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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.
# 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.
# 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.
# 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.
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
df1 = schools[["school_name", "average_math"]]
print(df1.isna().any()) # Checking for missing values
best_math_result_min = 800 * 8 / 10
print("The best math results are at least " + str(best_math_result_min))
df2 = df1[df1["average_math"] >= best_math_result_min] # Subsetting rows
df3 = df2.sort_values(by="average_math", ascending=False) # Sorting the new DataFrame
best_math_schools = df3
print("The NYC schools that have the best math results are : ")
print(best_math_schools)
# adding the column "total_SAT"
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
#print(schools.head())
# Subsetting the schools DataFrame
schools0 = schools[["school_name", "total_SAT"]]
schools1 = schools0.sort_values("total_SAT", ascending=False)
top_10_schools = schools1.sort_values(by="total_SAT", ascending=False)[:10]
print(top_10_schools)
# Code for the borough with the largest standard deviation
schools2 = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# print(schools2)
max_val_std = schools2["std"].max()
schools3 = schools2[schools2["std"] == max_val_std]
largest_std_dev = schools3
# print(largest_std_dev)
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
print(largest_std_dev)
print("The borough that has the largest standard deviation is Manhattan.")