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()
#Create a pandas DataFrame called best_math_schools containing the "school_name" and "average_math" score for all schools where the results are at least 80% of the maximum possible score, sorted by "average_math" in descending order.
best_math_schools= schools[schools["average_math"]>=640] [["school_name","average_math"]].sort_values("average_math", ascending=False)
best_math_schools#Identify the top 10 performing schools based on scores across the three SAT sections, storing as a pandas DataFrame called top_10_schools containing the school name and a column named "total_SAT", with results sorted by total_SAT in descending order.
schools["total_SAT"]=schools["average_math"] + schools["average_writing"] + schools["average_reading"]
schools
top_10_schools=schools.groupby("school_name", as_index=False)["total_SAT"].mean().sort_values("total_SAT", ascending=False).head(10)
top_10_schools#Locate the NYC borough with the largest standard deviation for "total_SAT", storing as a DataFrame called largest_std_dev with "borough" as the index and three columns: "num_schools" for the number of schools in the borough, "average_SAT" for the mean of "total_SAT", and "std_SAT" for the standard deviation of "total_SAT". Round all numeric values to two decimal places.
boroughs=schools.groupby("borough")["total_SAT"].agg(["count","mean","std"]).round(2)
boroughs
largest_std_dev=boroughs[boroughs["std"]== boroughs["std"].max()]
largest_std_devlargest_std_dev=largest_std_dev.rename(columns={"count":"num_schools","mean":"average_SAT","std":"std_SAT"})
largest_std_dev