<p>Fourier transform is most suited if your samples are each a time series. If they are you may extract frequency domain features for each sample from <code>transformed</code>. Here is a listing of common features in time and frequency domain that you can consider (<a href="https://arxiv.org/pdf/1401.8212.pdf" rel="nofollow noreferrer">reference</a>):</p>

<p><a href="https://i.stack.imgur.com/Gl8ao.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Gl8ao.png" alt="enter image description here"></a></p>
fourier transformation as a dimensional reduction technique in python
<p>My dataset has 2000 attributes and 200 samples. I need to reduce the dimensionality of it. To do this, I am trying to use Fourier transformation as a dimensional reduction. Fourier transformation returns the discrete Fourier transform when I feed data as an input. But I do not know how to use it for dimensional reduction. </p>

<pre><code>from scipy.fftpack import fft
import panda as pd

price = pd.read_csv(priceFile(), sep=",")
transformed = fft(price )
</code></pre>

<p>Can you please help me?   </p>