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...
# Which NYC schools have the best math results?
best_math_schools= schools[schools["average_math"]>=0.8*800][["school_name","average_math"]].sort_values("average_math",ascending=False)
# What are the top 10 performing schools based on the combined SAT scores?
#1º Creating the sum column
schools["total_SAT"]=schools["average_math"]+ schools["average_reading"]+schools["average_writing"]
#2º Creating the sort_values columns
top_10_schools = schools.sort_values("total_SAT",ascending=False)[["school_name","total_SAT"]].head(10)
# Which single borough has the largest standard deviation in the combined SAT score?
#1º Grouping the data by boroughs
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
#2º Locating the borough with the largest std deviation
largest_std_dev = boroughs[boroughs["std"]==boroughs["std"].max()]
#3º Naming the columns
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()