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
schools.head()
# Which NYC schools have the best math results?
# Best math schools
best_math_schools = schools[schools["average_math"] >= 0.8 * 800][["school_name", "average_math"]].sort_values("average_math", ascending=False)
# Plot a bar plot
#Additional values
max_y=max(best_math_schools["average_math"])
# Axis
x_bar = best_math_schools["school_name"]
y_bar = np.sort(best_math_schools["average_math"])
# Horizontal bar plot
plt.barh(x_bar, y_bar, align='center', color=['tab:blue', 'tab:gray'])
plt.xlabel("Average math score (points)")
plt.ylabel("School name")
plt.title("Best math results in NYC schools")
plt.xticks([0,100,200,300,400,500,600,700,max_y])
plt.show()
best_math_schools=pd.DataFrame(best_math_schools)
#What are the top 10 performing schools based on the combined SAT scores?
# Calculate total_SAT per school
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# Who are the top 10 performing schools?
top_10_schools = schools.sort_values("total_SAT", ascending=False)[["school_name", "total_SAT"]].head(10)
# Which NYC borough has the highest standard deviation for total_SAT?
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# Filter for max std and make borough a column
largest_std_dev = boroughs[boroughs["std"] == boroughs["std"].max()]
# Rename the columns for clarity
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
# Move borough from index to column
largest_std_dev.reset_index(inplace=True)
# Horizontal bar plot
#Additional values
max_value=max(boroughs["std"])
plt.barh(boroughs.index,boroughs["std"], align='center', color=['tab:orange', 'tab:gray'])
plt.xlabel("Std dev in SAT (points)")
plt.ylabel("Borough")
plt.title("Largest standard deviation in the combined SAT score")
plt.xticks([0,50,100,150,200,max_value])
plt.show()