Photo by Jannis Lucas on Unsplash.
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...
schools = schools.sort_values('average_math', ascending=False)
schools = schools[schools['average_math'] >= 640]
best_math_schools = schools[['school_name', 'average_math']]
print(best_math_schools)# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
schools = schools.sort_values('total_SAT', ascending=False)
top_10_schools = schools[['school_name', 'total_SAT']].head(10)
print(top_10_schools)# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
average_SAT_group = round(schools.groupby('borough')['total_SAT'].mean(), 2)
average_SAT = average_SAT_group.median()
std_SAT = round(schools.groupby('borough')['total_SAT'].std().max(), 2)
borough_highest = schools.groupby('borough')['total_SAT'].std()
schools_in_man = schools[schools['borough'] == 'Manhattan']
num_schools = schools_in_man['school_name'].count()
largest_std_dev = pd.DataFrame({
"borough": ['Manhattan'],
"num_schools": [num_schools],
"average_SAT": [average_SAT],
"std_SAT": [std_SAT]
})
print(largest_std_dev)