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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
#print(schools.columns)
# Start coding here...
# Add as many cells as you like...
#1.best math results:
best_math_schools = schools[schools['average_math'] >= 640][['school_name', 'average_math']].sort_values('average_math', ascending = False)
print(best_math_schools)
#2.top ten
#creating new column
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending = False).head(10)
print(top_10_schools)
#3. max_std
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
#bor = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
borough_stats = schools.groupby('borough').agg(
num_schools=('school_name', 'count'),
average_SAT=('total_SAT', 'mean'),
std_SAT=('total_SAT', 'std')).reset_index()
largest_std_dev_row = borough_stats.loc[borough_stats['std_SAT'].idxmax()]
#creating a DataFrame with results
largest_std_dev = pd.DataFrame([largest_std_dev_row]).round(2)
print(largest_std_dev)