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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...total_math = (800 * 80) / 100
print(total_math)#Filtering avg_math >= 640 and sorted by avg_math
best_math_schools = schools[schools['average_math'] >= 640][['school_name', 'average_math']].sort_values('average_math', ascending = False)
print(best_math_schools)#Calculation total_SAT
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
print(schools)#Sorted by top 10 schools
top_10_schools = schools.sort_values('total_SAT', ascending = False)[['school_name', 'total_SAT']].head(10)
print(top_10_schools)#Grouping the data by borough
borough_NYC = schools.groupby('borough')['total_SAT'].agg(['count','mean', 'std']).round(2)
#Filtering for the largest standard deviation
largest_std_dev = borough_NYC[borough_NYC['std'] == borough_NYC['std'].max()]
#Rename columns
largest_std_dev = largest_std_dev.rename(columns = {'count' : 'num_schools', 'mean' : 'average_SAT', 'std' : 'std_SAT'})
largest_std_dev.reset_index(inplace = True)
print(largest_std_dev)