SigmaAnalytics & Tech
B3PythonIntermediateAbout 110 min to complete

Broadcasting

The ability to predict — rather than guess at — how arrays of different shapes combine.

Notebook

About 110 minutes to complete

Enrol to read the notebook and download it to run.

What this module covers

The ability to predict — rather than guess at — how arrays of different shapes combine.

By the end of it

  • State the two broadcasting rules and apply them mechanically to any pair of shapes
  • Predict the output shape of any broadcast operation before running it
  • Diagnose a broadcasting ValueError from its message alone
  • Use np.newaxis, reshape, and keepdims to align shapes deliberately
  • Explain why broadcasting uses no extra memory
  • Normalise rows and columns of a matrix in one line each
  • Standardise a dataset (z-scores) — the universal machine-learning preprocessing step
  • Compute outer products and pairwise distance matrices without loops