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...
threshold = 640
# Create 'best_math_schools' DataFrame
best_math_schools = schools[schools['average_math'] >= threshold][['school_name', 'average_math']] \
.sort_values(by='average_math', ascending=False)
#creating top_10_schools
# Calculate the total SAT scores
schools['total_SAT'] = schools[['average_math', 'average_reading', 'average_writing']].sum(axis=1)
# Sort schools based on total_SAT in descending order and select the top 10
top_10_schools = schools.sort_values(by='total_SAT', ascending=False).head(10)[['school_name', 'total_SAT']]
#Locate NYC borough with largest standard deviation for "total_SAT"
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
largest_std_dev = borough_stats[borough_stats['std'] == borough_stats['std'].max()]
largest_std_dev.columns = ['num_schools', 'average_SAT', 'std_SAT']
# Display the result
print(top_10_schools)
print(largest_std_dev)
# Add as many cells as you like...