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()
#Adding a new column to calculate the percentage of the average math scores
schools['avg_math_percent'] = schools['average_math']/ 800 * 100
#NYC Schools with the best math results
best_math_schools = schools[schools['avg_math_percent'] >= 80.00]
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values('average_math', ascending = False)
best_math_schools = pd.DataFrame(best_math_schools)
print(best_math_schools)
#To calculate the total SAT scores
schools['total_SAT'] = schools[['average_math', 'average_reading', 'average_writing']].sum(axis = 1)
#Top 10 performing schools based on the combined SAT scores
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', ascending = False).head(10)
top_10_schools = pd.DataFrame(top_10_schools)
print(top_10_schools)
#Standard deviation of total SAT scores and the corresponding boroughs
boroughs = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
print(boroughs)
#Borough with the largest standard deviation of the total SAT score
largest_std_dev = boroughs[boroughs['std'] == 230.29]
print(largest_std_dev)
#Renaming the columns using a dictionary
largest_std_dev.rename(columns = {'count': 'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'}, inplace = True)
largest_std_dev = pd.DataFrame(largest_std_dev)
print(largest_std_dev)
```