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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()
# Start coding here...
# Add as many cells as you like...# Create a new DataFrame schools_math_80 containing only schools with average_math results that are at least 80% of 800 maximum possible score
schools_math_80 = schools[schools['average_math'] >= 0.80 * 800]
# Display school_name and average_math columns sorted by average_math in descending order
best_math_schools = schools_math_80[['school_name', 'average_math']].sort_values('average_math', ascending=False)
# Inspect result
best_math_schools.head()# Create new column total_SAT which is the sum of math, reading, and writing scores
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Display school_name and total_SAT of top 10 schools with the highest SAT scores
top_10_schools = schools[['school_name', 'total_SAT']].nlargest(n=10, columns='total_SAT')
top_10_schools# Group the data by borough and get the number of schools, mean of total_SAT, and std of total_SAT for each borough
schools_grouped = schools.groupby('borough').agg(num_schools = ('school_name', 'count'),
average_SAT=('total_SAT', 'mean'),
std_SAT = ('total_SAT', 'std')).round(2)
# Make a new DataFrame largest_std_dev containing the borough with the highest std
largest_std_dev = schools_grouped.nlargest(n=1, columns='std_SAT')
largest_std_dev