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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')
# Start coding here...
# Which NYC schools have the best math results? Note: The best math results are at least 80% of the *maximum possible score of 800* for math.
best_avemath_res = schools[schools['average_math'] >= 0.80 * 800]
sorted_best_avemath = best_avemath_res.sort_values(by='average_math', ascending=False)
best_math_schools = sorted_best_avemath[['school_name', 'average_math']]
# What are the top 10 performing schools based on the combined SAT scores?
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
sorted_total_SAT = schools.sort_values(by = 'total_SAT', ascending = False)
top_10_schools = sorted_total_SAT[['school_name', 'total_SAT']].head(10)
# Which single borough has the largest standard deviation in the combined SAT score?
borough = schools.groupby('borough').agg(num_schools = ('school_name', 'count'),
average_SAT = ('total_SAT', 'mean'),
std_SAT = ('total_SAT', 'std')).round(2)
largest_std_dev = largest_std_dev = borough[borough['std_SAT'] == borough['std_SAT'].max()]
# Add as many cells as you like...