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
import numpy as np
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()# filtering the schools with 80% of the max possible score of 800 for math
best_math_schools = schools[schools['average_math'] >= (800 * 0.8) ][['school_name', 'average_math']]
# Sort the results by average math score in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)
best_math_schools.head()# What are the top 10 performing schools based on the combined SAT scores?
schools['total_SAT'] = (schools['average_math'] +
schools['average_reading'] +
schools['average_writing'])
top_10_schools = schools[['school_name','total_SAT']].sort_values(by='total_SAT',ascending=False)
top_10_schools = top_10_schools.head(10)
top_10_schools # Which single borough has the largest standard deviation in the combined SAT score?
largest_std_dev = schools.groupby('borough')['total_SAT'].agg(['count', np.mean, np.std]).round()
largest_std_dev = largest_std_dev[largest_std_dev['std'] == max(largest_std_dev ['std'])]
largest_std_dev.rename(columns={'count':'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'}, inplace=True)
largest_std_dev