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...
# Subsetting the average_math column to find the best math schools
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending = False)
# Adding a new column, total_SAT, which is the sum of the 3 existing SAT columns
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# Sorting the DataFrame by school_name and total_SAT and storing the top 10 schools
top_10_schools = schools.sort_values("total_SAT", ascending = False)[["school_name", "total_SAT"]].head(10)
# Grouping the data by borough
borough = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# Filtering for the largest std
largest_std_dev = borough[borough["std"] == borough["std"].max()]
# Renaming the columns
largest_std_dev = large_std_dev.rename(columns = {"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})