Project: Exploring NYC Public School Test Result Scores

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...

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.

#Determine 80% of maximum score
per_max_score = 800 * 0.8


per_max_score

#DataFrame for best_math_schools with at least 80% maximum possible score
best_math_schools = schools[['school_name', 'average_math']][schools['average_math']>= per_max_score].sort_values('average_math', ascending=False)

best_math_schools

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.head()

#adding Total_sat column to DataFrame
schools['total_SAT'] = schools[['average_math', 'average_reading', 'average_writing']].sum(axis=1)

#subseting the top 10 schools
top_10_schools = schools.sort_values('total_SAT', ascending=False )
top_10_schools_1 = top_10_schools[['school_name', 'total_SAT']].reset_index(drop=True)
top_10_schools = top_10_schools_1.iloc[:10]
top_10_schools

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.

#Aggregating by Number of Boroughs with total_SAT average and std 
school_groups = schools.groupby('borough', as_index=True)['total_SAT'].agg(['count', 'mean', 'std'])

#Renaming columns
school_groups_rename = school_groups.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std':'std_SAT'}).round(2)
school_groups_rename

#Borough with largest standard deviation


largest_std_dev = school_groups_rename[school_groups_rename['std_SAT']== school_groups_rename['std_SAT'].max()] 
largest_std_dev