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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...Finding schools with the best math scores
Subset the data to find the schools with math scores of at least 80%, considering the maximum possible score is 800 and save this as a pandas DataFrame called best_math_schools.
#Calculating the math scores of atleast 80%(640.0)
math_score_80 = (80*800)/100
print(math_score_80)#Subset the data to find the schools with math scores of at least 80%
best_math_schools = schools[schools['average_math']>=math_score_80]
best_math_schools.head()#Selecting only school_name and average_math and sorting by average_math in descending order
best_math_schools = best_math_schools[['school_name', 'average_math']].sort_values(by='average_math', ascending=False)
best_math_schools Identifying the top 10 performing schools
Find the 10 best performing schools based on the total score across the three SAT sections.
#Adding a total_score column
schools['total_SAT'] = schools['average_math']+schools['average_reading']+schools['average_writing']
#Finding the top 10 schools for total_score
top_10_schools = schools.sort_values(by='total_SAT', ascending=False).head(10)
# Checking the type of top_10_schools
type(top_10_schools)
#selecting only school_name and total_SAT columns
top_10_schools = top_10_schools[['school_name','total_SAT']]print(top_10_schools)Locating the NYC borough with the largest standard deviation in SAT performance
Find out the number of schools, average SAT, and standard deviation of SAT for the NYC borough with the largest standard deviation, rounded to two decimal places.
#Group data by borough and find the number of schools and mean/std of total_SAT
borough_stats=schools.groupby("borough")["total_SAT"].agg(['count', 'mean','std']).round(2)
borough_stats#Create a dataframe with largest standard deviation
largest_std_dev=borough_stats[borough_stats['std'] == borough_stats['std'].max()]
print(largest_std_dev)# Renaming the columns
largest_std_dev = largest_std_dev.rename(columns={
'count':'num_schools',
'mean':'average_SAT',
'std':'std_SAT',
})
largest_std_dev