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...
schools['perc'] = round((schools["average_math"] / 800) *100,0)
#schools.head()
best_math_schools = schools[schools["perc"] >= 80]
best_math_schools = best_math_schools[["school_name", "average_math"]].sort_values(["average_math"], ascending = False)
#print(best_math_schools)
schools['total_SAT'] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools[["school_name", "total_SAT"]].sort_values(["total_SAT"], ascending = False)
top_10_schools = top_10_schools.reset_index(drop=True)
top_10_schools = top_10_schools.iloc[0:10]
#print(top_10_schools)
largest_std_dev = (schools.groupby("borough")
.agg(num_schools = ("school_name", "count"),
std_SAT = ("total_SAT", lambda x: round(x.std(), 2)),
average_SAT = ("total_SAT", lambda x: round(x.mean(),2)))
.reset_index()
.sort_values("std_SAT", ascending=False)
.iloc[0:1])
print(largest_std_dev)