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.
import pandas as pd
schools = pd.read_csv("schools.csv")
# Show all
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print(schools)# Start coding here...
# Q1. Finding schools with the best math scores
# Subset the data to find which schools have the 80% math scores
best_math_schools = schools[schools['average_math'] >= 640][['school_name', 'average_math']]
# Sort by average_math in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)
# Display the results
print(best_math_schools)# Q2. Identifying the top 10 performing schools
# Create new column for total_SAT
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Sort the DataFrame by total_SAT in descending order
top_10_schools = schools.sort_values(by='total_SAT', ascending=False)
# Select top 10 schools with relevant columns
top_10_schools = top_10_schools[['school_name', 'total_SAT']].head(10)
# Display the result
print(top_10_schools)# Q3. Locating the NYC borough with the largest standard deviation in SAT performance
# Group the data by borough and stats
borough_stats = schools.groupby('borough')['total_SAT'].agg(['count','mean', 'std']).round(2)
borough_stats.columns = ['num_schools', 'average_SAT', 'std_SAT']
# Find the largest Standard Deviation
largest_std_dev = borough_stats.sort_values(by='std_SAT', ascending=False).head(1).reset_index()
# Display the results
print(largest_std_dev)