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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...#schools.head()
#schools.shape
#schools.info()
schools.describe()
#schools.columns
# Which NYC schools have the best math results?
best_math_schools = schools[["school_name","average_math"]]
best_math_schools["%_score"] = (schools["average_math"] / 800) * 100
best_math_schools["%_score"] = best_math_schools["%_score"].round(1)
best_math_schools = best_math_schools.sort_values(["average_math"], ascending = False)
best_math_schools = best_math_schools[best_math_schools["%_score"] >= 80]
best_math_schools = best_math_schools[["school_name", "average_math"]]
print(best_math_schools)# What are the top 10 performing schools based on the combined SAT scores?
top_10_schools = schools[["school_name"]]
top_10_schools["total_SAT"] = (schools["average_math"] + schools["average_reading"] + schools["average_writing"])
top_10_schools = top_10_schools.sort_values(["total_SAT"], ascending = False)
top_10_schools = top_10_schools.iloc[0:10]
print(top_10_schools)# Which single borough has the largest standard deviation in the combined SAT score?
#print(schools.head())
school2 = schools
school2["total_sat"] = (schools["average_math"] + schools["average_reading"] + schools["average_writing"])
#school2["std_dev"] = school2["total_sat"].std()
print(school2.head())
largest_st_dev1 = pd.DataFrame()
largest_st_dev1["num_schools"] = school2.groupby("borough")["building_code"].count()
largest_st_dev1["average_SAT"] = school2.groupby("borough")["total_sat"].mean()
largest_st_dev1["std_SAT"] = school2.groupby("borough")["total_sat"].std()
largest_st_dev1 = largest_st_dev1.sort_values(["std_SAT"], ascending = False)
largest_st_dev1["average_SAT"] = largest_st_dev1["average_SAT"].round(2)
largest_st_dev1["std_SAT"] = largest_st_dev1["std_SAT"].round(2)
print(largest_st_dev1.head())
largest_std_dev = pd.DataFrame()
largest_std_dev = largest_st_dev1.iloc[0:1]
print(largest_std_dev)