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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 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.shape

Which NYC schools have the best math results?

# 1 - Finding schools with the best math scores and sorting in descending order

# Math results at least 80% of maximum score of 800
best_math_schools = schools[schools['average_math'] >= (800*.80)]
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values('average_math',
                                                                                ascending=False)

best_math_schools

What are the top 10 performing schools based on the combined SAT scores?

# 2 - Identifying the top 10 performing schools

# Creating a column for average scores
schools['total_SAT'] = schools.iloc[:,3] + schools.iloc[:,4] + schools.iloc[:,5]

# Sorting and limiting the data
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT', 
                                                                   ascending=False).head(10)

top_10_schools

Which single borough has the largest standard deviation in the combined SAT score?

# 3 - Locating the NYC borough with the largest standard deviation in SAT performance

# Grouping the data by borough and calculating count of schools, mean, and st. dev. 2 decimals
largest_std_dev = schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)

# Filtering for the largest standard deviation
largest_std_dev = largest_std_dev.sort_values('std', ascending=False).head(1)

## Different method for same result
# largest_std_dev = largest_std_dev[largest_std_dev['std'] == largest_std_dev['std'].max()]

# Renaming columns
largest_std_dev = largest_std_dev.rename(columns = {'count':'num_schools', 'mean':'average_SAT',
                                          'std':'std_SAT'})

largest_std_dev.reset_index(inplace=True)
largest_std_dev