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
import numpy as np
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
#schools.head()
#Which NYC schools have the best math results?
math_schools = schools.loc[:, ['school_name', 'average_math']].sort_values('average_math', ascending = False)
print(math_schools)
best_math_schools = math_schools[math_schools['average_math'] >= 640]
print(best_math_schools)
#What are the top 10 performing schools based on the combined SAT scores?
schools['total_SAT'] = schools[['average_math', 'average_reading', 'average_writing']].sum(axis=1)
schools_ordered_SAT= schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending = False)
top_10_schools= schools_ordered_SAT.iloc[0:10, :]
print(top_10_schools)
#Which single borough has the largest standard deviation in the combined SAT score?
boroughs = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
print(boroughs)
largest_std_dev = boroughs[boroughs['std'] == boroughs['std'].max()]
print(largest_std_dev)
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
print(largest_std_dev)
# Add as many cells as you like...