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I'm trying to choose between numpy.interp vs scipy.interpolate.interp1d (with kind='linear' of course). I realize they have different interfaces but that doesn't matter much to me (I can code around either interface). I'm wondering whether there are other differences I should be aware of. Thanks.
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interp. One-dimensional linear interpolation for monotonically increasing sample points. Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.
This class returns a function whose call method uses interpolation to find the value of new points. A 1-D array of monotonically increasing real values. A N-D array of real values.
The interp1d() function of scipy. interpolate package is used to interpolate a 1-D function. It takes arrays of values such as x and y to approximate some function y = f(x) and then uses interpolation to find the value of new points.
The scipy. interpolate is a module in Python SciPy consisting of classes, spline functions, and univariate and multivariate interpolation classes. Interpolation is done in many ways some of them are : 1-D Interpolation. Spline Interpolation.
Numpy.interp does not handle complex-valued data or ndim>1, while scipy.interp1d does both. OTOH, numpy's interpolator is much faster (and is likely faster still in more recent numpy version).
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While numpy returns an array with discrete datapoints, 'interp1d' returns a function. You can use the generated function later in your code as often as you want. Furthermore, you can choose other methods than linear interpoationn
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