Photo by Jannis Lucas on Unsplash.
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()
# Start coding here...
# Add as many cells as you like...b_m_schools = schools[schools["average_math"]/800 >= (0.8)].sort_values("average_math",ascending=False)
best_math_schools = pd.DataFrame(b_m_schools[["school_name","average_math"]])
best_math_schoolsschools["total_SAT"] = schools["average_math"]+schools["average_reading"]+schools["average_writing"]
t_10_schools = schools.sort_values("total_SAT",ascending=False).head(10)
top_10_schools = pd.DataFrame(t_10_schools[["school_name","total_SAT"]])
top_10_schools# Group by borough to calculate number of schools, average SAT, and standard deviation of SAT scores
borough_stats = schools.groupby('borough').agg(
num_schools=('school_name', 'size'),
average_SAT=('total_SAT', 'mean'),
std_SAT=('total_SAT', 'std')
).reset_index()
# Round the calculated statistics
borough_stats[['average_SAT', 'std_SAT']] = borough_stats[['average_SAT', 'std_SAT']].round(2)
# Find the borough with the largest standard deviation in SAT scores
largest_std_dev = borough_stats.sort_values(by='std_SAT', ascending=False).head(1)
# Output the result
largest_std_dev