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.
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()# I had filtered the schools which are the top performing schools scoring over 640 in the SAT.
best_math_schools = schools[schools["average_math"] >= 640][["school_name", "average_math"]].sort_values("average_math", ascending=False)
best_math_schools
# I had calculated the total SAT(Math, reading, writing) of the schools and stored it in a new column called "Total_SAT".Then I had filtered out the top 10 best schools by descending order and to finish it I used the head method showing only the first 10 rows.
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
top_10_schools = schools.sort_values("total_SAT", ascending=False)[["school_name", "total_SAT"]].head(10)
top_10_schools#The top 10 schools with the highest SAT scores are visualize with the bar plot below.
import matplotlib.pyplot as plt
fig , ax = plt.subplots()
ax.bar(top_10_schools['school_name'], top_10_schools['total_SAT'])
ax.tick_params(axis='x', rotation=90)#The task was to find the highest std in a NYC borough. The following code below is the my solution
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()]
# I had rename the columns for clarity base on the task
largest_std_dev = largest_std_dev.rename(columns={"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
# Optional: Move borough from index to column
largest_std_dev.reset_index(inplace=True)
#The row below shows the borough with the highest standard deviation
largest_std_dev