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("schools/{0}".format(f))
data[f.replace(".csv", "")] = d
all_survey = pd.read_csv("schools/survey_all.txt", delimiter="\t", encoding='windows-1252')
d75_survey = pd.read_csv("schools/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
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"]
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")
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"]
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")
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)
def get_first_two_chars(dbn):
return dbn[0:2]
combined["school_dist"] = combined["DBN"].apply(get_first_two_chars)
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, dtype: float64
%matplotlib inline
combined.corr()["sat_score"][survey_fields].plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x1073d62b0>
There are high correlations between N_s
, N_t
, N_p
and sat_score
. Since these columns are correlated with total_enrollment
, it makes sense that they would be high.
It is more interesting that rr_s
, the student response rate, or the percentage of students that completed the survey, correlates with sat_score
. This might make sense because students who are more likely to fill out surveys may be more likely to also be doing well academically.
How students and teachers percieved safety (saf_t_11
and saf_s_11
) correlate with sat_score
. This make sense, as it's hard to teach or learn in an unsafe environment.
The last interesting correlation is the aca_s_11
, which indicates how the student perceives academic standards, correlates with sat_score
, but this is not true for aca_t_11
, how teachers perceive academic standards, or aca_p_11
, how parents perceive academic standards.
combined.plot.scatter("saf_s_11", "sat_score")
<matplotlib.axes._subplots.AxesSubplot at 0x1073b6ef0>
There appears to be a correlation between SAT scores and safety, although it isn't thatstrong. It looks like there are a few schools with extremely high SAT scores and high safety scores. There are a few schools with low safety scores and low SAT scores. No school with a safety score lower than 6.5
has an average SAT score higher than 1500 or so.
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
districts = combined.groupby("school_dist").agg(numpy.mean)
districts.reset_index(inplace=True)
m = Basemap(
projection='merc',
llcrnrlat=40.496044,
urcrnrlat=40.915256,
llcrnrlon=-74.255735,
urcrnrlon=-73.700272,
resolution='i'
)
m.drawmapboundary(fill_color='#85A6D9')
m.drawcoastlines(color='#6D5F47', linewidth=.4)
m.drawrivers(color='#6D5F47', linewidth=.4)
# Temporary bug: if you run the following line of code in the Jupyter Guided Project interface on Dataquest, you'll get an error.
# We're working on a fix, thanks for your patience! This should work fine locally on your own computer though.
# m.fillcontinents(color='white',lake_color='#85A6D9')
longitudes = districts["lon"].tolist()
latitudes = districts["lat"].tolist()
m.scatter(longitudes, latitudes, s=50, zorder=2, latlon=True, c=districts["saf_s_11"], cmap="summer")
plt.show()
It looks like Upper Manhattan and parts of Queens and the Bronx tend to have lower safety scores, whereas Brooklyn has high safety scores.
race_fields = ["white_per", "asian_per", "black_per", "hispanic_per"]
combined.corr()["sat_score"][race_fields].plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x1053baf60>
It looks like a higher percentage of white or asian students at a school correlates positively with sat score, whereas a higher percentage of black or hispanic students correlates negatively with sat score. This may be due to a lack of funding for schools in certain areas, which are more likely to have a higher percentage of black or hispanic students.
combined.plot.scatter("hispanic_per", "sat_score")
<matplotlib.axes._subplots.AxesSubplot at 0x105436d30>
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
The schools listed above appear to primarily be geared towards recent immigrants to the US. These schools have a lot of students who are learning English, which would explain the lower SAT scores.
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
Many of the schools above appear to be specialized science and technology schools that receive extra funding, and only admit students who pass an entrance exam. This doesn't explain the low hispanic_per
, but it does explain why their students tend to do better on the SAT -- they are students from all over New York City who did well on a standardized test.
gender_fields = ["male_per", "female_per"]
combined.corr()["sat_score"][gender_fields].plot.bar()
<matplotlib.axes._subplots.AxesSubplot at 0x105483240>
In the plot above, we can see that a high percentage of females at a school positively correlates with SAT score, whereas a high percentage of males at a school negatively correlates with SAT score. Neither correlation is extremely strong.
combined.plot.scatter("female_per", "sat_score")
<matplotlib.axes._subplots.AxesSubplot at 0x105436a20>
Based on the scatterplot, there doesn't seem to be any real correlation between sat_score
and female_per
. However, there is a cluster of schools with a high percentage of females (60
to 80
), and high SAT scores.
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
These schools appears to be very selective liberal arts schools that have high academic standards.
combined["ap_per"] = combined["AP Test Takers "] / combined["total_enrollment"]
combined.plot.scatter(x='ap_per', y='sat_score')
<matplotlib.axes._subplots.AxesSubplot at 0x1097d27f0>
It looks like there is a relationship between the percentage of students in a school who take the AP exam, and their average SAT scores. It's not an extremely strong correlation, though.