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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...
# Fixed code: Added the 'by' parameter to specify the column to sort by
best_math_schools = schools[schools['average_math'] >= (800*0.8)][['school_name','average_math']].sort_values(by='average_math', ascending=False)
best_math_schools.head()
schools['total_SAT'] = schools['average_math']+schools['average_reading']+schools['average_writing']
top_10_schools = schools.sort_values('total_SAT',ascending=False)[['school_name','total_SAT']].head(10)
boroughs = schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
largest_std_dev = boroughs[boroughs['std'] == boroughs['std'].max()]
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.head()