Diamonds Analysis with Pandas

Manipulate and explore real-world data with Pandas. The diamonds dataset is a classic for practicing data manipulation. In this project, you’ll use Pandas to load the dataset directly from the Seaborn library and perform step-by-step analysis on the characteristics that define a diamond’s value: carat, cut, color, clarity, and price. You’ll begin by importing the dataset into a Pandas DataFrame and exploring its shape, columns, and summary statistics. From there, you’ll practice essential data manipulation skills: filtering rows based on conditions, selecting subsets of columns, and applying indexing techniques. Next, you’ll work with groupby and aggregation to examine average prices across different cuts, calculate price distributions by clarity, and compare carat weight across color categories. You’ll also learn how to sort results, create new calculated columns, and clean the dataset by handling duplicates or missing values if necessary. By the end of this project, you will be able to: Load the diamonds dataset from Seaborn into a Pandas DataFrame. Explore and summarize data with built-in Pandas functions. Apply filtering, indexing, and subsetting techniques. Use grouping and aggregation to analyze price and quality attributes. Create new columns and transform the dataset for deeper analysis. This project focuses purely on Pandas manipulation, giving you practical experience with real-world style data. By working hands-on, you’ll see how quickly Pandas helps uncover structure and relationships in a dataset—without needing external visualization or advanced tools.