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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()
# Start coding here...
# Add as many cells as you like...
school_name | borough | building_code | average_math | average_reading | average_writing | percent_tested | |
---|---|---|---|---|---|---|---|
0 | New Explorations into Science, Technology and ... | Manhattan | M022 | 657 | 601 | 601 | NaN |
1 | Essex Street Academy | Manhattan | M445 | 395 | 411 | 387 | 78.9 |
2 | Lower Manhattan Arts Academy | Manhattan | M445 | 418 | 428 | 415 | 65.1 |
3 | High School for Dual Language and Asian Studies | Manhattan | M445 | 613 | 453 | 463 | 95.9 |
4 | Henry Street School for International Studies | Manhattan | M056 | 410 | 406 | 381 | 59.7 |
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 = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)
best_math_schools
school_name | average_math | |
---|---|---|
88 | Stuyvesant High School | 754 |
170 | Bronx High School of Science | 714 |
93 | Staten Island Technical High School | 711 |
365 | Queens High School for the Sciences at York Co... | 701 |
68 | High School for Mathematics, Science, and Engi... | 683 |
280 | Brooklyn Technical High School | 682 |
333 | Townsend Harris High School | 680 |
174 | High School of American Studies at Lehman College | 669 |
0 | New Explorations into Science, Technology and ... | 657 |
45 | Eleanor Roosevelt High School | 641 |
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.
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools.groupby("school_name", as_index=False)["total_SAT"].mean().sort_values("total_SAT", ascending=False).head(10)
top_10_schools
school_name | total_SAT | |
---|---|---|
325 | Stuyvesant High School | 2144.0 |
324 | Staten Island Technical High School | 2041.0 |
55 | Bronx High School of Science | 2041.0 |
188 | High School of American Studies at Lehman College | 2013.0 |
334 | Townsend Harris High School | 1981.0 |
293 | Queens High School for the Sciences at York Co... | 1947.0 |
30 | Bard High School Early College | 1914.0 |
83 | Brooklyn Technical High School | 1896.0 |
121 | Eleanor Roosevelt High School | 1889.0 |
180 | High School for Mathematics, Science, and Engi... | 1889.0 |
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.
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
largest_std_dev
num_schools | average_SAT | std_SAT | |
---|---|---|---|
borough | |||
Manhattan | 89 | 1340.13 | 230.29 |