Project: Exploring NYC Public School Test Result Scores

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...

best_math_schools = schools[schools['average_math'] >= 0.8 * 800]
# Select only the 'school_name' and 'average_math' columns
best_math_schools = best_math_schools[['school_name', 'average_math']]
 #Sort the DataFrame by 'average_math' in descending order
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)
# Reset index to maintain a clean DataFrame
best_math_schools.reset_index(drop=True, inplace=True)
best_math_schools

import pandas as pd

# Assuming your original DataFrame is named 'df'

# Calculate the total SAT score for each school
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Create a DataFrame with school name and total SAT score
top_10_schools = schools[['school_name', 'total_SAT']]

# Sort the DataFrame by total SAT score in descending order and select the top 10
top_10_schools = top_10_schools.sort_values(by='total_SAT', ascending=False).head(10)

# Reset index for a clean DataFrame
top_10_schools.reset_index(drop=True, inplace=True)

# Calculate standard deviation of total SAT scores for each borough
std_dev_by_borough = schools.groupby('borough')['total_SAT'].std().reset_index()

# Find the borough with the largest standard deviation
largest_std_dev_borough = std_dev_by_borough.loc[std_dev_by_borough['total_SAT'].idxmax()]

# Print the top 10 performing schools and the borough with the largest standard deviation
print("Top 10 Performing Schools:")
print(top_10_schools)
print("\nBorough with Largest Standard Deviation:")
print(largest_std_dev_borough)

largest_std_dev_borough = std_dev_by_borough.loc[std_dev_by_borough['total_SAT'].idxmax()]

# Filter the original DataFrame to get schools only from the borough with the largest standard deviation
schools_in_largest_borough = schools[schools['borough'] == largest_std_dev_borough['borough']]

# Calculate the number of schools in the borough
num_schools = len(schools_in_largest_borough)

# Calculate the mean and standard deviation of total SAT scores for the borough
average_SAT = schools_in_largest_borough['total_SAT'].mean()
std_SAT = schools_in_largest_borough['total_SAT'].std()

# Create a DataFrame with the required statistics
largest_std_dev = pd.DataFrame({
    'borough': largest_std_dev_borough['borough'],
    'num_schools': [num_schools],
    'average_SAT': [round(average_SAT, 2)],
    'std_SAT': [round(std_SAT, 2)]
})

# Print the DataFrame
print("DataFrame for the Borough with the Largest Standard Deviation:")
print(largest_std_dev)