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Learn Scientific Computing with NumPy

NumPy as the tool: array operations instead of loops, shapes and slices, matrices and linear systems, seeded simulation, Monte Carlo error and floating point. Every program runs in your browser.

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Free to start · adaptive placement finds your level · reviews timed to your own forgetting.

30 lessons in Scientific Computing with NumPy

Why loops are slowShapes: rows, then columnsWhole numbers and decimalsSlicesA slice is a windowMasks: a question for every numberPiecewise rules on a whole arrayRows and columnsTotals along an axisBroadcasting: stretching to fitFunctions on every numberThe matrix productTranspose, identity, dot productSolving Ax = bDeterminants in floating pointEigenvaluesA seeded generatorTen thousand dice at onceHow wrong is a simulation?Running totals and differencesRandom walksSearching a gridSlopes from samplesAreas from samplesSpreadSorting and rankingA line through dataHow precise is a float?Integers that wrap aroundProject: measuring g

Built to be understood — and remembered.

Every idea is taught with motivation and a worked example before the drills, and an FSRS spaced-repetition engine schedules each review for the day its model predicts you would forget it. A short placement check finds what you already know, so you start Scientific Computing with NumPy exactly where it's useful.

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