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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 data frame with the 'school_name' and 'average_math' columns of the schools dataset.
#Filter the 'average_math' column by scores that are greater or equal than 80% of the total score, 640. The sort the the dataframe by the 'average_math' column in the descending order.
math_schools = schools[['school_name','average_math']]
best_math_schools = math_schools[math_schools['average_math'] >= 640].sort_values(by='average_math', ascending = False)
best_math_schools.head()#Created a new column that sums the 'average_math','average_reading', and 'average_writing'columns and store its values in 'total_SAT'
#Created a dataframe with the 'school_name' and 'total_SAT' columns sorted by the 'Total_SAT' column in descending order. It only shows the top 10 schools.
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name','total_SAT']].sort_values(by='total_SAT', ascending=False).head(10)
top_10_schools
boroughs = schools.groupby("borough")["total_SAT"].agg(["count", "mean", "std"]).round(2)
# Filter for max std and reset index so borough is 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"})
largest_std_dev