GP07: Analyzing NYC High School Data

1: Introduction



In [1]:

    
import pandas as pd
import numpy
import re

data_files = [
    "ap_2010.csv",
    "class_size.csv",
    "demographics.csv",
    "graduation.csv",
    "hs_directory.csv",
    "sat_results.csv"
]

data = {}

for f in data_files:
    d = pd.read_csv("../data/GP07/{0}".format(f))
    data[f.replace(".csv", "")] = d

2: Read in the surveys



In [2]:

    
all_survey = pd.read_csv("../data/GP07/survey_all.txt", delimiter="\t", encoding='windows-1252')
d75_survey = pd.read_csv("../data/GP07/survey_d75.txt", delimiter="\t", encoding='windows-1252')
survey = pd.concat([all_survey, d75_survey], axis=0)

survey["DBN"] = survey["dbn"]

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_10", 
    "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

3: Add DBN columns



In [3]:

    
data["hs_directory"]["DBN"] = data["hs_directory"]["dbn"]

def pad_csd(num):
    string_representation = str(num)
    if len(string_representation) > 1:
        return string_representation
    else:
        return "0" + string_representation
    
data["class_size"]["padded_csd"] = data["class_size"]["CSD"].apply(pad_csd)
data["class_size"]["DBN"] = data["class_size"]["padded_csd"] + data["class_size"]["SCHOOL CODE"]

4: Convert columns to numeric



In [4]:

    
cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']
for c in cols:
    data["sat_results"][c] = pd.to_numeric(data["sat_results"][c], errors="coerce")

data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]

def find_lat(loc):
    coords = re.findall("\(.+, .+\)", loc)
    lat = coords[0].split(",")[0].replace("(", "")
    return lat

def find_lon(loc):
    coords = re.findall("\(.+, .+\)", loc)
    lon = coords[0].split(",")[1].replace(")", "").strip()
    return lon

data["hs_directory"]["lat"] = data["hs_directory"]["Location 1"].apply(find_lat)
data["hs_directory"]["lon"] = data["hs_directory"]["Location 1"].apply(find_lon)

data["hs_directory"]["lat"] = pd.to_numeric(data["hs_directory"]["lat"], errors="coerce")
data["hs_directory"]["lon"] = pd.to_numeric(data["hs_directory"]["lon"], errors="coerce")

5: Condense datasets



In [5]:

    
class_size = data["class_size"]
class_size = class_size[class_size["GRADE "] == "09-12"]
class_size = class_size[class_size["PROGRAM TYPE"] == "GEN ED"]

class_size = class_size.groupby("DBN").agg(numpy.mean)
class_size.reset_index(inplace=True)
data["class_size"] = class_size

data["demographics"] = data["demographics"][data["demographics"]["schoolyear"] == 20112012]

data["graduation"] = data["graduation"][data["graduation"]["Cohort"] == "2006"]
data["graduation"] = data["graduation"][data["graduation"]["Demographic"] == "Total Cohort"]

6: Convert AP scores to numeric



In [6]:

    
cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']

for col in cols:
    data["ap_2010"][col] = pd.to_numeric(data["ap_2010"][col], errors="coerce")

7: Combine the datasets



In [7]:

    
combined = data["sat_results"]

combined = combined.merge(data["ap_2010"], on="DBN", how="left")
combined = combined.merge(data["graduation"], on="DBN", how="left")

to_merge = ["class_size", "demographics", "survey", "hs_directory"]

for m in to_merge:
    combined = combined.merge(data[m], on="DBN", how="inner")

combined = combined.fillna(combined.mean())
combined = combined.fillna(0)

8: Add a school district column for mapping



In [8]:

    
def get_first_two_chars(dbn):
    return dbn[0:2]

combined["school_dist"] = combined["DBN"].apply(get_first_two_chars)

9: Find correlations



In [9]:

    
correlations = combined.corr()
correlations = correlations["sat_score"]
print(correlations)









    



SAT Critical Reading Avg. Score         0.986820
SAT Math Avg. Score                     0.972643
SAT Writing Avg. Score                  0.987771
sat_score                               1.000000
AP Test Takers                          0.523140
Total Exams Taken                       0.514333
Number of Exams with scores 3 4 or 5    0.463245
Total Cohort                            0.325144
CSD                                     0.042948
NUMBER OF STUDENTS / SEATS FILLED       0.394626
NUMBER OF SECTIONS                      0.362673
AVERAGE CLASS SIZE                      0.381014
SIZE OF SMALLEST CLASS                  0.249949
SIZE OF LARGEST CLASS                   0.314434
SCHOOLWIDE PUPIL-TEACHER RATIO               NaN
schoolyear                                   NaN
fl_percent                                   NaN
frl_percent                            -0.722225
total_enrollment                        0.367857
ell_num                                -0.153778
ell_percent                            -0.398750
sped_num                                0.034933
sped_percent                           -0.448170
asian_num                               0.475445
asian_per                               0.570730
black_num                               0.027979
black_per                              -0.284139
hispanic_num                            0.025744
hispanic_per                           -0.396985
white_num                               0.449559
                                          ...   
rr_p                                    0.047925
N_s                                     0.423463
N_t                                     0.291463
N_p                                     0.421530
saf_p_11                                0.122913
com_p_11                               -0.115073
eng_p_11                                0.020254
aca_p_11                                0.035155
saf_t_11                                0.313810
com_t_11                                0.082419
eng_t_10                                     NaN
aca_t_11                                0.132348
saf_s_11                                0.337639
com_s_11                                0.187370
eng_s_11                                0.213822
aca_s_11                                0.339435
saf_tot_11                              0.318753
com_tot_11                              0.077310
eng_tot_11                              0.100102
aca_tot_11                              0.190966
grade_span_max                               NaN
expgrade_span_max                            NaN
zip                                    -0.063977
total_students                          0.407827
number_programs                         0.117012
priority08                                   NaN
priority09                                   NaN
priority10                                   NaN
lat                                    -0.121029
lon                                    -0.132222
Name: sat_score, Length: 67, dtype: float64

10: Plotting survey correlations



In [10]:

    
%matplotlib inline
combined.corr()["sat_score"][survey_fields].plot.bar()









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a712ab470>

11:Exploring safety



In [11]:

    
combined.plot.scatter("saf_s_11", "sat_score")









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a712f4828>

12: Racial differences in SAT scores



In [12]:

    
race_fields = ["white_per", "asian_per", "black_per", "hispanic_per"]
combined.corr()["sat_score"][race_fields].plot.bar()









    Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a712a5f98>



In [13]:

    
combined.plot.scatter("hispanic_per", "sat_score")









    Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a7132e160>



In [14]:

    
print(combined[combined["hispanic_per"] > 95]["SCHOOL NAME"])









    



44                         MANHATTAN BRIDGES HIGH SCHOOL
82      WASHINGTON HEIGHTS EXPEDITIONARY LEARNING SCHOOL
89     GREGORIO LUPERON HIGH SCHOOL FOR SCIENCE AND M...
125                  ACADEMY FOR LANGUAGE AND TECHNOLOGY
141                INTERNATIONAL SCHOOL FOR LIBERAL ARTS
176     PAN AMERICAN INTERNATIONAL HIGH SCHOOL AT MONROE
253                            MULTICULTURAL HIGH SCHOOL
286               PAN AMERICAN INTERNATIONAL HIGH SCHOOL
Name: SCHOOL NAME, dtype: object



In [15]:

    
print(combined[(combined["hispanic_per"] < 10) & (combined["sat_score"] > 1800)]["SCHOOL NAME"])









    



37                                STUYVESANT HIGH SCHOOL
151                         BRONX HIGH SCHOOL OF SCIENCE
187                       BROOKLYN TECHNICAL HIGH SCHOOL
327    QUEENS HIGH SCHOOL FOR THE SCIENCES AT YORK CO...
356                  STATEN ISLAND TECHNICAL HIGH SCHOOL
Name: SCHOOL NAME, dtype: object

13: Gender differences in SAT scores



In [16]:

    
gender_fields = ["male_per", "female_per"]
combined.corr()["sat_score"][gender_fields].plot.bar()









    Out[16]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a71347e48>



In [17]:

    
combined.plot.scatter("female_per", "sat_score")









    Out[17]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a713757f0>



In [18]:

    
print(combined[(combined["female_per"] > 60) & (combined["sat_score"] > 1700)]["SCHOOL NAME"])









    



5                         BARD HIGH SCHOOL EARLY COLLEGE
26                         ELEANOR ROOSEVELT HIGH SCHOOL
60                                    BEACON HIGH SCHOOL
61     FIORELLO H. LAGUARDIA HIGH SCHOOL OF MUSIC & A...
302                          TOWNSEND HARRIS HIGH SCHOOL
Name: SCHOOL NAME, dtype: object

14: AP Exam Scores vs SAT Scores



In [19]:

    
combined["ap_per"] = combined["AP Test Takers "] / combined["total_enrollment"]

combined.plot.scatter(x='ap_per', y='sat_score')









    Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f6a71385780>