Skip to content

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 tasked with answering three key questions about New York City (NYC) public school SAT performance:

### Which schools produce the highest math scores?

- Specifically, which schools have an average math SAT score of at least 80%?
- Save the results as a pandas DataFrame called
`best_math_schools`

.

### Who are the top 10 schools based on average results across reading, math, and writing?

- Save the results as a pandas DataFrame called
`top_10_schools`

.

### Which NYC borough has the largest standard deviation for SAT results?

- Save the results as a pandas DataFrame called
`largest_std_dev`

.

```
import pandas as pd
# Read the CSV file into a DataFrame
df = pd.read_csv('schools.csv')
# Filter schools with average math score >= 80% of the maximum score
best_math_schools = df[df['average_math'] >= 0.8 * 800]
# Sort by average math score in descending order
best_math_schools = best_math_schools.sort_values('average_math', ascending=False)[['school_name', 'average_math']]
best_math_schools
```

```
# Calculate the total SAT score for each school
df['total_SAT'] = df['average_reading'] + df['average_math'] + df['average_writing']
# Sort by total SAT score in descending order and select the top 10 schools
top_10_schools = df.sort_values('total_SAT', ascending=False)[['school_name', 'total_SAT']].head(10)
top_10_schools
```

```
# Group the DataFrame by borough and calculate mean and standard deviation of total SAT scores
borough_stats = df.groupby('borough')['total_SAT'].agg(['mean', 'std'])
# Find the borough with the largest standard deviation
largest_std_dev = borough_stats[borough_stats['std'] == borough_stats['std'].max()].reset_index
largest_std_dev['num_schools'] = df['borough'].value_counts()[largest_std_dev['borough']].values
largest_std_dev = largest_std_dev.round(2)
borough_stats
# largest_std_dev
```