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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()
# Filtering dataset to show only schools with scores 640 and above
best_math_schools = schools[schools['average_math'] >= 640]
# Subsetting school name and average_math
best_math_schools = best_math_schools[['school_name', 'average_math']]
print(best_math_schools.head())
# Sorting result in descending order
best_math_schools = best_math_schools.sort_values('average_math', ascending=False)
print(best_math_schools.head())
#Top 10 performing schools based on the combined SAT scores
print(schools.head())
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
#creating dataframe for top_10_ schools
top_10_schools = schools[['school_name', 'total_SAT']]
#sorting values by total_SAT
top_10_schools = top_10_schools.sort_values('total_SAT', ascending=False)
#Print top 10 schools
top_10_schools = top_10_schools.head(10)
print(top_10_schools)
#finding borough has the largest standard deviation in the combined SAT score
# Group by borough and compute statistics
borough = schools.groupby("borough")["total_SAT"].agg(num_schools="count", average_SAT="mean", std_SAT="std").reset_index()
# Round numerical values to 2 decimal places
borough = borough.round({"average_SAT": 2, "std_SAT": 2})
# Find the borough with the largest standard deviation
largest_std_dev = borough.loc[borough["std_SAT"].idxmax()]
#largest_std_dev as dataframe
largest_std_dev= pd.DataFrame([largest_std_dev])
# Display the result
print(largest_std_dev)