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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
import statistics as st
# Read in the data
schools = pd.read_csv("schools.csv")

# Preview the data
schools.head()
schools['total_SAT'] = schools['average_math'] + schools_top['average_reading'] + schools['average_writing']
# Start coding here...
# Add as many cells as you like...
#limit = 0.8 * schools.average_math.max()
schools_subset = schools.loc[schools['average_math']>=640].copy()
best_math_schools = schools_subset[['school_name','average_math']].sort_values(by='average_math', ascending=False)
# best_math_schools.plot(
#                     x='school_name',
#                     y='average_math',
#                     kind='bar'
# )
best_math_schools.head()
schools_top = schools[['school_name','average_math','average_reading','average_writing','total_SAT']].copy()
top_10_schools = schools_top.sort_values(by='total_SAT', ascending=False)[["school_name", "total_SAT"]].head(10)
top_10_schools
borough_group = schools[['school_name','borough','average_math','average_reading','average_writing']].copy()
borough_group.head()
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)

largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]

largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})

largest_std_dev.reset_index(inplace=True)

largest_std_dev.head()