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# Project: Exploring NYC Public School Test Result Scores

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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.

```.mfe-app-workspace-jfrv3u{font-size:13px;line-height:20px;font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;}```# Import required modules
import pandas as pd

# Preview the data

#Shape of the data
schools.shape

#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:")
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]
pd.DataFrame(top_10_schools)
#The top 10 performing schools
print("\nThe top 10 performing schools:")
print(top_10_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:")
print(largest_std_dev)``````
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