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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")
print(schools.head())
# First problem
#Getting the 0.8 of 800 maximum score
bestMath = 0.8 * 800
#copy the original dataframe to new
temp_schools = schools.copy()
#subset the temp schools showing only schools with 640 or greater scores in math
temp_schools = temp_schools[temp_schools["average_math"] > bestMath]
#storing to best_math_schools dataframe the school and their average math scores sorted by the scores in descending order
best_math_schools = temp_schools[["school_name", "average_math"]].sort_values("average_math", ascending=False)
print(best_math_schools)
print(temp_schools.columns)
#second problem
temp_schools = schools.copy()
#Adding TOTAL_SAT column in the temp_schools dataframe. Total SAT is all the average scores
temp_schools['total_SAT'] = temp_schools[["average_math", "average_reading", "average_writing"]].sum(axis=1)
temp_schools.sort_values(by="total_SAT", ascending=False, inplace=True)
#Getting top 10 schools
top_10_schools = temp_schools[["school_name", "total_SAT"]]
top_10_schools = top_10_schools[:10]
print(top_10_schools)
#Third problem
temp_schools = schools.copy()
#Adding TOTAL_SAT column in the temp_schools dataframe. Total SAT is all the average scores
temp_schools['total_SAT'] = temp_schools[["average_math", "average_reading", "average_writing"]].sum(axis=1)
borough_schools = round(temp_schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]), 2).sort_values(by="std", ascending=False).reset_index()
borough_schools.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"}, inplace=True)
#Getting the single borough
largest_std_dev = borough_schools.iloc[[0],0:]
largest_std_dev = pd.DataFrame(largest_std_dev)
print(largest_std_dev)