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...
# 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)
print(best_math_schools)
# 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)
print(top_10_schools)
# Task 3
borough = schools.groupby('borough')['total_SAT'].agg(['count','mean', 'std']).round(2)
print(borough)
largest_std_dev = borough[borough['std'] == borough['std'].max()]
# My code
#largest_std_dev.rename(columns={'count':'num_schools','mean':'average_SAT','std':'std_SAT'})
# Answer
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
print(largest_std_dev)