Flights Analysis with Pandas

Work with real-world flight data using Pandas. Flight data is a great example of how large datasets can be transformed into meaningful insights with Pandas. In this project, you will analyze the flights dataset from Seaborn, practicing the core operations that make Pandas such a powerful tool for data wrangling. You’ll begin by loading the dataset into a Pandas DataFrame and exploring its structure—rows, columns, and data types. Then, you’ll calculate descriptive statistics to understand passenger counts over time. From there, you’ll apply filtering and indexing to focus on specific years or months, and create subsets of the data for deeper analysis. The project then moves into groupby operations: summarizing passenger numbers by year, comparing monthly averages, and identifying long-term growth patterns. You’ll also work with date-related manipulations, such as extracting trends across decades or highlighting the busiest periods. Sorting and resetting indexes will help you organize results clearly. By the end of this project, you will be able to: Load and explore the flights dataset from Seaborn with Pandas. Apply filtering and indexing to select specific subsets of data. Use groupby and aggregation to summarize passengers by time period. Create new calculated columns and transform the dataset. Organize and compare results to reveal clear patterns. This project gives you hands-on practice with time-based data analysis using Pandas. By working with a dataset that reflects real-world flight trends, you’ll see how simple but powerful operations can uncover valuable insights—skills that apply to any industry where time series and categorical data matter.