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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.
# Re-run this cell
import pandas as pd
# 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.
best_math_schools = (
schools.loc[schools['average_math'] >= (.8 * 800), ["school_name", "average_math"]].sort_values("average_math", ascending = False)
)
# Checking this worked
best_math_schools.head()# 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).
#
# Calculating the total SAT score
schools["total_SAT"] = schools[["average_math", "average_reading", "average_writing"]].sum(axis=1)
#Creating a new DataFrame called SAT that contains this information
top_10_schools = schools[["school_name", "total_SAT"]].sort_values("total_SAT", ascending=False).head(10)
# Checking my code
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:
# (1) "borough" - the name of the NYC borough with the largest standard deviation of "total_SAT".
# (2) "num_schools" - the number of schools in the borough.
# (3) "average_SAT" - the mean of "total_SAT".
# (4) "std_SAT" - the standard deviation of "total_SAT".
#Round all numeric values to two decimal places.
largest_std_dev = (schools.groupby("borough").agg(
num_schools=("school_name", "count"),
average_SAT=("total_SAT", "mean"),
std_SAT=("total_SAT", "std")).round(2).sort_values("std_SAT", ascending=False).head(1))
# Note. the number of schools is the number of schools in the borough and not necessarily the number of schools with an average total_SAT score. In otherwords, missing data was not accounted for.
# Checking my work
print(largest_std_dev)