Python Tutorial: Learn Scipy - Fast Fourier Transform (scipy.fftpack) in 17 Minutes

SciPy offers Fast Fourier Transform pack that allows us to compute fast Fourier transforms.
Fourier transform is used to convert signal from time domain into the frequency domain. By doing so, it doesn’t not only allows us to check the signal’s behavior in the frequency domain, but also allows us to perform some functions, such as filtering, that would be otherwise not possible to be performed in time-domain. Fourier transformation finds its application in disciplines such as signal and noise processing, image processing, audio signal processing, etc.
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[@TimelyTimeSeries]

Thanks a lot for this video. As someone with no engineering background, this demonstration makes the concept much easier to understand.

[@eMasterClassAcademy]

Thanks

[@dhruv_195]

great video man, you explained everything so well not only that those small boxes at top right corner telling what the function does is a really smart idea. for the first time I understood everything in a programming tutorial, thank you brother : )

[@syremusic_]

Excellent video, a practical introduction to how to use FFT to find the peak frequency in a noisy signal. You can take this a step further and find the n peak frequencies in a signal and attempt to reconstruct the original signal. Thank you!

[@garrisonhustle3289]

A pretty easy tool for filtering, nice intro!

[@dsmith5272]

Nice explanation and tutorial; it helped me understand FFT a bit better.

[@dman2633]

Thank you very much. Your video is very clear and functional.

[@kaewkt8987]

Thank you, it's very clear explanation

[@user-tx1zr3xo3g]

Thanks man, very good video. Very simple from the information i looked online

[@ushapedcurve3831]

Great tutorial! I think it-s recommended to build graph of each intermediate output in JPEG format, for better understanding. So that the student had some pyplot practice.

[@eMasterClassAcademy]

Great suggestion!

[@alierencelik2188]

Excellent video for a starter like me, thanks

[@jiehuang9740]

Very helpful, perhaps can add more plotting when showing how the data is like.

[@princecharlesmbedzi3832]

what arguments did you use to plot the amplitude vs frequency graph

[@rajeshn2536]

Thank you so much, it helped a lot in understanding FFT

[@ht1qth5qh4qh9]

You're welcome sir

[@supundasanthakuruppu3496]

Thanks. This helped a lot!

[@anwarparadis7997]

Amazing video I like it

[@luisponce6594]

Excellent video. keep it going, you helped me a lot on my mechanical dynamics class.

[@eMasterClassAcademy]

Thanks bro.

[@kevinshao9148]

Thanks for the great video! One question, 9:40 why sample freq goes symmetric around zero, why not go with one side of the x axis [0 .... n ] ? so mathematically, what are these generated frequencies? are they unique?

[@Altekameraden79]

The range of the Discrete Fourier Transform is -infinity to positive infinity to include all possible discrete signals. It is common practice to truncate spectrum from 0 Hz to Fmax (defined by sample rate, max resolution of sensor accuracy etc.)

[@fjlord407]

Nice

[@HarpreetKaur-bx1ej]

am getting both amplitude position and peak frequency 0, what should i do i am not getting valid result

[@rio_agustian_]

Cool video, sir. Great explanation! But can you plot the bunch of numerical stuff instead of just print it? I think it'll be more intuitive for the viewers

[@eMasterClassAcademy]

thanks for watching, such a great suggestion!

[@nanoluisi]

Is it posible to generate a "clean" file out of fft?

[@nickpenacl]

thanks for the video, just don't understand how the amplitude could be 100 in the spectrum when it's supposed to expect be 1

[@apoorvmodak3579]

would have understood better with graphical representations

[@importantbiology7643]

Please code seen clear or share PDF link .

[@nyatoko8578]

Where is the magnitude sir?

[@aviadedell4826]

The argument of sin should be omega*t, not 2*pi*t( i see now you divided by the period)

[@pirate0bloodyskull]

noob

[@VanNguyen-yp7cd]

Thank you so much. It's very useful and your explain clearly.