📄️ Arrays and Attributes
Creating ndarrays, the six shape attributes, dtypes, and why an array is not a list.
📄️ Indexing, Slicing and Reshaping
1-D and 2-D access, why NumPy slices are views rather than copies, boolean masking, and reshape.
📄️ Operations and Broadcasting
Element-wise arithmetic, the broadcasting rules, universal functions, matrix multiplication, sorting and stacking.
📄️ Aggregations and Statistics
sum, mean, median, std and var; the axis argument; why NumPy and pandas disagree on std; and handling nan.