In many data-processing scenarios it is necessary to use a discrete set of available data-points to infer the value of a function at a new data-point. One approach to this problem is interpolation, which constructs a new model-function that goes through the original data-points. There are many forms of interpolation (polynomial, spline, kriging, radial basis function, etc.), and SciPy includes some of these interpolation forms. This webinar will review the interpolation modules available in SciPy and in the larger Python community and provide instruction on their use via example.