There are several general facilities available in SciPy for interpolation and smoothing for data in 1, 2, and higher dimensions. The choice of a specific interpolation routine depends on the data: …

Interpolation (scipy.interpolate) # Sub-package for functions and objects used in interpolation. See the user guide for recommendations on choosing a routine, and other usage details. Univariate …

The interp1d class in scipy.interpolate is a convenient method to create a function based on fixed data points, which can be evaluated anywhere within the domain defined by the given data …

The string has to be one of ‘linear’, ‘nearest’, ‘nearest-up’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, or ‘next’. ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of …

Multidimensional interpolation on regular or rectilinear grids. Strictly speaking, not all regular grids are supported - this function works on rectilinear grids, that is, a rectangular grid with even or …

Rescale points to unit cube before performing interpolation. This is useful if some of the input dimensions have incommensurable units and differ by many orders of magnitude.

All these interpolation methods rely on triangulation of the data using the QHull library wrapped in scipy.spatial.

While s = 0 formally corresponds to a bivariate spline interpolation, the FITPACK algorithm is not meant for interpolation, and may lead to unexpected results. For scattered data interpolation, …

In this example the cubic spline is used to interpolate a sampled sinusoid. You can see that the spline continuity property holds for the first and second derivatives and violates only for the …

The interp1d class in scipy.interpolate is a convenient method to create a function based on fixed data points, which can be evaluated anywhere within the domain defined by the given data …