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.
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
# Analysing NYC Schools with best math results
# Maximum possible score = 800
# Threshold for best maths result = 0.8 * 800 = 640
average_math_scores = schools[schools["average_math"] >= 640].sort_values(by="average_math", ascending=False)
best_math_schools = average_math_scores[["school_name", "average_math"]]
best_math_schools.plot(x="school_name", y="average_math", kind="bar")
# # Analysing Top 10 performing schools based on SAT scores
schools["total_SAT"] = schools[["average_math", "average_writing", "average_reading"]].sum(axis=1)
top_10_schools = schools[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending=False).head(10)
# Getting Borough with the largest standard deviation
borough_summary = schools.groupby("borough")["total_SAT"].agg(std_SAT="std", num_schools="count", average_SAT="mean").reset_index().sort_values(by="std_SAT", ascending=False).head(1)
largest_std_dev = borough_summary[["borough", "num_schools", "average_SAT", "std_SAT"]].round(2)
print(largest_std_dev)