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()
#Create a pandas DataFrame called best_math_schools containing the "school_name" and "average_math" score for all schools where the results are at least 80% of the maximum possible score, sorted by "average_math" in descending order.
max_math_score = schools['average_math'].max()
filtered_schools = schools[schools['average_math'] >= 640]
best_math_schools = filtered_schools[['school_name', 'average_math']]
best_math_schools = best_math_schools.sort_values(by='average_math', ascending= False)
print(schools)
print(best_math_schools)
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
#Identify the top 10 performing schools based on scores across the three SAT sections, storing as a pandas DataFrame called top_10_schools containing the school name and a column named "total_SAT", with results sorted by total_SAT in descending order.
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
sorted_schools = schools.sort_values(by='total_SAT', ascending = False)
top_10_schools = sorted_schools[['school_name', 'total_SAT']].head(10)
print(top_10_schools)
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
#Locate the NYC borough with the largest standard deviation for "total_SAT", storing as a DataFrame called largest_std_dev with "borough" as the index and three columns: "num_schools" for the number of schools in the borough, "average_SAT" for the mean of "total_SAT", and "std_SAT" for the standard deviation of "total_SAT". Round all numeric values to two decimal places.
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
grouped = schools.groupby('borough').agg(
num_schools=('school_name', 'count'),
average_SAT =('total_SAT', 'mean'),
std_SAT=('total_SAT','std')
)
grouped = grouped.round(2)
largest_std_dev = grouped[grouped['std_SAT']==grouped['std_SAT'].max()]
print(largest_std_dev)