Data visualization is a crucial component of data science, and Python's pandas library provides simple yet effective tools for plotting data directly from dataframes. Although primarily a data analysis tool, pandas offers a range of visualization options through its plotting functions, which serve as wrappers around the matplotlib library. This functionality facilitates straightforward plotting of various graph types, including line plots, bar plots, histograms, KDE plots, and scatter plots, among others, without requiring deep knowledge of matplotlib intricacies. The article explores these capabilities using the NIFTY-50 dataset from the National Stock Exchange of India, demonstrating how pandas can be used to visualize stock data from different sectors, such as banking, pharma, IT, and FMCG. Additionally, advanced plotting techniques like scatter matrix plots and bootstrap plots are discussed, showcasing pandas' potential as a versatile visualization library. The piece concludes by suggesting the pandas DataFrame documentation for further exploration of styling and customization options.