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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 source data
schools.head()# Finding schools with the best math scores
# The maximum score for each of the three SAT sections (math, reading, and writing) is 800
# Filter schools where the average math score is at least 80% of the maximum score
best_math_schools = schools[(schools["average_math"] / 800 >= 0.8)]
# Select the school name and average math score columns, then sort by average math score in descending order
best_math_schools = best_math_schools[["school_name", "average_math"]].sort_values("average_math", ascending=False)
# Display the top performing schools with their average math scores
best_math_schools.head()# Identifying the top 10 performing schools
# Calculate the total SAT score for each school by summing the average scores across the three sections
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# Select the school name and total SAT score columns, then sort by total SAT score in descending order
top_10_schools = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending=False).head(10)
# Display the top 10 performing schools with their total SAT scores
top_10_schools# Locating the NYC borough with the largest standard deviation in SAT performance
# Group the schools by borough and calculate the count, mean, and standard deviation of the total SAT scores
largest_std_dev = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# Rename the columns to provide more descriptive names
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
# Filter the dataframe to only include the borough with the largest standard deviation
largest_std_dev = largest_std_dev[largest_std_dev["std_SAT"] == largest_std_dev["std_SAT"].max()]
# Display the dataframe showing the borough with the largest standard deviation in SAT performance
largest_std_dev