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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...best_math_schools = schools[schools['average_math'] >= 0.8 * 800].loc[:,['school_name', 'average_math']].sort_values('average_math', ascending=False)
best_math_schoolstop_10_schools = schools.copy()
top_10_schools['total_SAT'] = schools.loc[:, ['average_math', 'average_reading', 'average_writing']].sum(axis=1)
top_10_schools = top_10_schools.loc[:, ['school_name', 'total_SAT']].sort_values('total_SAT', ascending=False).iloc[:10]
top_10_schoolslargest_std_dev = schools.copy()
largest_std_dev['total_SAT'] = schools.loc[:, ['average_math', 'average_reading', 'average_writing']].sum(axis=1)
largest_std_dev = largest_std_dev.pivot_table(
values=['total_SAT', 'school_name'],
index=['borough'],
aggfunc={'school_name':'count', 'total_SAT':['mean', 'std']}
).reset_index()
largest_std_dev.columns = ['borough', 'num_schools', 'average_SAT', 'std_SAT']
largest_std_dev = largest_std_dev.round({'average_SAT': 2, 'std_SAT': 2})
largest_std_dev = largest_std_dev.sort_values('std_SAT', ascending = False).iloc[[0], :]
largest_std_dev