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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...
passing_score = 0.8*800
# print(passing_score)
best_math_schools = schools[schools['average_math'] >= passing_score].sort_values(by='average_math', ascending=False)
# print(best_math_schools.head())
best_math_schools = best_math_schools[['school_name', 'average_math']]
best_math_schools.head()
schools['total_SAT'] = schools['average_math']+schools['average_reading']+schools['average_writing']
# schools.head()
# top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)
# top_10_schools.head()
top_10_schools = schools[['school_name', 'total_SAT']].nlargest(10, 'total_SAT')
# top_10_schools.head()
# borough_std_dev = schools.groupby('borough')['total_SAT'].std()
borough_std_dev = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
# print(borough_std_dev)
largest_std_dev = borough_std_dev[borough_std_dev['std'] == borough_std_dev['std'].max()]
# print(largest_std_dev)
# dic = {'num_schools': borough_std_dev['count'], 'average_SAT': borough_std_dev['mean'], 'std_SAT': borough_std_dev['std']}
# print(largest_std_dev)
# largest_std_dev = pd.DataFrame(dic)
largest_std_dev = largest_std_dev.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'})
# largest_std_dev.head()