Advanced

Linear Algebra & Advanced Patterns

Matrix operations, sorting/searching, and the tricks that come up in real code.

Function	What it finds	Example
`np.sort(a)`	sorted copy	`np.sort([3,1,2])` → `array([1, 2, 3])`
`a.argsort()`	indices that would sort the array	`array([3,1,2]).argsort()` → `array([1, 2, 0])`
`np.unique(a, return_counts=True)`	distinct values + how often each appears	`np.unique([1,1,2])` → `(array([1, 2]), array([2, 1]))`
`np.where(cond)` / `np.argwhere(cond)`	coordinates where a condition holds	`np.where([1,-1,2] > 0)` → `(array([0, 2]),)`
`np.searchsorted(a, v)`	insertion point to keep a sorted array sorted	`np.searchsorted([1,3,5], 4)` → `2`

⚠️ Watch out: np.reshape and matrix transpose are NOT the same operation: a frequent source of silent bugs when porting math notation into code. Reshape relabels the flat buffer; transpose actually reorders it.

Now practice it

Exercises that use what this lesson just covered.

Sort a VectorMedium Sort Rows by a Given ColumnEasy Locate Elements Matching a ConditionEasy Count Occurrences of Each Unique ValueEasy Delete Rows and ColumnsEasy

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