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...1 Finding schools with the best math scores
best_math_schools = schools[schools["average_math"] >= 640]
best_math_schools = best_math_schools[["school_name", "average_math"]]
best_math_schools = best_math_schools.sort_values("average_math", ascending=False)
best_math_schools.head()2 Identifying the top 10 performing schools
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools[["school_name", "total_SAT"]]
top_10_schools = top_10_schools.sort_values("total_SAT", ascending=False)
top_10_schools = top_10_schools[0:10]
top_10_schools
3 Locating the NYC borough with the largest standard deviation in SAT performance
import numpy as np
largest_std_dev = schools.groupby("borough")["total_SAT"].agg([np.size, np.mean, np.std])
largest_std_dev = round(largest_std_dev, 2)
largest_std_dev.columns = ["num_schools", "average_SAT", "std_SAT"]
largest_std_dev = largest_std_dev[largest_std_dev["std_SAT"] == largest_std_dev["std_SAT"].max()]
largest_std_dev