Python's most-used data library, end-to-end. NumPy arrays as the foundation, Series & DataFrame, reading CSV / Excel / JSON / Parquet / SQL, indexing & selection (loc, iloc, boolean masks), .query(), missing data (NaN, .fillna, .dropna), dtypes & .astype, vectorized math & broadcasting, .apply / .map / .applymap, groupby & aggregation (.agg, .transform, .filter), pivot & pivot_table, melt & wide-vs-long, merge & join (inner, outer, left, right), concatenation, indexing tricks (MultiIndex, .stack/.unstack), time series (.resample, .rolling, .shift, datetime accessors), categorical dtype, string accessor .str, .cut & .qcut, window functions, performance (chunked reads, eval, query, Polars vs Pandas, PyArrow backend, Dask, Modin, cuDF on GPU), I/O optimizations, visualization (.plot, hvplot), styling & conditional formatting, exporting, plus the new copy-on-write Pandas 2.x. 30 units, 450 lessons.