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Tabular data manipulation, indexing, grouping, missing data handling, and exploratory data analysis with Pandas.
A comprehensive guide to 2D charting, figure layouts, subplots, visual hierarchy, custom styling, and exploratory data visualization using Matplotlib and Pandas.
An engineering teardown of Pandas internal memory structures, 1D Series vs 2D DataFrame block managers, label-based (.loc) vs positional (.iloc) indexing, missing data imputation, split-apply-combine aggregations, pivot tables, and vectorized string feature extraction.
A complete data visualization laboratory and capstone suite featuring exploratory data analysis, multi-panel custom figures, statistical distributions, and correlation matrices built with Matplotlib and Pandas.