Exploring NYC Public School SAT Scores
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()# Which NYC schools have the best math results?
# calculate min required score for 'best math results'
min_best_score = int(0.80 * 800)
print(f"Minium required math score to be considered 'the best': {min_best_score}")
# get school name and avg math score for schools meeting that minimum
best_math_schools = schools[schools["average_math"] >= min_best_score][["school_name", "average_math"]].sort_values("average_math", ascending=False)
best_math_schools.head()# What are the top 10 performing schools based on the combined SAT scores?
# create a new column combining the average scores from each school
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# create a new DF that shows school name and the combined average SAT scores
top_10_schools = schools.sort_values("total_SAT", ascending=False)[["school_name", "total_SAT"]].head(10)
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)
# filter to find the max std
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
# rename columns for clarity
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
# reset the index
largest_std_dev.reset_index(inplace=True)
largest_std_dev