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Photo by Jannis Lucas on Unsplash.

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...
import pandas as pd

# Lire les données
schools = pd.read_csv("schools.csv")

# Filtrer les écoles avec un score en mathématiques d'au moins 80% du score maximum de 800
best_math_schools = schools[schools['average_math'] >= 0.8 * 800][['school_name', 'average_math']]

# Trier les écoles par score mathématique décroissant
best_math_schools = best_math_schools.sort_values(by='average_math', ascending=False)

# Afficher les résultats
print(best_math_schools)

import pandas as pd

# Lire les données
schools = pd.read_csv("schools.csv")

# Calculer le score total SAT
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Sélectionner les 10 écoles avec les scores SAT combinés les plus élevés
top_10_schools = schools[['school_name', 'total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)

# Ajouter une colonne avec le score total SAT
top_10_schools['total_SAT'] = top_10_schools['total_SAT']

# Afficher les résultats
print(top_10_schools)
import pandas as pd

# Lire les données
schools = pd.read_csv("schools.csv")

# Calculer le score total SAT
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']

# Groupement par borough pour calculer les statistiques
borough_stats = schools.groupby('borough').agg(
    num_schools=('school_name', 'count'),
    average_SAT=('total_SAT', 'mean'),
    std_SAT=('total_SAT', 'std')
).reset_index()

# Trouver l'arrondissement avec le plus grand écart-type
largest_std_dev_row = borough_stats.sort_values(by='std_SAT', ascending=False).iloc[0]

# Créer un DataFrame avec les résultats
largest_std_dev = pd.DataFrame([largest_std_dev_row])

# Arrondir les valeurs numériques à deux décimales
largest_std_dev = largest_std_dev.round(2)

# Afficher les résultats
print(largest_std_dev)