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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...For resolve the first question:
- Filter average math
- Select the columns school_name and average_math
- Sort by average_math
schools['borough'].unique()best_math_schools_filter = schools['average_math']>=640
best_math_schools = schools[best_math_schools_filter][["school_name","average_math"]]
best_math_schools = best_math_schools.sort_values("average_math",ascending=False)
best_math_schools.head()For resolve the Second question:
- SUM all scores (create a new column, total_SAT)
- Sort by total_SAT descending
- Take the first 10 rows
total_sat_schools =schools.copy()
total_sat_schools["total_SAT"] = total_sat_schools["average_math"]+total_sat_schools["average_reading"]+total_sat_schools["average_writing"]top_10_schools = total_sat_schools.sort_values("total_SAT",ascending=False)
top_10_schools = top_10_schools.iloc[:10,:]top_10_schools =top_10_schools[["school_name","total_SAT"]]For resolve the Third question:
- create column average_SAT, std_SAT
- Sort by std_SAT descending
- Take the first row
boroughs = total_sat_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.reset_index(inplace=True)largest_std_dev.head()