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...
#Best Math Schools
best_maths_schools = schools[schools["average_math"]>=640].sort_values(by='average_math',ascending=False)
best_math_schools = best_math_schools[["school_name", "average_math"]]
#Top 10 Schools
schools["total_SAT"] = schools["average_math"]+schools["average_reading"]+schools["average_writing"]
top_10_schools = schools[["school_name","total_SAT"]].sort_values(by = "total_SAT", ascending = False)
top_10_schools = top_10_schools.head(10)
print(top_10_schools)
# Borough with largest standard deviation
boroughs = schools.groupby("borough").agg({"total_SAT" : ["count","mean","std"]}).round(2)
print(boroughs)
large_std_dev = boroughs[boroughs[("total_SAT", "std")] == boroughs[("total_SAT", "std")].max()]
largest_std_dev = large_std_dev.rename(columns = {"count":"num_schools", "mean":"average_SAT", "std":"std_SAT"})
largest_std_dev.columns = ["num_schools", "average_SAT", "std_SAT"]
print(largest_std_dev)
# Add as many cells as you like...