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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.

# Re-run this cell 
import pandas as pd
import numpy as np
# Read in the data
schools = pd.read_csv("schools.csv")

# Preview the data
schools.head()

Which NYC schools have the best math results?

  • The best math results are at least 80% of the maximum possible score of 800 for math.
  • Save your results in a pandas DataFrame called best_math_schools, including "school_name" and "average_math" columns, sorted by "average_math" in descending order.
# Task 1

# calculate for the threshold value for schools with best math scores
best_math_result_criteria = 800*0.8
# subset schools dataframe for schools that are within the threshold 
subset1 = schools[schools["average_math"] >= best_math_result_criteria]
# subset again to only keep the "school_name" and "average_math" columns as well as order the by the average math grades in descending order 
best_math_schools = subset1[["school_name", "average_math"]].sort_values("average_math", ascending = False)

best_math_schools

What are the top 10 performing schools based on the combined SAT scores?

  • Save your results as a pandas DataFrame called top_10_schools containing the "school_name" and a new column named "total_SAT", with results ordered by "total_SAT" in descending order ("total_SAT" being the sum of math, reading, and writing scores).
# Task 2

# subset schools to include school name and all average scores
# the borough is also included here as we will re-use the same subset later on for Task 3
subset2 = schools[["school_name", "borough","average_math","average_reading","average_writing"]]

# Calculate for the total average SAT scores by summing the math, reading and writing scores
subset2["total_SAT"] =  subset2["average_math"] + subset2["average_reading"] + subset2["average_writing"]

# subset again, only keeping the school names and total average SAT scores for the top 10 schools
top_10_schools = subset2[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending = False).head(10)

top_10_schools

Which single borough has the largest standard deviation in the combined SAT score?

  • Save your results as a pandas DataFrame called largest_std_dev.
  • The DataFrame should contain one row, with:
    • "borough" - the name of the NYC borough with the largest standard deviation of "total_SAT".
    • "num_schools" - the number of schools in the borough.
    • "average_SAT" - the mean of "total_SAT".
    • "std_SAT" - the standard deviation of "total_SAT".

Round all numeric values to two decimal places.

# Task 3

# Group the subset2 by borough and get aggregated information for standard deviation, mean, and count. Sort the boroughs in descending order by standard deviation.
subset3 = subset2.groupby("borough")["total_SAT"].agg([np.std, 'count', np.mean]).sort_values("std", ascending = False).head(1)

# obtain the values needed
borough = subset3.index[0]
num_schools = subset3["count"].iloc[0]
average_SAT = round(subset3["mean"].iloc[0], 2)
std_SAT = round(subset3["std"].iloc[0], 2)

# set the values into the largest_std_dev dataframe
largest_std_dev = pd.DataFrame({"borough":[borough], "num_schools":[num_schools], "average_SAT":[average_SAT], "std_SAT":[std_SAT]})

largest_std_dev