.817077 -73.949251
5 06 40.848970 -73.932502
6 07 40.816815 -73.919971
7 08 40.823004 -73.864576
8 09 40.836349 -73.906240
9 10 40.870345 -73.898360
10 11 40.873138 -73.856120
11 12 40.831412 -73.886946
12 13 40.692865 -73.977016
13 14 40.711599 -73.948360
14 15 40.675972 -73.989255
15 16 40.686497 -73.928188
16 17 40.661319 -73.954519
17 18 40.641863 -73.914726
18 19 40.676547 -73.882158
19 20 40.626751 -74.006191
20 21 40.593596 -73.978465
21 22 40.618285 -73.952288
22 23 40.668586 -73.912298
23 24 40.740621 -73.911518
24 25 40.745414 -73.815558
25 26 40.748507 -73.759176
26 27 40.638828 -73.807823
27 28 40.709697 -73.805547
28 29 40.685276 -73.752740
29 30 40.755398 -73.932306
30 31 40.595680 -74.125726
31 32 40.696295 -73.917124
32 75 NaN NaN
33 79 NaN NaN
In [63]:
def fill_coords(x, coord='lat'):
    if (pd.isna(x[coord])) & (x['school_dist'] in ['75', '79']):
        return (boro_coords[coord][boro_coords['borough'] == x['borough']]).values[0]
    elif pd.isna(x[coord]):
        return (dist_coords[coord][dist_coords['school_dist'] == x['school_dist']]).values[0]
    else:
        return x[coord]
    
full['lat'] = full.apply(fill_coords, coord='lat', axis=1)
full['lon'] = full.apply(fill_coords, coord='lon', axis=1)
In [64]:
full.isna().sum()
Out[64]:
DBN                                       0
SCHOOL NAME                               0
Num of SAT Test Takers                    0
SAT Critical Reading Avg. Score           0
SAT Math Avg. Score                       0
SAT Writing Avg. Score                    0
sat_score                                 0
is_suppressed                             0
AP Test Takers                            0
Total Exams Taken                         0
Number of Exams with scores 3 4 or 5      0
NUMBER OF STUDENTS / SEATS FILLED        44
NUMBER OF SECTIONS                       44
AVERAGE CLASS SIZE                       44
SIZE OF SMALLEST CLASS                   44
SIZE OF LARGEST CLASS                    44
schoolyear                               28
frl_percent                              28
total_enrollment                         28
grade9                                   41
grade10                                  29
grade11                                  44
grade12                                  60
ell_num                                  28
ell_percent                              28
sped_num                                 28
sped_percent                             28
ctt_num                                  38
selfcontained_num                        38
asian_num                                28
asian_per                                28
black_num                                28
black_per                                28
hispanic_num                             28
hispanic_per                             28
white_num                                28
white_per                                28
male_num                                 28
male_per                                 28
female_num                               28
female_per                               28
Total Cohort                             89
Total Grads - % of cohort               110
Total Regents - % of cohort             110
Total Regents - % of grads              110
Advanced Regents - % of cohort          110
Advanced Regents - % of grads           110
Regents w/o Advanced - % of cohort      110
Regents w/o Advanced - % of grads       110
Local - % of cohort                     110
Local - % of grads                      110
Still Enrolled - % of cohort            110
Dropped Out - % of cohort               110
borough                                   0
grade_span_min                          111
grade_span_max                          109
city                                    109
postcode                                109
total_students                          109
school_type                               0
language_classes                          0
advancedplacement_courses                 0
online_ap_courses                         0
online_language_courses                   0
start_time                              109
end_time                                109
number_programs                         109
Location 1                              109
Community Board                         111
Council District                        111
lat                                       0
lon                                       0
has_lang                                  0
has_ap                                    0
has_online_lang                           0
has_online_ap                             0
has_spanish                               0
has_french                                0
has_chinese                               0
has_russian                               0
rr_s                                      8
rr_t                                      8
rr_p                                      8
N_s                                      12
N_t                                       8
N_p                                       9
saf_p_11                                  9
com_p_11                                  9
eng_p_11                                  9
aca_p_11                                  9
saf_t_11                                  8
com_t_11                                  8
eng_t_11                                  8
aca_t_11                                  8
saf_s_11                                 12
com_s_11                                 12
eng_s_11                                 12
aca_s_11                                 12
saf_tot_11                                8
com_tot_11                                8
eng_tot_11                                8
aca_tot_11                                8
school_dist                               0
District                                  0
YTD % Attendance (Avg)                    0
YTD Enrollment(Avg)                       0
is_consort                                0
is_CTE                                    0
is_allgirls                               0
is_intl                                   0
is_consort_intl                           0
is_specialized                            0
is_allboys                                0
dtype: int64

Unfortunately, our data leaves few if any clues for imputing the remaining missing values. While not perfect, our best option is to fill in the values with each column's mean.

After doing so — if everything has gone correctly — all of our numeric columns should be filled.

In [65]:
full = full.fillna(full.mean())
print(full.isna().sum())
DBN                                       0
SCHOOL NAME                               0
Num of SAT Test Takers                    0
SAT Critical Reading Avg. Score           0
SAT Math Avg. Score                       0
SAT Writing Avg. Score                    0
sat_score                                 0
is_suppressed                             0
AP Test Takers                            0
Total Exams Taken                         0
Number of Exams with scores 3 4 or 5      0
NUMBER OF STUDENTS / SEATS FILLED         0
NUMBER OF SECTIONS                        0
AVERAGE CLASS SIZE                        0
SIZE OF SMALLEST CLASS                    0
SIZE OF LARGEST CLASS                     0
schoolyear                                0
frl_percent                               0
total_enrollment                          0
grade9                                    0
grade10                                   0
grade11                                   0
grade12                                   0
ell_num                                   0
ell_percent                               0
sped_num                                  0
sped_percent                              0
ctt_num                                   0
selfcontained_num                         0
asian_num                                 0
asian_per                                 0
black_num                                 0
black_per                                 0
hispanic_num                              0
hispanic_per                              0
white_num                                 0
white_per                                 0
male_num                                  0
male_per                                  0
female_num                                0
female_per                                0
Total Cohort                              0
Total Grads - % of cohort                 0
Total Regents - % of cohort               0
Total Regents - % of grads                0
Advanced Regents - % of cohort            0
Advanced Regents - % of grads             0
Regents w/o Advanced - % of cohort        0
Regents w/o Advanced - % of grads         0
Local - % of cohort                       0
Local - % of grads                        0
Still Enrolled - % of cohort              0
Dropped Out - % of cohort                 0
borough                                   0
grade_span_min                            0
grade_span_max                            0
city                                    109
postcode                                  0
total_students                            0
school_type                               0
language_classes                          0
advancedplacement_courses                 0
online_ap_courses                         0
online_language_courses                   0
start_time                                0
end_time                                  0
number_programs                           0
Location 1                              109
Community Board                           0
Council District                          0
lat                                       0
lon                                       0
has_lang                                  0
has_ap                                    0
has_online_lang                           0
has_online_ap                             0
has_spanish                               0
has_french                                0
has_chinese                               0
has_russian                               0
rr_s                                      0
rr_t                                      0
rr_p                                      0
N_s                                       0
N_t                                       0
N_p                                       0
saf_p_11                                  0
com_p_11                                  0
eng_p_11                                  0
aca_p_11                                  0
saf_t_11                                  0
com_t_11                                  0
eng_t_11                                  0
aca_t_11                                  0
saf_s_11                                  0
com_s_11                                  0
eng_s_11                                  0
aca_s_11                                  0
saf_tot_11                                0
com_tot_11                                0
eng_tot_11                                0
aca_tot_11                                0
school_dist                               0
District                                  0
YTD % Attendance (Avg)                    0
YTD Enrollment(Avg)                       0
is_consort                                0
is_CTE                                    0
is_allgirls                               0
is_intl                                   0
is_consort_intl                           0
is_specialized                            0
is_allboys                                0
dtype: int64

With our data cleaning complete, we can take a look at our full dataset and save it to .csv format for later use.

In [66]:
full.to_csv('sat_data_clean.csv', index=False)
full
Out[66]:
DBN SCHOOL NAME Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score SAT Writing Avg. Score sat_score is_suppressed AP Test Takers Total Exams Taken Number of Exams with scores 3 4 or 5 NUMBER OF STUDENTS / SEATS FILLED NUMBER OF SECTIONS AVERAGE CLASS SIZE SIZE OF SMALLEST CLASS SIZE OF LARGEST CLASS schoolyear frl_percent total_enrollment grade9 grade10 grade11 grade12 ell_num ell_percent sped_num sped_percent ctt_num selfcontained_num asian_num asian_per black_num black_per hispanic_num hispanic_per white_num white_per male_num male_per female_num female_per Total Cohort Total Grads - % of cohort Total Regents - % of cohort Total Regents - % of grads Advanced Regents - % of cohort Advanced Regents - % of grads Regents w/o Advanced - % of cohort Regents w/o Advanced - % of grads Local - % of cohort Local - % of grads Still Enrolled - % of cohort Dropped Out - % of cohort borough grade_span_min grade_span_max city postcode total_students school_type language_classes advancedplacement_courses online_ap_courses online_language_courses start_time end_time number_programs Location 1 Community Board Council District lat lon has_lang has_ap has_online_lang has_online_ap has_spanish has_french has_chinese has_russian 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 school_dist District YTD % Attendance (Avg) YTD Enrollment(Avg) is_consort is_CTE is_allgirls is_intl is_consort_intl is_specialized is_allboys
0 01M292 HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES 29.0 355.0 404.0 363.0 1122.0 0 0.0 0.0 0.0 65.384615 2.923077 22.700000 20.115385 25.307692 20112012.0 88.600000 422.000000 98.00000 79.000000 80.000000 50.000000 94.000000 22.300000 105.000000 24.900000 34.000000 35.000000 59.000000 14.000000 123.000000 29.100000 227.000000 53.800000 7.000000 1.700000 259.000000 61.400000 163.000000 38.600000 56.000000 61.500000 41.675000 69.125000 0.000000 0.000000 41.675000 69.125000 19.850000 30.875000 20.275000 11.950000 Manhattan 6.000000 12.0 New York 10002.00000 323.00000 Traditional Chinese (Mandarin), Spanish Psychology Chinese Language and Culture, Spanish Literatu... Chinese (Mandarin), Spanish 830.000000 330.000000 1.000000 220 Henry Street\nNew York, NY 10002\n(40.7137... 3.000000 1.000000 40.713764 -73.985260 1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 89.000000 70.000000 39.000000 379.000000 26.000000 151.000000 7.800000 7.700000 7.400000 7.600000 6.300000 5.300000 6.100000 6.500000 6.000000 5.600000 6.100000 6.700000 6.700000 6.200000 6.600000 7.000000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
1 01M448 UNIVERSITY NEIGHBORHOOD HIGH SCHOOL 91.0 383.0 423.0 366.0 1172.0 0 39.0 49.0 10.0 103.923077 4.423077 23.800000 20.115385 28.000000 20112012.0 71.800000 394.000000 109.00000 97.000000 93.000000 95.000000 83.000000 21.100000 86.000000 21.800000 55.000000 10.000000 115.000000 29.200000 89.000000 22.600000 181.000000 45.900000 9.000000 2.300000 226.000000 57.400000 168.000000 42.600000 97.714286 60.485714 37.157143 62.471429 8.657143 14.071429 28.528571 48.385714 23.314286 37.528571 26.957143 9.828571 Manhattan 9.000000 12.0 New York 10002.00000 299.00000 Traditional Chinese, Spanish Calculus AB, Chinese Language and Culture, Eng... No courses Chinese (Cantonese), Chinese (Mandarin), Spanish 815.000000 315.000000 3.000000 200 Monroe Street\nNew York, NY 10002\n(40.712... 3.000000 1.000000 40.712332 -73.984797 1.0 1.0 1.0 0.0 1.0 0.0 1.0 0.0 84.000000 95.000000 10.000000 385.000000 37.000000 46.000000 7.900000 7.400000 7.200000 7.300000 6.600000 5.800000 6.600000 7.300000 6.000000 5.700000 6.300000 7.000000 6.800000 6.300000 6.700000 7.200000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
2 01M450 EAST SIDE COMMUNITY SCHOOL 70.0 377.0 402.0 370.0 1149.0 0 19.0 21.0 0.0 53.535714 2.464286 21.928571 20.464286 23.250000 20112012.0 71.800000 598.000000 101.00000 93.000000 77.000000 86.000000 30.000000 5.000000 158.000000 26.400000 91.000000 19.000000 58.000000 9.700000 143.000000 23.900000 331.000000 55.400000 62.000000 10.400000 327.000000 54.700000 271.000000 45.300000 79.571429 70.385714 66.000000 93.828571 0.000000 0.000000 66.000000 93.828571 4.357143 6.171429 17.614286 10.742857 Manhattan 6.000000 12.0 New York 10009.00000 649.00000 Consortium School No Language Classes Calculus AB, English Literature and Composition No courses American Sign Language, Arabic, Chinese (Manda... 830.000000 330.000000 1.000000 420 East 12 Street\nNew York, NY 10009\n(40.72... 3.000000 2.000000 40.729783 -73.983041 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.000000 98.000000 28.000000 516.257511 42.000000 150.000000 8.700000 8.200000 8.100000 8.400000 7.300000 8.000000 8.000000 8.800000 6.725751 6.166953 6.719313 7.429828 7.900000 7.900000 7.900000 8.400000 01 DISTRICT 01 91.18 12367 1 0 0 0 0 0 0
3 01M458 FORSYTH SATELLITE ACADEMY 7.0 414.0 401.0 359.0 1174.0 0 0.0 0.0 0.0 28.600000 1.200000 23.000000 22.600000 23.400000 20112012.0 72.800000 224.000000 131.00000 49.000000 44.000000 147.334928 9.000000 4.000000 20.000000 8.900000 3.000000 0.000000 5.000000 2.200000 77.000000 34.400000 133.000000 59.400000 8.000000 3.600000 97.000000 43.300000 127.000000 56.700000 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.719022 -73.982377 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40.000000 100.000000 23.000000 66.000000 10.000000 37.000000 8.100000 7.000000 6.700000 7.600000 8.500000 8.200000 8.900000 8.900000 6.800000 6.100000 6.100000 6.800000 7.800000 7.100000 7.200000 7.800000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
4 01M509 MARTA VALLE HIGH SCHOOL 44.0 390.0 433.0 384.0 1207.0 0 0.0 0.0 0.0 49.851852 2.296296 19.370370 17.370370 21.481481 20112012.0 80.700000 367.000000 143.00000 100.000000 51.000000 73.000000 41.000000 11.200000 95.000000 25.900000 28.000000 36.000000 34.000000 9.300000 116.000000 31.600000 209.000000 56.900000 6.000000 1.600000 170.000000 46.300000 197.000000 53.700000 73.571429 49.914286 31.385714 61.157143 10.571429 19.628571 20.814286 41.514286 18.514286 38.842857 29.857143 14.342857 Manhattan 9.000000 12.0 New York 10002.00000 401.00000 Traditional French, Spanish English Literature and Composition, Studio Art... No courses Spanish 800.000000 330.000000 1.000000 145 Stanton Street\nNew York, NY 10002\n(40.72... 3.000000 1.000000 40.720569 -73.985673 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 90.000000 100.000000 21.000000 306.000000 29.000000 69.000000 7.700000 7.400000 7.200000 7.300000 6.400000 5.300000 6.100000 6.800000 6.400000 5.900000 6.400000 7.000000 6.900000 6.200000 6.600000 7.000000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
5 01M515 LOWER EAST SIDE PREPARATORY HIGH SCHOOL 112.0 332.0 557.0 316.0 1205.0 0 24.0 26.0 24.0 131.117647 5.529412 22.876471 15.764706 28.588235 20112012.0 77.000000 562.000000 199.09611 261.000000 209.000000 92.000000 453.000000 80.600000 7.000000 1.200000 0.000000 2.000000 476.000000 84.700000 29.000000 5.200000 50.000000 8.900000 5.000000 0.900000 302.000000 53.700000 260.000000 46.300000 183.285714 48.942857 36.414286 74.571429 22.028571 44.771429 14.371429 29.785714 12.628571 25.585714 34.385714 16.442857 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.719022 -73.982377 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 95.000000 100.000000 86.000000 454.000000 36.000000 389.000000 8.300000 7.200000 7.400000 7.500000 9.100000 7.300000 8.700000 9.100000 8.000000 6.300000 7.000000 7.300000 8.500000 7.000000 7.700000 8.000000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
6 01M539 NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND ... 159.0 522.0 574.0 525.0 1621.0 0 255.0 377.0 191.0 156.368421 6.157895 25.510526 19.473684 31.210526 20112012.0 23.000000 1613.000000 117.00000 123.000000 147.000000 157.000000 4.000000 0.200000 43.000000 2.700000 2.000000 0.000000 448.000000 27.800000 189.000000 11.700000 229.000000 14.200000 725.000000 44.900000 794.000000 49.200000 819.000000 50.800000 40.428571 98.520000 98.180000 99.660000 77.380000 78.660000 20.820000 21.000000 0.340000 0.340000 0.820000 0.660000 Manhattan 8.457766 12.0 New York 10002.00000 1725.00000 Traditional Chinese (Mandarin), French, Italian, Latin, Sp... Biology, Calculus AB, Calculus BC, Chemistry, ... No courses No courses 815.000000 400.000000 1.000000 111 Columbia Street\nNew York, NY 10002\n(40.7... 3.000000 2.000000 40.718725 -73.979426 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 98.000000 68.000000 51.000000 923.000000 67.000000 736.000000 8.500000 7.900000 7.900000 8.400000 7.600000 5.600000 5.900000 7.300000 7.300000 6.400000 7.000000 7.700000 7.800000 6.700000 6.900000 7.800000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
7 01M650 CASCADES HIGH SCHOOL 18.0 417.0 418.0 411.0 1246.0 0 0.0 0.0 0.0 64.125000 2.937500 21.781250 18.687500 24.750000 20112012.0 69.800000 218.000000 5.00000 89.000000 59.000000 65.000000 7.000000 3.200000 15.000000 6.900000 1.000000 0.000000 1.000000 0.500000 99.000000 45.400000 108.000000 49.500000 9.000000 4.100000 87.000000 39.900000 131.000000 60.100000 87.285714 38.414286 18.557143 42.585714 0.000000 0.000000 18.557143 42.585714 19.828571 57.414286 52.614286 8.142857 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.719022 -73.982377 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 75.000000 86.000000 19.000000 125.000000 12.000000 30.000000 9.000000 8.400000 8.100000 8.600000 7.600000 7.500000 8.300000 8.700000 8.100000 6.900000 7.900000 8.400000 8.300000 7.600000 8.100000 8.600000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
8 01M696 BARD HIGH SCHOOL EARLY COLLEGE 130.0 624.0 604.0 628.0 1856.0 0 0.0 0.0 0.0 214.166667 10.250000 20.975000 17.166667 24.250000 20112012.0 18.000000 617.000000 184.00000 162.000000 128.000000 143.000000 1.000000 0.200000 5.000000 0.800000 0.000000 0.000000 93.000000 15.100000 93.000000 15.100000 112.000000 18.200000 307.000000 49.800000 193.000000 31.300000 424.000000 68.700000 123.571429 95.100000 94.728571 99.600000 0.000000 0.000000 94.728571 99.600000 0.371429 0.400000 4.071429 0.814286 Manhattan 9.000000 12.0 New York 10002.00000 560.00000 Traditional Chinese (Mandarin), Latin, Spanish No courses No courses No courses 900.000000 350.000000 1.000000 525 East Houston Street\nNew York, NY 10002\n(... 3.000000 2.000000 40.718962 -73.976066 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 98.000000 81.000000 50.000000 561.000000 30.000000 283.000000 8.800000 8.200000 8.300000 9.100000 8.200000 7.400000 7.500000 8.300000 8.300000 7.300000 8.000000 8.900000 8.500000 7.600000 8.000000 8.700000 01 DISTRICT 01 91.18 12367 0 0 0 0 0 0 0
9 02M047 47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECO... 16.0 395.0 400.0 387.0 1182.0 0 0.0 0.0 0.0 28.150000 1.650000 16.365000 15.550000 17.250000 20112012.0 66.900000 174.000000 50.00000 63.000000 38.000000 23.000000 14.000000 8.000000 56.000000 32.200000 34.000000 10.000000 3.000000 1.700000 56.000000 32.200000 103.000000 59.200000 11.000000 6.300000 74.000000 42.500000 100.000000 57.500000 19.142857 63.166667 26.900000 42.866667 0.000000 0.000000 26.900000 42.866667 36.266667 57.133333 23.166667 6.833333 Manhattan 9.000000 12.0 New York 10010.00000 184.00000 Traditional American Sign Language No courses No courses No courses 845.000000 345.000000 1.000000 223 East 23 Street\nNew York, NY 10010\n(40.73... 6.000000 2.000000 40.738599 -73.982512 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 72.000000 76.000000 30.000000 109.000000 16.000000 43.000000 8.900000 7.700000 7.900000 8.100000 8.100000 6.100000 7.700000 7.200000 7.300000 6.300000 7.000000 7.500000 8.100000 6.700000 7.500000 7.600000 02 DISTRICT 02 89.01 60823 0 0 0 0 0 0 0
10 02M288 FOOD AND FINANCE HIGH SCHOOL 62.0 409.0 393.0 392.0 1194.0 0 0.0 0.0 0.0 73.416667 3.166667 22.645833 20.416667 25.000000 20112012.0 68.400000 433.000000 135.00000 122.000000 88.000000 88.000000 9.000000 2.100000 80.000000 18.500000 69.000000 4.000000 13.000000 3.000000 191.000000 44.100000 206.000000 47.600000 21.000000 4.800000 195.000000 45.000000 238.000000 55.000000 52.857143 80.100000 68.825000 86.275000 0.000000 0.000000 68.825000 86.275000 11.275000 13.725000 11.025000 7.075000 Manhattan 9.000000 12.0 New York 10019.00000 443.00000 CTE School Spanish English Language and Composition, Environmenta... No courses No courses 740.000000 230.000000 1.000000 525 West 50Th Street\nNew York, NY 10019\n(40.... 4.000000 3.000000 40.765027 -73.992517 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 86.000000 100.000000 57.000000 348.000000 35.000000 229.000000 7.600000 7.000000 6.900000 7.600000 7.300000 7.100000 7.800000 7.700000 6.200000 5.700000 6.100000 7.200000 7.000000 6.600000 6.900000 7.500000 02 DISTRICT 02 89.01 60823 0 1 0 0 0 0 0
11 02M294 ESSEX STREET ACADEMY 53.0 394.0 384.0 378.0 1156.0 0 0.0 0.0 0.0 52.000000 3.428571 14.980952 13.000000 17.095238 20112012.0 60.800000 343.000000 93.00000 97.000000 69.000000 84.000000 13.000000 3.800000 70.000000 20.400000 41.000000 2.000000 14.000000 4.100000 95.000000 27.700000 208.000000 60.600000 26.000000 7.600000 183.000000 53.400000 160.000000 46.600000 59.333333 77.175000 64.025000 83.650000 0.000000 0.000000 64.025000 83.650000 13.150000 16.350000 15.550000 6.350000 Manhattan 9.000000 12.0 New York 10002.00000 349.00000 Consortium School French, Spanish Spanish Language and Culture No courses No courses 800.000000 245.000000 1.000000 350 Grand Street\nNew York, NY 10002\n(40.7168... 3.000000 1.000000 40.716867 -73.989532 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 48.000000 56.000000 9.000000 150.000000 15.000000 28.000000 8.700000 8.100000 7.900000 8.300000 8.000000 7.700000 7.900000 8.900000 7.400000 6.500000 7.300000 7.600000 7.900000 7.300000 7.700000 8.200000 02 DISTRICT 02 89.01 60823 1 0 0 0 0 0 0
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67 02M600 THE HIGH SCHOOL OF FASHION INDUSTRIES 335.0 423.0 423.0 411.0 1257.0 0 70.0 87.0 26.0 221.461538 7.384615 27.046154 24.692308 28.307692 20112012.0 68.300000 1599.000000 449.00000 431.000000 388.000000 331.000000 45.000000 2.800000 184.000000 11.500000 71.000000 42.000000 60.000000 3.800000 626.000000 39.100000 836.000000 52.300000 66.000000 4.100000 116.000000 7.300000 1483.000000 92.700000 388.000000 81.971429 62.642857 76.285714 16.371429 19.700000 46.271429 56.557143 19.371429 23.771429 10.585714 4.957143 Manhattan 9.000000 12.0 New York 10011.00000 1732.00000 CTE School Spanish Biology, Calculus AB, English Literature and C... No courses No courses 800.000000 345.000000 1.000000 225 West 24 Street\nNew York, NY 10011\n(40.74... 4.000000 3.000000 40.745112 -73.995906 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 94.000000 91.000000 17.000000 1495.000000 85.000000 263.000000 7.900000 6.800000 7.100000 7.300000 7.400000 7.200000 7.300000 7.700000 6.500000 5.600000 6.500000 7.300000 7.300000 6.500000 6.900000 7.400000 02 DISTRICT 02 89.01 60823 0 1 0 0 0 0 0
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71 02M625 HIGH SCHOOL OF GRAPHIC COMMUNICATION ARTS 191.0 395.0 382.0 376.0 1153.0 0 79.0 85.0 9.0 257.437500 10.031250 23.806250 17.093750 29.812500 20112012.0 72.700000 1433.000000 380.00000 415.000000 337.000000 301.000000 131.000000 9.100000 246.000000 17.200000 77.000000 78.000000 45.000000 3.100000 434.000000 30.300000 914.000000 63.800000 37.000000 2.600000 667.000000 46.500000 766.000000 53.500000 391.142857 38.928571 21.642857 55.785714 0.942857 2.314286 20.685714 53.457143 17.300000 44.214286 27.400000 28.785714 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.739540 -73.991099 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 75.000000 66.000000 24.000000 1192.000000 77.000000 377.000000 6.700000 6.800000 6.800000 7.000000 6.700000 6.400000 6.700000 7.200000 5.700000 5.400000 5.700000 6.500000 6.300000 6.200000 6.400000 6.900000 02 DISTRICT 02 89.01 60823 0 0 0 0 0 0 0
72 02M630 ART AND DESIGN HIGH SCHOOL 270.0 444.0 441.0 430.0 1315.0 0 84.0 100.0 33.0 189.241379 6.482759 27.868966 22.965517 30.827586 20112012.0 53.300000 1344.000000 345.00000 304.000000 335.000000 360.000000 22.000000 1.600000 149.000000 11.100000 52.000000 32.000000 121.000000 9.000000 360.000000 26.800000 685.000000 51.000000 169.000000 12.600000 628.000000 46.700000 716.000000 53.300000 293.714286 66.900000 47.071429 70.214286 8.071429 12.114286 38.985714 58.071429 19.828571 29.785714 24.557143 5.214286 Manhattan 9.000000 12.0 New York 10019.00000 1438.00000 CTE School Spanish Art History, Biology, Calculus AB, Chemistry, ... No courses No courses 830.000000 400.000000 3.000000 231 249 East 56 Street\nNew York, NY 10019\n(4... 6.000000 4.000000 40.765402 -73.981768 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 83.000000 46.000000 23.000000 1019.000000 39.000000 280.000000 8.000000 6.900000 7.200000 7.100000 8.100000 6.400000 7.100000 7.800000 6.700000 5.900000 6.500000 7.200000 7.600000 6.400000 6.900000 7.400000 02 DISTRICT 02 89.01 60823 0 1 0 0 0 0 0
73 02M655 LIFE SCIENCES SECONDARY SCHOOL 61.0 409.0 424.0 403.0 1236.0 0 50.0 90.0 10.0 99.928571 4.285714 21.678571 16.500000 26.428571 20112012.0 70.300000 717.000000 177.00000 173.000000 125.000000 117.000000 57.000000 7.900000 141.000000 19.700000 43.000000 43.000000 24.000000 3.300000 205.000000 28.600000 461.000000 64.300000 17.000000 2.400000 370.000000 51.600000 347.000000 48.400000 119.571429 79.485714 48.828571 61.014286 5.928571 7.542857 42.900000 53.471429 30.642857 38.985714 11.771429 7.042857 Manhattan 6.000000 12.0 New York 10128.00000 771.00000 Traditional French, Spanish Biology, Calculus AB, English Language and Com... No courses No courses 800.000000 300.000000 2.000000 320 East 96 Street\nNew York, NY 10128\n(40.78... 8.000000 5.000000 40.783644 -73.945742 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 73.000000 45.000000 14.000000 474.000000 23.000000 90.000000 8.000000 7.300000 7.300000 7.600000 7.300000 6.600000 7.200000 7.700000 5.900000 5.700000 6.400000 6.900000 7.000000 6.500000 7.000000 7.400000 02 DISTRICT 02 89.01 60823 0 0 0 0 0 0 0
74 03M283 MANHATTAN THEATRE LAB HIGH SCHOOL 27.0 406.0 379.0 408.0 1193.0 0 0.0 0.0 0.0 103.823529 3.941176 22.870588 19.470588 26.470588 20112012.0 59.600000 409.000000 170.00000 119.000000 73.000000 47.000000 35.000000 8.600000 43.000000 10.500000 5.000000 26.000000 7.000000 1.700000 188.000000 46.000000 202.000000 49.400000 9.000000 2.200000 130.000000 31.800000 279.000000 68.200000 42.500000 63.800000 24.775000 38.225000 0.450000 0.900000 24.325000 37.325000 39.025000 61.775000 22.650000 8.600000 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.781574 -73.977370 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 53.000000 57.000000 17.000000 186.000000 13.000000 61.000000 7.000000 6.800000 7.000000 6.900000 5.800000 4.900000 5.200000 5.300000 6.100000 6.000000 6.300000 7.000000 6.300000 5.900000 6.200000 6.400000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
75 03M299 HIGH SCHOOL FOR ARTS, IMAGINATION AND INQUIRY 49.0 374.0 384.0 374.0 1132.0 0 0.0 0.0 0.0 77.095238 3.238095 22.890476 17.714286 27.523810 20112012.0 66.400000 418.000000 155.00000 101.000000 92.000000 70.000000 60.000000 14.400000 82.000000 19.600000 38.000000 13.000000 4.000000 1.000000 156.000000 37.300000 246.000000 58.900000 6.000000 1.400000 233.000000 55.700000 185.000000 44.300000 70.250000 62.000000 52.166667 84.233333 0.733333 1.066667 51.400000 83.166667 9.800000 15.766667 26.633333 8.966667 Manhattan 9.000000 12.0 New York 10023.00000 435.00000 Traditional French United States History No courses No courses 845.000000 326.000000 1.000000 122 Amsterdam Avenue\nNew York, NY 10023\n(40.... 7.000000 6.000000 40.774296 -73.984823 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 91.000000 96.000000 14.000000 344.000000 27.000000 51.000000 7.600000 7.000000 6.800000 7.000000 6.500000 5.800000 6.100000 6.500000 6.500000 5.800000 6.400000 7.100000 6.900000 6.200000 6.400000 6.900000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
76 03M307 URBAN ASSEMBLY SCHOOL FOR MEDIA STUDIES, THE 41.0 384.0 390.0 370.0 1144.0 0 0.0 0.0 0.0 49.346154 2.423077 20.050000 18.846154 21.576923 20112012.0 68.800000 366.000000 120.00000 86.000000 84.000000 76.000000 47.000000 12.800000 56.000000 15.300000 41.000000 10.000000 8.000000 2.200000 137.000000 37.400000 205.000000 56.000000 11.000000 3.000000 212.000000 57.900000 154.000000 42.100000 74.800000 62.225000 40.375000 65.325000 0.000000 0.000000 40.375000 65.325000 21.850000 34.675000 22.200000 12.700000 Manhattan 9.000000 12.0 New York 10023.00000 387.00000 Traditional Spanish Calculus AB, Chemistry, English Literature and... No courses No courses 900.000000 315.000000 1.000000 122 Amsterdam Avenue\nNew York, NY 10023\n(40.... 7.000000 6.000000 40.774296 -73.984823 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 80.000000 96.000000 22.000000 263.000000 25.000000 71.000000 8.700000 7.900000 7.800000 8.400000 7.800000 7.900000 7.900000 8.200000 6.900000 6.200000 7.100000 7.600000 7.800000 7.400000 7.600000 8.100000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
77 03M402 THE URBAN ASSEMBLY SCHOOL FOR GREEN CAREERS 2.0 401.0 413.0 394.0 1208.0 1 0.0 0.0 0.0 12.333333 1.000000 12.333333 12.333333 12.333333 20112012.0 73.200000 283.000000 124.00000 84.000000 75.000000 147.334928 71.000000 25.100000 61.000000 21.600000 33.000000 9.000000 5.000000 1.800000 56.000000 19.800000 215.000000 76.000000 5.000000 1.800000 161.000000 56.900000 122.000000 43.100000 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 Manhattan 9.000000 12.0 New York 10024.00000 395.00000 CTE School Spanish English Literature and Composition No courses No courses 830.000000 330.000000 1.000000 145 West 84 Street\nNew York, NY 10024\n(40.78... 7.000000 6.000000 40.785743 -73.974415 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 53.000000 30.000000 27.000000 93.000000 6.000000 48.000000 8.200000 7.900000 7.700000 8.000000 4.400000 4.200000 5.200000 5.700000 6.300000 6.400000 6.300000 7.200000 6.300000 6.200000 6.400000 7.000000 03 DISTRICT 03 89.28 21962 0 1 0 0 0 0 0
78 03M403 THE GLOBAL LEARNING COLLABORATIVE 2.0 401.0 413.0 394.0 1208.0 1 0.0 0.0 0.0 86.100000 3.400000 25.240000 19.600000 29.900000 20112012.0 68.400000 358.000000 192.00000 108.000000 58.000000 147.334928 69.000000 19.300000 69.000000 19.300000 41.000000 7.000000 13.000000 3.600000 99.000000 27.700000 224.000000 62.600000 21.000000 5.900000 184.000000 51.400000 174.000000 48.600000 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 Manhattan 9.000000 12.0 New York 10024.00000 480.00000 Traditional Arabic, Chinese (Mandarin), Italian, Spanish Biology, Calculus AB, English Literature and C... No courses No courses 830.000000 330.000000 1.000000 145 West 84 Street\nNew York, NY 10024\n(40.78... 7.000000 6.000000 40.785743 -73.974415 1.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 79.000000 100.000000 24.000000 162.000000 16.000000 47.000000 8.500000 7.900000 7.700000 7.600000 7.200000 7.600000 8.300000 8.200000 7.000000 6.700000 7.100000 7.400000 7.600000 7.400000 7.700000 7.700000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
79 03M404 INNOVATION DIPLOMA PLUS 12.0 416.0 403.0 381.0 1200.0 0 0.0 0.0 0.0 57.538462 3.538462 17.223077 14.461538 20.230769 20112012.0 50.400000 199.000000 23.00000 83.000000 65.000000 28.000000 7.000000 3.500000 20.000000 10.100000 1.000000 0.000000 97.000000 48.700000 66.000000 33.200000 33.000000 16.600000 3.000000 1.500000 95.000000 47.700000 104.000000 52.300000 39.000000 28.200000 20.500000 72.700000 0.000000 0.000000 20.500000 72.700000 7.700000 27.300000 66.700000 5.100000 Manhattan 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.781574 -73.977370 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 73.000000 100.000000 21.000000 147.000000 13.000000 41.000000 9.100000 8.400000 8.000000 8.700000 6.800000 5.800000 6.400000 7.000000 7.300000 6.400000 6.800000 7.500000 7.700000 6.800000 7.100000 7.700000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
80 03M415 WADLEIGH SECONDARY SCHOOL FOR THE PERFORMING &... 32.0 371.0 368.0 370.0 1109.0 0 65.0 73.0 0.0 95.190476 4.190476 19.990476 16.619048 22.857143 20112012.0 76.600000 516.000000 184.00000 108.000000 71.000000 63.000000 38.000000 7.400000 97.000000 18.800000 5.000000 60.000000 2.000000 0.400000 309.000000 59.900000 200.000000 38.800000 4.000000 0.800000 198.000000 38.400000 318.000000 61.600000 131.857143 63.857143 47.514286 74.328571 8.128571 12.757143 39.385714 61.571429 16.328571 25.671429 20.585714 5.371429 Manhattan 6.000000 12.0 New York 10026.00000 531.00000 Traditional French, Spanish Calculus AB, English Literature and Compositio... No courses No courses 800.000000 345.000000 6.000000 215 West 114 Street\nNew York, NY 10026\n(40.8... 10.000000 9.000000 40.802175 -73.954009 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 71.000000 68.000000 55.000000 336.000000 27.000000 262.000000 7.600000 6.900000 7.100000 7.100000 6.100000 5.200000 5.900000 6.100000 6.000000 5.500000 5.900000 6.600000 6.500000 5.900000 6.300000 6.600000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
81 03M417 FRANK MCCOURT HIGH SCHOOL 2.0 401.0 413.0 394.0 1208.0 1 0.0 0.0 0.0 50.000000 2.000000 25.000000 24.500000 26.000000 20112012.0 24.000000 219.000000 131.00000 88.000000 155.308756 147.334928 2.000000 0.900000 38.000000 17.400000 14.000000 0.000000 15.000000 6.800000 40.000000 18.300000 81.000000 37.000000 81.000000 37.000000 102.000000 46.600000 117.000000 53.400000 175.547166 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 Manhattan 9.000000 12.0 New York 10024.00000 373.00000 Traditional Spanish No courses No courses No courses 840.000000 300.000000 1.000000 145 West 84 Street\nNew York, NY 10024\n(40.78... 7.000000 6.000000 40.785743 -73.974415 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 98.000000 100.000000 47.000000 97.000000 8.000000 47.000000 8.400000 7.600000 7.300000 7.600000 6.900000 7.000000 7.000000 7.900000 7.300000 7.300000 7.100000 7.700000 7.500000 7.300000 7.200000 7.800000 03 DISTRICT 03 89.28 21962 0 0 0 0 0 0 0
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177 09X525 BRONX LEADERSHIP ACADEMY HIGH SCHOOL 114.0 384.0 394.0 388.0 1166.0 0 40.0 42.0 8.0 103.111111 3.888889 25.277778 22.185185 27.925926 20112012.0 76.900000 705.000000 261.00000 172.000000 123.000000 149.000000 68.000000 9.600000 143.000000 20.300000 25.000000 69.000000 5.000000 0.700000 290.000000 41.100000 402.000000 57.000000 0.000000 0.000000 305.000000 43.300000 400.000000 56.700000 159.285714 66.828571 36.171429 55.042857 4.800000 7.228571 31.371429 47.814286 30.671429 44.957143 16.614286 12.228571 Bronx 9.000000 12.0 Bronx 10457.00000 711.00000 Traditional Spanish Biology, English Language and Composition, Uni... No courses No courses 800.000000 344.000000 1.000000 1710 Webster Avenue\nBronx, NY 10457\n(40.8435... 3.000000 15.000000 40.843581 -73.903220 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 81.000000 76.000000 10.000000 507.000000 31.000000 59.000000 8.300000 7.800000 7.600000 7.900000 7.000000 6.600000 7.200000 7.600000 6.100000 5.800000 6.300000 7.300000 7.100000 6.700000 7.000000 7.600000 09 DISTRICT 09 89.27 34518 0 0 0 0 0 0 0
178 09X543 HIGH SCHOOL FOR VIOLIN AND DANCE 27.0 353.0 315.0 371.0 1039.0 0 0.0 0.0 0.0 53.315789 2.157895 24.468421 22.631579 26.473684 20112012.0 84.300000 323.000000 162.00000 85.000000 42.000000 34.000000 28.000000 8.700000 72.000000 22.300000 38.000000 16.000000 1.000000 0.300000 119.000000 36.800000 202.000000 62.500000 0.000000 0.000000 47.000000 14.600000 276.000000 85.400000 45.333333 75.816667 50.166667 64.950000 5.116667 6.316667 45.033333 58.633333 25.633333 35.066667 14.450000 7.133333 Bronx 9.000000 12.0 Bronx 10456.00000 378.00000 Traditional Spanish No courses No courses No courses 800.000000 300.000000 1.000000 1110 Boston Road\nBronx, NY 10456\n(40.8276026... 3.000000 16.000000 40.827603 -73.904475 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 87.000000 88.000000 30.000000 192.000000 15.000000 62.000000 8.600000 7.900000 7.900000 8.100000 6.800000 6.100000 7.000000 7.300000 6.600000 6.000000 6.800000 7.400000 7.300000 6.700000 7.200000 7.600000 09 DISTRICT 09 89.27 34518 0 0 0 0 0 0 0
179 10X141 RIVERDALE / KINGSBRIDGE ACADEMY (MIDDLE SCHOOL... 113.0 438.0 455.0 440.0 1333.0 0 133.0 184.0 119.0 83.441176 3.235294 22.455882 19.529412 24.617647 20112012.0 39.700000 1337.000000 175.00000 190.000000 135.000000 130.000000 90.000000 6.700000 203.000000 15.200000 11.000000 65.000000 117.000000 8.800000 174.000000 13.000000 709.000000 53.000000 333.000000 24.900000 692.000000 51.800000 645.000000 48.200000 122.428571 82.942857 59.514286 71.214286 11.442857 13.700000 48.057143 57.528571 23.442857 28.785714 12.800000 3.057143 Bronx 6.000000 12.0 Bronx 10463.00000 1355.00000 Traditional French, Spanish Biology, Calculus AB, English Literature and C... No courses No courses 820.000000 240.000000 1.000000 660 West 237 Street\nBronx, NY 10463\n(40.8883... 8.000000 11.000000 40.888373 -73.914093 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 89.000000 85.000000 27.000000 1165.000000 76.000000 307.000000 7.100000 6.800000 6.900000 6.900000 5.900000 5.600000 5.700000 6.300000 5.900000 5.500000 6.200000 7.000000 6.300000 6.000000 6.300000 6.700000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
180 10X213 BRONX ENGINEERING AND TECHNOLOGY ACADEMY 86.0 373.0 412.0 365.0 1150.0 0 107.0 228.0 10.0 81.285714 3.571429 22.790476 19.190476 26.619048 20112012.0 79.800000 444.000000 148.00000 126.000000 70.000000 100.000000 69.000000 15.500000 95.000000 21.400000 70.000000 4.000000 23.000000 5.200000 131.000000 29.500000 285.000000 64.200000 4.000000 0.900000 367.000000 82.700000 77.000000 17.300000 57.200000 80.300000 66.400000 82.150000 17.600000 22.400000 48.800000 59.750000 13.900000 17.850000 8.875000 6.500000 Bronx 9.000000 12.0 Bronx 10463.00000 420.00000 Traditional No Language Classes Calculus AB, Chemistry, English Language and C... No courses No courses 815.000000 430.000000 1.000000 99 Terrace View Avenue\nBronx, NY 10463\n(40.8... 8.000000 10.000000 40.877049 -73.912337 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 81.000000 87.000000 39.000000 332.000000 27.000000 157.000000 8.400000 7.600000 7.500000 7.700000 6.300000 6.300000 6.600000 7.000000 6.500000 5.900000 6.800000 7.500000 7.100000 6.600000 7.000000 7.400000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
181 10X225 THEATRE ARTS PRODUCTION COMPANY SCHOOL 59.0 405.0 391.0 394.0 1190.0 0 17.0 27.0 0.0 54.285714 2.214286 24.842857 23.642857 26.000000 20112012.0 67.400000 524.000000 86.00000 70.000000 82.000000 65.000000 23.000000 4.400000 99.000000 18.900000 73.000000 3.000000 6.000000 1.100000 143.000000 27.300000 365.000000 69.700000 6.000000 1.100000 162.000000 30.900000 362.000000 69.100000 65.000000 92.833333 77.533333 83.500000 15.866667 17.000000 61.733333 66.533333 15.300000 16.500000 5.533333 1.533333 Bronx 6.000000 12.0 Bronx 10457.00000 581.00000 Traditional Spanish Calculus AB, English Literature and Composition Calculus AB, English Language and Composition,... Spanish 830.000000 330.000000 3.000000 2225 Webster Avenue\nBronx, NY 10457\n(40.8546... 5.000000 15.000000 40.854647 -73.896646 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 88.000000 89.000000 21.000000 450.000000 32.000000 98.000000 8.600000 7.800000 7.600000 8.200000 6.100000 5.000000 5.700000 6.700000 7.700000 6.900000 7.500000 8.000000 7.500000 6.600000 6.900000 7.600000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
182 10X237 THE MARIE CURIE SCHOOL FOR MEDICINE, NURSING, ... 79.0 382.0 393.0 382.0 1157.0 0 15.0 15.0 0.0 77.266667 3.033333 25.196667 21.600000 28.866667 20112012.0 71.500000 540.000000 189.00000 125.000000 135.000000 91.000000 36.000000 6.700000 84.000000 15.600000 39.000000 19.000000 8.000000 1.500000 192.000000 35.600000 310.000000 57.400000 18.000000 3.300000 89.000000 16.500000 451.000000 83.500000 72.000000 82.500000 61.400000 74.325000 0.350000 0.375000 61.050000 73.925000 21.125000 25.675000 13.925000 3.250000 Bronx 9.000000 12.0 Bronx 10463.00000 573.00000 Traditional Spanish Biology, English Literature and Composition, U... No courses No courses 800.000000 245.000000 1.000000 120 West 231 Street\nBronx, NY 10463\n(40.8759... 8.000000 14.000000 40.875975 -73.901235 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 62.000000 59.000000 47.000000 287.000000 17.000000 212.000000 8.100000 7.600000 7.400000 7.800000 6.000000 5.400000 6.100000 6.400000 5.900000 5.900000 5.900000 6.900000 6.700000 6.300000 6.500000 7.000000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
183 10X243 WEST BRONX ACADEMY FOR THE FUTURE 26.0 382.0 406.0 370.0 1158.0 0 25.0 29.0 14.0 58.000000 2.400000 23.106667 20.800000 25.033333 20112012.0 86.300000 636.000000 185.00000 85.000000 61.000000 39.000000 84.000000 13.200000 124.000000 19.500000 89.000000 15.000000 22.000000 3.500000 157.000000 24.700000 442.000000 69.500000 8.000000 1.300000 368.000000 57.900000 268.000000 42.100000 42.000000 46.425000 28.425000 59.150000 1.400000 2.650000 27.025000 56.500000 18.000000 40.850000 29.350000 16.600000 Bronx 6.000000 12.0 Bronx 10458.00000 611.00000 Traditional French, Spanish Art History, Chemistry, English Language and C... No courses No courses 830.000000 315.000000 1.000000 500 East Fordham Road\nBronx, NY 10458\n(40.86... 6.000000 15.000000 40.860012 -73.888230 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 83.000000 100.000000 41.000000 490.000000 46.000000 223.000000 8.000000 7.400000 7.400000 7.700000 7.000000 6.300000 7.400000 7.700000 6.600000 6.000000 7.000000 7.600000 7.200000 6.600000 7.300000 7.700000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
184 10X268 KINGSBRIDGE INTERNATIONAL HIGH SCHOOL 52.0 304.0 356.0 302.0 962.0 0 32.0 37.0 0.0 195.071429 6.571429 29.628571 27.000000 32.428571 20112012.0 95.100000 497.000000 111.00000 127.000000 129.000000 130.000000 441.000000 88.700000 2.000000 0.400000 0.000000 0.000000 34.000000 6.800000 27.000000 5.400000 421.000000 84.700000 15.000000 3.000000 260.000000 52.300000 237.000000 47.700000 121.000000 58.733333 34.466667 58.733333 10.466667 17.833333 24.000000 40.933333 24.233333 41.266667 25.533333 14.666667 Bronx 9.000000 12.0 Bronx 10468.00000 443.00000 International School No Language Classes English Language and Composition No courses No courses 745.000000 245.000000 1.000000 2780 Reservoir Avenue\nBronx, NY 10468\n(40.87... 7.000000 11.000000 40.870377 -73.898163 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 84.000000 88.000000 36.000000 389.000000 21.000000 155.000000 8.200000 7.900000 7.500000 7.900000 6.900000 6.000000 7.600000 7.900000 6.900000 6.000000 6.600000 7.400000 7.300000 6.600000 7.200000 7.700000 10 DISTRICT 10 88.92 56757 0 0 0 1 0 0 0
185 10X284 BRONX SCHOOL OF LAW AND FINANCE 54.0 402.0 395.0 385.0 1182.0 0 21.0 27.0 0.0 63.551724 2.793103 20.968966 16.448276 24.896552 20112012.0 74.200000 422.000000 137.00000 108.000000 91.000000 86.000000 30.000000 7.100000 64.000000 15.200000 30.000000 21.000000 3.000000 0.700000 133.000000 31.500000 275.000000 65.200000 11.000000 2.600000 239.000000 56.600000 183.000000 43.400000 67.142857 67.760000 55.780000 82.500000 7.160000 10.480000 48.640000 72.020000 11.980000 17.500000 16.020000 13.200000 Bronx 9.000000 12.0 Bronx 10463.00000 435.00000 Traditional Latin Calculus AB, English Language and Composition,... No courses No courses 800.000000 245.000000 1.000000 99 Terrace View Avenue\nBronx, NY 10463\n(40.8... 8.000000 10.000000 40.877049 -73.912337 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 85.000000 79.000000 32.000000 320.000000 23.000000 120.000000 8.000000 7.400000 7.300000 7.600000 6.800000 6.700000 6.700000 7.000000 6.100000 6.000000 6.400000 7.100000 7.000000 6.700000 6.800000 7.200000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
186 10X319 PROVIDING URBAN LEARNERS SUCCESS IN EDUCATION ... 2.0 401.0 413.0 394.0 1208.0 1 0.0 0.0 0.0 61.285714 3.571429 16.957143 14.571429 22.000000 20112012.0 81.800000 213.000000 45.00000 70.000000 24.000000 74.000000 11.000000 5.200000 35.000000 16.400000 14.000000 1.000000 0.000000 0.000000 80.000000 37.600000 131.000000 61.500000 1.000000 0.500000 86.000000 40.400000 127.000000 59.600000 64.000000 26.916667 13.783333 57.400000 0.000000 0.000000 13.783333 57.400000 13.150000 42.600000 50.700000 20.866667 Bronx 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.870345 -73.898360 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 87.000000 73.000000 1.000000 145.000000 11.000000 2.000000 9.100000 6.800000 6.100000 7.500000 7.700000 5.700000 6.100000 6.900000 8.400000 7.300000 7.900000 8.300000 8.200000 6.600000 6.800000 7.600000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
187 10X342 INTERNATIONAL SCHOOL FOR LIBERAL ARTS 49.0 300.0 333.0 301.0 934.0 0 55.0 73.0 45.0 141.692308 5.538462 23.723077 16.615385 28.769231 20112012.0 78.300000 606.000000 87.00000 96.000000 121.000000 112.000000 484.000000 79.900000 28.000000 4.600000 11.000000 3.000000 1.000000 0.200000 0.000000 0.000000 605.000000 99.800000 0.000000 0.000000 297.000000 49.000000 309.000000 51.000000 65.000000 63.400000 53.500000 83.533333 10.000000 13.833333 43.500000 69.666667 9.966667 16.466667 25.500000 9.100000 Bronx 6.000000 12.0 Bronx 10468.00000 543.00000 Traditional Spanish No courses No courses No courses 800.000000 345.000000 1.000000 2780 Reservoir Avenue\nBronx, NY 10468\n(40.87... 7.000000 11.000000 40.870377 -73.898163 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 92.000000 92.000000 65.000000 488.000000 34.000000 316.000000 9.000000 8.300000 7.800000 8.300000 7.100000 6.500000 6.500000 7.200000 7.400000 6.400000 7.100000 7.700000 7.800000 7.100000 7.100000 7.700000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
188 10X368 INsTECH ACADEMY (M.S. / HIGH SCHOOL 368) 111.0 390.0 408.0 383.0 1181.0 0 40.0 54.0 17.0 95.375000 3.500000 26.691667 22.250000 29.791667 20112012.0 77.900000 1126.000000 171.00000 163.000000 107.000000 114.000000 136.000000 12.100000 161.000000 14.300000 53.000000 36.000000 45.000000 4.000000 134.000000 11.900000 922.000000 81.900000 23.000000 2.000000 551.000000 48.900000 575.000000 51.100000 109.600000 63.900000 32.080000 50.200000 4.260000 6.780000 27.780000 43.420000 31.800000 49.800000 22.360000 12.420000 Bronx 6.000000 12.0 Bronx 10463.00000 1036.00000 Traditional Spanish Calculus AB, English Language and Composition,... No courses No courses 830.000000 330.000000 1.000000 2975 Tibbett Avenue\nBronx, NY 10463\n(40.8800... 8.000000 11.000000 40.880050 -73.909209 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 90.000000 91.000000 51.000000 978.000000 72.000000 496.000000 8.600000 7.600000 7.600000 7.900000 7.400000 6.200000 6.600000 7.200000 6.500000 6.200000 6.600000 7.500000 7.500000 6.700000 6.900000 7.500000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
189 10X374 KNOWLEDGE AND POWER PREPARATORY ACADEMY INTERN... 61.0 392.0 401.0 387.0 1180.0 0 0.0 0.0 0.0 111.333333 4.809524 22.690476 17.857143 27.571429 20112012.0 75.300000 324.000000 98.00000 92.000000 68.000000 66.000000 30.000000 9.300000 36.000000 11.100000 15.000000 3.000000 3.000000 0.900000 114.000000 35.200000 194.000000 59.900000 10.000000 3.100000 153.000000 47.200000 171.000000 52.800000 1.666667 62.915625 45.121491 66.876392 10.910809 14.105702 34.210494 52.770587 17.803826 33.140172 24.645990 9.779497 Bronx 9.000000 12.0 Bronx 10458.00000 467.00000 Traditional French, Spanish No courses No courses No courses 730.000000 400.000000 1.000000 500 East Fordham Road\nBronx, NY 10458\n(40.86... 6.000000 15.000000 40.860012 -73.888230 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 92.000000 100.000000 75.000000 329.000000 31.000000 258.000000 8.800000 8.500000 8.200000 8.800000 7.900000 6.900000 7.900000 8.500000 7.200000 6.600000 7.200000 8.000000 8.000000 7.300000 7.800000 8.400000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
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191 10X433 HIGH SCHOOL FOR TEACHING AND THE PROFESSIONS 57.0 370.0 371.0 365.0 1106.0 0 62.0 83.0 13.0 88.080000 4.080000 20.532000 17.440000 22.960000 20112012.0 88.100000 473.000000 230.00000 107.000000 89.000000 47.000000 84.000000 17.800000 95.000000 20.100000 8.000000 44.000000 12.000000 2.500000 119.000000 25.200000 333.000000 70.400000 9.000000 1.900000 200.000000 42.300000 273.000000 57.700000 114.428571 63.500000 37.342857 57.771429 2.871429 4.642857 34.442857 53.128571 26.157143 42.228571 20.457143 14.028571 Bronx 9.000000 12.0 Bronx 10468.00000 499.00000 Traditional Spanish English Literature and Composition, Spanish La... No courses No courses 845.000000 345.000000 1.000000 2780 Reservoir Avenue\nBronx, NY 10468\n(40.87... 7.000000 11.000000 40.870377 -73.898163 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 90.000000 100.000000 27.000000 384.000000 35.000000 112.000000 8.400000 8.000000 7.900000 7.900000 7.300000 7.800000 8.000000 8.800000 5.900000 5.400000 6.100000 6.700000 7.200000 7.100000 7.400000 7.800000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
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195 10X439 BRONX HIGH SCHOOL FOR LAW AND COMMUNITY SERVICE 65.0 366.0 364.0 352.0 1082.0 0 44.0 65.0 9.0 74.967742 3.483871 20.593548 16.838710 23.612903 20112012.0 80.200000 432.000000 154.00000 143.000000 53.000000 82.000000 58.000000 13.400000 90.000000 20.800000 29.000000 33.000000 10.000000 2.300000 133.000000 30.800000 279.000000 64.600000 4.000000 0.900000 192.000000 44.400000 240.000000 55.600000 89.285714 42.450000 28.550000 66.500000 1.900000 4.333333 26.650000 62.166667 13.900000 33.500000 34.666667 20.200000 Bronx 9.000000 12.0 Bronx 10458.00000 403.00000 Traditional Italian, Spanish Calculus AB, English Language and Composition,... No courses No courses 815.000000 400.000000 2.000000 500 East Fordham Road\nBronx, NY 10458\n(40.86... 6.000000 15.000000 40.860012 -73.888230 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 87.000000 92.000000 43.000000 332.000000 33.000000 159.000000 7.500000 7.600000 7.200000 7.700000 6.900000 6.000000 6.200000 7.000000 6.000000 6.000000 6.500000 7.300000 6.800000 6.500000 6.600000 7.300000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
196 10X440 DEWITT CLINTON HIGH SCHOOL 443.0 419.0 426.0 410.0 1255.0 0 366.0 540.0 262.0 398.511628 15.697674 23.400000 16.488372 27.511628 20112012.0 74.800000 4159.000000 1380.00000 1119.000000 976.000000 684.000000 791.000000 19.000000 621.000000 14.900000 135.000000 352.000000 278.000000 6.700000 1282.000000 30.800000 2478.000000 59.600000 86.000000 2.100000 2126.000000 51.100000 2033.000000 48.900000 990.142857 57.114286 40.442857 70.957143 14.457143 25.414286 25.985714 45.528571 16.714286 29.100000 24.057143 14.757143 Bronx 9.000000 12.0 Bronx 10468.00000 2746.00000 Traditional Latin, Spanish Biology, Calculus AB, Calculus BC, Chemistry, ... No courses No courses 800.000000 246.000000 5.000000 100 West Mosholu Parkway South\nBronx, NY 1046... 7.000000 11.000000 40.880082 -73.885902 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 67.000000 87.000000 33.000000 2820.000000 244.000000 1325.000000 5.700000 6.400000 6.500000 6.800000 6.000000 6.900000 7.300000 7.600000 5.200000 5.400000 6.000000 6.900000 5.700000 6.200000 6.600000 7.100000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
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198 10X445 BRONX HIGH SCHOOL OF SCIENCE 731.0 632.0 688.0 649.0 1969.0 0 1190.0 2435.0 2189.0 728.894737 23.000000 31.163158 23.368421 34.210526 20112012.0 34.200000 3013.000000 764.00000 770.000000 755.000000 724.000000 2.000000 0.100000 3.000000 0.100000 0.000000 0.000000 1912.000000 63.500000 105.000000 3.500000 216.000000 7.200000 764.000000 25.400000 1741.000000 57.800000 1272.000000 42.200000 614.000000 98.057143 98.057143 100.000000 97.271429 99.185714 0.785714 0.814286 0.000000 0.000000 1.785714 0.085714 Bronx 9.000000 12.0 Bronx 10468.00000 3037.00000 Specialized School Chinese, French, Italian, Japanese, Latin, Mod... Art History, Biology, Calculus AB, Calculus BC... No courses No courses 800.000000 345.000000 1.000000 75 West 205 Street\nBronx, NY 10468\n(40.87705... 7.000000 11.000000 40.877056 -73.889780 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 98.000000 50.000000 45.000000 2956.000000 74.000000 1314.000000 8.100000 7.800000 7.600000 7.900000 7.400000 5.000000 5.300000 6.900000 6.800000 6.300000 6.700000 7.600000 7.400000 6.400000 6.500000 7.500000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 1 0
199 10X475 JOHN F. KENNEDY HIGH SCHOOL 109.0 351.0 362.0 339.0 1052.0 0 46.0 48.0 19.0 117.400000 4.800000 22.797143 19.171429 25.457143 20112012.0 77.800000 706.000000 56.00000 258.000000 191.000000 201.000000 168.000000 23.800000 147.000000 20.800000 43.000000 57.000000 5.000000 0.700000 248.000000 35.100000 443.000000 62.700000 10.000000 1.400000 482.000000 68.300000 224.000000 31.700000 711.142857 37.714286 17.514286 47.271429 2.600000 7.271429 14.928571 40.014286 20.200000 52.785714 28.485714 25.314286 Bronx 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.870345 -73.898360 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.000000 43.000000 8.000000 314.000000 30.000000 80.000000 7.300000 6.900000 6.700000 6.800000 4.700000 3.600000 3.800000 4.500000 5.600000 5.200000 5.700000 6.200000 5.600000 4.900000 5.100000 5.600000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
200 10X477 MARBLE HILL HIGH SCHOOL FOR INTERNATIONAL STUDIES 99.0 414.0 435.0 414.0 1263.0 0 83.0 141.0 37.0 90.533333 3.666667 25.023333 20.933333 28.000000 20112012.0 83.900000 456.000000 114.00000 109.000000 133.000000 100.000000 147.000000 32.200000 31.000000 6.800000 21.000000 0.000000 50.000000 11.000000 105.000000 23.000000 264.000000 57.900000 34.000000 7.500000 213.000000 46.700000 243.000000 53.300000 84.571429 89.016667 81.366667 91.400000 16.300000 18.366667 65.066667 73.066667 7.650000 8.600000 7.866667 1.816667 Bronx 9.000000 12.0 Bronx 10463.00000 447.00000 Traditional English, Italian, Japanese Biology, Calculus AB, English Literature and C... No courses No courses 830.000000 440.000000 2.000000 99 Terrace View Avenue\nBronx, NY 10463\n(40.8... 8.000000 10.000000 40.877049 -73.912337 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 97.000000 97.000000 45.000000 428.000000 28.000000 190.000000 8.800000 8.200000 7.900000 8.300000 8.200000 6.600000 7.000000 7.600000 7.100000 6.300000 6.900000 7.700000 8.000000 7.100000 7.300000 7.900000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
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202 10X546 BRONX THEATRE HIGH SCHOOL 68.0 348.0 362.0 354.0 1064.0 0 34.0 34.0 0.0 75.250000 3.041667 25.983333 22.125000 29.875000 20112012.0 73.700000 445.000000 179.00000 127.000000 80.000000 59.000000 42.000000 9.400000 67.000000 15.100000 17.000000 21.000000 2.000000 0.400000 128.000000 28.800000 307.000000 69.000000 8.000000 1.800000 143.000000 32.100000 302.000000 67.900000 81.833333 63.760000 37.740000 58.900000 2.460000 3.960000 35.280000 54.960000 26.020000 41.100000 19.700000 14.840000 Bronx 9.000000 12.0 Bronx 10463.00000 435.00000 Traditional French, Spanish Biology, Calculus AB, English Literature and C... No courses No courses 840.000000 400.000000 2.000000 99 Terrace View Avenue\nBronx, NY 10463\n(40.8... 8.000000 10.000000 40.877049 -73.912337 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 91.000000 94.000000 88.000000 388.000000 30.000000 360.000000 9.600000 9.300000 9.300000 9.400000 7.200000 6.600000 7.100000 7.500000 7.400000 6.400000 7.600000 8.100000 8.000000 7.400000 8.000000 8.300000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
203 10X549 DISCOVERY HIGH SCHOOL 55.0 372.0 379.0 364.0 1115.0 0 0.0 0.0 0.0 83.272727 3.772727 19.731818 15.818182 23.454545 20112012.0 80.000000 529.000000 164.00000 196.000000 131.000000 38.000000 109.000000 20.600000 94.000000 17.800000 6.000000 53.000000 11.000000 2.100000 107.000000 20.200000 400.000000 75.600000 7.000000 1.300000 277.000000 52.400000 252.000000 47.600000 75.666667 59.380000 44.340000 74.060000 15.340000 25.000000 29.000000 49.060000 15.060000 25.940000 22.540000 11.620000 Bronx 9.000000 12.0 Bronx 10468.00000 542.00000 Traditional Spanish Biology, English Language and Composition, Env... No courses No courses 845.000000 345.000000 1.000000 2780 Reservoir Avenue\nBronx, NY 10468\n(40.87... 7.000000 11.000000 40.870377 -73.898163 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 59.000000 70.000000 8.000000 283.000000 23.000000 37.000000 8.700000 8.000000 7.700000 8.100000 8.300000 7.300000 7.100000 8.200000 6.200000 5.500000 6.100000 6.800000 7.600000 6.700000 6.800000 7.600000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
204 10X660 GRACE DODGE CAREER AND TECHNICAL EDUCATION HIG... 73.0 362.0 382.0 356.0 1100.0 0 0.0 0.0 0.0 137.625000 5.593750 21.668750 16.093750 25.187500 20112012.0 71.000000 1162.000000 377.00000 364.000000 190.000000 231.000000 219.000000 18.800000 280.000000 24.100000 38.000000 153.000000 17.000000 1.500000 350.000000 30.100000 780.000000 67.100000 10.000000 0.900000 465.000000 40.000000 697.000000 60.000000 331.571429 40.985714 20.657143 50.014286 2.600000 6.414286 18.042857 43.585714 20.357143 49.985714 29.114286 25.685714 Bronx 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.870345 -73.898360 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 63.000000 87.000000 10.000000 806.000000 83.000000 119.000000 7.500000 7.500000 7.400000 7.600000 7.900000 8.100000 8.500000 8.700000 5.900000 5.500000 5.900000 6.800000 7.100000 7.000000 7.300000 7.700000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 0 0
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206 10X696 HIGH SCHOOL OF AMERICAN STUDIES AT LEHMAN COLLEGE 92.0 636.0 648.0 636.0 1920.0 0 194.0 302.0 243.0 120.789474 5.526316 22.189474 18.421053 25.421053 20112012.0 20.800000 377.000000 89.00000 101.000000 99.000000 88.000000 0.000000 0.000000 5.000000 1.300000 0.000000 0.000000 63.000000 16.700000 40.000000 10.600000 68.000000 18.000000 203.000000 53.800000 203.000000 53.800000 174.000000 46.200000 77.333333 95.650000 95.650000 100.000000 80.666667 84.233333 14.966667 15.766667 0.000000 0.000000 3.416667 0.716667 Bronx 9.000000 12.0 Bronx 10468.00000 387.00000 Specialized School Spanish Calculus AB, Chemistry, English Language and C... No courses No courses 800.000000 300.000000 1.000000 2925 Goulden Avenue\nBronx, NY 10468\n(40.8712... 8.000000 11.000000 40.871255 -73.897516 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 98.000000 100.000000 52.000000 359.000000 27.000000 188.000000 9.100000 8.100000 8.100000 8.700000 8.800000 7.400000 8.200000 9.000000 8.100000 7.100000 7.800000 8.600000 8.700000 7.500000 8.000000 8.700000 10 DISTRICT 10 88.92 56757 0 0 0 0 0 1 0
207 11X249 BRONX HEALTH SCIENCES HIGH SCHOOL 74.0 370.0 381.0 382.0 1133.0 0 0.0 0.0 0.0 100.578947 4.263158 25.710526 19.368421 32.421053 20112012.0 73.900000 377.000000 153.00000 86.000000 61.000000 77.000000 23.000000 6.100000 28.000000 7.400000 15.000000 8.000000 18.000000 4.800000 214.000000 56.800000 137.000000 36.300000 2.000000 0.500000 133.000000 35.300000 244.000000 64.700000 62.000000 92.150000 88.375000 95.650000 13.700000 14.575000 74.675000 81.075000 3.775000 4.350000 3.100000 4.750000 Bronx 9.000000 12.0 Bronx 10475.00000 345.00000 Traditional Spanish No courses No courses No courses 715.000000 300.000000 1.000000 750 Baychester Avenue\nBronx, NY 10475\n(40.87... 10.000000 12.000000 40.874132 -73.833805 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 90.000000 87.000000 14.000000 266.000000 20.000000 39.000000 8.300000 7.900000 7.300000 8.000000 8.200000 6.800000 7.400000 7.900000 6.300000 5.400000 5.800000 7.200000 7.600000 6.700000 6.800000 7.700000 11 DISTRICT 11 89.84 38230 0 0 0 0 0 0 0
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246 13K350 URBAN ASSEMBLY HIGH SCHOOL OF MUSIC AND ART 52.0 360.0 364.0 356.0 1080.0 0 0.0 0.0 0.0 60.333333 2.629630 22.229630 19.037037 26.888889 20112012.0 74.100000 431.000000 139.00000 101.000000 100.000000 91.000000 20.000000 4.600000 97.000000 22.500000 36.000000 39.000000 8.000000 1.900000 316.000000 73.300000 98.000000 22.700000 4.000000 0.900000 184.000000 42.700000 247.000000 57.300000 94.333333 74.133333 51.500000 69.333333 0.000000 0.000000 51.500000 69.333333 22.666667 30.666667 15.966667 7.800000 Brooklyn 9.000000 12.0 Brooklyn 11201.00000 394.00000 Traditional Spanish English Language and Composition No courses No courses 830.000000 400.000000 1.000000 49 Flatbush Avenue Extension\nBrooklyn, NY 112... 2.000000 33.000000 40.697175 -73.984960 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 58.000000 69.000000 18.000000 215.000000 18.000000 65.000000 7.600000 7.100000 6.900000 7.000000 6.600000 6.400000 6.600000 7.100000 6.500000 6.100000 6.400000 7.300000 6.900000 6.500000 6.600000 7.100000 13 DISTRICT 13 89.56 22785 0 0 0 0 0 0 0
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249 13K430 BROOKLYN TECHNICAL HIGH SCHOOL 1277.0 587.0 659.0 587.0 1833.0 0 2117.0 3692.0 2687.0 1329.052632 41.157895 32.452632 28.578947 33.947368 20112012.0 50.700000 5332.000000 1413.00000 1399.000000 1319.000000 1201.000000 4.000000 0.100000 29.000000 0.500000 1.000000 0.000000 3214.000000 60.300000 542.000000 10.200000 420.000000 7.900000 1135.000000 21.300000 3106.000000 58.300000 2226.000000 41.700000 985.714286 92.014286 91.985714 99.971429 82.514286 89.671429 9.485714 10.285714 0.028571 0.028571 7.042857 0.585714 Brooklyn 9.000000 12.0 Brooklyn 11217.00000 5458.00000 Specialized School Chinese (Mandarin), French, Italian, Spanish Biology, Calculus AB, Calculus BC, Chemistry, ... Microeconomics, Physics B No courses 845.000000 315.000000 1.000000 29 Ft Greene Place\nBrooklyn, NY 11217\n(40.68... 2.000000 35.000000 40.688107 -73.976745 1.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 94.000000 77.000000 45.000000 4768.000000 189.000000 2192.000000 7.900000 6.900000 7.300000 7.400000 7.800000 6.300000 7.000000 7.500000 7.000000 5.900000 6.700000 7.600000 7.500000 6.400000 7.000000 7.500000 13 DISTRICT 13 89.56 22785 0 0 0 0 0 1 0
250 13K439 BROOKLYN INTERNATIONAL HIGH SCHOOL 90.0 305.0 364.0 312.0 981.0 0 0.0 0.0 0.0 138.785714 7.642857 19.585714 17.214286 21.357143 20112012.0 80.000000 367.000000 93.00000 97.000000 87.000000 90.000000 305.000000 83.100000 0.000000 0.000000 34.384091 29.997727 156.000000 42.500000 74.000000 20.200000 105.000000 28.600000 32.000000 8.700000 177.000000 48.200000 190.000000 51.800000 86.714286 80.957143 50.271429 61.428571 0.000000 0.000000 50.271429 61.428571 30.671429 38.571429 14.014286 5.000000 Brooklyn 9.000000 12.0 Brooklyn 11201.00000 343.00000 Consortium, International School French No courses No courses No courses 915.000000 330.000000 1.000000 49 Flatbush Avenue Extension\nBrooklyn, NY 112... 2.000000 33.000000 40.697175 -73.984960 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 94.000000 92.000000 84.000000 360.000000 24.000000 292.000000 8.700000 8.100000 8.000000 8.200000 8.600000 7.500000 8.500000 9.000000 7.900000 7.200000 7.800000 8.000000 8.400000 7.600000 8.100000 8.400000 13 DISTRICT 13 89.56 22785 0 0 0 0 1 0 0
251 13K483 THE URBAN ASSEMBLY SCHOOL FOR LAW AND JUSTICE 105.0 400.0 417.0 389.0 1206.0 0 31.0 57.0 24.0 75.206897 3.344828 21.886207 18.931034 24.482759 20112012.0 60.500000 450.000000 122.00000 127.000000 99.000000 102.000000 4.000000 0.900000 60.000000 13.300000 44.000000 8.000000 4.000000 0.900000 356.000000 79.100000 77.000000 17.100000 5.000000 1.100000 178.000000 39.600000 272.000000 60.400000 81.800000 86.175000 67.550000 78.350000 12.850000 15.050000 54.700000 63.300000 18.675000 21.650000 12.125000 1.150000 Brooklyn 9.000000 12.0 Brooklyn 11201.00000 449.00000 Traditional Spanish English Language and Composition, Environmenta... No courses No courses 840.000000 340.000000 1.000000 283 Adams Street\nBrooklyn, NY 11201\n(40.6946... 2.000000 33.000000 40.694620 -73.988624 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 95.000000 88.000000 45.000000 422.000000 30.000000 198.000000 8.700000 8.100000 8.000000 8.500000 7.300000 6.900000 7.400000 8.000000 6.800000 6.300000 6.600000 7.800000 7.600000 7.100000 7.300000 8.100000 13 DISTRICT 13 89.56 22785 0 0 0 0 0 0 0
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253 13K509 FREEDOM ACADEMY HIGH SCHOOL 29.0 400.0 390.0 403.0 1193.0 0 74.0 89.0 0.0 53.666667 2.500000 22.391667 20.000000 25.125000 20112012.0 68.000000 218.000000 65.00000 46.000000 53.000000 54.000000 4.000000 1.800000 31.000000 14.200000 16.000000 7.000000 3.000000 1.400000 179.000000 82.100000 33.000000 15.100000 2.000000 0.900000 95.000000 43.600000 123.000000 56.400000 47.285714 57.383333 51.716667 90.366667 3.950000 6.400000 47.750000 83.933333 5.650000 9.633333 35.700000 4.916667 Brooklyn 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.692865 -73.977016 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 78.000000 95.000000 43.000000 191.000000 18.000000 103.000000 7.300000 7.100000 6.800000 7.000000 6.400000 6.100000 6.500000 6.900000 5.900000 5.800000 5.900000 7.000000 6.500000 6.300000 6.400000 7.000000 13 DISTRICT 13 89.56 22785 0 0 0 0 0 0 0
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256 13K575 BEDFORD STUYVESANT PREPARATORY HIGH SCHOOL 13.0 398.0 391.0 394.0 1183.0 0 0.0 0.0 0.0 47.916667 2.750000 18.191667 16.083333 20.916667 20112012.0 74.600000 152.000000 1.00000 55.000000 50.000000 46.000000 1.000000 0.700000 0.000000 0.000000 34.384091 29.997727 0.000000 0.000000 127.000000 83.600000 19.000000 12.500000 3.000000 2.000000 54.000000 35.500000 98.000000 64.500000 62.285714 33.457143 13.042857 39.071429 0.000000 0.000000 13.042857 39.071429 20.471429 60.928571 55.557143 9.000000 Brooklyn 8.457766 12.0 NaN 10725.96477 772.02168 Traditional No courses No courses No courses No courses 816.252033 315.111111 1.821138 NaN 6.782016 22.237057 40.692865 -73.977016 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 84.000000 82.000000 29.000000 81.000000 9.000000 30.000000 9.300000 8.700000 8.300000 8.600000 7.400000 6.200000 6.800000 7.800000 8.000000 7.600000 8.000000 8.100000 8.200000 7.500000 7.700000 8.200000 13 DISTRICT 13 89.56 22785 0 0 0 0 0 0 0
257 13K595 BEDFORD ACADEMY HIGH SCHOOL 89.0 438.0 443.0 431.0 1312.0 0 43.0 57.0 16.0 104.062500 4.687500 21.793750 17.562500 25.437500 20112012.0 46.700000 367.000000 80.00000 96.000000 98.000000 93.000000 4.000000 1.100000 15.000000 4.100000 0.000000 1.000000 10.000000 2.700000 328.000000 89.400000 26.000000 7.100000 1.000000 0.300000 180.000000 49.000000 187.000000 51.000000 73.500000 93.840000 89.020000 94.880000 60.780000 64.740000 28.260000 30.120000 4.820000 5.120000 3.800000 2.160000 Brooklyn 9.000000 12.0 Brooklyn 11216.00000 361.00000 Traditional Spanish Biology, Calculus AB, Chemistry, English Langu... No courses No courses 800.000000 415.000000 2.000000 1119 Bedford Avenue\nBrooklyn, NY 11216\n(40.6... 3.000000 36.000000 40.685381 -73.954268 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 96.000000 96.000000 66.000000 347.000000 23.000000 239.000000 9.300000 8.200000 8.100000 8.300000 7.600000 7.900000 7.300000 7.900000 6.600000 6.000000 6.700000 7.700000 7.800000 7.400000 7.400000 8.000000 13 DISTRICT 13 89.56 22785 0 0 0 0 0 0 0
258 13K605 GEORGE WESTINGHOUSE CAREER AND TECHNICAL EDUCA... 85.0 406.0 391.0 392.0 1189.0 0 35.0 35.0 0.0 97.857143 4.023810 22.190476 19.000000 24.952381 20112012.0 65.000000 927.000000 206.00000 232.000000 247.000000 242.000000 18.000000 1.900000 131.000000 14.100000 43.000000 60.000000 26.000000 2.800000 740.000000 79.800000 142.000000 15.300000 12.000000 1.300000 700.000000 75.500000 227.000000 24.500000 210.000000 54.185714 21.428571 39.957143 6.714286 11.942857 14.714286 27.985714 32.771429 60.042857 23.885714 11.771429 Brooklyn 9.000000 12.0 Brooklyn 11201.00000 702.00000 CTE School Spanish English Literature and Composition, United Sta... No courses No courses 830.000000 330.000000 4.000000 105 Johnson Street\nBrooklyn, NY 11201\n(40.69... 2.000000 33.000000 40.694944 -73.986035 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 83.000000 92.000000 33.000000 736.000000 60.000000 282.000000 7.900000 7.300000 7.200000 7.200000 6.500000 6.400000 7.000000 7.100000 5.500000 5.400000 5.800000 6.800000 6.600000 6.400000 6.700000 7.000000 13 DISTRICT 13 89.56 22785 0 1 0 0 0 0 0
259 13K616 BROOKLYN HIGH SCHOOL FOR LEADERSHIP AND COMMUN... 2.0</