Interpolation (`scipy.interpolate`)#

There are several general facilities available in SciPy for interpolation andsmoothing for data in 1, 2, and higher dimensions. The choice of a specificinterpolation routine depends on the data: whether it is one-dimensional,is given on a structured grid, or is unstructured. One other factor is thedesired smoothness of the interpolator. In short, routines recommendedforinterpolation can be summarized as follows:

	kind	routine	continuity	comment
1D	linear	`numpy.interp`	piecewise continuous	Alternatively,`make_interp_spline(...,k=1)`
	cubic spline	`CubicSpline`	2nd derivative
	monotone cubic spline	`PchipInterpolator`	1st derivative	non-overshooting
	non-cubic spline	`make_interp_spline`	(k-1)th derivative	`k=3` is equivalent to`CubicSpline`
	nearest	`interp1d`		kind=’nearest’, ‘previous’, ‘next’
N-D curve	nearest, linear, spline	`make_interp_spline`	(k-1)th derivative	use N-dimy array
N-D regular(rectilinear)grid	nearest	`RegularGridInterpolator`		method=’nearest’
	linear			method=’linear’
	splines		2nd derivatives	method=’cubic’, ‘quintic’
	monotone splines		1st derivatives	method=’pchip’
N-D scattered	nearest	`NearestNDInterpolator`		alias:`griddata`
	linear	`LinearNDInterpolator`
	cubic (2D only)	`CloughTocher2DInterpolator`	1st derivatives
	radial basis function	`RBFInterpolator`

Smoothing and approximation of data#

1D spline functions	`make_smoothing_spline`	classic smoothing splines,GCV penalty
1D spline functions	`make_splrep`	automated/semi-automated knotselection
spine curves in N-D	`make_splprep`
unconstrained least squaresspline fit	`make_lsq_spline`
2D smoothing surfaces	`bisplrep`	scattered data
	`RectBivariateSpline`	gridded data
Radial basis functions in N-D	`RBFInterpolator`

Further details are given in the links below

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Interpolation (scipy.interpolate)#

Smoothing and approximation of data#

Interpolation (`scipy.interpolate`)#