In this project, we will expore variouos data sets which range from 2011 to 2012 to understand if there are any patterns or factors effecting SAT scores. I will focus on finding which factors drive difference in SAT score.
Descriptive statistics(eg,r-value,mean) and data visulations will be used in this project.
I will focus on cleaning data,combining 7 data sets into one data set at the begining. Then I will focuse on analysing correlation between safety scoroe factor,race factor,gender factor and SAT score.
Data set will be used:
1,https://data.cityofnewyork.us/Education/SAT-Results/f9bf-2cp4 - SAT scores for each high school in New York City 2,https://data.cityofnewyork.us/Education/School-Attendance-and-Enrollment-Statistics-by-Dis/7z8d-msnt- Attendance information for each school in New York City 3,https://data.cityofnewyork.us/Education/2010-2011-Class-Size-School-level-detail/urz7-pzb3 - Information on class size for each school 4,https://data.cityofnewyork.us/Education/AP-College-Board-2010-School-Level-Results/itfs-ms3e - Advanced Placement (AP) exam results for each high school (passing an optional AP exam in a particular subject can earn a student college credit in that subject) 5,https://data.cityofnewyork.us/Education/Graduation-Outcomes-Classes-Of-2005-2010-School-Le/vh2h-md7a - The percentage of students who graduated, and other outcome information 6,https://data.cityofnewyork.us/Education/School-Demographics-and-Accountability-Snapshot-20/ihfw-zy9j - Demographic information for each school 7,https://data.cityofnewyork.us/Education/NYC-School-Survey-2011/mnz3-dyi8 - Surveys of parents, teachers, and students at each school
import pandas as pd
import numpy
import re
#Put all csv data in to one file
data_files = [
"ap_2010.csv",
"class_size.csv",
"demographics.csv",
"graduation.csv",
"hs_directory.csv",
"sat_results.csv"
]
data = {}
#Create a data disctionary like {'ap_2010':'schools/ap_2010.csv'}
for f in data_files:
d = pd.read_csv("schools/{0}".format(f))
key_name=f.replace(".csv", "")
data[key_name] = d
#Servey data sets are not in csv format, we need to conbined them
#and read them seperatly.
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)
#Change column name upper case
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_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]
#Put servey data set in to data
data["survey"] = survey
#When we explored all of the data sets, we noticed that some of them, like class_size and hs_directory, don't have a DBN column. hs_directory does have a dbn column,
#though, so we can create DBN.
data["hs_directory"]["DBN"] = data["hs_directory"]["dbn"]
def pad_csd(num):
string_representation = str(num)
if len(string_representation) > 1:
return string_representation
#put 2 zeros in front of string_repersentaion
else:
return "0"+ string_representation
#Create a new column called padded_csd in the class_size data set.
data["class_size"]["padded_csd"] = data["class_size"]["CSD"].apply(pad_csd)
#Combined padded_csd and class size to create DBN
data["class_size"]["DBN"] = data["class_size"]["padded_csd"]+data["class_size"]["SCHOOL CODE"]
data['class_size']['DBN']
0 01M015 1 01M015 2 01M015 3 01M015 4 01M015 5 01M015 6 01M015 7 01M015 8 01M015 9 01M015 10 01M015 11 01M015 12 01M019 13 01M019 14 01M019 15 01M019 16 01M019 17 01M019 18 01M019 19 01M019 20 01M019 21 01M019 22 01M019 23 01M019 24 01M019 25 01M020 26 01M020 27 01M020 28 01M020 29 01M020 ... 27581 32K556 27582 32K556 27583 32K556 27584 32K556 27585 32K556 27586 32K556 27587 32K556 27588 32K556 27589 32K556 27590 32K556 27591 32K556 27592 32K556 27593 32K556 27594 32K556 27595 32K564 27596 32K564 27597 32K564 27598 32K564 27599 32K564 27600 32K564 27601 32K564 27602 32K564 27603 32K564 27604 32K564 27605 32K564 27606 32K564 27607 32K564 27608 32K564 27609 32K564 27610 32K564 Name: DBN, Length: 27611, dtype: object
#Change 3 columns into numeric
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")
#Create new column
data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]
#Find lat and lon by function and create columns called "lat" and "lon"
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_11 0.036906 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
# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.
survey_fields.remove("DBN")
# There are several fields in combined that originally came from a survey of parents, teachers, and students.
# Make a bar plot of the correlations between these fields and sat_score.
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.figure(figsize=(10, 6))
colors=['yellow' if (x>0.2) else 'darkgreen' for x in combined.corr()['sat_score'][survey_fields]]
ax=combined.corr()["sat_score"][survey_fields].plot.bar(color=colors,title="Correlation of survey responses and SAT-scores")
for p in ax.patches:
if p.get_height()>0.20:
ax.annotate(format(p.get_height(), '.2f'), (p.get_x(), p.get_height()*1.02),fontsize="small", fontweight="demibold")
ax.axhline(y=0.0,color='black',linestyle='-',linewidth=1)
<matplotlib.lines.Line2D at 0x7f5ad2220a90>
According to the above chart, yellow bars mean that r-value is higher than 0.2. We will focus on looking at those factors. 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.
rr_s, the student rsponse rate, is higher than rr_t, the teachers response rate,correlates with sat_score, which makes sense because students who are more likely to fill out surveys may be more likely to also be doing well academically.
You may have noticed that saf_t_11 and saf_s_11, which measure how teachers and students perceive safety at school, correlated highly with sat_score. In addistion to N_s,N_t,N_p, saf_t_11 and saf_s_11 has hight r-value,which make senseas 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.
I will dig into safety and sat score to figure out which schools have low safety scores.
# Make a scatter plot of the saf_s_11 column vs. the sat_score
ax=combined.plot.scatter(y='sat_score',x='saf_s_11',figsize=(10,6),title="Correlation of Students Safety Score and SAT Scores,by School")
sns.set_style('dark')
There appears to be a correlation between SAT scores and safety,even though it is not a strong indication. We can see a cluster between 6-7 safety score. No school with a safety score lower than 6.5 has an average SAT score higher than 1500 or so.
#Map out safety scores
from mpl_toolkits.basemap import Basemap
#Create a new dataset grouby school dist
districts = combined.groupby("school_dist").agg(numpy.mean)
districts.reset_index(inplace=True)
#Set up a map
fig = plt.figure(figsize=(10, 6))
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)
#Change lon and lat columns to a list
longitudes = districts["lon"].tolist()
latitudes = districts["lat"].tolist()
# Create a map
ax=m.scatter(longitudes, latitudes, s=80, zorder=2, latlon=True, c=districts["saf_s_11"], cmap="summer")
plt.title('Average Safety Scores in Each District')
plt.show()
Yellow points mean that schools have the higher average safety score, green points means the shcools have low average safety score. From the map we can tell that schools in upper mahaantan,part of Queens and Bronx have a higher safety score.
By plotting out the correlations between racial columns and sat_score, we can determine whether there are any racial differences in SAT performance.
racial_col=["white_per", "asian_per", "black_per", "hispanic_per"]
ax=combined.corr()['sat_score'][racial_col].plot.bar(figsize=(8,5),title='Correlation of race to SAT-scores')
sns.set_style("dark")
ax.axhline(y=0.0, color='black', linestyle='-', linewidth=2)
<matplotlib.lines.Line2D at 0x7f5acc2522b0>
From the above bar chart, we can see that 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. We can explore the correlation of hispanic parent with sat_score since it has the highest negative correlation.
#Make a scatter plot of hispanic_per vs. sat_score
his_sat=combined.plot.scatter('hispanic_per','sat_score',figsize=(10,6))
plt.title('Correlation between Hispanic Parent and SAT Scores')
<matplotlib.text.Text at 0x7f5acee89a90>
From the above chart, we can see the school with the higher percentage of hispanic parent has the lower sat score, which also explian that more studens learning English in the shcool, the lower SAT scores.
#Research any schools with a hispanic_per greater than 95%
hispanic_95=combined.loc[combined["hispanic_per"] > 95,"SCHOOL NAME"]
print(hispanic_95)
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
We can google the name of those school. 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.
#Research any schools with a hispanic_per less than 10% and an average SAT score greater than 1800.
combined.loc[(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
After google above schools, I find that 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.
I am intereted in exploring race distribution in elite school which has average sat over 1800 compared to all schools
racial_dbn_col=["white_per", "asian_per", "black_per", "hispanic_per",'DBN']
elite_school=combined.loc[combined['sat_score']>1800,racial_dbn_col]
all_school=combined.loc[combined['sat_score']<1800,racial_dbn_col]
#Select racial columns
race=[c for c in elite_school if c.endswith('per')]
#Change multipel racial columns into one column--create a new dataset for plotting
elite_school_race=pd.melt(elite_school,id_vars='DBN',value_vars=race, value_name='percentage')
elite_school_race.rename({'variable':'Race'},axis=1,inplace=True)
fig,ax=plt.subplots(figsize=(10,6))
#creat bar graphs for elit school
ax1=plt.subplot(1,2,1)
ax1=sns.barplot(x='Race', y='percentage', data=elite_school_race,ci=None)
ax1.set_title('Race Distribution in Elite Schools',fontsize='medium')
#Do the same work on all_school dataset
race=[c for c in all_school if c.endswith('per')]
all_school_race=pd.melt(all_school,id_vars='DBN',value_vars=race,value_name='percentage')
all_school_race.rename({'variable':'Race'},axis=1,inplace=True)
ax2=plt.subplot(1,2,2)
ax2=sns.barplot(x='Race',y='percentage',data=all_school_race,ci=None)
ax2.set_title('Race Distribution in Regular Schools',fontsize='medium')
/dataquest/system/env/python3/lib/python3.4/site-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.
<matplotlib.text.Text at 0x7f5aceabda20>
Above 2 charts further explain that schools with over 1800 sat score have less black people and hispanic people.On the contrary, schools with lower sat score have less white peopple and Asian people.
We can say that sat scroe has significant correlation with race.
#We can plot out the correlations between each percentage and sat_score.
gender_col=['male_per','female_per']
ax=combined.corr()['sat_score'][gender_col].plot.bar(title='Correlation of gender to SAT-scores')
ax.axhline(y=0.0,linestyle='-',linewidth=1,color='black')
<matplotlib.lines.Line2D at 0x7f131dcb1a20>
We can see that there is a positive correlation between female and 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',figsize=(10,6))
<matplotlib.axes._subplots.AxesSubplot at 0x7f5accf66048>
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 (40 to 80), and high SAT scores.
#Research any schools with a female_per greater than 60% and an average SAT score greater than 1700.
combined.loc[(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.
In the U.S., high school students take Advanced Placement (AP) exams to earn college credit. There are AP exams for many different subjects.
It makes sense that the number of students at a school who took AP exams would be highly correlated with the school's SAT scores. Let's explore this relationship. Because total_enrollment is highly correlated with sat_score, we don't want to bias our results. Instead, we'll look at the percentage of students in each school who took at least one AP exam.
#Calculate the percentage of students in each school that took an AP exam.
combined['ap_per']=combined["AP Test Takers "]/combined['total_enrollment']
combined.plot.scatter(x='ap_per',y='sat_score',figsize=(10,6))
<matplotlib.axes._subplots.AxesSubplot at 0x7f131daf4e80>
There is a relationship between the percentage of students in a school who take the AP exam, and their average SAT scores. SAT score above 1300, the the positive correlation is clear.SAT less than 1200, the correlation is not clear, but we still can see a little negative correlation.All in all, It's not an extremely strong relationship betwee Ap-test taking and SAT performance.
#Plotting scatter chart to see
combined.plot.scatter(x=['AVERAGE CLASS SIZE'],y=['sat_score'],figsize=(10,6))
<matplotlib.axes._subplots.AxesSubplot at 0x7f5aceaa7f98>
From above chart,we can the trend but it is not significnat. I will dig deeper to see the SAT Score fall in average calss szie 20-30.
class_szie_2030=combined.loc[(combined['AVERAGE CLASS SIZE']>20) & (combined['AVERAGE CLASS SIZE']<30),:]
class_szie_2030.plot.scatter(x=['AVERAGE CLASS SIZE'],y=['sat_score'],figsize=(10,6))
<matplotlib.axes._subplots.AxesSubplot at 0x7f5acee69d30>
We still can't see the strong correlation between average class size and sat score.
After conducting various analysis, I drawn below conclusions.
1, There is correlation betwee average safety score and sat_score.Looking at the safety score by district,I found no school with a safety score lower than 6.5 has an average SAT score higher than 1500 or so. 2, There is statistic significant correlation between racial factor and SAT score. The school with less balck and hispanic students has the higher average SAT score. It could be the reason that schools with more black and hispanic students have less funding and the schools have lower saftey score. 3,The schools focuse on science and technology have tend to have a higher SAT score. The schools with recent immigrants to the US have a lot of students who are learning English with lower SAT score.