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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 tasked with answering three key questions about New York City (NYC) public school SAT performance:

Which schools produce the highest math scores?

  • Specifically, which schools have an average math SAT score of at least 80%?
  • Save the results as a pandas DataFrame called best_math_schools.

Who are the top 10 schools based on average results across reading, math, and writing?

  • Save the results as a pandas DataFrame called top_10_schools.

Which NYC borough has the largest standard deviation for SAT results?

  • Save the results as a pandas DataFrame called largest_std_dev.
# Start coding here... 
import pandas as pd
# Showing all DF columns:
pd.set_option('display.max_columns', None)
# Function to check if column is numeric:
def is_numeric(col):
    try:
        pd.to_numeric(col)
        return True
    except:
        return False
    

# Reading in schools csv
schools = pd.read_csv("schools.csv")
# print(schools)

# Maximum  math score
max_math = 800
# Creating 80% of max avg math filter
math_filter = max_math * .8

# Creating best math schools df
best_math_schools = schools[schools["average_math"] >= math_filter].sort_values("average_math", ascending=False)
best_math_schools = best_math_schools[["school_name", "average_math"]]
# print(best_math_schools)

# Creating schools copy with total_sat column
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# print(schools)

# Sorting by top 10 total SAT score
top_10_schools = schools.sort_values("total_SAT", ascending=False).head(10)
top_10_schools = top_10_schools[["school_name","total_SAT"]]
# print(top_10_schools)

# Locating borough with largest total_SAT standard deviation --> Manhattan
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()].reset_index()
largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"}, inplace=True)
print(largest_std_dev)