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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 numpy as np
import matplotlib.pyplot as plt

# Read in the data
schools = pd.read_csv("schools.csv")

# Preview the data
print(schools.info())
print(schools.describe())
schools.head()

Data Cleaning

Keep only rows with complete (non-missing) values for all variables

schools.isna().sum().plot(kind="bar", rot=45) # only percent_tested variable has missing entries
schools.dropna()
Hidden output

Exploratory Analysis

To get acquainted with the dataset, we first conduct exploratory analysis by calculating summary statistics and by generating some plots for visualization

best_math_schools = schools[schools["average_math"] >= 800*.80][['school_name','average_math']].sort_values("average_math", ascending=False)

print(best_math_schools)

schools["total_SAT"] = schools.loc[:, "average_math":"average_writing"].sum(axis=1)
top_10_schools = schools.sort_values("total_SAT", ascending=False).iloc[:10][["school_name","total_SAT"]]

print(top_10_schools)

largest_std_dev = (schools.groupby("borough")["total_SAT"]
                   .agg(
                       num_schools = "count",
                       average_SAT = "mean",
                       std_SAT = "std")
                   .round(2)
                   .sort_values("std_SAT", ascending = False)
                   .head(1)
                   .reset_index()
)
print(largest_std_dev)