Every year, American high school students take SATs, which are standardized tests intended to measure literacy, numeracy, and writing skills. There are three sections - reading, math, and writing, each with a maximum score of 800 points. These tests are extremely important for students and colleges, as they play a pivotal role in the admissions process.

Analyzing the performance of schools is important for a variety of stakeholders, including policy and education professionals, researchers, government, and even parents considering which school their children should attend.

You have been provided with a dataset called schools.csv, which is previewed below.

You have been tasked with answering three key questions about New York City (NYC) public school SAT performance.

Import data from csv, import pandas library

In [ ]:
# Import pandas library
import pandas as pd

# Read in the data
schools = pd.read_csv("./nyc_schools.csv")

Examine first 10 rows

In [ ]:
# Preview the data
schools.head(10)
Out[ ]:
school_name borough building_code average_math average_reading average_writing percent_tested
0 New Explorations into Science, Technology and ... Manhattan M022 657 601 601 NaN
1 Essex Street Academy Manhattan M445 395 411 387 78.9
2 Lower Manhattan Arts Academy Manhattan M445 418 428 415 65.1
3 High School for Dual Language and Asian Studies Manhattan M445 613 453 463 95.9
4 Henry Street School for International Studies Manhattan M056 410 406 381 59.7
5 Bard High School Early College Manhattan M097 634 641 639 70.8
6 Urban Assembly Academy of Government and Law Manhattan M445 389 395 381 80.8
7 Marta Valle High School Manhattan M025 438 413 394 35.6
8 University Neighborhood High School Manhattan M446 437 355 352 69.9
9 New Design High School Manhattan M445 381 396 372 73.7

Data info

In [ ]:
# Data info
schools.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 375 entries, 0 to 374
Data columns (total 7 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   school_name      375 non-null    object 
 1   borough          375 non-null    object 
 2   building_code    375 non-null    object 
 3   average_math     375 non-null    int64  
 4   average_reading  375 non-null    int64  
 5   average_writing  375 non-null    int64  
 6   percent_tested   355 non-null    float64
dtypes: float64(1), int64(3), object(3)
memory usage: 20.6+ KB

Q1. Which NYC schools have the best math results?

In [ ]:
# Subsetting best math results that are at least 80% of the maximum possible score of 800 for math 
school_math_filtered = schools[schools['average_math']>=0.8*800]

# Get the best_math_schools with school_name and average_math columns, sorted by average_math
best_math_schools = school_math_filtered.loc[:,['school_name','average_math']].sort_values('average_math', ascending=False)

# Q1. Display the top 30 schools with best math results
print(best_math_schools.head(30))

                                           school_name  average_math
88                              Stuyvesant High School           754
170                       Bronx High School of Science           714
93                 Staten Island Technical High School           711
365  Queens High School for the Sciences at York Co...           701
68   High School for Mathematics, Science, and Engi...           683
280                     Brooklyn Technical High School           682
333                        Townsend Harris High School           680
174  High School of American Studies at Lehman College           669
0    New Explorations into Science, Technology and ...           657
45                       Eleanor Roosevelt High School           641

Q2. What are the top 10 performing schools based on the combined SAT scores?

In [ ]:
# Adding total_SAT column
schools['total_SAT']=schools['average_math']+schools['average_reading']+schools['average_writing']

# Get top_10_schools with highest total SAT scores
top_10_schools = schools.loc[:,['school_name','total_SAT']].sort_values('total_SAT', ascending=False).head(10)

# Q2.Display the top 10 schools with highest total SAT scores
print(top_10_schools)

                                           school_name  total_SAT
88                              Stuyvesant High School       2144
170                       Bronx High School of Science       2041
93                 Staten Island Technical High School       2041
174  High School of American Studies at Lehman College       2013
333                        Townsend Harris High School       1981
365  Queens High School for the Sciences at York Co...       1947
5                       Bard High School Early College       1914
280                     Brooklyn Technical High School       1896
45                       Eleanor Roosevelt High School       1889
68   High School for Mathematics, Science, and Engi...       1889

Q3. Which single borough has the largest standard deviation in the combined SAT score?

In [ ]:
# Get the count, mean and std of total_SAT column
largest_std_dev = schools.groupby("borough")['total_SAT'].agg(['count','mean','std'])

# Filter the largest standard deviation of 'total_SAT' column
largest_std_dev = largest_std_dev[largest_std_dev['std']==largest_std_dev['std'].max()].round(2)

# Rename the columns for clarity
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})

# Change borough from index to column
largest_std_dev.reset_index(inplace=True)

# Q3. print the borough with the largest standard deviation in the combined SAT score
print(largest_std_dev)

     borough  num_schools  average_SAT  std_SAT
0  Manhattan           89      1340.13   230.29