Why does numpy FFT only work for samples of 10,20,30 sec?

1. How many samples do I need for FFT?
2. What are the limitations of FFT?
3. How does Numpy FFT work?

How many samples do I need for FFT?

The number of samples (N) in the FFT must be an integer power of 2. Therefore, N = 2^p, where p is a positive integer. This rule minimizes the number of multiplications—and therefore the computation time—needed to compute the coefficients of the Fourier series.

What are the limitations of FFT?

A disadvantage associated with the FFT is the restricted range of waveform data that can be transformed and the need to apply a window weighting function (to be defined) to the waveform to compensate for spectral leakage (also to be defined). An alternative to the FFT is the discrete Fourier transform (DFT).

How does Numpy FFT work?

The fft function which uses the functionality of the SciPy package works in a way that, it uses the basic data structures that are used in the numpy arrays, in order to create a module that is required for scientific calculations and programming.