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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.

# Importing needed libraries
import pandas as pd

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

# Preview the data
schools.head()

Creating a 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.

schools_top_math = schools[schools["average_math"] > 0.8*800]
schools_top_math_subset = schools_top_math[["school_name", "average_math"]]
best_math_schools = schools_top_math_subset.sort_values(by="average_math", ascending=False)

print(best_math_schools)

Identifing 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_reading"] + schools["average_writing"]
schools_subset = schools[["school_name","total_SAT"]]
schools_subset_sorted = schools_subset.sort_values(by="total_SAT", ascending=False)

top_10_schools = schools_subset_sorted.iloc[0:10] 

print(top_10_schools)

Locating 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.

borough_stats = schools.groupby("borough")["total_SAT"].agg(['mean', 'std', 'count']).round(2)

largest_std_dev_borough = borough_stats['std'].idxmax()
largest_std_dev = pd.DataFrame(
    {
    'num_schools': borough_stats.loc[largest_std_dev_borough, 'count'],
    'average_SAT': borough_stats.loc[largest_std_dev_borough, 'mean'],
    'std_SAT': borough_stats.loc[largest_std_dev_borough, 'std']
    },index=[largest_std_dev_borough])

print(largest_std_dev)