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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
print(schools.head())
# Start coding here...
# Add as many cells as you like...
max_score = 800
min_percentage = schools['average_math'] / max_score
# second method : with loc
best_math_schools = schools.sort_values('average_math', ascending=False).loc[min_percentage > 0.8, ['school_name','average_math']]
print(best_math_schools)
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
print(schools.head())
# 2nd challenge
# Calculate the SAT score and attribute to a new column
schools['total_SAT'] = schools[['average_math','average_writing', 'average_reading']].sum(axis=1)
# Attribute the SAT score to a new column
top_10_schools = schools.loc[:, ['school_name','total_SAT']].sort_values('total_SAT', ascending=False).head(10)
print(top_10_schools)
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
print(schools.head())
#3rd challenge
# group by borough : and agg all different calculation type
schools['total_SAT'] = schools[['average_math','average_writing', 'average_reading']].sum(axis=1)
borough_df = schools.groupby('borough').agg({
'school_name': ['count'],
'total_SAT': ['sum', 'mean', 'std']
}).round(2)
print(borough_df)
max_std = borough_df[('total_SAT','std')].max()
print(max_std)
# Create a new dataframe to filter
largest_std_dev = borough_df[borough_df[('total_SAT','std')] == max_std]
largest_std_dev.columns = ['num_schools', 'total_SAT', 'average_SAT', 'std_SAT']
print(largest_std_dev)