MPG Analysis with Pandas

Explore vehicle performance data with Pandas. The MPG dataset (Miles Per Gallon) is a classic resource for practicing data manipulation, containing technical details of cars such as horsepower, cylinders, weight, origin, and fuel efficiency. In this project, you’ll use Pandas to load, explore, and analyze this dataset to uncover how different vehicle characteristics affect MPG. You’ll start by importing the dataset into a Pandas DataFrame, reviewing its structure, and handling missing or inconsistent values. Then, you’ll apply filtering and subsetting to focus on cars with specific attributes—for example, selecting vehicles from certain origins or filtering based on engine size. Next, you’ll use groupby and aggregation to compare average MPG across categories like the number of cylinders, vehicle origin, or model year. You’ll also practice creating new calculated columns, such as weight-to-horsepower ratios, to examine how technical factors influence efficiency. Sorting and ranking methods will help you identify the most and least fuel-efficient cars. By the end of this project, you will be able to: Load and explore the MPG dataset with Pandas. Clean and prepare data by handling missing values. Filter and subset vehicles based on specific attributes. Use grouping and aggregation to compare MPG across categories. Create calculated columns to reveal deeper performance insights. This project demonstrates how Pandas can turn raw technical data into meaningful analysis. By the time you finish, you’ll not only sharpen your skills in data manipulation but also develop an analytical perspective on how vehicle features connect to fuel efficiency—knowledge that’s transferable to many real-world datasets.