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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.
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
# '''Which NYC schools have the best math results?'''
# Selects schools that have at least 80% of the Max 800 math score:
best_math_schools = schools[schools['average_math'] > (0.8*800)]
# Keeping only school name and math score, sorting values in descending:
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values('average_math', ascending=False)
#print(best_math_schools.head(10))
# '''What are the top 10 performing schools based on the combined SAT scores?'''
# Creates and calculates total SAT score as sum of Math, Reading, and Writing:
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools.sort_values('total_SAT', ascending=False)[0:10][['school_name', 'total_SAT']]
#print(top_10_schools)
# '''Which single borough has the largest standard deviation in the combined SAT score?'''
# Calculates standard deviation of SAT per NY borough
SAT_borough_STD = schools.groupby('borough')['total_SAT'].std()
print(SAT_borough_STD)
# Identifies name and value of the NY borough with highest STA standard deviation...:
max_STD_borough = SAT_borough_STD.idxmax()
max_STD_value = SAT_borough_STD.max()
print(f"Borough with the largest standard deviation in combined SAT score: {max_STD_borough} with a standard deviation of {max_STD_value}")
# ...and record them into largest_std_dev df
largest_std_dev = pd.DataFrame({'borough': [max_STD_borough], 'std_SAT': [max_STD_value]})
# Subset the schools from the NY borough with highest SAT standard deviation:
bor_schools = schools[schools['borough'] == max_STD_borough]
# Adds number of schools and average SAT to the df largest_std_dev:
largest_std_dev['num_schools'] = len(bor_schools)
largest_std_dev['average_SAT'] = bor_schools['total_SAT'].mean()
# Rounds all values of df largest_std_dev to 2 decimals
largest_std_dev = round(largest_std_dev, 2)