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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()# NYS schools with the best math result {schools with maths score >= 640 points }
max_pos_score = 0.8 * 800
best_math_schools = schools[schools["average_math"] >= max_pos_score][["school_name", "average_math"]].sort_values(by="average_math", ascending=False)# Top 10 performing schools based on combined SAT scores
schools["total_SAT"] = schools.loc[:,["average_math","average_reading","average_writing"]].sum(axis=1)
top_10_schools = schools[["school_name","total_SAT"]].sort_values(by = "total_SAT", ascending = False).head(10)# borough with the largest standard dev, {num_schools, std_SAT, average_SAT}
largest_std_dev = schools.groupby(by="borough").agg( # group by borough
num_schools=('total_SAT', 'count'), # number of schools
average_SAT=('total_SAT', 'mean'), # mean of each borough
std_SAT=('total_SAT', 'std') # std of each borough
).sort_values(by="std_SAT", ascending=False).round(2).head(1) # sort by std_SAT and round to 2dp
largest_std_dev