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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. Schools with the best math results
# (Math scores are considered "best" if at least 80% of 800, which is 640)
# ---------------------------------------------------------
best_math_schools = (
schools.loc[schools["average_math"] >= 640, ["school_name", "average_math"]]
.sort_values("average_math", ascending=False)
)
# ---------------------------------------------------------
# 2. Top 10 performing schools based on the combined SAT score
# Total SAT is the sum of math, reading, and writing scores.
# ---------------------------------------------------------
# First, create a new column with the total SAT score:
schools["total_SAT"] = (
schools["average_math"] + schools["average_reading"] + schools["average_writing"]
)
top_10_schools = (
schools.loc[:, ["school_name", "total_SAT"]]
.sort_values("total_SAT", ascending=False)
.head(10)
)
# ---------------------------------------------------------
# 3. Identify the borough with the largest standard deviation
# in the combined SAT score.
# For each borough, compute:
# - num_schools: number of schools in the borough
# - average_SAT: mean of total_SAT for that borough
# - std_SAT: the standard deviation of total_SAT
#
# Round the numeric values to two decimals.
# ---------------------------------------------------------
# Group by borough and compute statistics.
borough_stats = (
schools.groupby("borough")["total_SAT"]
.agg(num_schools="count", average_SAT="mean", std_SAT="std")
.reset_index()
)
# Round the numeric columns to two decimal places.
borough_stats["average_SAT"] = borough_stats["average_SAT"].round(2)
borough_stats["std_SAT"] = borough_stats["std_SAT"].round(2)
borough_stats["num_schools"] = borough_stats["num_schools"].astype(float).round(2)
# Identify the borough with the largest standard deviation in total SAT.
max_std_idx = borough_stats["std_SAT"].idxmax()
largest_std_dev = borough_stats.loc[[max_std_idx], ["borough", "num_schools", "average_SAT", "std_SAT"]]
# ---------------------------------------------------------
# Display the resulting DataFrames:
# ---------------------------------------------------------
print("Best Math Schools (average_math >= 640):")
print(best_math_schools.to_string(index=False))
print("\nTop 10 Schools by Combined SAT Score:")
print(top_10_schools.to_string(index=False))
print("\nBorough with the Largest Standard Deviation in Total SAT Score:")
print(largest_std_dev.to_string(index=False))