So far, you've learned to create various plots using Matplotlib and Seaborn with basic Python data structures or NumPy arrays. However, data for analysis often resides in external files like spreadsheets or CSVs. The Pandas library is the standard tool in Python for loading, managing, and manipulating such structured data, primarily using its DataFrame object.

This chapter focuses on bridging the gap between data handling with Pandas and visualization with Matplotlib and Seaborn. You will learn how to:

• Load data from common file formats into Pandas DataFrames.
• Utilize the basic plotting methods built directly into Pandas DataFrames.
• Integrate Pandas DataFrames effectively with both Matplotlib and Seaborn functions, passing data columns directly to create visualizations.
• Perform elementary data filtering and preparation steps using Pandas before plotting.

Working directly with DataFrames simplifies the plotting process, especially when dealing with labeled datasets containing multiple variables.