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()
schools.info()
# check if there is duplicates in school name
len(schools) == len(schools.school_name.unique())
# Which schools are best for math?
best_math_schools = schools[['school_name', 'average_math']].sort_values(['average_math'], ascending = False).head(1)
best_math_schools
# Calculate total_SAT per school# Who are the top 10 performing schools?
schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
# Who are the top 10 performing schools?
top_10_schools = schools[['school_name', 'total_SAT']].sort_values('total_SAT' ,ascending = False).reset_index(drop=True).head(10)
top_10_schools
# Which NYC borough has the highest standard deviation for total_SAT?
# group the schools with borough and calculate the count, mean, std, round the aggregations to 2 decimal point
boroughs = schools.groupby('borough')['total_SAT'].agg(['count', 'mean', 'std']).round(2)
# Filter for max std and reset index so borough is a column
largest_std_dev = boroughs[boroughs['std']==boroughs['std'].max()].reset_index()
# Rename the columns for clarity
largest_std_dev =largest_std_dev.rename(columns = {"count": "num_schools", "mean": "average_SAT", "std": "std_SAT"})
largest_std_dev
Here is what I learned from this project.
- Using .sort_values() method to
sort the dataframe
Create a new column
to the dataframe- Using
groupby with aggregations
to summarize the data - Rename the columns with dictionary