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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...print(schools.head())# Creating the data Frame
all_math_schools = pd.DataFrame(schools, columns = ["school_name", "average_math"])
# Filtering the maths schools based on the condition given
best_math_schools= all_math_schools[all_math_schools["average_math"] >= 640].sort_values("average_math", ascending = False)
print(best_math_schools.head())
# Creating the column to contain sum of math, wriitng and reading
schools["total_SAT"] = schools["average_math"] + schools["average_reading"] + schools["average_writing"]
# # Creating top 10 schools sorting the total_SAT column
# top_10_schools = pd.DataFrame(schools, columns = ["school_name", "total_SAT"]).sort_values("total_SAT", ascending = False)
# print(top_10_schools.head(10))
top_10_schools = schools.sort_values("total_SAT", ascending=False)[["school_name", "total_SAT"]].head(10)# borough = schools[schools["borough"]== schools.groupby("borough")["total_SAT"].std().max()]
# borough = schools.groupby("borough")["total_SAT"].agg("std").round(2)
# # Arranging the standard deviation in descending order
# borough_sorted = borough.sort_values(ascending = False)
# # Getting the largest value of the standard deviation
# # print(borough_sorted[0:1])
# # Creating the largest_std_dev dataframe
# largest_std_dev = pd.DataFrame(largest_std_dev, values =["borough_sorted"])
# print(largest_std_dev)# borough = schools[schools["borough"]== schools.groupby("borough")["total_SAT"].std().max()]
# borough = schools.groupby("borough")["total_SAT"].agg("std").round(2)
# # Arranging the standard deviation in descending order
# borough_sorted = borough.sort_values(ascending = False)
# # Getting the largest value of the standard deviation
# # print(borough_sorted[0:1])
# # Getting the number of schools in the borough
# num_schools = schools.groupby("borough")[schools[schools["school_name"] == "Manhattan"]].count()
# print(num_schools)
# # Creating the largest_std_dev dataframe
# largest_std_dev = pd.DataFrame(borough_sorted[0:1],)
# print(largest_std_dev)# print(schools.head())
# 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"})
# Optional: Move borough from index to column
largest_std_dev.reset_index(inplace=True)
print(largest_std_dev)