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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...# Filter for average_maths > 80%
best_avg_maths = schools['average_math'] >= (0.8 * 800)
#Subset with Filter
best_in_maths = schools[best_avg_maths]
best_in_maths_srt = best_in_maths.sort_values('average_math', ascending=False)
best_math_schools = best_in_maths_srt.loc[:, ['school_name', 'average_math']]
best_math_schools
# Calculate SAT Column
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Find Top 10 schools in in total_SAT Scores
top_10_schools = (schools.sort_values('total_SAT', ascending=False)).head(10)
# Slicing required columns and set total_SAT in descending order
top_10_schools = top_10_schools.loc[:, ['school_name', 'total_SAT']]
top_10_schools# Calculate Standard Deviation and Mean of total_SAT column grouped by borough
borough_sat_mean_std = schools.groupby('borough')['total_SAT'].agg(['std', 'mean']).round(2)
borough_sat_mean_std
# Add num_schools column with number of schools in each borough
borough_sat_mean_std['num_schools'] = schools['borough'].value_counts()
borough_sat_mean_std
# Create filter for maximum std_dev
std_max = borough_sat_mean_std['std'].max()
print(std_max)
# Subset with max std_dev filter
largest_std_dev = borough_sat_mean_std[borough_sat_mean_std['std'] == std_max]
largest_std_dev
# Rearrange columns to num_schools, mean and std_dev
largest_std_dev = largest_std_dev[['num_schools', 'mean', 'std']]
# Dictionary for renaming columns
names_dict = {'mean': 'average_SAT', 'std':'std_SAT'}
# Rename Columns
largest_std_dev = largest_std_dev.rename(columns=names_dict)
largest_std_dev