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...best_score = 0.8 * 800
best_schools = schools[schools["average_math"] >= best_score]
best_math_schools = best_schools.loc[:, ["school_name", "average_math"]].sort_values("average_math", ascending = False)
print(best_math_schools)schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
schools_sorted = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending=False)
top_10_schools = schools_sorted.iloc[:10]
print(top_10_schools)total_SAT_by_borough = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
largest_std_borough = total_SAT_by_borough[total_SAT_by_borough["std"] == total_SAT_by_borough["std"].max()]
largest_std_dev = largest_std_borough.rename(columns = {
"count":"num_schools",
"mean":"average_SAT",
"std": "std_SAT"
})
print(largest_std_dev)