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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.
# Import required modules
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
#Shape of the data
schools.shape
#Finding 80%
best_math_schools = schools[schools['average_math'] >= 0.8 * 800]
#Sorting values for average math
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values('average_math', ascending=False)
print("Best math schools:")
print(best_math_schools)
#Creating SAT column
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
#Finding best 10 school
sorted_totalsat = schools.sort_values("total_SAT", ascending = False)
#Subsetting columns
cols_to_sub = ["school_name", "total_SAT"]
top_schools = sorted_totalsat[cols_to_sub]
top_10_schools = top_schools[:10]
pd.DataFrame(top_10_schools)
#The top 10 performing schools
print("\nThe top 10 performing schools:")
print(top_10_schools)
#Top sd
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
print("\nLargest standard deviation:")
print(largest_std_dev)