Python for Finance, 3rd Edition

Chapter 5. Numerical Computing with NumPy

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Computers are useless. They can only give answers.

Pablo Picasso

Numerical computing is at the core of quantitative finance workflows: pricing, risk, backtesting, and simulation all rely on fast operations over large arrays of numbers. This chapter introduces NumPy, the array-based numerical foundation that underpins later chapters on pandas, time series, simulation, and machine learning.

The chapter shows how to work with NumPy arrays (ndarray objects) for fast numerical computing. It explains how NumPy arrays differ from Python lists, how to create and inspect arrays, how to reshape and aggregate them, how to express computations with vectorization and broadcasting, how to use Boolean masks and np.where, and how to generate random numbers with numpy.random’s modern Generator interface.

To follow along, you need a working python 3.11+ interpreter with numpy installed (see Chapter 3), an interactive environment such as IPython or Jupyter, and familiarity ...