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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()# VERIFYING THE NUMBER OF OBSERVATIONS AND IF THERE IS NANs
# Number of observations
num_observations = schools.shape[0]
# Check for NaNs
nan_info = schools.isna().sum()
nan_info2 = schools.isna().any()
print(num_observations, nan_info, nan_info2, sep= "\n\n")#DOING SOME CLEANING ON THE DATAFRAME
schools_math = schools.drop(columns = ["borough", "building_code","average_reading", "percent_tested"])
#Add a new column with the percentage scored on the math
schools_math["math%"] = schools_math["average_math"] / 8
#print(schools_math.head())
#Subsett com base na percentagem
schools_math_80 = schools_math[schools_math["math%"] >= 80]
#print(schools_math_80.head())
# Finishing touch
best_math_schools = schools_math_80.sort_values("average_math", ascending = False)
best_math_schools = best_math_schools[["school_name","average_math"]]#Limpeza do dataset original
schools_top = schools.drop(columns=["building_code","percent_tested"])
# Criação da coluna total_SAT - o total médio de SAT por escola
schools_top["total_SAT"] =schools_top[["average_math","average_reading","average_writing"]].sum(axis=1)
# Nova limpeza do dataset
schools_top = schools_top.drop(columns = ["average_reading","average_math","average_writing"])
schools_top = schools_top.sort_values("total_SAT", ascending = False)
top_10_schools = schools_top[0:10].drop("borough",axis=1)
print(top_10_schools)# Calcular a média por agrupamento e o número de escolas por agrupamento
schools_std = schools_top.groupby("borough")["total_SAT"].agg(["mean","count","std"])
#Arrendondamente de todos os valores
schools_std = schools_std[["mean","count","std"]].round(2)
#rename das colunas
schools_std = schools_std.rename(columns={"mean":"average_SAT", "count":"num_schools","std":"std_SAT"})
#seleção do maior agrupamento
largest_std_dev = schools_std[schools_std["std_SAT"]==schools_std["std_SAT"].max()]
print(largest_std_dev)