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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()
Create a pandas 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.
# Best Math Schools
## Filter the schools where the results are at least 80% of the max possible score, sorted by "average_math" in descending order
best_math_schools = schools[schools["average_math"] >= (0.8 * 800)]
## Contain only the "school_name" and "average_math" score for all schools meeting the threshold, then sort the values by "average_math" in descending order
best_math_schools = best_math_schools[["school_name", "average_math"]].sort_values(by="average_math", ascending=False)
## Print the result
print(best_math_schools)
Identify 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.
# Top 10 Highest SAT Schools
## Adding total_SAT into the dataframe
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
## Identifying the top 10 performing schools based on total_SAT
top_10_schools = schools[["school_name", "total_SAT"]].sort_values(by="total_SAT", ascending=False).head(10)
## Showing the result
print (top_10_schools)Locate 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.
# NYC borough with largest standard deviation for "total_SAT"
## Group schools by borough and adding relevant stats, rounding all numeric values to two decimal places
schools_by_borough = schools.groupby("borough")["total_SAT"].agg(['count', 'mean', 'std']).round(2)
schools_by_borough.columns = ["num_schools", "average_SAT", "std_SAT"]
## Locate the NYC borough with the largest standard deviation for "total_SAT"
largest_std_dev = schools_by_borough[schools_by_borough["std_SAT"] == schools_by_borough["std_SAT"].max()]
print (largest_std_dev)