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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 tasked with answering three key questions about New York City (NYC) public school SAT performance:

Which schools produce the highest math scores?

  • Specifically, which schools have an average math SAT score of at least 80%?
  • Save the results as a pandas DataFrame called best_math_schools.

Who are the top 10 schools based on average results across reading, math, and writing?

  • Save the results as a pandas DataFrame called top_10_schools.

Which NYC borough has the largest standard deviation for SAT results?

  • Save the results as a pandas DataFrame called largest_std_dev.
# Start coding here... 
import pandas as pd

data=pd.read_csv('schools.csv')

Lets see how that data look like:

data.head()

Now we want to find the school with math score of 80% or higher, lets first calculate what that score is:

score_min=0.80*800
print('minimum score is',score_min)

lets filter on school which have avg math score of our calculated minimum score and sort decening

best_math_schools=data.loc[data['average_math']>=score_min,['school_name','average_math']].sort_values('average_math',ascending=False)
best_math_schools.head()

Next we will idendtify top 10 performing schools, based on sat accros all three sections

data['total_SAT']=data['average_math']+data['average_reading']+data['average_writing']
print(data.head())
top_10_schools=data.groupby('school_name',as_index=False)['total_SAT'].mean().sort_values('total_SAT',ascending=False).head(10)
print(top_10_schools)

now we will locate nyc borough with largest std deviation for total sat

new_data=data.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
print(new_data)
largest_std_dev= new_data[new_data['std']==new_data['std'].max()].reset_index()
print(largest_std_dev)
largest_std_dev.rename(columns={"count": "num_schools","mean":"average_SAT","std":"std_SAT"},inplace=True)
print(largest_std_dev)