Koryo Kakinoki/

Project: Exploring NYC Public School Test Result Scores


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

# Start coding here...
# Add as many cells as you like...
# New df where schools acheived more than 80% of average maths score. 
maths_80 = schools[schools['average_math'] >= 800 * 0.8]
best_math_schools = maths_80[['school_name', 'average_math']].sort_values('average_math', ascending = False)
# Identify top 10 schools
# Add total score column
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Top 10 schools
top_10_schools = schools[['school_name','total_SAT']].sort_values('total_SAT', ascending=False).head(10)
# Task 3
borough = schools.groupby('borough')['total_SAT'].agg(['count','mean', 'std']).round(2)

largest_std_dev = borough[borough['std'] == borough['std'].max()]

# My code

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

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