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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...import numpy as np
# Subset schools for best math result
#best_math_results = schools[schools["average_math"]>=0.8*800]
# Save result in new sorted DataFrame best_math_schools
#best_math_schools = best_math_results[["school_name", "average_math"]].sort_values("average_math", ascending=False)
best_math_schools = schools.loc[schools["average_math"]>=0.8*800, ["school_name", "average_math"]].sort_values("average_math", ascending=False)
# Preview the data
best_math_schools.head()
# adding a column "total_SAT" to schools
schools["total_SAT"] = schools["average_math"]+schools["average_reading"]+schools["average_writing"]
# Sort the top 10 performing schools on SAT score and save as a new DataFrame top_10_schools
top_10_schools = schools[["school_name","total_SAT"]].sort_values("total_SAT", ascending=False)
top_10_schools = top_10_schools.head(n=10)
# group by borough to find school count, mean and std_dev of total_SAT
boroughs = schools.groupby("borough")["total_SAT"].agg([np.mean, np.std]).round(2)
boroughs["num_schools"] = schools["borough"].value_counts()
# Filter for largest standard deviation
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
# Rename the columns
largest_std_dev = largest_std_dev.rename({"mean":"average_SAT", "std":"std_SAT"}, axis = 1)
print(largest_std_dev)