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...
```

#### Create a pandas DataFrame called best_math_schools containing the "school_name" and "average_math" score for all schools where the results are at least 80% of the maximum possible score, sorted by "average_math" in descending order.

```
# calculate best_math_schools
# the maximum score for each of the three SAT sections (math, reading, and writing) is 800, so you can use this to find the threshold of 80%.
_80pct_of_maximum_score = 800 * 0.8
_80pct_of_maximum_score
```

```
# Filtering the data
best_schools = schools[schools['average_math'] >= _80pct_of_maximum_score]
best_math_schools = best_schools[['school_name','average_math']].sort_values('average_math',ascending=False)
best_math_schools
```

#### Identify the top 10 performing schools based on scores across the three SAT sections, storing as a pandas DataFrame called top_10_schools containing the school name and a column named "total_SAT", with results sorted by total_SAT in descending order.

```
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name','total_SAT']]
top_10_schools = top_10_schools.sort_values('total_SAT',ascending=False).head(10)
top_10_schools
```

#### Locate the NYC borough with the largest standard deviation for "total_SAT", storing as a DataFrame called largest_std_dev with "borough" as the index and three columns: "num_schools" for the number of schools in the borough, "average_SAT" for the mean of "total_SAT", and "std_SAT" for the standard deviation of "total_SAT". Round all numeric values to two decimal places.

```
# Grouping the data by borough
# Group the data by "borough" and find the number of schools, mean and standard deviation of "total_SAT"
# You can chain .groupby() with .agg() to calculate these values. The .agg() method accepts a list of statistics, as strings, to calculate, e.g., .agg(["min", "max", "median"]).
largest_std_dev = round(schools.groupby('borough')['total_SAT'].agg(['count','mean','std']),2)
largest_std_dev = largest_std_dev.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'})
largest_std_dev
```

```
# Filtering for the largest standard deviation
# You can subset for the row where "std" is equal to the largest value for that column across the DataFrame using boolean indexing.
largest_std_dev = largest_std_dev[largest_std_dev['std_SAT'] >= 230]
largest_std_dev
```