For more pure research and prototyping things both can do, I still think matlab is better though I rarely use it. I just like the idea of being able to easily deploy the code later somehow. Kind of an entrepreneurial feature.

In fact, it's not an issue at all since Julias ecosystem is a superset of that of Python: with PyCall you can use Python libraries and Julia libraries in one program without issues.

julia> data = rand(ComplexF64, 1024^2);

# python fft from julia:

julia> res = @btime np.fft.fft(data);

  78.613 ms (39 allocations: 16.00 MiB)

In [11]: %timeit res = np.fft.fft(data)

89.3 ms ± 1.65 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

As expected, julia has its own fft package (based on FFTW):

julia> res = @btime fft(data);

  61.540 ms (33 allocations: 16.00 MiB)