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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()
# Creating df containing the best math schools
best_math_schools = schools[schools['average_math'] >= 640][['school_name','average_math']].sort_values('average_math',ascending=False)
#sorting by average math scores in descending order
#best_math_schools.sort_values('average_math',ascending=False)#Getting Total SAT Score for schools
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
#Grouping by school name and then getting the average for Total SAT sorting by descending and limiting to only top 10 using the head function. Its long, but it is efficient
top_10_schools = schools.groupby('school_name')['total_SAT'].mean().reset_index().sort_values('total_SAT' ,ascending = False).head(10)#Analyzing all of the schools by boroughs total SAT scores to understand how the boroughs schools compare and performed
boroughs = schools.groupby('borough')['total_SAT'].agg(['count','mean','std']).round(2)
#Finding the borough with the largest standard deviation
#boroughs#Finding the borough with the largest standard deviation
largest_std_dev = boroughs[boroughs['std'] == boroughs['std'].max()]
#Renaming the Columns to provide cleaer understanding of the data
largest_std_dev = largest_std_dev.rename(columns = {'count':'num_schools', 'mean':'average_SAT', 'std':'std_SAT'})
#Displaying Resutls
largest_std_dev