I need a clear, well-structured explanation of Python’s Pandas library that goes beyond the official docs and blog snippets. The goal is to understand exactly when and how to use Series, DataFrames, indexing, grouping, merging, reshaping, and basic time-series handling so I can move confidently from raw CSVs to clean analytical datasets. You’ll walk me through the concepts in plain English, then back each idea with concise, runnable code samples. Think “mini handbook” rather than a generic tutorial: short sections, real-world examples, and notes on common pitfalls or gotchas (e.g., chained assignment, dtype surprises). If a feature overlaps with NumPy, flag it so I know why Pandas behaves the way it does under the hood. Deliverables are straightforward: • A readable guide (Markdown or Google Doc) covering the core topics above • Annotated .py files that reproduce every example in the guide • A brief Q&A wrap-up so I can clarify anything that’s still fuzzy Keep the tone friendly and practical—this is about grasping Pandas quickly, not academic depth.