  We introduce the shape module of the Python package Geomstats to analyze
shapes of objects represented as landmarks, curves and surfaces across fields
of natural sciences and engineering. The shape module first implements widely
used shape spaces, such as the Kendall shape space, as well as elastic spaces
of discrete curves and surfaces. The shape module further implements the
abstract mathematical structures of group actions, fiber bundles, quotient
spaces and associated Riemannian metrics which allow users to build their own
shape spaces. The Riemannian geometry tools enable users to compare, average,
interpolate between shapes inside a given shape space. These essential
operations can then be leveraged to perform statistics and machine learning on
shape data. We present the object-oriented implementation of the shape module
along with illustrative examples and show how it can be used to perform
statistics and machine learning on shape spaces.
