# Analyzing NYC High School Data¶

## Introduction¶

In this project, we are going to analyze relationships between SAT scores and demographic factors in New York City public schools. The SAT, or Scholastic Aptitude Test, is an exam that U.S. high school students take before applying to college. It has three sections, each of which is worth a maximum of 800 points. High average SAT scores are usually indicative of a good school and of overall school district quality.

New York City has published data on student SAT scores by high school, along with additional demographic datasets.

• SAT scores by school - SAT scores for each high school in NYC.
• School attendance - Attendance information for each school in NYC.
• Class size - Information on class size for each school.
• AP test results - 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).
• Graduation outcomes - The percentage of students who graduated, and other outcome information.
• Demographics - Demographic information for each school.
• School survey - Surveys of parents, teachers, and students at each school.

New York City has a significant immigrant population and is very diverse, so comparing demographic factors such as race, income, and gender with SAT scores is a good way to determine whether the SAT is a fair test. For example, if certain categories of students consistently perform better on the SAT, we would have some evidence that the SAT is unfair.

### Summary of Results¶

During our research, we found out that some factors (safety and respect scores, total enrollment, female percentage, White and Asian race percentages, AP test takers percentage, class size) show a positive correlation with SAT scores, while some others (the percentages of english learners, males, Black and Hispanic races, free and reduced lunches, as an indicator of a student's family income) - a negative one. The district with the highest SAT score has the highest safety and respect score. In the survey parents tend to give the highest scores, while students - the lowest.

In [1]:
import pandas as pd
import numpy as np
import re

# Creating a list of all csv files
data_files = [
'ap_2010.csv',
'class_size.csv',
'demographics.csv',
'hs_directory.csv',
'sat_results.csv'
]
# Reading all the datasets from the list and adding them to the dictionary
data = {}
for f in data_files:
data[f.replace('.csv', '')] = d


Two other datasets (surveys) are tab delimited and encoded with Windows-1252 encoding, so we'll need to specify it when reading this data. Next, we'll combine the survey datasets into a single dataframe.

In [2]:
# Reading the survey datasets

# Combining the survey datasets
survey = pd.concat([all_survey, d75_survey], axis=0)


## Data Cleaning¶

We'll start with renaming the 'dbn' column (which is a unique ID for each school) of the survey dataframe to 'DBN' to make the column name consistent with the other datasets. Then, we'll filter the data to leave only relevant columns, which we can figure out from a data dictionary at the original data download location.

In [3]:
# Renaming the column
survey['DBN'] = survey['dbn']

# Creating a list of relevant columns
survey_fields = [
'DBN',            # School identification code (district borough number)
'rr_s',           # Student Response Rate
'rr_t',           # Teacher Response Rate
'rr_p',           # Parent Response Rate
#------------NUMBER OF RESPONDENTS------------
'N_s',            # students
'N_t',            # teachers
'N_p',            # parents
#-------------------SCORES--------------------
#--------------STUDENT RESPONSES--------------
'saf_s_11',       # Safety and Respect
'com_s_11',       # Communication
'eng_s_11',       # Engagement
#--------------TEACHER RESPONSES--------------
'saf_t_11',
'com_t_11',
'eng_t_11',
'aca_t_11',
#---------------PARENT RESPONSES--------------
'saf_p_11',
'com_p_11',
'eng_p_11',
'aca_p_11',
#-----------------TOTAL SCORES----------------
'saf_tot_11',
'com_tot_11',
'eng_tot_11',
'aca_tot_11',
]

# Filtering only relevant columns
survey = survey.loc[:,survey_fields]

# Assigning the dataset to the dictionary
data['survey'] = survey


Now we'll rename the 'dbn' column to 'DBN' also for the hs_directory dataset and add the 'DBN' column to the class_size dataset. In the last case, it's practically a combination of the 'CSD' and 'SCHOOL CODE' columns.

In [4]:
# Renaming the column
data['hs_directory']['DBN'] = data['hs_directory']['dbn']

return str(num).zfill(2)

# Creating the 'DBN' column
data['class_size']['DBN'] = data['class_size']['padded_csd'] + data['class_size']['SCHOOL CODE']


Let's create a column that totals up the SAT scores for the different sections of the exam. Before we do, we have to convert those values to numeric.

In [5]:
# Converting the columns to numeric
cols = ['SAT Math Avg. Score',
'SAT Writing Avg. Score']
for c in cols:
data['sat_results'][c] = pd.to_numeric(data['sat_results'][c], errors='coerce')

# Calculating a column with total SAT scores
data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]


We'll convert to numeric also some columns of the ap_2010 dataset.

In [6]:
# Converting the columns to numeric
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')


The last step in our data cleaning process will be parsing the latitude and longitude coordinates for each school, using the 'Location 1' column of the hs_directory dataset.

In [7]:
# Creating functions for parsing the coordinates
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

# Parsing the coordinates and converting them to numeric
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')


## Data Combining¶

Before combining the datasets, we'll condense some of them so that each value in the 'DBN' column is unique.

In [8]:
# Condensing the class_size dataset

## Filtering only high schools and 'GEN ED' as the most popular program type
class_size = data['class_size']
class_size = class_size[(class_size['GRADE '] == '09-12')
& (class_size['PROGRAM TYPE'] == 'GEN ED')]

## Calculating average class size per school
class_size = class_size.groupby('DBN').agg(np.mean)

## Resetting index
class_size.reset_index(inplace=True)

## Re-assigning the class_size dataset back to the dictionary
data['class_size'] = class_size

###########################################################################################
# Condensing the demographics dataset

## Filtering the most recent years
data['demographics'] = data['demographics'][data['demographics']['schoolyear'] == 20112012]

###########################################################################################

## Filtering the most recent full cohort


Now, we'll combine all the datasets using the following strategy:

1. Being the sat_results dataset our main concern, we'll want to preserve as many rows as possible from it while minimizing null values.

2. While some of the datasets have a lot of missing DBN values, we don't want to lose too many rows (i.e., the data for many high schools) when merging.

3. Some datasets have DBN values almost identical to those in the sat_results dataset. They also have information we need to keep, and most of our analysis would be impossible if a significant number of rows was missing from those datasets. Therefore, we must avoid missing data in them.

After that, we'll fill missing values.

In [9]:
# Step 1: preserving at maximum the sat_results dataset
combined = data['sat_results']

# Step 2: using left joins
combined = combined.merge(data['ap_2010'], on='DBN', how='left')

# Step 3: using inner joins
to_merge = ['class_size',
'demographics',
'survey',
'hs_directory']
for m in to_merge:
combined = combined.merge(data[m], on='DBN', how='inner')

# Filling missing values
combined = combined.fillna(combined.mean())
combined = combined.fillna(0)


Let's add a column to our combined dataset that specifies the school district.

In [10]:
# Creating a function for extracting the district
def get_first_two_chars(dbn):
return dbn[0:2]

# Creating the 'school_dist' column
combined['school_dist'] = combined['DBN'].apply(get_first_two_chars)


## Finding Correlations¶

Here we'll take a look at how the 'sat_score' column correlates with the others.

In [11]:
correlations = combined.corr()['sat_score']
print(correlations[:35])
print(correlations[35:])

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
white_per                               0.620718
male_num                                0.325520
male_per                               -0.112062
female_num                              0.388631
female_per                              0.112108
Name: sat_score, dtype: float64
rr_s                 0.232199
rr_t                -0.023386
rr_p                 0.047925
N_s                  0.423463
N_t                  0.291463
N_p                  0.421530
saf_s_11             0.337639
com_s_11             0.187370
eng_s_11             0.213822
aca_s_11             0.339435
saf_t_11             0.313810
com_t_11             0.082419
eng_t_11             0.036906
aca_t_11             0.132348
saf_p_11             0.122913
com_p_11            -0.115073
eng_p_11             0.020254
aca_p_11             0.035155
saf_tot_11           0.318753
com_tot_11           0.077310
eng_tot_11           0.100102
aca_tot_11           0.190966
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


Unsurprisingly, 'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score', and 'SAT Writing Avg. Score' are strongly correlated with 'sat_score'.

We can also make some other observations:

• 'total_enrollment' and its proxies ('total_students', 'N_s', 'N_p', 'N_t', 'AP Test Takers', 'Total Exams Taken', and 'NUMBER OF SECTIONS') have a strong positive correlation with 'sat_score'. This seems counterintuitive: we'd expect smaller schools where students receive more attention to have higher scores.
• The percentage and number of females correlate positively with SAT scores, whereas those of males - negatively. This could indicate that women do better on the SAT than men.
• Teacher and student ratings of school safety and respect ('saf_t_11' and 'saf_s_11') correlate with SAT scores.
• Student ratings of school academic standards ('aca_s_11') correlate with SAT scores, but this doesn't hold for ratings from teachers and parents.
• There is significant racial inequality in SAT scores.
• The percentage of English language learners at the school ('ell_percent') has a strong negative correlation with SAT scores.

## Plotting Survey Correlations¶

Let's now visualize how the survey columns of our combined dataset correlate with SAT scores. Before, we've created a list of those columns called 'survey_fields'.

In [12]:
import matplotlib.pyplot as plt
%matplotlib inline

print(survey_fields)

['DBN', 'rr_s', 'rr_t', 'rr_p', 'N_s', 'N_t', 'N_p', 'saf_s_11', 'com_s_11', 'eng_s_11', 'aca_s_11', 'saf_t_11', 'com_t_11', 'eng_t_11', 'aca_t_11', 'saf_p_11', 'com_p_11', 'eng_p_11', 'aca_p_11', 'saf_tot_11', 'com_tot_11', 'eng_tot_11', 'aca_tot_11']


First, we'll remove DBN since it's a unique identifier, not a useful numerical value for correlation.

In [13]:
# Removing DBN
survey_fields.remove('DBN')

# Plotting the correlations
combined.corr()['sat_score'][survey_fields].plot.barh(figsize=(15, 15),
color='slateblue',
fontsize=23)
plt.title('SAT scores vs. Survey correlations', fontsize=35)
plt.xlabel('SAT scores', size=23)
plt.show()


From this plot, we can confirm once again some of our observations above:

• Numbers of students, parents, and teachers ('N_s', 'N_p', 'N_t') have a strong positive correlation with SAT scores.
• Teacher and student ratings of school safety and respect correlate with SAT scores, which looks reasonable.
• Student academic expectations scores correlate with SAT scores, but this doesn't hold for those from teachers and parents.

In addition, this plot allows us to discern some new patterns:

• Only 2 survey fields show a negative correlation with SAT scores. It looks logical: things like safety, respect, communication, academic standards, and engagement are indeed positive, and are supposed to be beneficial for learning outcomes.
• Scores from parents mostly show poor correlation with SAT scores, up to even be negative in case of communication score. This can probably mean their lack of involvement in those questions and/or misunderstanding of some concepts (for example, considering communication as a distracting factor for learning process).
• The student response rate shows rather strong positive correlation with SAT scores. It seems that students who demonstrate high learning results tend also to be more active socially (participating in such surveys).

## Exploring Safety & Respect and SAT Scores¶

Let's investigate in more detail the correlation of how teachers and students perceive safety and respect at school with SAT scores.

In [14]:
# Plotting 'sat_score' vs. 'saf_s_11'
combined.plot.scatter(x='saf_s_11',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT vs. Safety and Respect (from students)', fontsize=22)
plt.xlabel('Safety and Respect scores', size=16)
plt.ylabel('SAT scores', size=16)
plt.show()

# Plotting 'sat_score' vs. 'saf_t_11'
combined.plot.scatter(x='saf_t_11',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT vs. Safety and Respect (from teachers)', fontsize=22)
plt.xlabel('Safety and Respect scores', size=16)
plt.ylabel('SAT scores', size=16)
plt.show()


We can clearly see that a positive correlation between safety and respect perception by both students and teachers with SAT scores really exists, even if it isn't so strong. Also, all the schools with "anomalously" high values of SAT scores are related exactly to those with rather high safety and respect scores (starting from around 6.7 according to students and from 7 according to teachers). However, the highest safety and respect scores on both plots are not related to the schools with the highest SAT scores.

### Safety & Respect Scores by Borough and District¶

It would be interesting to take a look at safety and respect scores by borough.

In [15]:
# Sorting safety and respect scores by borough from the student survey
combined.groupby('boro').agg(np.mean)['saf_s_11'].sort_values(ascending=False)

Out[15]:
boro
Manhattan        6.831370
Queens           6.721875
Bronx            6.606577
Staten Island    6.530000
Brooklyn         6.370755
Name: saf_s_11, dtype: float64
In [16]:
# Sorting safety and respect scores by borough from the teacher survey
combined.groupby('boro').agg(np.mean)['saf_t_11'].sort_values(ascending=False)

Out[16]:
boro
Queens           7.365625
Manhattan        7.287778
Staten Island    7.210000
Bronx            7.026882
Brooklyn         6.985849
Name: saf_t_11, dtype: float64

We can deduce that in general, according to both students and teachers, the schools in Manhattan and Queens have the highest safety and respect scores, while those in Brooklyn - the lowest.

Let's look at these scores in more detail, mapping them by district. For this purpose, we'll use the 'saf_tot_11' column.

In [17]:
from mpl_toolkits.basemap import Basemap

# Grouping the dataframe by district
districts = combined.groupby('school_dist').mean()
districts.reset_index(inplace=True)

# Creating a map
plt.figure(figsize=(15, 10))
m = Basemap(
projection='merc',
llcrnrlat=40.496044,
urcrnrlat=40.915256,
llcrnrlon=-74.255735,
urcrnrlon=-73.700272,
resolution='h'
)
m.drawmapboundary(fill_color='#c1f2f8')
m.drawcoastlines(color='#6D5F47', linewidth=0.4)
m.drawrivers(color='#6D5F47', linewidth=0.4)
m.fillcontinents(color='white')
m.drawcounties(color='black',zorder=999)

longitudes = districts['lon'].tolist()
latitudes = districts['lat'].tolist()

# Plotting districts
m.scatter(
longitudes,
latitudes,
s=200,
zorder=2,
latlon=True,
c=districts['saf_tot_11'],
cmap='RdYlGn'               # A traffic-light template, with green - the safest,
)                               # red - the least safe

plt.colorbar()
plt.title('Average Safety & Respect scores by district', fontsize=23)
plt.show()


It seems that our previous conclusions were actually biased: there is only one district with very low safety and respect score, located in Brooklyn (where we see also the district with the highest score), and this value definitely influences the whole distribution of safety and respect scores, making it heavily left-skewed.

### SAT Scores by Borough and District¶

Let's do the same analysis for SAT scores.

In [18]:
# Sorting SAT scores by borough
sat_by_borough = combined.groupby('boro').agg(np.mean)['sat_score'].sort_values(ascending=False)
print(sat_by_borough)

# Creating a map
plt.figure(figsize=(15, 10))
m = Basemap(
projection='merc',
llcrnrlat=40.496044,
urcrnrlat=40.915256,
llcrnrlon=-74.255735,
urcrnrlon=-73.700272,
resolution='h'
)
m.drawmapboundary(fill_color='#c1f2f8')
m.drawcoastlines(color='#6D5F47', linewidth=0.4)
m.drawrivers(color='#6D5F47', linewidth=0.4)
m.fillcontinents(color='white')
m.drawcounties(color='black',zorder=999)

# Plotting districts
m.scatter(
longitudes,
latitudes,
s=200,
zorder=2,
latlon=True,
c=districts['sat_score'],
cmap='rainbow'               # A rainbow template, with red - the highest SAT score,
)                                # violet - the lowest

plt.colorbar()
plt.title('Average SAT scores by district', fontsize=23)
plt.show()

boro
Staten Island    1382.500000
Queens           1286.753032
Manhattan        1278.331410
Brooklyn         1181.364461
Bronx            1157.598203
Name: sat_score, dtype: float64


If to look only at the statistics by borough, it looks like the highest SAT scores are related to Staten Island. However, the map shows that this borough is actually represented by only one district, hence it's definitely under-sampled. In Brooklyn and Bronx there are many districts characterized by very low SAT scores. On the other hand, in Brooklyn there is a district with the highest SAT score, and it's the same district that had also the highest safety and respect score on the previous map.

## Exploring Total Enrollment and SAT Scores¶

When looking for correlations, we noticed that the total enrollment highly correlates with SAT scores. Let's take a closer look at their relationships.

In [19]:
# Plotting 'sat_score' vs. 'total_enrollment'
combined.plot.scatter(x='total_enrollment',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT vs. Total enrollment', fontsize=22)
plt.xlabel('Total enrollment scores', size=16)
plt.ylabel('SAT scores', size=16)
plt.show()


It looks like this correlation really exists, even if not that strong.

We see that the majority of schools have total enrollment less than 1000 students, so it would be interesting to zoom this range.

In [20]:
# Plotting 'sat_score' vs. 'total_enrollment'
combined.plot.scatter(x='total_enrollment',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT vs. Total enrollment', fontsize=22)
plt.xlabel('Total enrollment scores', size=16)
plt.ylabel('SAT scores', size=16)

# Zooming the range 0-1000
plt.xlim(0, 1000)
plt.show()


Hence, for the schools with the total enrollment less than 1000 students (that is, for the majority of the schools) the correlation with SAT scores is even more poor than the global trend.

## Exploring Races and SAT Scores¶

Next, we are going to plot correlations of percentages of different races vs. SAT scores, to determine whether there are any racial differences in SAT performance.

In [21]:
# Creating a list of columns with races
races = ['white_per',
'asian_per',
'black_per',
'hispanic_per']

# Plotting race correlations vs. SAT scores
combined.corr()['sat_score'][races].plot.barh(figsize=(15, 7),
color='slateblue',
fontsize=23)
plt.title('SAT scores vs. Race correlations', fontsize=35)
plt.xlabel('SAT score correlations', size=23)
plt.xlim(-0.4, 0.7)
plt.show()


From these 4 races, White and Asian show strong positive correlation with SAT scores, with the White slightly dominating, while Black and Hispanic - negative correlation, especially strong for the last one.

This strong negative correlation for the Hispanic race looks curious, let's take a closer look at it.

In [22]:
# Plotting 'sat_score' vs. 'hispanic_per'
combined.plot.scatter(x='hispanic_per',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT scores vs. Hispanic students percentage', fontsize=22)
plt.xlabel('Hispanic students %', size=16)
plt.ylabel('SAT scores', size=16)
plt.show()


From this plot, we can confirm a strong negative correlation between Hispanic students percentage and SAT scores. For all the schools where this percentage is higher than 25%, the maximum SAT score is never higher than 1500. And as soon as this percentage decreases from 25%, the number of schools with high SAT scores (as well as SAT scores themselves) drastically increases.

Let's find the schools with more than 95% of Hispanic people.

In [23]:
hispanic_greater_95 = combined[combined['hispanic_per'] > 95][['school_name', 'sat_score']]
hispanic_greater_95

Out[23]:
school_name sat_score
44 Manhattan Bridges High School 1058.0
82 Washington Heights Expeditionary Learning School 1174.0
89 Gregorio Luperon High School for Science and M... 1014.0
125 Academy for Language and Technology 951.0
141 International School for Liberal Arts 934.0
176 Pan American International High School at Monroe 970.0
253 Multicultural High School 887.0
286 Pan American International High School 951.0

Researching these schools on Google, we discovered that all of them are specialized on teaching immigrants recently arrived from Spanish-speaking countries, who may not be well-prepared for high school. Among these people, there are many unaccompanied minors and students with limited or interrupted formal education in their countries, having trouble reading in Spanish as well as English. Many of these students are older than traditional high school students and must leave school to support their families. From this context, it's not surprising that these schools demonstrate low SAT results.

Now let's turn our attention to the schools with less than 10% of Hispanic students and an average SAT score greater than 1800.

In [24]:
hispanic_less_10_SAT_greater_1800 = combined[(combined['hispanic_per'] < 10)
& (combined['sat_score'] > 1800)][['school_name', 'sat_score']]
hispanic_less_10_SAT_greater_1800

Out[24]:
school_name sat_score
37 Stuyvesant High School 2096.0
151 Bronx High School of Science 1969.0
187 Brooklyn Technical High School 1833.0
327 Queens High School for the Sciences at York Co... 1868.0
356 Staten Island Technical High School 1953.0

Google research showed that all of them are prestigious magnet high schools (which in the U.S. education system means public schools with specialized courses or curricula), specialized in technology and science, having very strict admission tests and accepting only city residents. It looks now quite explainable that, first, there are few immigrants and, second, that average SAT scores are very high there.

## Exploring Genders and SAT Scores¶

Let's investigate gender differences in SAT scores.

In [25]:
# Creating a list of columns with genders
genders = ['male_per', 'female_per']

# Plotting genders correlations vs. SAT score correlations
combined.corr()['sat_score'][genders].plot.barh(figsize=(15, 4),
fontsize=23,
color='slateblue')
plt.title('SAT scores vs. Gender correlations', fontsize=35)
plt.xlabel('SAT score correlations', size=23)
plt.show()


We see that the percentage of females shows a slight positive correlation with SAT scores, and those of males, correspondingly, a slight negative one. It can mean that women do better on the SAT than men. To get more insights, we'll plot the female percentage values and SAT scores.

In [26]:
# Plotting 'sat_score' vs. 'female_per'
combined.plot.scatter(x='female_per',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT vs. Female percentage', fontsize=22)
plt.xlabel('Female %', size=16)
plt.ylabel('SAT scores', size=16)

plt.axvline(50, color='black', linestyle='--')
plt.show()


Naturally, if we plot the percentage of males, the graph would be mirrored with respect to the 50% separation line.

We can make the following observations here:

• In general, the average results of women on the SAT are indeed slightly higher than those of men.
• The majority of schools have more or less comparable percentages of men and women (with each gender from 40 to 60%).
• There are around 6 schools with only female students and 1 school with only male ones. For both of these groups the SAT scores are just normal, without any anomalous values.
• A cluster of 6 schools with high SAT scores up to 2100 is detected to the left of the separation line, with the amount of women from 35 to 50%.
• Another group of high SAT scores, the bigger and more spread-out one, is located immediately to the right of the seperatinn line, with the amount of women from 50 to 80%.

It would be curious to have a look at the schools from the last 2 clusters.

In [27]:
female_less_50_SAT_greater_1700 = combined[(combined['female_per'] < 50)
& (combined['sat_score'] > 1700)][['school_name', 'sat_score', 'female_per']]
female_less_50_SAT_greater_1700

Out[27]:
school_name sat_score female_per
37 Stuyvesant High School 2096.0 40.7
79 High School for Mathematics, Science and Engin... 1847.0 34.2
151 Bronx High School of Science 1969.0 42.2
155 High School of American Studies at Lehman College 1920.0 46.2
187 Brooklyn Technical High School 1833.0 41.7
198 Brooklyn Latin School, The 1740.0 45.1
327 Queens High School for the Sciences at York Co... 1868.0 43.5
356 Staten Island Technical High School 1953.0 43.0

Here we recognize the same elite high schools, specialized in technology and science, which we've already seen before. It's logical that average SAT scores in these schools are so high. In addition, it seems reasonable that the gender gap for these schools, despite being present (taking also into account that science and techology generally attract more men than women), is not that significant, with the minimum percentage of women around 35%. Most probably, this can be conditioned by gender policy applied in such schools.

Let's now find out the schools from the second cluster of high SAT scores. We are especially interested in the uppermost five schools of this cloud, with the amount of women greater than 60% and average SAT scores greater than 1700.

In [28]:
female_greater_60_SAT_greater_1700 = combined[(combined['female_per'] > 60)
& (combined['sat_score'] > 1700)][['school_name', 'sat_score', 'female_per']]
female_greater_60_SAT_greater_1700

Out[28]:
school_name sat_score female_per
5 Bard High School Early College 1856.0 68.7
26 Eleanor Roosevelt High School 1758.0 67.5
60 Beacon High School 1744.0 61.0
61 Fiorello H. LaGuardia High School of Music & A... 1707.0 73.6
302 Townsend Harris High School 1910.0 71.1

Google reserch showed that those are schools with high educational standards, intense curricula, and strict admission requirements. They are mostly specialized in languages, history, social sciences, literature, arts, and music. The highest percentage of women is related to Fiorello H. LaGuardia High School of Music & Art and Performing Arts, which is, as its name suggests, specialized predominantly in different kinds of arts.

## Exploring English Leaners Percentage and SAT Scores¶

Now we'll take a look at the relationship between English learners percentage and SAT scores, since earlier we've noticed a strong negative correlation between them.

In [29]:
# Plotting 'sat_score' vs. 'ell_percent'
combined.plot.scatter(x='ell_percent',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT scores vs. English learners percentage', fontsize=22)
plt.xlabel('English learners %', size=16)
plt.ylabel('SAT scores', size=16)
plt.show()


We can notice several things here:

• A strong negative correlation really exists in this case.
• There are some schools with very low percentage of English learners (less than 1%) and very high SAT scores - presumably, the elite schools for city residents which we've seen while analyzing race diversity.
• There is a big gap on the plot, with no schools with the English learners percentage between 50 and 70%.
• There are schools with more than 70% of English learners and rather low SAT scores. Obviously, those are the schools for the immigrants, who have an additional challenge of learning English, apart from following the main curriculum. Having difficulties with English and, most probably, with the adaptation in a new country, they have also difficulties with the program itself, which results in low SAT scores.
• One school stands out of the trend, having around 40% of English learners and a relatively high SAT score. Let's find its name.
In [30]:
combined[(combined['ell_percent'] > 35) & (combined['sat_score'] > 1400)]['school_name']

Out[30]:
46    High School for Dual Language and Asian Studies
Name: school_name, dtype: object

This resulted to be a dual language English-Mandarin Chinese academically demanding school.

## Exploring AP Scores vs. SAT Scores¶

In the U.S., high school students can take Advanced Placement (AP) exam in a particular subject to earn college credit in that subject.

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 [31]:
# Calculate the percentage of students in each school who took an AP exam
combined['ap_per'] = combined['AP Test Takers '] * 100/ combined['total_enrollment']

# Plotting 'sat_score' vs. 'ap_per'
combined.plot.scatter(x='ap_per',
y='sat_score',
figsize=(10, 8),
color='slateblue',
fontsize=14,
s=150,
alpha=0.5)
plt.title('SAT vs. AP test taker percentage', fontsize=22)
plt.xlabel('AP test takers %', size=16)
plt.ylabel('SAT scores', size=16)
plt.show()


On this plot, we can distinguish two clusters:

• The first one (a big cluster on the left side of the plot) demonstrates a strong positive correlation.
• The second big cluster doesn't show any correlation at all, even though here we can see rather high percentages of AP test takers up to more than 95%.

However, there seems to be an issue with the second cluster: many of the values of AP test takers percentage, especially those high, correspond to almost the same value of SAT score, which looks a kind of an average value. One possible explanation here is that this is a consequence of filling missing values, which we did at the beginning of the project after combining datasets. To check this assumption, let's re-run those steps for a test dataset (combined_test) without filling missing values and then re-create the plot above.

In [32]:
# Re-running the code cell [9] for combined_test, without filling missing values
combined_test = data['sat_results']
combined_test = combined_test.merge(data['ap_2010'], on='DBN', how='left')
to_merge_test = ['class_size', 'demographics', 'survey'