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Project: Exploring NYC Public School Test Result Scores


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.

# Import required modules
import pandas as pd

# Read in the data
schools = pd.read_csv("schools.csv")

# Preview the data

#Shape of the data

#Finding 80%
best_math_schools = schools[schools['average_math'] >= 0.8 * 800]

#Sorting values for average math
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values('average_math', ascending=False)
print("Best math schools:")

#Creating SAT column
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]

#Finding best 10 school
sorted_totalsat = schools.sort_values("total_SAT", ascending = False)

#Subsetting columns
cols_to_sub = ["school_name", "total_SAT"]
top_schools = sorted_totalsat[cols_to_sub]
top_10_schools = top_schools[:10]
#The top 10 performing schools 
print("\nThe top 10 performing schools:")

#Top sd
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
print("\nLargest standard deviation:")
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