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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...import pandas as pd
# Reading the data
df = pd.read_csv('schools.csv')
# Show few first rows of the data
df.head()
# Finding the schools with the best math results
best_math_schools = df[df['average_math'] > 0.8 * 800][['school_name', 'average_math']].sort_values(by='average_math', ascending=False)
# 10 top performing schools based on SAT
df['total_SAT'] = df['average_math'] + df['average_reading'] + df['average_writing']
top_10_schools = df[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)
# Finding the borough with largest std_dev
std_dev_SAT = df.groupby('borough')['total_SAT'].std().sort_values(ascending=False).head(1)
num_schools = df[df['borough'] == 'Manhattan']['school_name'].count()
SAT_avg = df[df['borough'] == 'Manhattan']['total_SAT'].mean()
std_sat = df[df['borough'] == 'Manhattan']['total_SAT'].std()
dict = {'borough': ['Manhattan'],
'num_schools': [num_schools],
'average_SAT': [SAT_avg],
'std_SAT': [std_sat]}
largest_std_dev = round(pd.DataFrame(dict), 2)
print(largest_std_dev)