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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...Selecting the rows with "average_math" score results are at least 80% of the maximum possible score.
schools_at_least_80 = schools[schools['average_math'] >= 800*0.8]
print(schools_at_least_80.head())Creating 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 = pd.DataFrame(schools_at_least_80[['school_name','average_math']].sort_values(by='average_math', ascending=False))
print(best_math_schools)Creating a column with the sum of the average scores as "total_SAT".
schools['total_SAT'] = (schools['average_math'] + schools['average_reading'] + schools['average_writing'])
print(schools.head())Identifying 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_schools = pd.DataFrame(schools[['school_name','total_SAT']].sort_values(by='total_SAT', ascending=False).iloc[0:10])
print(top_10_schools)Creating a DataFrame cwith "borough" as the index and three columns: number of schools in the borough, the mean of "total_SAT", and the standard deviation of "total_SAT", rounding all numeric values to two decimal places.
select_measures = pd.DataFrame(schools.groupby('borough')['total_SAT'].agg(['count','mean','std'])).round(2)
print(select_measures)Renaming the 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".
select_measures_renamed = select_measures.rename(columns={'count': 'num_schools','mean': 'average_SAT','std': 'std_SAT'})
print(select_measures_renamed)Locating the NYC borough with the largest standard deviation for "total_SAT", storing as a DataFrame called largest_std_dev.