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
# 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...
# filter schools with average math score at least 80% of 800 which is (640)
best_math_schools = schools[schools["average_math"] > 640]
# subset the DataFrame with columns "school_name" and "average_math"
best_math_schools = best_math_schools[["school_name", "average_math"]]
# Sort by "average_math" in descending order
best_math_schools = best_math_schools.sort_values(by="average_math", ascending=False)
best_math_schools
best_math_schools.head()#create a new column
schools["total_SAT"] = schools["average_math"]+schools["average_reading"] +schools["average_writing"]
#creating and sorting the dataframe
top_10_schools = schools[["school_name","total_SAT"]].sort_values("total_SAT",ascending = False).head(10)
top_10_schools
#group by borough
std_dev = schools.groupby("borough")["total_SAT"].agg(['count','mean','std']).round(2)
# rename the columns
std_dev.rename(columns={"count": "num_schools", "mean":"average_SAT","std": "std_SAT"},inplace=True)
#sort the dataframe by std column to give in decending order
largest_std = std_dev.sort_values("std_SAT",ascending= False)
largest_std
#slice the boroughs index to get first row
largest_std_dev=largest_std.iloc[[0]]
largest_std_dev