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...# Question 1 - Which NYC schools have the best math results
math_threshold = 0.80 * 800
math_school_subset = schools[['school_name', 'average_math']].sort_values(by='average_math', ascending=False)
best_math_schools = math_school_subset.loc[math_school_subset['average_math'] >= math_threshold]
best_math_schools.head()# Question 2 - What are the top 10 performing schools based on the combined SAT scores
# selected_cols = schools[['average_math', 'average_reading', 'average_writing']]
# selected_cols.head()
schools['total_SAT'] = schools[['average_math', 'average_reading', 'average_writing']].sum(axis=1)
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)
top_10_schools# Question 3 - Which single borough has the largest standard deviation in the combined SAT score
borough = schools.groupby('borough')['total_SAT'].std().idxmax()
num_schools = schools[schools['borough'] == borough].shape[0]
schools_within_borough = schools.loc[schools['borough'] == borough]
average_SAT = schools_within_borough['total_SAT'].mean().round(2)
std_SAT = schools_within_borough['total_SAT'].std().round(2)
largest_std_dev = {
'borough': borough,
'num_schools': num_schools,
'average_SAT': average_SAT,
'std_SAT': std_SAT
}
largest_std_dev = pd.DataFrame([largest_std_dev])
largest_std_dev
# print(borough, num_schools, average_SAT, std_SAT)