Why is FFT module for smaller df showing uexpected malfunctioning in Python?

1. How do you normalize FFT?
2. What are the disadvantages of FFT?
3. How to extract frequency from FFT Python?

How do you normalize FFT?

Normalise the fft by dividing it by the length of the original signal in the time domain. Zero values within the signal are considered to be part of the signal, so 'non-zero samples' is inappropriate. The length to use to normalise the signal is the length before adding zero-padding.

What are the disadvantages 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 to extract frequency from FFT Python?

We can obtain the magnitude of frequency from a set of complex numbers obtained after performing FFT i.e Fast Fourier Transform in Python. The frequency can be obtained by calculating the magnitude of the complex number. So simple ab(x) on each of those complex numbers should return the frequency.