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

In [1]:

```
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
```

In [2]:

```
#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
```

In [3]:

```
#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"]
```

In [4]:

```
data['class_size']['DBN']
```

Out[4]:

In [5]:

```
#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")
```

In [6]:

```
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"]
```

In [7]:

```
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")
```

In [8]:

```
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)
```

In [9]:

```
def get_first_two_chars(dbn):
return dbn[0:2]
combined["school_dist"] = combined["DBN"].apply(get_first_two_chars)
```

In [10]:

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

In [11]:

```
# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.
survey_fields.remove("DBN")
```

In [12]:

```
# 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)
```

Out[12]:

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.

In [13]:

```
# 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.

In [14]:

```
#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.

In [57]:

```
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)
```

Out[57]:

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.

In [16]:

```
#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')
```

Out[16]:

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.

In [17]:

```
#Research any schools with a hispanic_per greater than 95%
hispanic_95=combined.loc[combined["hispanic_per"] > 95,"SCHOOL NAME"]
print(hispanic_95)
```

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.

In [18]:

```
#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']
```

Out[18]:

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

In [24]:

```
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')
```

Out[24]:

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.

In [20]:

```
#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')
```

Out[20]:

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.

In [45]:

```
combined.plot.scatter('female_per','sat_score',figsize=(10,6))
```

Out[45]:

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.

In [22]:

```
#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']
```

Out[22]:

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.

In [30]:

```
#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))
```

Out[30]:

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.

In [32]:

```
#Plotting scatter chart to see
combined.plot.scatter(x=['AVERAGE CLASS SIZE'],y=['sat_score'],figsize=(10,6))
```

Out[32]:

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.

In [41]:

```
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))
```

Out[41]:

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.