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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...#subset schools to find the schools with math score of at least 80% of the maximum possible score of 800
min_score_80_percent = 0.8 * 800
best_math_schools = schools[schools.average_math >= min_score_80_percent]
#sort the dataframe by average_math score in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)
#extract the school_name and average_math columns
best_math_schools = best_math_schools[['school_name', 'average_math']]
print(best_math_schools)
#create a new column 'total_SAT' to calculate the average total scores for each school
schools['total_SAT'] = schools.average_reading + schools.average_math + schools.average_writing
#sort the schools dataframe by total_SAT in descending order
schools_sorted = schools.sort_values(by='total_SAT', ascending=False)
#extract the school_name and total_SAT columns
top_schools = schools_sorted[['school_name','total_SAT']]
#get only the top 10 schools by total_SAT
top_10_schools = top_schools.head(10)
print(top_10_schools)
#group the schools data by borough and return the borough with the highest total_SAT standard deviation, along with its mean and the number of schools in the borough
grouped_df = schools.groupby('borough').agg({
'school_name':'count',
'total_SAT':['mean', 'std']
}).round(2)
grouped_df.columns = ['num_schools', 'average_SAT', 'std_SAT']
largest_std_dev = grouped_df.sort_values(by='std_SAT', ascending=False).head(1)
print(largest_std_dev)