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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()
F_S = schools[schools['average_math'] > (800 * 0.8)]
best_math_schools = F_S[['school_name', 'average_math']]
best_math_schools = best_math_schools.sort_values(['average_math'], ascending = False)
print(best_math_schools)
schools['total_SAT'] = schools[['average_math', 'average_reading', 'average_writing']].sum(axis=1)
T_S = schools.sort_values('total_SAT', ascending = False).head(10)
top_10_schools = T_S[['school_name','total_SAT']]
print(top_10_schools)
borough = schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
largest_std_dev = borough[borough['std'] == borough['std'].max()]
largest_std_dev = largest_std_dev.rename(columns = {'count': 'num_schools','mean': 'average_SAT','std':'std_SAT'})
largest_std_dev.reset_index(inplace = True)